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I have used Casadi in interpreter mode and through mex file. However, the script is not very flexible, especially when the control problem involves tracking of an arbitrary reference signal. Have anyone tried ICLOCS, Acado or other tools in Simulink? Kindly share your experience.
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I already saw some examples of GA (genetic algorithm) applications to tune PID parameters, but I (until now) don't know a way to define the bounds. The bounds are always presented in manuscripts, but they appear without great explanations. I suspect that they are obtained in empirical methods.
Could Anyone recommend me research?
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Dear Italo,
In general, the bounds selection is made empirically because the "suitable range" of a PID controller is problem-dependent. The way I use to select the bounds is: 1. I tune a PID controller that produces a stable response to the closed-loop system. Then, 2. I choose a range around this "nominal" value large enough such that the GA has still some degree of freedom to search in the optimization space. Finally, 3. if the GA converges, I start decreasing/increasing this range till I got a more or less good behavior of the GA, i.e., the GA doesn't stick in a sub-optimal minimum or so.
If you want to use a more rigorous approach, I would suggest computing the set of all stabilizing PID controllers for the particular system. Then, I would establish the bounds for the GA search space to be the this computed set. In that way, you would search for the optimal controller only within those producing a stable closed-loop response.
Best,
Jorge
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Please suggest the most useful of software on Linear Matrix Inequalities to solve Fuzzy control systems?
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These LMIs can be solved using MATLAB's LMI Toolbox software packages.
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Please explain, what is the importance to introduce the disturbances in the process?
Also explain, why most of the disturbances in the process are commonly represented by an impulse function? provide me reference also.
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Hi Vishal,
Pro cess identification can be identified and its characteristics are characterized by using impulse function in the process control system. In the water or fluid plant, the water variable is inconsistent and highly disturbed when chemicals or other agents are added to it. In the air purification process, the temperature of the circulating air control process is also constantly affected by heat or coolant agents. Thus, these processes need a disturbance acting representative in control formulation in the close loop system. Disturbances help to represent the system closer in relation to the actual plant process control. Therefore fulfilling the process stabilization and control technique. In a way, disturbances in real life are non-linear. So we can introduce a disturbance in the state-space equation to make it become a linear system for control system. Further studies can be explored at the webpage's search at:" impulse identification of process control" - Search (bing.com)
SIncerely,
Ng Tian Seng
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We know that robot pose errors are very common and that robot trajectories are pre-constructed and generated from CAD models in machining scenarios. However, for the trajectory points, there are inherent errors, and we need to compensate for the position and attitude of these trajectory points.
The existing off-line compensation is very common, but it lacks real-time, and the compensation objects are all the results obtained from pre-experiments. In fact such compensation, in a new experiment, the whole process is different from that of the pre-experiment because of the compensation done, so the final compensation also all stay at the level of being able to improve the performance, and theoretically such compensation is also all incomplete.
How to compensate for the new point errors based on the information obtained in real time, and update the point information of the generated trajectory in real time?
My robot is an ABB, and it would be great if you could offer some advice on the robot control system,transmission of data, branching of the perception model, etc.
Thanks to all the researchers who discussed and replied.
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Especially for industrial/process control systems, there is a lot of resistance to having historized data stored in the cloud as it is seen as much less secure. Is this perception correct and if so is there any evidence for it?
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I do not have exact data regarding the question you are asking.
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Hi guys, I am simulating a horizontal axis small scale wind turbine using Ansys Fluent. In order to validate the manufacturing data, I vary the wind speed and its corresponded angular velocity, but unfortunately the obtained results exceeds the experimental ones especially in high wind speeds (15m/s). According to the wind turbine manufacturer the turbine have a pitch control system, but there is no information about the used pitch angle in the experimental results. What must I do in this case? Can I choose the pitch angle that gives the same results as experimental ones ?
Thank you.
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Dear Somaya,
I hope this will help you
I think the optimum pitch angle which is based on BEMT. Most of designs based on the theory make the pitch angle at root very high and at the tip nears to zero. Therefore my opinion is to try to simulate your wind turbine at the pitch angle that makes the tip pitch angle zero or makes the tip perpendicular to the wind direction.
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Hi
I am trying to learn control systems for job opportunities.
I have some background bu rather to start from zero and go from there to to infinity and beyond
Also need to work on my matlab coding abilities for this
What are your suggestions?
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The best ref.
OIE publication
Infectious diseases books according to subject you need
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I am trying to get bode plot for a boost converter with closed loop control. if the input output signals were a certain waveforms, than their phase difference would be easily calculated but in this case how do i determine the phase (lag or lead) for the given frequency? is it related to sampling period of the control loop and how much time would it take to bring the change to output after a certain change in input has been done?
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A bode plot always applies to sinus shaped signals only. (cosine simply is a sine wave shifted by Pi/2.) Non sine shaped signals have to be represented by Fourrier series or Laplace transformation.
Simulating a boost converter either is done in time domain (Then most signals are rectangular or trapezoid shaped. But then you don't get a bode plot straight forward.) or in frequency domain.
If you want to run an AC simulation to get a bode plot you first have to replace the inductor+switch+rectifier+control loop by a linearized equivalent circuit (to make it compatible with an AC simulation in frequency domain).
Most simple: regard the converter as a voltage controlled current source (current mode regulation. I~(Vtarget-Vout)) and the filtering capacitor as an integrator (Vout~Integral(Iin-Iout)converting the current into a voltage.
Voltage mode regulation is more ugly because you get two poles in the loop. (I~Integral(Vtarget-Vout), Vout~integral(Iin-Iout))
Best consult the book:
Christophe P. Basso, "Switch-Mode Power Supplies", Mc Graw Hill
Expect about 2 weeks reading and calculating to catch the first 5 chapters and set up your own models for your specific regulator topology and regulator parameters.
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Is it sampling frequency of control loop or some kind of resonance frequency of the system ??
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The frequency response, or variations in magnitude and phase as a function of frequency, is depicted using Bode graphs. This is accomplished using two semi-log scale charts. The top plot is usually magnitude or "gain" in decibels (dB). The bottom plot represents a phase, which is usually expressed in degrees.
A Bode plot /ˈboʊdi/ is a graph representing a system's frequency response in electrical engineering and control theory. It is frequently a mix of a Bode magnitude plot, which expresses the amplitude of the frequency response (usually in dB), and a Bode phase plot, which expresses the phase shift.
