Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook

ISBN-13: 9781852332273 / Angielski / Miękka / 2000 / 246 str.

M. Norgaard;O. Ravn;N.K. Poulsen

Opis

The technology of neural networks has attracted much attention in recent years. Their ability to learn nonlinear relationships is widely appreciated and is utilized in many different types of applications; modelling of dynamic systems, signal processing, and control system design being some of the most common. The theory of neural computing has matured considerably over the last decade and many problems of neural network design, training and evaluation have been resolved. This book provides a comprehensive introduction to the most popular class of neural network, the multilayer perceptron, and shows how it can be used for system identification and control. It aims to provide the reader with a sufficient theoretical background to understand the characteristics of different methods, to be aware of the pit-falls and to make proper decisions in all situations. The subjects treated include: System identification: multilayer perceptrons; how to conduct informative experiments; model structure selection; training methods; model validation; pruning algorithms. Control: direct inverse, internal model, feedforward, optimal and predictive control; feedback linearization and instantaneous-linearization-based controllers. Case studies: prediction of sunspot activity; modelling of a hydraulic actuator; control of a pneumatic servomechanism; water-level control in a conical tank. The book is very application-oriented and gives detailed and pragmatic recommendations that guide the user through the plethora of methods suggested in the literature. Furthermore, it attempts to introduce sound working procedures that can lead to efficient neural network solutions. This will make the book invaluable to the practitioner and as a textbook in courses with a significant hands-on component.

Spis treści

1. Introduction.- 1.1 Background.- 1.1.1 Inferring Models and Controllers from Data.- 1.1.2 Why Use Neural Networks?.- 1.2 Introduction to Multilayer Perceptron Networks.- 1.2.1 The Neuron.- 1.2.2 The Multilayer Perceptron.- 1.2.3 Choice of Neural Network Architecture.- 1.2.4 Models of Dynamic Systems.- 1.2.5 Recurrent Networks.- 1.2.6 Other Neural Network Architectures.- 2. System Identification with Neural Networks.- 2.1 Introduction to System Identification.- 2.1.1 The Procedure.- 2.2 Model Structure Selection.- 2.2.1 Some Linear Model Structures.- 2.2.2 Nonlinear Model Structures Based on Neural Networks.- 2.2.3 A Few Remarks on Stability.- 2.2.4 Terminology.- 2.2.5 Selecting the Lag Space.- 2.2.6 Section Summary.- 2.3 Experiment.- 2.3.1 When is a Linear Model Insufficient?.- 2.3.2 Issues in Experiment Design.- 2.3.3 Preparing the Data for Modelling.- 2.3.4 Section Summary.- 2.4 Determination of the Weights.- 2.4.1 The Prediction Error Method.- 2.4.2 Regularization and the Concept of Generalization.- 2.4.3 Remarks on Implementation.- 2.4.4 Section Summary.- 2.5 Validation.- 2.5.1 Looking for Correlations.- 2.5.2 Estimation of the Average Generalization Error.- 2.5.3 Visualization of the Predictions.- 2.5.4 Section Summary.- 2.6 Going Backwards in the Procedure.- 2.6.1 Training the Network Again.- 2.6.2 Finding the Optimal Network Architecture.- 2.6.3 Redoing the Experiment.- 2.6.4 Section Summary.- 2.7 Recapitulation of System Identification.- 3. Control with Neural Networks.- 3.1 Introduction to Neural-Network-based Control.- 3.1.1 The Benchmark System.- 3.2 Direct Inverse Control.- 3.2.1 General Training.- 3.2.2 Direct Inverse Control of the Benchmark System.- 3.2.3 Specialized Training.- 3.2.4 Specialized Training and Direct Inverse Control of the Benchmark System.- 3.2.5 Section Summary.- 3.3 Internal Model Control (IMC).- 3.3.1 Internal Model Control with Neural Networks.- 3.3.2 Section Summary.- 3.4 Feedback Linearization.- 3.4.1 The Basic Principle of Feedback Linearization.- 3.4.2 Feedback Linearization Using Neural Network Models..- 3.4.3 Feedback Linearization of the Benchmark System.- 3.4.4 Section Summary.- 3.5 Feedforward Control.- 3.5.1 Feedforward for Optimizing an Existing Control System.- 3.5.2 Feedforward Control of the Benchmark System.- 3.5.3 Section Summary.- 3.6 Optimal Control.- 3.6.1 Training of an Optimal Controller.- 3.6.2 Optimal Control of the Benchmark System.- 3.6.3 Section Summary.- 3.7 Controllers Based on Instantaneous Linearization.- 3.7.1 Instantaneous Linearization.- 3.7.2 Applying Instantaneous Linearization to Control.- 3.7.3 Approximate Pole Placement Design.- 3.7.4 Pole Placement Control of the Benchmark System.- 3.7.5 Approximate Minimum Variance Design.- 3.7.6 Section Summary.- 3.8 Predictive Control.- 3.8.1 Nonlinear Predictive Control (NPC).- 3.8.2 NPC Applied to the Benchmark System.- 3.8.3 Approximate Predictive Control (APC).- 3.8.4 APC applied to the Benchmark System.- 3.8.5 Extensions to the Predictive Controller.- 3.8.6 Section Summary.- 3.9 Recapitulation of Control Design Methods.- 4. Case Studies.- 4.1 The Sunspot Benchmark.- 4.1.1 Modelling with a Fully Connected Network.- 4.1.2 Pruning of the Network Architecture.- 4.1.3 Section Summary.- 4.2 Modelling of a Hydraulic Actuator.- 4.2.1 Estimation of a Linear Model.- 4.2.2 Neural Network Modelling of the Actuator.- 4.2.3 Section Summary.- 4.3 Pneumatic Servomechanism.- 4.3.1 Identification of the Pneumatic Servomechanism.- 4.3.2 Nonlinear Predictive Control of the Servo.- 4.3.3 Approximate Predictive Control of the Servo.- 4.3.4 Section Summary.- 4.4 Control of Water Level in a Conic Tank.- 4.4.1 Linear Analysis and Control.- 4.4.2 Direct Inverse Control of the Water Level.- 4.4.3 Section Summary.- References.