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Cyber security question
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A Linux system's permission and access control system is built on three primary permission categories: read (r), write (w), and execute (x), applied to three user classes: owner, group, and others. Each file or directory has these permissions managed via the chmod and chown commands. In my experience, while setting up secure environments for IT projects, I assigned minimal permissions to files to prevent unauthorized access, ensuring only specific users could modify or execute critical scripts. Linux also supports Access Control Lists (ACLs) for fine-grained permission control, which I’ve utilized to define custom rules for specific user needs in collaborative environments.
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2025 4th International Conference on Energy Utilization and Automation (ICEUA 2025) will be held in Beijing, China from January 17-19, 2025.
Conference Website: https://ais.cn/u/quqeE3
---Call for papers---
The topics of interest for submission include, but are not limited to:
1. Energy Engineering
· Simulation and optimization of energy conversion systems
· Energy materials
· Carbon capture and storage
· Energy equipment
· Modelling and optimization of heat pumps, refrigeration and air conditioning systems
· Urban energy systems
· Energy storage systems
· Transport and distribution of electric Energy
·New energy systems and control technology
2. Automation Engineering
· Measurement and control technology and instrumentation
· Modern signal processing and detection technology
· Automation technology applications
· Microwave millimeter wave test technology and remote sensing
· Automatic control application theory
· Control systems engineering
· Control system simulation technology
· Navigation guidance and control
· Fluid transmission and control
· Automation instrumentation and devices
· Robot control
· Control science and technology
---Publication---
Submitted paper will be peer reviewed by conference committees, and accepted papers after registration and presentation will be published in Journal of Physics: Conference Series (ISSN:1742-6596), which will be submitted for indexing by EI Compendex, Scopus.
---Important Dates---
Full Paper Submission Date: December 27, 2024
Registration Date: January 6, 2025
Final Paper Submission Date: January 10, 2025
Conference Dates: January 17-19, 2025
--- Paper Submission---
Please send the full paper(word+pdf) to Submission System:
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Energy Utilization and Automation " > Utilizatopn versus Automation -scope not cleare
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In deriving a model of an LTI dynamic system, the model of the system does have no RHP zeros, however, when inspecting the step response, it reveals a non-minimum phase system represented by a small negative (backward) time response. How to relate?
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Hello Salman, typically, this is associated with zeros in the right half plane (RHP) of the system’s transfer function. However, it’s possible to observe non-minimum phase behavior in the time domain response even if the system doesn’t have RHP zeros. This could be due to the presence of time delays or certain types of nonlinearities in the system.
A time delay in the system can cause a phase lag, which might appear as a non-minimum phase behavior in the time domain. This is because a time delay can cause the system’s output to initially move in the opposite direction before moving towards the final steady-state value.
Nonlinearities can also cause similar effects. For instance, if the system has a backlash or hysteresis, it might show a non-minimum phase behavior in the time domain.
So, while your system might not have RHP zeros, the observed non-minimum phase behavior in the step response could be due to time delays or nonlinearities in the system. You might need to consider these factors when modeling and controlling your system3.
These links could help more.
For robust systems, this could help too:
Chap 26, pages 383-393, also pages: 375-381
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How can advanced wastewater treatment processes be integrated with real-time monitoring and adaptive control systems to enhance the removal efficiency of micropollutants and pathogens, while also optimizing energy consumption and minimizing the production of secondary pollutants?
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Gaurav H Tandon Advanced wastewater treatment can get better at removing tiny pollutants and germs by using real-time monitoring and adaptive control systems. These systems can adjust treatment processes on the fly, making them more efficient. For example, they can cut energy use by up to 30% and reduce secondary pollutants by over 20%, all while improving overall water quality.
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I am seeking information about the 'actual' real-life applications of metaheuristic algorithms, specifically beyond academic research papers. I understand that these algorithms are powerful optimization tools in fields like Engineering Design, Scheduling, Finance, Robotics, and Control Systems, among others. Could you specifically tell me about the industries or companies that currently use these optimization techniques, and for which tasks or business operations? I am interested in understanding their true practical utilization in the real world.
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Another algorithms used: simulated annealing (SA), gray wolf Optimization (GWO), EOA, SCA, etc.
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S. Hasan Saeed treated wrote on Automatic Control Systems (with MATLAB programs) and one of his books has Chapter 12 as Robust Control Systems. If you have the book, kindly take snap shot of chapter 12 only and send to jocianvef2004@gmail.com, please. I am dare in need of his explanation in that topic.
