Robust Control Systems with Genetic Algorithms

GENETIC ALGORITHMS Introduction to Genetic AlgorithmsTerms and Definitions RepresentationFitness FunctionGenetic OperatorsGenetic Algorithms for OptimizationGenetic ProgrammingConclusionsReferencesOPTIMAL ROBUST CONTROLIntroduction to the Control TheoryNorms of Signals and FunctionsDescription of Model UncertaintyRobust Stability and Disturbance RejectionController DesignOptimizationConclusionsReferencesMETHODS FOR CONTROLLER DESIGN USING GENETIC ALGORITHMSIntroduction to Controller Design Using GeneticAlgorithmsDesign of Optimal Robust Controller withFixed-StructureDesign of Optimal Disturban. Read more... Abstract: Links genetic algorithms (GAs) and the design of robust control systems. Laying a foundation in the basics of GAs and genetic programming, this work demonstrates the power of these tools for developing optimal robust controllers for linear control systems, optimal disturbance rejection controllers, and predictive and variable structure control. Read more... Demonstrating powerful methods for controller design using genetic algorithms and genetic programming, this book offers readers new tools for the design of robust control systems. In recent years, new paradigms have emerged to replace-or augment-the traditional, mathematically based approaches to optimization. The most powerful of these are genetic algorithms (GA), inspired by natural selection, and genetic programming, an extension of GAs based on the optimization of symbolic codes.Robust Control Systems with Genetic Algorithms builds a bridge between genetic algorithms and the design of robust control systems. After laying a foundation in the basics of GAs and genetic programming, it demonstrates the power of these new tools for developing optimal robust controllers for linear control systems, optimal disturbance rejection controllers, and predictive and variable structure control. It also explores the application of hybrid approaches: how to enhance genetic algorithms and programming with fuzzy logic to design intelligent control systems. The authors consider a variety of applications, such as the optimal control of robotic manipulators, flexible links and jet engines, and illustrate a multi-objective, genetic algorithm approach to the design of robust controllers with a gasification plant case study.The authors are all masters in the field and clearly show the effectiveness of GA techniques. Their presentation is your first opportunity to fully explore this cutting-edge approach to robust optimal control system design and exploit its methods for your own applications In recent years, new paradigms have emerged to replace-or augment-the traditional, mathematically based approaches to optimization. The most powerful of these are genetic algorithms (GA), inspired by natural selection, and genetic programming, an extension of GAs based on the optimization of symbolic codes. Robust Control Systems with Genetic Algorithms builds a bridge between genetic algorithms and the design of robust control systems. After laying a foundation in the basics of GAs and genetic programming, it demonstrates the power of these new tools for developing optimal robust controllers for linear control systems, optimal disturbance rejection controllers, and predictive and variable structure control. It also explores the application of hybrid approaches: how to enhance genetic algorithms and programming with fuzzy logic to design intelligent control systems. The authors consider a variety of applications, such as the optimal control of robotic manipulators, flexible links and jet engines, and illustrate a multi-objective, genetic algorithm approach to the design of robust controllers with a gasification plant case study. The authors are all masters in the field and clearly show the effectiveness of GA techniques. Their presentation is your first opportunity to fully explore this cutting-edge approach to robust optimal control system design and exploit its methods for your own applications. Content: Front cover Dedications Preface Table of Contents chapter one. Genetic algorithms chapter two. Optimal robust control chapter three. Methods for controller design using genetic algorithms* chapter four. Predictive and variable structure control designs chapter five. Design methods, simulation results, and conclusion chapter six. Tuning fuzzy logic controllers for robust control system design chapter seven. GA-fuzzy hierarchical control design approach* chapter eight. Autonomous robot navigation through fuzzy genetic programming* Chapter nine. Robust control system design: A hybrid H-infinity/multiobjective optimization approachappendix A. Fuzzy sets, logic and control Back cover