In electronics, frequency response is a quantifiable measure of a system's or device's output spectrum in response to a stimulus that is used to define the system's dynamics. It is a measure of the amplitude and phase of the output in relation to the input as a function of frequency.
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Usually, we analyze the observability of a system through an observability matrix. For example, for a VO/VIO system, we can calculate its observability matrix according to its state space equation and analyze its unobservable dimensions: monocular VO is 7, monocular VIO is 4; however, for the monocular VO based on the optimization method, we can also get its unobservable dimension of 7 by calculating the zero space of its Hessian matrix. Is there any connection between them?
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Hi,
In fact the matrix M represents the space tests of the observability or the unobservability of your nonlinear system while the Hessian matrix is the observability matrix by which we see the outputs of your system and with it we can build the tests space of observability and inobservability M, it should be emphasized that matrix H plays the same role of the matrix C for linear systems.
Best regards
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I am working on the real data of the physical property. I have gotten the bode plot data (I am working on frequency domain data). I have tried to identify the data and get the model. But the fitness is very low (40%).
When I checked using state space identification with 10 state, I got a better result around 80%. But the requirement is I have to use second order system.
My question is, how to increase the fitness score of my model with the limitation I have to use second order system?
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PID controller is one of the most techniques that are used to improve control system operation, now with control valve how PID controller achieves practically?
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Since you advised researchers to use GA and PSO to "design" (sort of) the PID controller on the other thread, I'm sure you are an expert PID control designer.
Allow me to ask something because I have a genuine confusion about your question: "What does the practicality of the PID controller have to do with the control valve?" The control valve is an actuator that physically regulates the process flow, but it technically does NOT open or close the valve on its own.
Perhaps you intend to ask how effective a PID controller is in controlling a control valve that behaves nonlinearly. For example, the process plant is Gp(s) = 10/(s + 1)3, and the input flow is u4, where u is the valve opening.
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Which SCI/SCIE journals publish work in the field of control systems with a high acceptance rate and also respond fast?
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Sorry question outside of my field
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I made a PID-based control system and now am asked to augment my finding with a prototype. What this prototype refers to? Should I make a total system in Matlab or I will have to implement it physically?
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Dear Md Rayid Hasan Mojumder,
I completely agree with what is said and I add. A prototype is a rudimentary working model of a product, information system, or control system typically built for demonstration purposes or as part of the development process.
For more details and information about this subject, I suggest seeing links on the topic.
Best regards
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M.tech project topic in control system
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Ankur Jyoti Das Implementation of dual axis tracking solar panel control
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I want to implement Fuzzy Control in an actual production process based on S7-1200 PLC , I have simulated the fuzzy control system in Matlab .But ,When i want to programe the PLC code using Siemens TIA Portral software, I found the fuzzy inference and defuzzication is difficult to programme. I have read many articles and papers disscusing fuzzy control based on PLC, they all omited the key step ,that is how to programme the fuzzy inference and defuzzication, so i still confused about that. I am very long for someone to be able to give me some help or share me some fragments of the PLC code related to this question.
Thanks for all reading my question!
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Dear Zhang laifa,
I suggest you to develop an external fuzzy controller using an existing process controller or on an embedded controller according to your application. then you can share required parameters through an available communication link (MODBUS, SERIAL) with a PLC.
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In control theory, using Routh array test, it can be established that a quadratic polynomial
p(x) = a x^2 + b x + c (where a > 0)
is a Hurwitz polynomial (i.e. it has roots with negative real part) if and only if a > 0, b > 0 and c > 0. In an equivalent way, this can be proved using Hurwitz determinants.
I am looking for a simple proof for this fact. Without loss of generality, we can assume a = 1.
For p(x) = x^2 + b x + c, we can use the root finder formula and discuss various cases.
Is there any simple proof? I welcome your ideas and suggestions. Thank you!
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I thank Victor for pointing me in the right direction.. Vieta's formulas give the easiest proof for the assertion.
Indeed, we suppose that p(x) = x^2 + b x + c as the given polynomial.
Suppose that its roots are: x1 = m + i n, x2 = m - i n (where n = 0 or n > 0).
If p(x) is Hurwitz, it has stable roots. This implies that m < 0.
By Vieta's formulas, x1 + x2 = - b and x1 x2 = c.
This gives: b = - (x1 + x2) = - 2 m > 0 and c = x1 x2 = m^2 + n^2 > 0.
Conversely, suppose that b > 0 and c > 0. We show that p(x) is a Hurwitz polynomial. In other words, we must show that m < 0.
This is quite obvious since 2 m = - b < 0 and so m < 0.
We have proved the claim that p(x) = x^2 + b x + c is a Hurwitz polynomial if and only if b > 0 and c > 0.
This is quite an elegant proof and I thank Victor again.
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I'm looking for a exhaustive paper (or book) regardind a four (or two) wheeled robot trajectory control system in order to reproduce and fully understand the kinematics and control system equations.
My rover has four motor, one for wheel, and turns through a wheel speed control.
Would be amazing find the PID control design on the angle and speed control as well as the trigonometric equations of the robot trajectory.
Thank you very much
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Dear researchers
What is your opinion about the criterion recommended in seismic codes for determining scaling period, which are used to scale ground motion records?
As you know, the mentioned criterion is the period of the structure’s dominant mode, which has the largest modal participating mass ratio (usually the first vibration mode). Hence, the period of the mode with the second largest modal participating mass ratio is not considered in the scaling process. Consequently, although this criterion usually results in the largest value of scaling period, it is not logical ones.
This is especially important when Tuned Mass damper (TMD) or Base-Isolation system is utilized, which cause the modal properties of the structures to change.
I used a new criterion based on the weighted mean value of the periods for the structures equipped with TMD.
Have you used any criteria other than the criterion mentioned in the seismic codes?
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Dear Mikayel Gregor Melkumyan , it is my pleasure if u can read my latest article
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To be specific, the rotation of the aircraft around the three axis can be controlled by several part, the rudder and vertical stabilizer or differential thrust for multi engine aircraft control the yaw , the pitch and the roll are controlled by the wing flaps and elevators.
Exactly which of those are controlled by a computer in modern aircraft (military or commercial) and to which extent.