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Modern Control Engineering
Book by Katsuhiko Ogata
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What is the latest research progress of adaptive signal control system in the field of transportation?and I feel that if it is just to improve efficiency, it is meaningless.
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I think Adaptive signal control systems (ASCS) bring several benefits, including improved traffic flow, reduced congestion, and decreased vehicle emissions, which collectively enhance urban mobility and environmental quality.
However, these systems also face challenges, such as high implementation and maintenance costs, privacy concerns related to data collection, and the potential for technological failures that could disrupt traffic rather than streamline it.
These technologies thus present a balance of significant improvements in efficiency and safety, with substantial considerations in terms of cost, privacy, and dependability.
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I'm currently tasked with designing the control system for a reverse osmosis (RO) feasibility project. I'm looking for guidance on how to approach this design process and what software options are available for designing control systems. Additionally, I'm interested in exploring how artificial intelligence (AI) can be integrated into this project to enhance its functionality and efficiency. Can you provide insights on these aspects?
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Lee Clawson thank you sir for your valuable response
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I have a Control System Panel having a PLC system and Control Relays installed in the panel. This panel is to be subjected to the "Seismic Shake Table Test with 5OBE + 1 SSE vibration level". The "Electromechanical SLIM Relay, 24V DC operated, 6mm Thick SLIM Type" is a major component in this panel (250+ Nos quantity).
I understand that electromechanical relay has a reed switch that may move due to vibrations hence the Shake Table Test will fail if any of the relay becomes non operative after the vibrations.
Whether I need to go for alternate type of relays (Such as Solid state type relay SSR ? An SSR activates the Transistor base and the output (emitter of Transistor) will drive the field output load .
Will the electromechanical relay pass a Seismic vibrations of SSE level ?
Appreciate any expert opinion and suggestions on the above.
Regards
Amol
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Yes, as the seismic vibration frequencies are lower in Hz/sec
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2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2024) will be held in Shenzhen, China, from June 14 to 16, 2024.
---Call For Papers---
The topics of interest for submission include, but are not limited to:
(1) Artificial Intelligence
- Intelligent Control
- Machine learning
- Modeling and identification
......
(2) Sensor
- Sensor/Actuator Systems
- Wireless Sensors and Sensor Networks
- Intelligent Sensor and Soft Sensor
......
(3) Control Theory And Application
- Control System Modeling
- Intelligent Optimization Algorithm and Application
- Man-Machine Interactions
......
(4) Material science and Technology in Manufacturing
- Artificial Material
- Forming and Joining
- Novel Material Fabrication
......
(5) Mechanic Manufacturing System and Automation
- Manufacturing Process Simulation
- CIMS and Manufacturing System
- Mechanical and Liquid Flow Dynamic
......
All accepted papers will be published in the Conference Proceedings, which will be submitted for indexing by EI Compendex, Scopus.
Important Dates:
Full Paper Submission Date: April 1, 2024
Registration Deadline: May 31, 2024
Final Paper Submission Date: May 14, 2024
Conference Dates: June 14-16, 2024
For More Details please visit:
Invitation code: AISCONF
*Using the invitation code on submission system/registration can get priority review and feedback
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Data science
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Hi,
I am going to graduate soon InshaaALLAH.
I have a plan to write a patent, I am currently working in the control and design laboratory and my Prof. expertise is also in control systems.
I will continue as a researcher in my current lab after my MS degree. So, I want to make this journey joyful and full of learning, and doing something great. I want to present some proposals to write a patent during my job as a researcher.
I believe that mechanical engineering is so old and already a lot of areas are explored and it is difficult to find some novel topics for research except integrating AI to make it novel.
I am open to recommendations that use AI in the control systems domain or LiDAR.
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Some fields in the area of control systems, mechanical engineering where you might consider writing a patent, including those that use AI in control systems or LiDAR. Here are a few possibilities:
  1. AI-based control systems for robotics
  2. Mechanical engineering systems with AI-based fault detection and diagnosis
  3. AI-based control systems for energy-efficient buildings
  4. LiDAR-based control systems for autonomous vehicles
  5. AI-based control systems for precision agriculture
  6. Mechanical engineering systems with AI-based predictive maintenance
  7. AI-based control systems for wind turbines
  8. LiDAR-based control systems for drones
These are just a few possibilities. I hope this gives you some ideas to get
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Good morning, I newly joined research scholar, suggest me how to start my research on
“High gain DC-DC converters using control systems.”
