Control Systems - Science topic

Kamal Abdulraheem

Thank you for sharing the step response plot. The vertical line artifact you see before the controller response kicks in is likely caused by the predictive model and receding horizon control mismatch:

- At the step change, the model predictive controller is basing its control actions on predicting the future plant output using its internal model.

- However, the real plant output does not start responding until the actual control input from MPC starts actuating it. This causes the temporary mismatch.

- Once the plant starts receiving the optimal control inputs and its output aligns with the predictions, the vertical artifact disappears.

Some ways to potentially minimize this:

- Tune the prediction horizon Hp to be as small as possible while still maintaining control performance. Shorter horizons reduce the artifact duration.

- Set the control horizon Hc shorter than Hp so control inputs react faster to changes. But stability has to be ensured.

- Improve dynamic accuracy of the internal process model to better match real plant lag. Model mismatch exacerbates the issue.

Please let me know if this explanation helps point you in the right direction.

1/ Monitoring techniques are distinguished by identifying the internal state of the system, whereas monitoring methods focus on adjusting the internal state of the system.

2/ Yes, the application of the monitoring type does affect the performance of air conditioning devices, especially in cases of hybridization. This depends on the complexity of the system and specific control requirements.

3/ Both strategies can be applied to the machine using different techniques, but this requires a careful study of the compatibility of the methods and specific needs.

4/ MRAS, LGI (Kalman filter), and SMO differ in purpose and application, and are not limited to a specific time for machine systems but depend on the complexity of the system and control conditions.

5/ A linear technology can be applied to a nonlinear model of the machine, but careful consideration of system analysis and adaptation to its characteristics is essential to achieve optimal performance.

Do you have any other questions or need further clarification?

Thank you, for sharing this useful link.

People do many things without adaptive control.

However, at some point, we may understand that the performance may not be what we were looking for. Either the plant parameters were not exactly what we thought, or they might have changed from what they were in the beginning. Moreover, the object of control could be time-varying, nonlinear, etc.

This is what led people to think about “adapting” the control parameter to the actual situation.

A first idea was to only change the main control loop gain. This was called the MIT rule and it worked nicely and was even implemented on a plane that flew nicely…until it crashed. Comes out that working with time-varying control parameters is not the same as fixed parameters.

This made people reluctant to the idea of adaptive control.

Then, some names, such as Prof. Kumpati (Bob) Narendra from Yale, contributed a lot to the theory of stability and also came up with their own idea of adaptive control. This appears in literature as Model Reference Adaptive Control (MRAC) and this is what you usually see in literature.

It requires the plant to behave like some ideal model of the same order and indeed, it tries to modify the parameters of the big plant.

As it happens, by 1980, after some engineering experience, I came to RPI (Rensselaer) for a PhD. The “problem” was that the Professor’s grant was for Large Flexible Structures (LFS). They were not only large plants but also MIMO, so there was not much we could do with the classical MRAC.

They started using another idea, which was called Command Generator Tracking (CGT). You don’t try to make all state variables of the big plant follow all state variables of some big model. Instead, you define some low-order model that only defines the trajectory that you would like your plant output to track. They had some publications, yet the idea was negatively received, so they were almost on their way out. However, the grant forced my advisor to let me “try to do something.”

As I was coming from the industry, I liked this idea of a simplified controller with varying parameters, and I even “felt” that it should work. The plant can be of order 20 or 50 and yet the model and the controller can be of order 5.

What can I say? I ended up with a low-order adaptive controller that managed to control the pretty big LFS. It was interesting that people at JPL liked the idea and even dared to implement it on their large flexible antenna.

After playing with it, I decided to call our approach “Simple Adaptive Control (SAC).”

I won’t make this message a dissertation, but if you write Simple Adaptive Control on Google, you can see quite a bit, of publications from all around the world.

Also, please always feel free to come back with any questions.

Control system engineers are concerned with three main topics: observability, controllability and stability. Observability is the ability to observe all the parameters or state variables in the system. Controllability is the ability to move a system from any state to any desired state.

Voltage profile improvement in industries with series compenstor using AI predictive models

There are several drawbacks to using harmonic potential functions for obstacle avoidance:

Overall, while harmonic potential functions are a popular approach to obstacle avoidance, they have some limitations that should be considered when choosing an algorithm for a particular application.

A two company sample? Is that enough? That will not allow any generalised conclusions. You may wish to rethink your research aims and method.

Marco Rinaldi

in the PID, the integral part is already limited to anti-windup. I am not an expert in control however, I think the selected (red marked) part is used to readjust the setting point to put the system in active all the time. when the system works the setpoint is swinging in a range +- 10 degrees around the actual setpoint which is 1200 degrees.

Great, Elaine Lu Happy to Collaborate.

Based on your final statement it appears that you are already monitoring the power to your sensors and instrumentation. However, a simple amp-meter on the input to the electronics package would work.