And where I can learn the most technical details about this subject (a book is preferred)
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In the normal mode, the aircraft control (relative to three axes) with the help of steering surfaces and mechanization can be carried out both in manual mode by the pilot and in the autopilot mode, which, depending on the type of aircraft, can operate in different modes, for example,
STAB - Manual stabilization mode, where the autopilot helps the pilot to control the position of the aircraft more easily, avoiding unexpected movements caused by turbulence and wind.
TAKE - STAB mode submode with additional climb support for fast, controlled takeoffs.
AUTO - autonomous flight, where the autopilot takes full control of the aircraft. It has many sub-modes, each with different autopilot behavior.
If we talk about abnormal conditions, for example, a stall into a tailspin, then modern aircraft have automatic systems to prevent stalls into a tailspin. For example, the Automatic Stall Prevention System on Boeing 737 MAX 8 and 9 airplanes turns on itself if the aircraft climbs too quickly in manual control mode, which is fraught with a loss of lift and speed.
Conclusion. All regular flight modes, as well as warning and prevention of abnormal ones, can be carried out using the autopilot. Aerobatics, entry and exit from the spin are carried out in manual mode by the pilot. You can find literature on the topic Piloting aircraft, automatic flight control systems, automatic aircraft control systems on the Internet. Usually these are the disciplines of aviation and aviation engineering educational institutions.
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Hi
How can i correct this error?? I think it's about matrix dimensions for port e.
Error in default port dimensions function of S-function 'FeedbackLinearization/Controller'. This function does not fully set the dimensions of output port 2
I'm running a simulation based on feedback linearization control method that comes from a paper attached below.
the model is also attached.
Anyone help me, helps a poor student. (if it makes sense lol)
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Hi,
file name "sfun_ abcaaaaa.c" is not available in the folder mentioned.
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hi every one
I'm trying to test my protection algorithm with an on-grid PV system (at least 2 parallel strings containing minimum of 4 modules per string).
I would appreciate if anyone can provide me a test simulation file ( matlab is preferred) with its full control system (with mppt)
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If you're using 1400 kWh per month, let's consider an average of 6 hours of sunlight per day, this implies you would need 1400 / 6×30 = 7.7 kW at least every hour, i.e. ~ an 8-10 kW system. To test solar panel voltage output, put your solar panel in direct sunlight, set your multi-meter to the "volts" setting and... touch the multi-meter's (red) positive lead to your solar panel's positive wire. Then touch, the multi-meter's (black) negative lead to your solar panel's negative wire.
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I have to compare five level MLI in term of efficiency and THD which has less number of switches.
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hello,
i work on a controlled Microgrid and i want to test the robustness of my controller againt a white noise that may be added to the output or the input. Is there is any specific condition to follow in order to take a good choise of a noise power ? or it is somthing random ?
- Actually i tried to take it about 3% of the nominal measurement value, is this enough to be good choice ?
- in addition, i tried the two types of noises, but i noticed that the one applied on the output affects much more the system than the one applied on the output (in such a way, my system looses its stability with the output noise, but gives an acceptable performance with the input noise ) , is this reasonnable ? if yes, why ?
thank you in advance
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Hi Sarah
The Microgrid controller design and its' robustness testing is different from communication system or control system. The white noise concept will not work here. The controlled Microgrid testing depends on operational scenarios and several robustness metrics are proposed by researchers for those scenarios.
One of the testing protocol is published by IEEE Standards--
2030.8-2018 - IEEE Standard for the Testing of Microgrid Controllers
DOI: 10.1109/IEEESTD.2018.8444947
It is useful to simulate operational scenarios and testing of designed controlled Microgrid.
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Hi Everyone. what are the hot topics for a theoretical and practical PHD research in control systems. It would be nice if you recommend some, with mentioning some related works through these years.
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25 years ago I was facing the same problems when I was starting my PhD I was very very much excited and asked my Professor that I want to do my PhD on such topic. My Professor agreed but just after one year I realized I completely lost my time I did wrong decision, No one was helping me even my Professor, because he could not change his interest for my PhD. Then I immediately changed my field according to my Professor's interest. So after 25 years my advice is that try the find the best Professor and best university and then follow your Professor's interest for your successful PhD.
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How can I effectively construct one state-space description, which is in fact a combination of multiple individual state space descriptions? Algebraic relationships between state-variables and input signals, will lead to off-diagonal terms in the resulting state-space matrix. For small systems this can easily be done by hand, but how can this be done effectively for larger systems?
To clarify my question:
suppose there are 2 state-space descriptions: \dot{x}_1 = A1(x1,t) x1 + B1(x1,t) u1 and \dot{x}_2 = A2(x2,t) x2 + B2(x2,t) u2, which are interconnected as described by the algebraic equation: f(x1,x2,u1,u2)=0. How can these two state-space descriptions be integrated efficiently to one big \dot{x} = A(x,t) x + B(x,t) u?
The resulting state-space description will be used for stability analysis purposes. Maybe it is better to analyze DAE system descriptions in order to analyze the stability?
It would be especially helpful if there are any symbolic methods, which also allow for nonlinear state-space descriptions.
If you can provide me with some good references, this would be a great help.
Thanks in advance.
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maybe this will help you
A report about using multiple state space models to establish an overall model.
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LQR based control system is offline because it is computed once before running of simulation or experiment.
As input-output data we use LQR based optimal control system applied to motor speed tracking application. We would like to estimate K optimal gain based Moorse-Penrose pseudo -inverse derivation. So, this control system is not based on the model, therefore A,B matrices are unknown. Model is black box.
Except the Neural Networks based control system, I would like to know whether it possible to implement online control system when estimated K optimal gain matrix will be updated each instant (each cycle).
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For a RG discussion on the meaning of the 'online' (or 'on-line') terminology at this forum:
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Dear friends:
In some calculation in control theory, I need to show that the following matrix
E = I - (C B)^{-1} B C
is a singular matrix. Here, B is (n X 1) column vector and C is (1 X n) row vector. Also, I is the identify matrix of order n. So, the matrix E is well-defined.
I have verified this by trying many examples from MATLAB, but I need a mathematical proof.
This is perhaps a simple calculation in linear algebra, but I don't see it!
Any help on this is highly appreciated.. Thanks..
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(B*C) is always a rank-one matrix, a singular matrix. (C*B) is a scalar, say a and nonzero eigenvalue of (B*C)=a, then that matrix E=I-(C*B)^{-1}(B*C) will singular.
