Chapter 1 Introduction to Control Engineering00
1.1 Introduction00
1.2 Historical Review00
1.3 Development of Control Theory00
1.4 Control Strategies00
1.5 Control System Fundamentals00
1.6 Examples of Control Systems0
Chapter 2 Modeling of Dynamic Systems0
2.1 Introduction0
2.2 Differential Equation0
2.3 Transfer Function0
2.4 Block Diagrams0
2.5 StateSpace Model0
2.6 TimeDomain Analysis of Control System0
Chapter 3 Optimal Control0
3.1 Introduction0
3.2 Typical Optimal Control Problems and Solutions0
3.3 Fundamentals of Optimal Control0
3.4 Linear Quadratic Regulator0
3.5 Kalman Filter0
3.6 Linear Quadratic Gaussian Control System0
3.7 Optimal Control Examples0
Chapter 4 Model Predictive Control0
4.1 Introduction0
4.2 MPC Control Problem0
4.3 Key Aspects of MPC0
4.4 Programming of MPC0
4.5 A Study on EEGBased Vehicle Control with MPC Controller0
4.6 Recommendation on Control Parameters of MPC0
Chapter 5 Sliding Mode Control
5.1 Introduction
5.2 Definition of Sliding Mode Control
5.3 Basic Problems in SMC Design
5.4 Characteristics and Mathematical Description of Sliding Mode
5.5 Sliding Mode Control Design
5.6 Chattering
5.7 Control Examples
5.8 A Study on BrainControlled Mobile Robots
Chapter 6 Artificial Neural Network Control
6.1 Introduction
6.2 Fundamentals of Artificial Neural Network
6.3 Property of Neural Network
6.4 The Structure (Architecture) of Artificial Neural Network
6.5 Activation Function
6.6 Learning in Neural Network
6.7 Generalization
6.8 Selected Types of Neural Network
Chapter 7 Fuzzy Logic Control Systems
7.1 Introduction
7.2 Fuzzy Sets
7.3 Fuzzy Inference
7.4 Fuzzy Control
7.5 Self Organizing Fuzzy Logic Control (SOFLC)
7.6 Application of Fuzzy Controllers
Chapter 8 NeuroFuzzy Control Systems
8.1 Introduction
8.2 Types of NeuroFuzzy Systems
8.3 General Architecture of NeuroFuzzy Systems
8.4 Various Architecture of NeuroFuzzy Models
8.5 Learning in NeuroFuzzy Systems
8.6 Selected NeuroFuzzy Systems
Chapter 9 Genetic Algorithm for Control Systems
9.1 Introduction
9.2 GA Optimization Process
9.3 Binary Representation and Real Representation
9.4 Selection Operator
9.5 Genetic Operators for Binary Representation
9.6 Genetic Operators for Real Representation
9.7 Control Examples with Genetic Algorithm
Chapter 10 Reinforcement Learning Control
10.1 Introduction
10.2 Fundamentals of Reinforcement Learning
10.3 Markov Decision Process
10.4 Dynamic Programming for Finite MDP
10.5 Model Learning for MDP
10.6 Continuous State MDP
10.7 QLearning of Temporal Difference Control
10.8 Control Example with Reinforcement Learning