The best way to reduce the power consumption is to first eliminate any sensor, and it's related instrumentation, that is not strictly required for your project. The next step would be to evaluate the sensors to determine if they are the most energy efficient type available. Finally, analyze your instrumentation to eliminate any redundancy. For example, if one sensor has a power supply of 5 watts but only requires 3 watts to operate and a second sensor has a power supply of 7 watts but only requires 4 watts to operate. You may be able to eliminate the first power supply and use the second to power both. This reduces the overall power consumption because a power supply rated at 5 watts will actually consume 6 or 7 watts due to internal resistances and parasitic losses in transformers.

Integral of Time-weighted Absolute Error (ITAE) can be used as a cost function for tuning PID, however, the question here is how to combine errors of two measured parameters to calculate PID output.

Hi,

By using a monitoring and control system like blockchain, organizations unequivocally signal that they do not trust their employees. When used for management, blockchain's primary function is to coordinate and record actions so that network participants are assured of safe interactions.

For more information about this subject, I suggest you see the links on the topic.

Best regards

You mean a four rotor aircraft? You can refer to the products of Dajiang Company

Hi,

I suggest you see the links on the topic.

Best regards

My experience was to implement myself the NMPC directly in Matlab using IPOPT. It took a while to make all the code all the hessians and jacobians. The benefits is that you get a lot of confidence on the work and the code is much faster to run. Nowadays, there are now some toolboxes available that would make you to save a lot of time.

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

These LMIs can be solved using MATLAB's LMI Toolbox software packages.

For high wind speed operation, most turbines produce its nominal power at pitch angles that range between -1 and 10 degrees. You can select that range as input to your BEM code and check whether you obtain results close to experiment ones. It can agree within +/- 5%

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

Teng Zhang Take a look:

1. https://www.scitepress.org/papers/2018/69057/69057.pdf

2.

3. https://www.diva-portal.org/smash/get/diva2:459937/FULLTEXT02

I do not have exact data regarding the question you are asking.

The best ref.

OIE publication

Infectious diseases books according to subject you need

Hello Anup Marahatta

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.

Anup Marahatta

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.

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

you can see the following links:

1. https://itectec.com/matlab/matlab-problem-with-system-identification-toolbox-and-sim-command/

2. https://www.dcsc.tudelft.nl/~jwvanwingerden/lti/doc/companion.pdf

3. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.461.1702&rep=rep1&type=pdf

4.

Hi Ali Tebaan Hassan Al-ghiz'zy

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 G_p(s) = 10/(s + 1)³, and the input flow is u⁴, where u is the valve opening.

Image source: https://blackhawksupply.com/blogs/articles/different-types-of-control-valves-key-features-and-applications

Sorry question outside of my field

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

Ankur Jyoti Das Implementation of dual axis tracking solar panel control

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.

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.

Dear Stefano Leonori,

I suggest you to see links and attached files on topic.

Best regards

Dear Mikayel Grigor Melkumyan , it is my pleasure if u can read my latest article

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.

Hi,

file name "sfun_ abcaaaaa.c" is not available in the folder mentioned.

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.

You can use T type converter.

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.

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.

Hello Giel Van den Broeck

maybe this will help you

A report about using multiple state space models to establish an overall model.

For a RG discussion on the meaning of the 'online' (or 'on-line') terminology at this forum:

Dear Sir Victor Palamodov,

You are right, it is not true for the matrix. But in the question, the order of B is n*1 and order of C is 1*n. So B*C is a n*n matrix and that is a rank-one matrix.

Thank you Len Leonid Mizrah sir for the detailed answer, can you please tell me something more about charge plasma gate all around MOSFET?

Waqar Ahmed

In Chemical Engineering Process control system like PLC can be linked to MATLAB simulation - https://www.mathworks.com/discovery/plc-simulation.html#:~:text=You%20can%20perform%20PLC%20simulation,workflow%20for%20Model%2DBased%20Design.

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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.

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.

Advances and Applications in Nonlinear Control Systems

The question is how to measure the 21st Century Skills?

Dear Sreeraj Arole ,

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

Hi Saeb AmirAhmadi Chomachar

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.

Aren’t the links working?

Hi,

It would be that this reference can help:

PD: Matlab implementation codes are available.

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.

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:

@ Vinayak Kumar

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.

Best regards

1-IMPROVED INTEGRAL SLIDING MODE CONTROL METHODS FOR SPEED CONTROL OF PMSM SYSTEM

By: Cunjian Xia, Xiaocui Wang, Shihua Li and Xisong Chen

2-http://www.ftik.usm.ac.id/matlab-code-for-sliding-mode-controller-converter.pdf

Dear Sreeraj Arole

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.

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.

Refer to this document: https://picture.iczhiku.com/resource/eetop/ShKYEeUGEgWPTVnv.pdf

See this page for any alternative software:

Best regards

For Video tutorials , we have:

Introduction | Nonlinear Control Systems: https://www.youtube.com/watch?v=Xgnwn0G9qoo

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.

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.

Dear Kouba Nour El Yakine

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

Regards

You can use control methods in everywhere. Below, there are some areas:

Hi Majid Tavakolinia

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.