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I want to know that the junctionless GAA MOSFET is used for creating induced source and drain through charge plasma concept but can that junctionless bar of semiconductor be fully intrinsic or it has to be fully doped.
Do we used intrinsic junctionless bar or lightly doped bar for creating induced source and drain?
Can someone explain it in simple words?
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Thank you Len Leonid Mizrah sir for the detailed answer, can you please tell me something more about charge plasma gate all around MOSFET?
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Hello, Can we find Kp, Ki, and Kd values of PID, Lead-lag, or Type2/Type3 compensator? I have designed a type2 compensator for my system but now I need a PID controller for the same system can I access Kp and Ki values from the Type2 compensator? Please guide me
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We use many mathematical models and state-space models in digital control systems. Before applying these concepts in real life scenarios, how useful (or less useful) it is to do a simulation in MATLAB?
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Hi everyone,
I'm currently designing a control system for a swimming pool. Could you please suggest a sensor which can be used to measure the concentration of chlorine in the pool?
Thanks in advance :)
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$ 50 per Piece Get Latest Price
Model Name/NumberGIPC101Type Of Ph MeterHand-HeldUsage/ApplicationMeasurementBrandGABY INSTRUMENTS
Chlorine is a standard chemical element that’s oftentimes used to provide people all over the world with clean drinking water. The reason that chlorine is highly effective and important for water quality monitoring is because it’s able to kill bacteria via a chemical reaction. Whether you’re treating your swimming pool or work in a water treatment facility using the right amount of chlorine can keep the water free from impurities.
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I get some negative vibe from the internet saying root locus is limited to education instrument to study pole and zero in s-plane which then forgotten after the university study. Does this suggest it a useless instrument for control system engineering in industry or alike. I have seen many bode plots and Nyquist analyses from the electronics arena by Analog Device or Texas Instrument website and app notes. Not so much on root locus design (and analysis).
Based on Nise Control System (now on Chapter 11), I gather zero can be placed close to the pole to mitigate the dominant pole to improve transient or steady-state error or both (Darth Vader versa Jedi battle there). I have seen a few comments that pole-zero cancellation is not a practical solution due to system tolerance and drift, is this a realistic issue for an electronic system, given that component's tolerance and precision, could be more precise than some mechanical systems?
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Root locus can not only determine the stability of a system but also it can predict the transfer function of a real time system by just providing a sine wave input to it. Then the root locus can be obtained by having a plot between magnitude and phase of ratio of FFT of output with that of input. Then by analysing the poles and zeros the transfer function of unknown system can be predicted.
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I have come across some Nonlinear Control System problems where I need to implement Matlab code but unfortunately, it's getting too tough to start with. I can some of the problems such as,
1. Population Dynamics using Lotka–Volterra differential equations.
2. Continuous Stirred Tank Reactor (CSTR) using van–der–Vusse
reaction scheme.
3. PI Controller for Linear and Nonlinear system
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So I'm in my first year Masters program, with option in Automatic Control systems. When I look up research topics, I see plenty ambiguous topics , twisted and I wonder how this topics are formulated or is there a pattern and people are improving on them.
I currently still cannot come up with a topic to work on.
My interest is in control systems engineering and it should address industrial challenges.
I don't want to choose what won't be of importance and impact.
Your input will go a long way to help me decide on what to work on.
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The question is how to measure the 21st Century Skills?
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Hello !
Was trying to build interleaved PFC architecture in simscape but getting the error (attached) "The following inductors are connected together " after switching has started.
Some matlab answers say its inherent issue. any one can help how to solve it ?
Thanks in Advance
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Would you please show us your complete circuit. In order to locate the error one has to work out some idealized analytical solution to the problem.
Or one can start with a simplified circuit with a known analytical solutions.
What is type of sources you have? Voltage or current source?
Best wishes
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As I know, the ordinary differential equation (ODE), xdot= -x^3+u, where x is the state variable, and u the control variable, is the control system associated to a falling object in atmosphere with viscous drag. I am not sure to be correct on that! Please comment on that!.
Update 1: xdot= -x^3+u, is called the hyper-sensitive system.
c.f.: A Collection of Optimal Control Test Problems: John T Betts.
Another example is velocity control for aircrafts in horizontal flight, which has an ODE evolution:
xdot=-x^2+u. Notice the attachment picked from:
Optimal Control with Engineering Applications; By: Hans Peter Geering.
I want to also know the real model associated to the control system described by the ODE: xdot= x^3+u. I guess more probably, this is associated to electrical systems.
Update 2: My own intuition says, positively damped systems as:
x_dot+x^3=u
are mechanical. Meanwhile, negatively damped systems as:
x_dot-x^3=u
are electrical.
You can yourself find some other examples in this regard.
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Thanks for showing the disturbance suppression capability of your proposed controller. However, I'm unsure what you meant by "injecting a step-function as a disturbance input". The disturbance is a sinusoidal signal.
I have tuned the PD gains so that you can compare both of them meaningfully. The setpoint or the reference target value for the process variable (x) of the double integrator is 1. The double integrator is also loaded with a sinusoidal disturbance of 0.1*sin(t).
x'' = - Kp*(x - 1) - Kd*x' + 0.1*sin(t); x(0) = 0; x'(0) = 0
where Kp = 0.002304, and Kd = 0.096.
The final value of the process variable (x) oscillates within the magnitudes of 1 ± 0.1.
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Greetings All,
I'm a MSc in Electrical Engineering Student and interested in Control System, anybody has an Idea of Theses Title for Control System where I can implement the concept of AI?
Thanks and Regards.
Thaer Ibrahim.
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Aren’t the links working?
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I am working on a project in which I need to control the duty cycle of the pulse given to mosfet in an AC to DC converter.The mosfets are connected to LCL filter to give a regualted 5V.The output voltage and a reference of 5V error is taken and a compensator is connected.The output is compared using PWM and then sent to mosfet.Do we need mosfet driver in between?What kind of mosfet driver should I use in multisim?Can I get any reference?Should we solve this using bode plot?Should I use PID controller to decrease the error and should I simulate in MATLAB using PID controller ?As I find it easy
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Hi,
It would be that this reference can help:
PD: Matlab implementation codes are available.
