Neural Networks Modeling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time (Paperback)

1. Introduction
1.1. Time-delay system
1.2. System model
1.3. Neural Identification
1.4. Neural state observer
1.5. Neural block control 1.5.1. Discrete-time Sliding mode control
1.5.2. Inverse optimal control
1.6. Problem Statement
1.7. Objectives
1.8. Background information
1.9. Book Structure
2. Mathematical preliminaries
2.1. Time-delay systems
2.1.1. Time-delay
2.1.2. Time-delay system
2.1.3. Nonlinear discrete-time system with time-delays
2.2. Recurrent high order neural network 2.2.1. Discrete-time recurrent high order neural network
2.2.2. Extended Kalman Filter based training for recurrent high order neural networks
3. Recurrent high order neural network identification of nonlinear discrete-time unknown system with time-delays.
3.1. System identification
3.2. Neural Identification
3.3. Design of a neural identifier based on a recurrent high order neural network for a nonlinear discrete-time unknown system with
time-delays.
3.4. Simulation results of the recurrent high order neural network identifier
3.4.1. Van der Pol oscillator
3.4.2. Differential Robot
4. Neural identifier-control scheme for nonlinear discrete-time unknown system with time-delays
4.1. Neural identifier-control scheme, discrete-time sliding modes
4.1.1. Discrete-time sliding mode control
4.1.2. Real-time results of the neural identifier-control scheme using sliding mode control
4.1.2.1. Linear Induction motor with time-delays, test 1
4.1.2.2. Linear Induction motor with time-delays, test 2
4.1.2.3. Linear Induction motor with time-delays, test 3
4.2. Neural identifier-control scheme, inverse optimal control
4.2.1. inverse optimal control
4.2.2. Real-time results of the neural identifier-control scheme using inverse optimal control
4.2.2.1. Application to a differential robot
4.2.2.1.1. Differential robot, test 1
4.2.2.1.2. Differential robot, test 2
5. Recurrent high order neural network observer of nonlinear discrete-time unknown systems with time-delays.
5.1. Neural observer
5.2. Design of a full order neural observer based on a recurrent high order neural network for a nonlinear discrete-time unknown
system with time-delays.
5.2.1. Simulation results of the recurrent high order neural network full order observer
5.3. Design of a reduced order neural observer based on a recurrent high order neural network for a nonlinear discrete-time
unknown system with time-delays.
5.3.1. Simulation results of the recurrent high order neural network reduced order observer
6. Neural observer-control scheme for nonlinear discrete-time unknown system with time-delays
6.1. Design of a reduced order neural observer based on a recurrent high order neural network for a nonlinear discrete-time
unknown system with time-delays. 6.1.1. Simulation results of the neural observer-control
6.1.2. Real-time results of the neural observer-control
7. Concluding remarks and future trends
Appendix
A. Artificial neural networks
a. Biological neural networks
i. Biological neuron
ii. Biological synapse
iii. Classification of neurons
b. Artificial neural networks
c. Activation functions
d. Artificial neural networks classification
i. Single-layer neural networks
ii. Multilayer neural networks
iii. Recurrent neural networks
B. Linear induction motor prototype
a. Linear induction motor
b. How does a linear induction motor work?
c. Linear induction motor model
d. Flux observer
e. Linear induction motor prototype
i. Electric drive by induction motor
ii. Linear induction motor prototype
iii. Prototype del robot differential
C. Differential robot prototype
a. All-terrain tracked robot
b. All-terrain tracked prototype