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Intro; Foreword; Preface; Acknowledgements; Contents; Contributors; 1 Control Engineering from Classical to Intelligent Control Theory-An Overview; 1.1 Introduction; 1.2 Control System Classifications, Properties, and Specifications; 1.2.1 System Classification; 1.2.2 Transient Response-Dynamic Performance; 1.2.3 Stability; 1.2.4 Robustness; 1.3 Modern Control Strategies; 1.3.1 Intelligent Control; 1.4 Conclusion; References; 2 Main Metaheuristics Used for the Optimization of the Control of the Complex Systems; 2.1 Introduction; 2.2 Local Methods; 2.2.1 Taboo Search
2.2.2 Simulated Annealing2.2.3 Tunneling Algorithms; 2.2.4 GRASP Methods; 2.3 Global Methods; 2.3.1 Genetic Algorithms and Evolutionary Algorithms; 2.3.2 Ant Colony Optimization (ACO); 2.3.3 Particle Swarm Optimization (PSO); 2.4 Multi-objective Optimization; 2.4.1 Ordered Weighted Averaging (OWA); 2.4.2 OWA Using Choquetintegral; 2.4.3 Pareto Optimality Approach; 2.5 Conclusion; References; 3 Optimal Controller Parameter Tuning from Multi/Many-objective Optimization Algorithms; 3.1 Introduction; 3.2 Benchmark Problems; 3.2.1 Ball and Beam System; 3.2.2 Stirring Tank with Heat Exchanger
3.2.3 Distillation Column3.3 Optimization Algorithms; 3.3.1 Strength Pareto Evolutionary Algorithm II; 3.3.2 Non-dominated Sorting Genetic Algorithm II; 3.3.3 Multi-objective Particle Swarm Optimization; 3.3.4 Multi-objective Evolutionary Algorithm Based on Decomposition; 3.3.5 Approximation-guided Evolutionary Algorithm II; 3.3.6 Reference Vector Guided Evolutionary Algorithm; 3.4 Implementation; 3.4.1 Control Algorithms; 3.4.2 Objective Functions; 3.4.3 Results; 3.5 Conclusion; References; 4 Fuzzy and Neuro-fuzzy Control for Smart Structures; 4.1 Introduction; 4.2 Fuzzy Control
4.2.1 Fuzzy Logic4.2.2 Membership Functions; 4.2.3 Fuzzification and Defuzzification; 4.2.4 Fuzzy Inference Systems; 4.2.5 Fuzzy Inference Methods; 4.2.6 Fuzzy Control Applications; 4.3 Artificial Neural Networks; 4.3.1 What Is a Neural Network?; 4.3.2 The Concept of the Artificial Neuron; 4.3.3 Calculation of Outputs; 4.3.4 Training of Neural Networks; 4.4 Adaptive Neuro-fuzzy Inference Systems (ANFIS); 4.4.1 What Is ANFIS?; 4.4.2 What Is the ANFIS Routine of MATLAB?; 4.4.3 Training of Adaptive Neuro-fuzzy Inference Systems Through MATLAB; 4.5 Fuzzy and Neuro-fuzzy Controllers
4.5.1 Fuzzy Controller4.5.2 Neuro-fuzzy Controller; 4.6 Numerical Examples; 4.6.1 Fuzzy Control of a Cantilever Beam; 4.6.2 Adaptive Neuro-fuzzy Control of a Cantilever Beam; 4.7 Conclusions; References; 5 Computational Intelligence in the Desalination Industry; 5.1 Introduction; 5.2 Basic Concepts of Reverse Osmosis Technology; 5.2.1 Basic Notions on the Reverse Osmosis Process; 5.2.2 Components of a Typical SWRO Plant; 5.2.3 Some Observations on the Operating Parameters of an SWRO Plant; 5.3 Overview of the Use of Computational Intelligence in the Desalination Industry
2.2.2 Simulated Annealing2.2.3 Tunneling Algorithms; 2.2.4 GRASP Methods; 2.3 Global Methods; 2.3.1 Genetic Algorithms and Evolutionary Algorithms; 2.3.2 Ant Colony Optimization (ACO); 2.3.3 Particle Swarm Optimization (PSO); 2.4 Multi-objective Optimization; 2.4.1 Ordered Weighted Averaging (OWA); 2.4.2 OWA Using Choquetintegral; 2.4.3 Pareto Optimality Approach; 2.5 Conclusion; References; 3 Optimal Controller Parameter Tuning from Multi/Many-objective Optimization Algorithms; 3.1 Introduction; 3.2 Benchmark Problems; 3.2.1 Ball and Beam System; 3.2.2 Stirring Tank with Heat Exchanger
3.2.3 Distillation Column3.3 Optimization Algorithms; 3.3.1 Strength Pareto Evolutionary Algorithm II; 3.3.2 Non-dominated Sorting Genetic Algorithm II; 3.3.3 Multi-objective Particle Swarm Optimization; 3.3.4 Multi-objective Evolutionary Algorithm Based on Decomposition; 3.3.5 Approximation-guided Evolutionary Algorithm II; 3.3.6 Reference Vector Guided Evolutionary Algorithm; 3.4 Implementation; 3.4.1 Control Algorithms; 3.4.2 Objective Functions; 3.4.3 Results; 3.5 Conclusion; References; 4 Fuzzy and Neuro-fuzzy Control for Smart Structures; 4.1 Introduction; 4.2 Fuzzy Control
4.2.1 Fuzzy Logic4.2.2 Membership Functions; 4.2.3 Fuzzification and Defuzzification; 4.2.4 Fuzzy Inference Systems; 4.2.5 Fuzzy Inference Methods; 4.2.6 Fuzzy Control Applications; 4.3 Artificial Neural Networks; 4.3.1 What Is a Neural Network?; 4.3.2 The Concept of the Artificial Neuron; 4.3.3 Calculation of Outputs; 4.3.4 Training of Neural Networks; 4.4 Adaptive Neuro-fuzzy Inference Systems (ANFIS); 4.4.1 What Is ANFIS?; 4.4.2 What Is the ANFIS Routine of MATLAB?; 4.4.3 Training of Adaptive Neuro-fuzzy Inference Systems Through MATLAB; 4.5 Fuzzy and Neuro-fuzzy Controllers
4.5.1 Fuzzy Controller4.5.2 Neuro-fuzzy Controller; 4.6 Numerical Examples; 4.6.1 Fuzzy Control of a Cantilever Beam; 4.6.2 Adaptive Neuro-fuzzy Control of a Cantilever Beam; 4.7 Conclusions; References; 5 Computational Intelligence in the Desalination Industry; 5.1 Introduction; 5.2 Basic Concepts of Reverse Osmosis Technology; 5.2.1 Basic Notions on the Reverse Osmosis Process; 5.2.2 Components of a Typical SWRO Plant; 5.2.3 Some Observations on the Operating Parameters of an SWRO Plant; 5.3 Overview of the Use of Computational Intelligence in the Desalination Industry