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I would like to know application of learning and control in MAS setting. And where could I find simulation codes of learning in MAS control
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Dear Amol Patil
Basically, the graph theory has been used to model the relationships among agents of the multi-agent or multi-robot system where the vertices of the graph correspond to the agents (robots), and the edges model the relation among those agents. The mathematics approach used for the edges represents the nature of the relationship or communication among agents. From this approach, a big area of theoretical and practical research is developed. I am attaching some links, books, and paper references that can be helpful to understand this area.
Books and Chapter books
Papers
  • Y. Cao, W. Yu, W. Ren and G. Chen, "An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination," in IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 427-438, Feb. 2013, doi: 10.1109/TII.2012.2219061.
  • S. Knorn, Z. Chen and R. H. Middleton, "Overview: Collective Control of Multiagent Systems," in IEEE Transactions on Control of Network Systems, vol. 3, no. 4, pp. 334-347, Dec. 2016, doi: 10.1109/TCNS.2015.2468991.
  • Zuo, Zongyu, Qing-Long Han, Boda Ning, Xiaohua Ge, and Xian-Ming Zhang. "An overview of recent advances in fixed-time cooperative control of multiagent systems." IEEE Transactions on Industrial Informatics 14, no. 6 (2018): 2322-2334.
  • Ding, Lei, Qing-Long Han, Xiaohua Ge, and Xian-Ming Zhang. "An overview of recent advances in event-triggered consensus of multiagent systems." IEEE transactions on cybernetics 48, no. 4 (2017): 1110-1123.
  • Wang, Xiaoling, Housheng Su, Xiaofan Wang, and Guanrong Chen. "An overview of coordinated control for multi-agent systems subject to input saturation." Perspectives in Science 7 (2016): 133-139.
  • Herrera, Manuel, Marco Pérez-Hernández, Ajith Kumar Parlikad, and Joaquín Izquierdo. "Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering." Processes 8, no. 3 (2020): 312.
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please suggest me to choose a topic in non linear control system . from where i choose and read research papers.
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There are quite interesting subjects to study under the nonlinear control theory. You can pick up one of the following nonlinear controllers: back-stepping controller, nonlinear predictive controller, or sliding mode controller. Also, applying the Laplace transform to a nonlinear system is another appealing subject. You can start off by reading this paper:
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Hi,
I'm trying to optimize a PI controller for a MIMO system called Vinante–Luyben (VL). I need to introduce a stable region to algorithm in order to have a reasonable search space. Do you know any paper or publications which has done it before so i can check if i didn't make any mistake?
(I found two stable for g11 and g22 as figure but it's not working some how)
Thank you for your helping
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Hi,
Also i suggest you to see attached files on topic.
Best regards
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Hi all, I have a question for Control system development specialists.
I compute controllability of linear system through MATLAB function ctrb, and I know that system have 1 uncontrollable state. How to define which state is uncontrollable?
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Dear Niccolo Lemonis,
Your system is under a state space representation (A,B,C,D), I presume it is in any form it will be necessary to transform it by a change of vector basis in diagonal canonical form if the poles (eigenvalue of matrix A) are distinct case 1 or that of Jordan form if the poles are multiple cases 2.
The state case 1 cannot be controlled if the corresponding row of matrix B is zero, for case 2 it will be the last row of Jordan's block (degree of multiplicity of the pole) and its correspondence of matrix B which must be zero. It is also the same principle applied to the Kalman decomposition.
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Dear friends:
While one can easily write an *.m file for implementing a sliding mode control,
for example, to stabilize a control system, integral sliding mode control is also
a popular scheme and used in many papers.
I like to know how to write an *.m file for implementing integral sliding mode control?
I like to kindly request Sliding Mode Control (SMC) experts to illustrate this control
and MATLAB code with a simple example of a control system on the plane (with twostates x1, x2) and how to set up the integral sliding mode control in MATLAB.
Thanks a lot!
With best wishes, Sundar
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1-IMPROVED INTEGRAL SLIDING MODE CONTROL METHODS FOR SPEED CONTROL OF PMSM SYSTEM
By: Cunjian Xia, Xiaocui Wang, Shihua Li and Xisong Chen
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I have two PI controller running in parallel both controls the same output variable, based predefined input conditions I would like to transfer control from Control A to Control B without any variation in the output response. would be great to get any suggestion on efficient way of doing it.
Using Matlab Simulink as implementation plattform
Thanks
Sreeraj A
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I have illustrated my idea for your problem, attached as an image to this comment.
In the image, t1, t2, t3, and t4, are discretionary, and you can set them to optional values. Even you may change the time signal to any shape you prefer, square wave, saw-toothed, sine wave, several pulses, with respect to your control-selector strategy. But beware that, the times of transfer from control A to control B, should not over-lap, because the control signal then becomes multiple-valued and hence you may encounter an error in Simulink simulation. You can as well modify the amplitude of the pulse, by multiplying it to a constant gain, or even multiplying it by a time-varying function described as another time-signal.
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T1 =
-67.34 s + 1.563e06
----------------------------------------------------------
0.0008762 s^2 + 0.09793 s + 2273
T2 =
2.447e07 s^2 + 7.695e11 s + 2.687e13
------------------------------------------------------------------------------------
2.483e-05 s^4 + 77.99 s^3 + 1364 s^2 + 3.142e06 s
Screen shots of Bode plots for the above transfer functions are attached with respective file names. Yes, I was taught in control system course that both the Gain Margin (GM) and Phase Margin (PM) should be positive for a stable system, but the bode plot of T2 shows that the system is stable even with negative GM. Therefore, I intrepreted this as the system is unstable only when its phase is equal to ± 180 ° at its open loop gain of unity. Because, only at this condition the closed loop gain becomes infinity.
However, my interpretation goes wrong with the system function T1, where both GM and Phase Margine (PM) are negative but its phase is not equal to ± 180 ° at its open loop gain of unity.
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Transfer function T1 has a zero in right half plane, hence it is a non-minimum phase system, and bode plot is not applicable for such systems. Note that, bode plot is only sufficient, but not necessary condition for system stability/instability. You can go for Nyquist criterion or eigenvalue analysis for stability assessment.
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Recently I have designed a automated gas leakage detection system using IoT. Now I want to analysis it further. What is the process to design/summarize it's control system.
