Adaptive Control and Fuzzy Control | Chapter 10 | Modern Control Systems: Theory, Design and Applications

The theme of this chapter focused on adaptive control and fuzzy control. Adaptive control and fuzzy control were narrated and explained separately, with related theories briefly introduced and explained. The design and analysis of controllers for adaptive control and fuzzy control were discussed. Additionally, adaptive control and fuzzy control were briefly applied to some examples, and their relative mathematical models were constructed. The design of a self-tuning regulator and an adaptive fuzzy controller was also highlighted. Furthermore, examples were provided to enhance the understanding of adaptive control and fuzzy control.

 

Author (s) Details

 

Zhou Gaofeng
Zhongyuan University of Technology, China.

 

Zhang Qi
Zhongyuan University of Technology, China.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49473-76-8/CH10 

Modern Control Systems: Theory, Design and Applications| Book Publisher International

Relevant theories on modern control systems are narrated and explained to help readers grasp some crucial factors when a control system needs to be analyzed to realize a given action or aim. Classical examples are also provided and explained for a specific idea in detail. Deep discussion and analysis can provide an enough comprehension for readers. Review section is also added to the end of each chapter to strengthen possible consideration for some special themes.

 

This book systematically focuses on modern control systems and timely explains some available tools on control system analysis. The whole book is divided into 12 chapters and several appendices. Different chapter narrates different theme according to the whole work course of modern control system. Main contents include: introduction to classical control theory, common mathematical models of linear system, the solution of state equation for linear continuous and discrete system, steady-state error, stability analysis of control system, controllability and observability of linear system, state feedback and state observer design, optimal control, PID control and robust control, dynamic system identification and state evaluation, adaptive control and fuzzy control, modern digital control systems and overview of nonlinear control. Besides, auxiliary basic knowledge and specific examples are also added in the book or appendix section.

 

This book can be utilized as the undergraduate or graduate’s textbook or reference, also as basic technology textbook of engineer cultivation. Besides, this book can be read and applied by industrialists, engineering researcher, engineer, engineering technologist and mechanics. Of course, this book can be also taught as a textbook in the field of industry for international students or researchers or engineers. This book may help readers completely comprehend modern control systems for their possible application in reality.

 

Author (s) Details

Zhou Gaofeng
Zhongyuan University of Technology, China.

 

Zhang Qi
Zhongyuan University of Technology, China.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49473-76-8/CH0

Applications of a Stochastic–Fuzzy Approach to Modeling and Optimal Control of Discrete Time Systems by Using Large Scale Data Processing: An advanced Approach | Chapter 5 | Current Topics on Mathematics and Computer Science Vol. 10

 The major goal of this chapter is to present a mathematical method for stochastic-fuzzy object fuzzy control. The proposed hybrid method calculates probability of fuzzy conditional statements in the knowledge base using both a stochastic–fuzzy knowledge base defining the object under control and large scale data. The presentation of multistage fuzzy optimization methods with reference to dynamical discrete systems represented by deterministic, stochastic, and fuzzy models (paragraph 2), as well as the proposition of modelling stochastic-fuzzy object under control in the form of knowledge base (paragraph 3). (paragraph 3). The theoretical description is illustrated with examples of calculations.

Author(s) Details

Anna Walaszek-Babiszewska
Department of Computer Engineering, Opole University of Technology, Opole, Poland.

View Book :- https://stm.bookpi.org/CTMCS-V10/article/view/3747

Adaptive Fuzzy Robust Control for a Class of Nonlinear Systems via Small Gain Theorem: Recent Study | Chapter 6 | Emerging Trends in Engineering Research and Technology Vol. 6

Practical nonlinear systems can usually be represented by partly linearizable models with unknown nonlinearities and external disturbances. Based on this consideration, we propose a novel adaptive fuzzy robust control (AFRC) algorithm for such systems. The AFRC effectively combines techniques of adaptive control and fuzzy control and it improves the performance by retaining the advantages of both methods. The linearizable part will be linearly parameterized with unknown but constant parameters, and the discontinuous-projection-based adaptive control law is used to compensate these parts. The Takagi-Sugeno fuzzy logic systems are used to approximate unknown nonlinearities. Robust control law ensures the robustness of closed-loop control system. A systematic design procedure of the AFRC algorithm by combining the back stepping technique and small-gain approach is presented. Then the closed-loop stability is studied by using small gain theorem and the result indicates that the closed-loop system is semi-globally uniformly ultimately bounded. 

Author(s) Details

Xingjian Wang
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.  

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/200

Design of Adaptive Neuro-Fuzzy Controller for Load Frequency Control of Two-Area Power System Using Clustering Algorithm | Chapter 12 | Emerging Trends in Engineering Research and Technology Vol. 3

To fullfil of increasing power demands and reduce carbon emission additional renewable sources of power are added to existing thermal unit. Interconnection of hydro units to existing thermal system can allow to system to operate at full capacity. The aim of this research is to study LFC by adding non-linearity’s to the thermal - thermal and hydro-thermal systems. Further this work investigates the modeling of hydro thermal systems and use of Adaptive Neuro-fuzzy controllers (ANFC) in these models. Network frequency change due to change in speed, may result in loss of synchronization with the rest of power system and finally resulting in power system black-out.  In this research a multi section steam turbine with re-heater was used for a single area system modeled in MATLAB, later two single area systems were combined to create a two area system, and its dynamics are studied by creating load perturbations. The proposed frequency controller was able to attain a setting time of 10 Sec which is comparatively lower than other existing speed controllers. .  The time domain response result of hydropower system proves the benefit of Neuro-fuzzy system, i.e. more rapid output response and minimal overshoot.

Author(s) Details

Ajay Kumar Maurya
Department of Electrical Engineering, IFTM University, Moradabad, Uttar Pradesh, India.


Dr. Anil Kumar
 Department of Electrical Engineering, IFTM University, Moradabad, Uttar Pradesh, India.


Dr. Piush Kumar
Department of Electrical and Electronics Engineering, Future Institute of Engineering and Technology, Bareilly, Uttar Pradesh, India.


Professor G. K. Banerjee
Department of Electrical Engineering, IFTM University, Moradabad, Uttar Pradesh, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/179