Neural Network Control of Nonlinear Discrete Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems.

Features:

• Presents the first comprehensive treatise on neurocontroller design in discrete-time
• Helps you navigate through the complexity of discrete-time design with progressive development of the concepts
• Includes a complete derivation of an output feedback controller along with a practical example of spark-engine ignition control using neural networks
• Develops a framework for implementation in embedded industrial systems

Contents

Background on Neural Networks

• NN Topologies and Recall
• Properties of NN
• NN Weight Selection and Training
• NN Learning and Control Architectures

Background and Discrete-Time Adaptive Control

• Dynamical Systems
• Mathematical Background
• Properties of Dynamical Systems
• Nonlinear Stability Analysis and Controls Design
• Robust Implicit STR

Neural Network Control of Nonlinear Systems and Feedback Linearization

• NN Control with Discrete-Time Tuning
• Feedback Linearization
• NN Feedback Linearization
• Multilayer NN for Feedback Linearization
• Passivity Properties of the NN

Neural Network Control of Uncertain Nonlinear Discrete-Time Systems with Actuator Nonlinearities

• Background on Actuator Nonlinearities
• Reinforcement NN Learning Control with Saturation
• Uncertain Nonlinear System with Unknown Deadzone and Saturation Nonlinearities
• Adaptive NN Control of Nonlinear System with Unknown Backlash

Output Feedback Control of Strict Feedback Nonlinear Mimo Discrete Time Systems

• Class of Nonlinear Discrete-Time Systems
• Output Feedback Controller Design
• Weight Updates for Guaranteed Performance

Neural Network Control of Nonstrict Feedback Nonlinear Systems

• Adaptive NN Control Design Using State Measurements
• Output Feedback NN Controller Design

System Identification Usin Discrete-Time Neural Networks

• Identification of Nonlinear Dynamical Systems
• Identifier Dynamics for MIMO Systems
• NN Identifier Design
• Passivity Properties of the NN

Discrete-Time Model Reference Adaptive Control

• Dynamics of an mnth-Order Multi-Input and Multi-Output System
• NN Controller Design
• Projection Algorithm

Neural Network Control in Discrete-Time Using Hamilton-Jacobi-Bellman Formulation

• Optimal Control and Generalized HJB Equation in Discrete-Time
• NN Least-Squares Approach
• Numerical Examples

Neural Network Output Feedback Controller Design and Embedded Hardware Implementation

• Embedded Hardware-PC Real-Time Digital Control System
• SI Engine Test Bed
• Lean Engine Controller Design and Implementation
• EGR Engine Controller Design and Implementation

Index

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