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See this page for any alternative software:
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I have the nonlinear systems of Khalil, however some definitions are no so clear, is there a newer book with Matlab examples
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For Video tutorials , we have:
Introduction | Nonlinear Control Systems: https://www.youtube.com/watch?v=Xgnwn0G9qoo
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Hello everybody
Despite a few TMD cost models available in the literature, I am searching for more accurate initial and lifetime cost models of translational TMDs for Life Cycle Cost Analysis (LCCA) of TMD-equipped structures.
In fact, the provided cost model affiliated with one of the companies designs and manufactures transitional TMD (such as LeMessurier CO.), which this model consists TMD initial cost (construction and installation of the TMD) and TMD damage cost (maintenance and repair losses of TMD before structural collapse)
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The effectiveness of tuned mass dampers (TMDs) in reducing the seismic response of civil structures is still a debated issue. The few studies regarding TMDs on inelastic structures indicate that they would perform well under moderate earthquake loading, when the structure remains linear or weakly nonlinear, while tending to fail under severe ground shaking, when the structure experiences strong nonlinearities. TMD seismic efficiency should be therefore rationally assessed by considering to which extent moderate and severe earthquakes respectively contribute to the expected cost of damages and losses over the lifespan of the structure.
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I design feedback close loop system with a robust static feedback controller using Hinf approach where control low is u(t)=Kx(t). What is the good method to show robustness for this type of system? What plots we can make to show robustness for designed system?
Regards
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We all have been accustomed to see a block diagram for a linear control system, where in the forward path, first sits the controller, then actuator, and then plant.
What if we flip the blocks of actuator and plant. I mean what if we place plant before actuator?
We know that, the closed-loop transfer function does not depend on the consecutive order of blocks in forward path, since: C(s)/R(s)=G(s)/(1+G(s)H(s)), where G(s) is the forward-path gain and H(s) is the feedback-path gain (sensor). The forward path gain could be decomposes as:
G(s)=Gcontroller(s)*Gactuator(s)*Gplant(s).
Therefore, since these transfer functions in forward-path are multiplied, then it does not differ what are their consecutive order.
Hence, let's flip the plant and actuator blocks in the forward path: G(s)=Gcontroller(s)*Gplant(s)*Gactuator(s).
What happens next? Nothing about the stability, but a great revamp of the required embedded system and controller design. Because instead of measuring plant out-put to send it into the feedback path, we only need to measure actuator out-put which is more convenient through the control engineering practice. In other words, we have mixed controller and plant blocks together, to establish a new controller which physically controls the actuator.
For stabilization, I think it works well. For example, assume an aircraft autopilot (controller). Through this revamped controller design, instead of measuring and regulating aircraft states as pitch-rate, we only measure and regulate actuator (elevator) deflection. If we can stabilize the elevator angular position to stay at zero, then we have stabilized the aircraft at a level-flight (stabilization), since zero elevator-deflection means level flight has been sustained.
For command following (not stabilization), we may set a mathematical trick as:
G(s)=Gcontroller(s)*Gactuator(s)*[Gplant(s)/Gactuator(s)]*Gactuator(s),
in this way, even for the case of command following (control) we still need to only measure actuator output.
Advantage:
- in the classical loop, we use actuator model as a software block in the controller simulation and design, while using plant as a hardware to be regulated by physical sensing of its output.
-in my revamped controller setup, we use plant model as a software block in the controller simulation and design, while using actuator as a hardware to be regulated by physical sensing of its output.
It seems physically sensing and regulating actuator dynamics, would be much convenient than physically sensing and regulating plant dynamics.
Please share with me, your comments and ideas about my plan.
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A trailer capable of steering the cabin (honestly I've never heard of) is a different physical system than a cabin steering a trailer. Controllers are designed taking into account a certain mathematical description of physical systems, but any mathematical description implies a series of assumptions which should be true in order for the controller to have the expected behaviour.
So, I may assume there can be systems where you can replace actuator and plant, but I can imagine this as a pure theoretical exercise.
For example, you can imagine to use a PID to control a boost converter's output and, let's say, you can find a boost converter which forces a given PID to behave in some way, but honestly this wouldn't make much sense in practical terms.
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I want to find PID parameters to regulate my system.
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Manual PID tuning is done by setting the reset time to its maximum value and the rate to zero and increasing the gain until the loop oscillates at a constant amplitude. (When the response to an error correction occurs quickly a larger gain can be used.
The best method for tuning PID controller parameters is depend of your system
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I want to know all the things related and necessary which help me in control system field.
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You can use control methods in everywhere. Below, there are some areas:
  1. Power systems (smart grid , micro-grid , power generation , power devices (motors,...))
  2. Electrical drivers for electrical motors
  3. Biological systems
  4. Economic systems
  5. Social systems
  6. Robotics
  7. Aerospace and aeronautical systems
  8. Automobile industry
  9. Chemical Process
  10. Multi agent systems
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Dear researches
In general, the value of modal participating mass ratio (MPMR) for each vibration mode represents the participation of each mode in the structural responses.
when a structure equipped with TMD, a vibration mode is added to the others. MPMR value of the added vibration mode can be significant even when TMD mass is very small and as a result, TMD has no effect on the structural responses.
How can this contradiction be justified?
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I second Einar Strømmen & Pranoy Nair responses, also I would like to add a point that when we add a TMD we are introducing damping into the system. I am not really sure how you would define MPMR in such a system as modal analysis works only for undamped systems.
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Dear fellow researchers, I would be obliged if anyone of you could kindly advise me on the amount of the publication fees for the Journal of Advanced Research in Dynamical and Control Systems (1943023X).
JazakAllah and thank you very much.
Regards
Cikgu Arman
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In my reply of 14th of May I expressed my concern about this journal “Journal of Advanced Research in Dynamical and Control Systems”.
Indeed according to the latest list of discontinued titles in Scopus this journal is no longer indexed in Scopus (see attachment).
Best regards.
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What is the significance of a bode plot obtained by the load disturbance transfer function G(s)/(1 + G(s)C(s))? I have two control systems and have attached the bode plot of both. The time response of the one controller is better than the other one. But how to conclude this from the frequency response? (The better controller's frequency response is shown in red color). How does a bode plot justify that the red one is better than the other in disturbance rejection?
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Dear Purav,
As per my analysis, if you look into the phase plot of bode diagrams then can see the red one never cross the -180 degree line whereas green one cross -180 degree line. So obviously red one has infinite gain margin and better than green one. The gain margin is infinite means the path never crosses the real axis in the left half plane (the path goes to the origin as |s|→∞. from nyquist/polar plot perspective.
