This paper presents the design and simulation of high-performance brain emotional learning and fuzzy-based intelligent controller (BELFBIC) for three-phase induction motors V/f speed control. BELFBIC is an improved version of a brain emotional learning-based intelligent controller (BELBIC) controller. V/f control is simple and relatively easy to implement. It provides motor performance, which is adequate in most applications. For the first time, BELFBIC is used for space vector pulse width modulation inverter-fed induction motor V/f speed control. A comparative analysis with a proportional-integral-derivative (PID) controller and BELBIC are also carried out. The simulation is conducted with MATLAB/Simulink. The proposed controller is implemented in hardware, and its performance is shown to be better than either PID or BELBIC.

Induction motors are the most important workhorses in industry. They are mostly used as constant-speed drives when fed from a voltage source of fixed frequency. Advent of advanced power electronic converters and powerful digital signal processors, however, has made possible the development of high performance, adjustable speed AC motor drives. This book aims to explore new areas of induction motor control based on artificial intelligence (AI) techniques in order to make the controller less sensitive to parameter changes. Selected AI techniques are applied for different induction motor control strategies. The book presents a practical computer simulation model of the induction motor that could be used for studying various induction motor drive operations. The control strategies explored include expert-system-based acceleration control, hybrid-fuzzy/PI two-stage control, neural-network-based direct self control, and genetic algorithm based extended Kalman filter for rotor speed estimation. There are also chapters on neural-network-based parameter estimation, genetic-algorithm-based optimized random PWM strategy, and experimental investigations. A chapter is provided as a primer for readers to get started with simulation studies on various AI techniques. Presents major artificial intelligence techniques to induction motor drives Uses a practical simulation approach to get interested readers started on drive development Authored by experienced scientists with over 20 years of experience in the field Provides numerous examples and the latest research results Simulation programs available from the book's Companion Website This book will be invaluable to graduate students and research engineers who specialize in electric motor drives, electric vehicles, and electric ship propulsion. Graduate students in intelligent control, applied electric motion, and energy, as well as engineers in industrial electronics, automation, and electrical transportation, will also find this book helpful. Simulation materials available for download at www.wiley.com/go/chanmotor

This paper presents the design and simulation of a high-performance brain emotional learning and fuzzy-based intelligent controller (BELFBIC) for three-phase induction motor V/f speed control. V/Hz control is simple and relatively easy to implement. It provides motor performance that is adequate for most applications. For the first time, this new design brain emotional learning and fuzzy-based intelligent controller is used for a space vector pulse width modulation inverter fed induction motor V/f speed control. A comparative analysis with a PID controller and a fuzzy controller is also carried out. The simulation is carried out by MATLAB/Simulink.

Conventional controllers like the proportional integral differential (PID) would have being very effective not just for speed control alone, if not for some complexities with individually controlling its respective controllers and summing up their individual contributions to effectively yield controlled signal output. Also, for its insensitivity to changes made to model parameters which may be as a result of misrepresentation of some control variables. As a result of this, developing an intelligent based fuzzy logic controller (FLC) became eminent, and on this basis, this book is written. By varying the motor speed with input reference speed, an error signal and a feedback loop is generated. The FLC then operates on the principles of mapping with corrective measure of the error signal generated and it is regulated by sets of IF-THEN rules integrating the Mamdani fuzzy inference approach. The rules projected and formed are used to overcome the drawbacks of conventional controllers. Since induction motors are widely used in many industries today, most especially squirrel caged, an intelligent approach to the control of these motors will save cost, increase reliability and efficiency

Book relates to the speed control of an induction motor introduced intelligent methods such as Fuzzy Logic Control (FLC), Artificial Neural Networks (ANN), Adaptive Neural Fuzzy Inference System (ANFIS) and Optimization Techniques such as Genetic Algorithm (GA), Sequential Quadratic Programming (SQP) and Particle Swarm Optimization Algorithms(PSO).The results showed that the PSO-PI controller can perform with an efficient way for searching for the optimal PI controller. Comparison study among fuzzy logic, neural network, Adaptive Neural Fuzzy Inference System, genetic algorithm, sequential quadratic programming and particle swarm optimization controllers are performed. These methods can improve the dynamic performance of the system in a better way.The PI-PSO controller is the best method based on integrated of time weight absolute error (ITAE)criteria which presented satisfactory performances and possesses good robustness (no overshoot, minimal rise time, steady state error almost to zero value). A comparison study has been done between selected methods and some other technique which showed that the proposed controller has setting time less than other methods by 40%.

This book focuses on the intelligent control design for both the induction motor (IM) and the permanent magnet synchronous motor (PMSM). Compared with traditional control schemes, such as the field-oriented control (FOC) and the direct torque control (DTC), the intelligent controllers designed in this book could overcome the influence of parameter uncertainty and load torque disturbance. This book is a research monograph, which provides valuable reference material for researchers who wish to explore the area of AC motor. In addition, the main contents of the book are also suitable for a one-semester graduate course.

This text book is priceless for those researchers and engineers who are enthusiastic in carrying out research in designing of speed controller of Variable Frequency Induction Motor Drives (VFIMDs) which are the vertebrae of the industrial world. The chapters in the book have considered the various Artificial Intelligence (AI) techniques in order to properly design and tune the speed controller for DFOC and DTC based induction motor drives. The AI based controllers have given improved dynamic response of the two VFIMDs for disturbance rejection capability, robustness and stability under various dynamic conditions. Moreover, this book has given an insight into the quantitative and qualitative comparisons of transient performance parameters of VFIMDs controlled by AI (i.e. FL, ANFIS, ANN, GA and MOGA) techniques. The comparisons of the AI based techniques with the conventional one, and those among themselves facilitate need based selection of VFIMDs for various applications. The qualitative comparison has also been carried out to highlight the pros and cons of the use of AI techniques in the designing and tuning of the speed controllers.