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If you know how to obtain the dynamic model of DC-DC converters, whether they are high-gain or not, classical or modern control theory can be applied.
In principle, all power electronic converters are nonlinear in nature, but obtaining averaged dynamic models helps in the implementation of both linear and nonlinear controllers.
Control knowledge can be basic, although it's not an easy subject; it's an entire branch of knowledge that can be applied to power electronics.
I would recommend the following:
  1. Dynamic modeling of power electronic converters.
  2. Study of switching functions (PWM).
  3. Studies in control theory.
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Hello researchers;
when I am trying to apply model reference adaptive controller in controlling of 2-dof robotic manipulators, some difficulties faced me. as we know all, in model reference adaptive control system we need the transfer function of both the plant and the model. this is so possible in linear systems. but what we have to do in case of if our system is nonlinear? such as in 2-dof robotic manipulators.
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I think about strategies to modify things, for instance, combining an inner loop MRAC for dealing with system uncertainties and an outer loop using a Fuzzy Proportional-Integral Controller for external disturbances, as found in recent research.
I'm sorry that I can't help you much.
You can do it; never give up.
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How to integrate Cupcarbon and Sumo to implement Iot-aware neurofuzzy-based traffic control system? I am working on the implementation of my research. I need the best approach to do the above . I need the best programming choice python or matlab or java
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Here's a step-by-step guide but You MUST understand and run simulation at every step
1. Understand the Tools:
CupCarbon is a wireless sensor network simulator, while SUMO is a traffic simulation software.
2. Set up the Environment:
Install CupCarbon and SUMO on your machine, ensuring that they are properly configured and working individually. Make sure you have Python installed as well.
3. Define the System Architecture:
Design the architecture of your IoT-aware neurofuzzy-based traffic control system. Identify the components, interfaces, and data flow between CupCarbon, SUMO, and your neurofuzzy-based traffic control system.
4. Implement the Neurofuzzy-Based Traffic Control System:
Develop the neurofuzzy-based traffic control system using Python. You can use libraries such as scikit-fuzzy or neuro-fuzzy systems to implement the neurofuzzy logic.
5. Interface CupCarbon with Python:
CupCarbon provides a Python API that allows you to control and interact with the simulator programmatically. Use the CupCarbon Python API to create, configure, and control the wireless sensor network in CupCarbon from your Python code.
6. Interface SUMO with Python:
Similarly, SUMO also provides a Python API called "traci" (Traffic Control Interface) that allows you to interact with the SUMO traffic simulation. Use the traci API to control the traffic simulation from your Python code.
7. Establish Communication between CupCarbon and SUMO:
Develop a communication mechanism between CupCarbon and SUMO. You can use sockets or a message queue system to exchange data between the two simulators. For example, CupCarbon can provide sensor information to SUMO, which can then adjust the traffic simulation based on the received data.
8. Integrate the Neurofuzzy-Based Traffic Control System:
Integrate traffic control system with CupCarbon and SUMO. Use the data received from CupCarbon and SUMO to make intelligent decisions in your traffic control system and control the traffic in the simulation accordingly.
9. Test and Evaluate:
Run simulations and evaluate the performance of your integrated system. Collect relevant metrics and analyze the results to assess the effectiveness of your IoT-aware neurofuzzy-based traffic control system.
Good luck: partial credit AI
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The step response of an MPC control system for a nonlinear plant gives a response with vertical line before it starts tracking. I have been trying to figure out what could be responsible for the vertical line and how to eliminate it. suggestions on how to eliminate the vertical or what could be repsonsible is welcome. I have attached the response below
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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.
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Dear Colleagues ; I am interested in studying advanced techniques for AC machines and i need to answers some questions they are as follows:
1/ Which basic distinctions set observation techniques apart from control approaches?
2/ Does the application of the type of observation impact the performance of the AC machines when hybridization between advanced techniques approaches occurs?
3/ Is it possible to apply both strategy to the machine by applying different techniques for each strategy?
3/ If we improve the rates of each strategy using advanced artificial intelligence techniques, will this increase the data of the controlled system?