Now if you look into the phase margin the red one also have substantial amount phase margin though green one has infinite phase margin.
Overall we can say red one is better than green one.
For more better analysis you can check the bandwidth as well as sensitivity measurement as per type of the disturbance ( whether input/output/feedback path disturbance)
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Dear researchers
I can model tuned mass damper (TMD)in opensees platform. Now I want to model active mass damper (AMD) in opensees. So, how can I model its auxiliary equipment including sensors, control computer systems and actuator?
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The plan is to control an average power generator speed to achieve a MPPT strategy.
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In short, the benefits of using fuzzy controllers can be illustrated as follows:
They are quick to comprehend conceptually: the ideas underlying them are very basic and fundamental;
They are flexible: they enable emerging Fuzzy structures to be applied to their features by applying new information to established rules;
They are tolerant of incorrect data: calculation precision errors or randomness affect device efficiency little;
You might model nonlinear processes of subjective complexity: structures like "Adaptive Neuro-Fuzzy Inference Processes" will do that;
They can be founded on the experience and actions of the system;
They can be combined easily and deliver improved efficiency with conventional control systems;
They are built on the normal human language and allows new rules to be introduced, exceptions and new behaviours to established Fuzzy structures.
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I have the transfer function of MPPTC obtained by the dynamic modeling of MPPTC. I am trying to design the control system for MPPTC to be used for Cubesat application. Any help will be highly appreciated.
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in fact , as to MPPT, the P&O is dramatically useful . you can have a try.
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Please i need recommnedation on texts or literature that can improve my knowledge and skills on tuning of control systems ranging from sliding mode, LQR/LQG and others. I alwys have problem at this stage after rigor of modeling.
Most of control design problem involves tuning heuristically. In my opinion, this is randomness that doesnt have strategies. Even PID control with popular Ziegler Nichols still involve randomness!
there should be a way to know the range of tuning.
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Hi,
Can methods used to diagnose faults in control systems be used to detect cyber attacks in control systems?
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It depends on the type of Cyber Attack. Some can, and some can be difficult to detect (like the Replay Attack).
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We have always thought of broader research and joining hands in research and analysis of electronics and computer science. As a computer science expert you may be a seasoned programmer and a thinker. I would like to introduce the different domains where electronics and computer science can merge and play a role
Applied Electromagnetics & RF Circuits: Applied electromagnetics (EM) plays an essential role in areas such as wireless technologies, the environment, life sciences, transportation, and more. Faculty and students perform research in all aspects of applied EM, including Microwave and Millimeter-Wave Circuits, MEMS Circuits, Antennas, Wave Propagation Studies for Wireless Applications, Scattering, Computational Electromagnetics, Active and Passive Microwave Remote Sensing, Plasma Electrodynamics, and EM Metamaterials.
Computer Vision: Research goals include: i) the semantic understanding of materials, objects, and actions within a scene; ii) modeling the spatial organization and layout of the scene and its behavior in time. The algorithms developed in this area of research enable the design of machines that can perform real-world visual tasks such as autonomous navigation, visual surveillance, or content-based image and video indexing.
Control Systems: The development of sophisticated computer aided design software has enabled analysis and controller design for complex multivariable systems. The needs of society for improved transportation safety and a cleaner environment have posed challenges that can only be solved with feedback control.
Embedded Systems: Designing embedded systems is a huge challenge because they have so many requirements: they often need to be tiny, high-performance, inexpensive, reliable, and last a long time on poor power sources, all while sensing and influencing their surroundings. Faculty and students are applying their skills to the entire “stack,” from transistors and circuits to operating systems and applications.
ECE Education Research: ECE Education Research is a rigorous, interdisciplinary field in which scholars focus on and apply research methods from education, learning sciences, and social-behavioral sciences to address a variety of issues pertaining to: teaching and learning; college access and persistence; workforce development; and other issues critical to the success of the field of engineering. Scholars in the subfield of ECE Education Research focus on issues pertinent to the discipline of electrical and computer engineering.
Integrated Circuits and VLSI: Research in Very-large-scale integration (VLSI) digital circuits includes microprocessor and mixed signal (microcontroller) circuits, with emphasis on low-power and high-performance; computer-aided design, including logic synthesis, physical design, and design verification; testing and design for testability; advanced logic families and packaging; integrated circuit micro-architectures; and system integration. 
MEMS and Microsystems: Devices such as micromachined neural probes for implantable prostheses, ultra-miniature low-power pressure sensors for catheters, tactile sensors arrays for fingerprint analysis, infra-red imagers for manufacturing process control, and micro gas chromatography systems for environmental monitoring are some of the past contributions of this program.
Network, Communication & Information Systems (NCIS): Communication networks are collections of receiving and transmitting stations that may relay information from one station to another by means of other stations acting as relays. There are many components in the process of transmitting information in a communication system. One component is information representation in minimal form, that is data compression. A second aspect of communication is modulation; the process whereby information is mapped into waveforms suitable for propagation. A third aspect is error control coding; the method by which errors made in receiving information can be corrected. The performance of a communication system is usually measured in terms of the probability of incorrectly decoding the information or the distortion between the original information-bearing signal and the reconstruction, and the energy used.
Optics & Photonics: Specific areas presently under investigation include nonlinear optics, optical MEMS (coupling optical fields to mechanical motion), ultrafast optics, semiconductor quantum optoelectronics, Terahertz generation and applications, fiber and integrated photonics and lasers, high-power fiber lasers, x-ray and EUV generation, quantum optics and quantum computing, optical microcavities, nanophotonics, spectroscopy of single quantum dots, biophotonics, and biophysical studies of biomolecular structure. 
Plasma Science & Engineering: PSE has incredibly broad and strategic impact on national and economic security, and providing societal benefit. Modern microelectronic devices could not be fabricated in the absence of plasma etching, deposition and cleaning processes. Thin film solar cell technologies depend upon plasma deposition to be economically viable. Fabrication of biotechnology devices depends on plasma processes to harden artificial joints and prepare biocompatible surfaces on tissue scaffolding. Interplanetary probes are powered by plasma thrusters.
Power and Energy: Faculty are investigating energy conversion systems where enhanced performance of electrical machines and power electronics is being exploited to develop a variety of novel applications, from automotive propulsion systems to wind generators. Power systems research is seeking new tools and techniques for improving grid efficiency and robustness.
Quantum Science & Technology: Quantum mechanics has played an important role in many areas of engineering for decades now, fueling an increasing number of fundamental breakthroughs, as available devices become smaller and individual particles can be precisely controlled in the lab. Newly observed phenomena are often best explained using quantum theory, facilitating new technologies and applications. In particular, accounting for quantized energy levels and the Fermi nature of electrons in semiconductors has lead to more accurate modeling and optimization of CMOS transistors, as well as new results on capacitively-coupled quantum dots.
Robotics & Autonomous Systems: We also use artificial intelligence techniques for dealing with planning and uncertainty, localization and mapping, sensor processing and classification, and continuous learning.
Signal & Image Processing and Machine Learning: Signal processing is a broad engineering discipline that is concerned with extracting, manipulating, and storing information embedded in complex signals and images. Methods of signal processing include: data compression; analog-to-digital conversion; signal and image reconstruction/restoration; adaptive filtering; distributed sensing and processing; and automated pattern analysis.
Solid-State Devices and Nanotechnology: Research in organic and molecular electronics includes organic field-effect transistors, integrated circuits and light-emitting devices on glass and plastic substrates, hydrogenated amorphous silicon thin-film transistors and active-matrix arrays on glass and plastic substrates for flat panel displays and sensors, and active-matrix organic light-emitting display technology. 
What are the other areas where there can be similar synergy and let us discuss further
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I think an other vision may be based on another topical classification which may be more natural as:
On the top is the electronic systems which may be classified in more branches such as:
The communications systems ; the Instrumentation systems, the control systems and the digital computers as wall as other subbranches as biomedical systems.
Every system contains many subsystems and many building blocks.
A communication systems such as a communication network may contain the other systems and subsystems.
The building blocks are built from electronic circuits analog and digital.
The circuits are built from digital, analog and mixed circuits
The circuits themselves are built from electronic devices.
The electronic devices are build fro material by fabrication processes and technology.
This classification may be more transparent and may be easier to correlate the research tasks with it.
The electronic systems are now configured such that it has a digital core and an analog and digital periphery.
So, co design of software/hard ware is always required with the weight tends to the software tasks.
The hard ware design is more or less made by the semicondcutor companies.
The soft ware is always left to customize the hardware. More software engineers
are required than hardware engineers. The electronic systems after digitization became software oriented.
Best wishes
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I am planning to develop a test rig to control the valve opening and closing. The input signal for the control system is SSI input from absolute encoder and voltage (up to 5V) from pressure sensor (or 4-20mA current). The output from the control system is ON/OFF voltage signal (24VDC) to open and close the valve. Can anyone advise me what are the suitable instruments to be used & to link between all those devices (sensors and valves) to the controller and laptop/PC?
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Nowadays a lot of people are using the Arduino system for this sort of task, because of the huge range of input-output modules and code examples and libraries. Components suitable for industrial use are available.
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I some papers, the mean square error is considered. In some other, the mse is normalized by dividing the error by the (total sampling instants x the total length of the reference trajectory).
Which solution does represent the factual error?
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Dear Samira Eshghi,
The performance of the controller is generally measured using the following parameters such as :
1) Maximum Overshoot
2) Settling Time
3) Rise Time
4) Steady State Error.
Additionally, there are other ways to measure performance in literature. This includes integrated absolute error (IAE), the integral of squared-error
(ISE), or the integrated of time-weighted-squared-error (ITSE). These performance measures have their own advantages and disadvantages. For example, the disadvantage of the IAE and ISE criteria is that its minimization can result in a response with relatively small overshoot but a long settling time because the ISE performance criterion weights all errors equally independent of time. Although the ITSE performance criterion can overcome the disadvantage of the ISE criterion, the derivation processes of the analytical formula are complex and time-consuming. The formulas for these performance measures or the performance measure based on overshoot, settling time, rise time and steady-state error can be found in the paper given below.
In discrete domain, ISE refers to Mean Square Error. I am unfamiliar with normalized-mean-square-error .
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I want to use "adv_no_gui" functionality of ADVISOR in order to implement a new power management strategy and an optimization routine for a parallel hybrid electirc vehicle. But there is not enough information available in ADVISOR help. If anyone have experience in this field, please help me.
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Are there tutorials for this software?
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About the recent aspects of deep learning in control systems
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Dear Firas
I would like to advise you on these three papers that can give you a wide perspective about the use and application of Deep Learning (DL) and Deep Reinforcement Learning (DRL) in dynamic systems and control.
DL and DRL learning are very related in the area of control and dynamic systems, given DRL has been used by some researchers as an adaptive control new perspective. Additionally, DL is part of the motor of DRL
[1]- This first paper is going to find a DL and Reinforcement Learning (RL) historic review, finally you will find an application perspective of these two topics to control and dynamic systems (General introduction)
[2]- This is going to model the control problem as a Markov Decision Process and it is going to face the problem focusing on Dynamic Programming (DP) and Artificial Neural Networks(ANN). This is going to be a very good starting point for your question answer.
[3]- It is a very good review from Benjamin Recht. It is going to give you a control systems review from the LQR perspective passing through DP, RL, and finally DRL. At this point, you are going to have clear the relation at DL and DRL and how these ideas are applied to dynamic systems and Control.
[1] Lee, Jay H., Joohyun Shin, and Matthew J. Realff. "Machine learning: Overview of the recent progresses and implications for the process systems engineering field." Computers & Chemical Engineering 114 (2018): 111-121.
[2] Lee, Jay H., and Jong Min Lee. "Approximate dynamic programming based approach to process control and scheduling." Computers & chemical engineering 30.10-12 (2006): 1603-1618.
[3] Recht, Benjamin. "A tour of reinforcement learning: The view from continuous control." Annual Review of Control, Robotics, and Autonomous Systems 2 (2019): 253-279.
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i want to submit my research paper
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The interesting this, if correct, how Scopus successfully listed this journal. This shows that the system has gaps and should be reviewed. What did Scopus mention about this problem?
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I started to learn the Kalman filter. However, I couldn't understand its' difference with the feedback/feedforward control system. Ultimately both systems are used to minimize the errors between the measurement and estimated state.
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