4/ What's the difference between the MRAS, LGI (Kalman filter) and SMO. Are they limited to a specific time for machine systems?.
5/ Is it logical to apply a technique subject to a linear strategy to a nonlinear model of a machine like the Kalman filter or the extended-based adaptive observation?
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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?
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How can advanced fuzzy logic and machine learning techniques be integrated to enhance decision-making and control systems in complex, uncertain, and dynamic environments, such as autonomous vehicles navigating real-world urban traffic scenarios?
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Thank you, for sharing this useful link.
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Hello:
I have a question about the definition of adaptive control, since I´m researching about making a model-free adaptive control system. I will appreciate your help.
The definition I found says that an adaptive control modifies its parameters or structure in order to achieve a performance index. Reading about the subject in different sources I noticed that when they refer to an adaptive controller, it always has a model of the plant and implies an adaptation law which is usually obtained by taking the model and manipulating expressions.
My deduction is that when these sources refers to adaptive control, is about a kind of this instead. Am I right? I will really appreciate your support on this.
Thanks.
Pablo.
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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.
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Hello there!
I'm looking for some decent Control System Idea titles that can be used for my undergraduate thesis at the Undergraduate level.
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Voltage profile improvement in industries with series compenstor using AI predictive models
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Various domains have methods for compatibility testing. For example: Software, design, electrical (keyword E-Plan), control systems, network technology,
it does not have to be exclusively domains in the mechatronics field
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There are several drawbacks to using harmonic potential functions for obstacle avoidance:
  1. Local minima
  2. Overshoot
  3. Difficulty in tuning
  4. Limited applicability
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.
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I will like to conduct a study to comparatively assess the internal control system of 2 companies in other to find out which of them is more efficent
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A two company sample? Is that enough? That will not allow any generalised conclusions. You may wish to rethink your research aims and method.
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What term or name is appropriate for the control system that incorporates a form of feedback from the overall controller signal, which accumulates within a limit and results in a slight adjustment to the system's setpoint, in addition to the typical PID control?
Thank you...
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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.
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IASC-Intelligent Automation & Soft Computing new special issue “New Advances and Applications in Intelligent Control Systems” is open for submission now.We are calling for papers.Details:https://www.techscience.com/iasc/special_detail/intelligent-control-systems
Keywords
1.New theories, methods and performance evaluation of intelligent control systems 2.Advanced neural networks and fuzzy controllers 3.Machine learning and deep learning-based control systems 4.Wireless networked control 5.Automatic control using cyber-physical systems and internet of things (IoT) 6.Advanced control of manipulators and robotics 7.Bio-inspired optimization algorithms for auto-tuning control design 8.Recent intelligent control applications of industrial manufacturing and biomedical systems
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Great, Elaine Lu Happy to Collaborate.
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I am working on a small wind turbine as part of my internship for my course. The rated capacity of the small wind turbine is 700 watts at a rated wind speed of (…) m/s. The turbine is installed on a (height of the pole) m- steel pole/ tower. Further, the turbine is connected to the local electricity grid.
Challenges faced: At 3 m/s- windspeed, I am observing that the connected sensors/ electronics are consuming 15W
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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.
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How errors should be manipulated for a PID control system in which control two parameters and as a result it takes two errors to calculate the input signal for the system?
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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.
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Hey, I’m looking for a thesis topic within blockchain and MCS. Thinking more of how organization can adapt it in their system.
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I want to design a control system which can control the fan on all four sides, by this i mean a single fan can be lifted [upwards downwards left and right] ,is this possible ?
The idea i want to use it in the agriculture system for pesticides control.
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You mean a four rotor aircraft? You can refer to the products of Dajiang Company
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I am new to MD simulation and as part of my learning I am trying different methods to control temperature and pressure of a system by changing the factors. I am trying to apply velocity rescaling now but I am not sure how it works on lammps. I checked the documentation but I still can't figure it out how can I apply it.
Thanks
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Dear all,
Can we say, robust iterative learning control design, in other form is Data driven control design
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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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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.
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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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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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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%
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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
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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M.tech project topic in control system
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Dear sir
1. Modeling and management of photovoltaic and fuel cell based alternative power systems
2. Implementation of Solar PV- Battery and Diesel Generator Based Electric Vehicle Charging Station
3. A Novel Design of Hybrid Energy Storage System for Electric Vehicles
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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 Grigor 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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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.
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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 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 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?