Decentralized Estimation and Control for Multisensor Systems

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ISBN: 9780849318658 | 0849318653
Cover: Hardcover
Copyright: 1/29/1998

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This book explores the problem of developing scalable, decentralized estimation and control algorithms for linear and nonlinear multisensor systems. Decentralized estimation and control systems have become indispensable tools in a range of applications, including process control systems, aerospace, and mobile robotics. The book extends the decentralized estimation principle to problems involving local control and actuation, removing current limitations in decentralized data fusion algorithms.

1 Introduction

(18)

1.1 Background

(2)

1.2 Motivation

(9)

1.2.1 Modular Robotics

(1)

1.2.2 The Mars Sojourner Rover

(2)

1.2.3 The MIT Humanoid Robot (Cog)

(1)

1.2.4 Large Scale Systems

(1)

1.2.5 The Russian Mir Space Station

(2)

1.2.6 The Space Shuttle Columbia

(3)

1.3 Problem Statement

(1)

1.4 Approach

(1)

1.4.1 Estimation

(1)

1.4.2 Control

(1)

1.4.3 Applications

(1)

1.5 Principal Contributions

(1)

1.6 Book Outline

(4)

2 Estimation and Information Space

(36)

2.1 Introduction

(1)

2.2 The Kalman Filter

(3)

2.2.1 System Description

(1)

2.2.2 Kalman Filter Algorithm

(1)

2.3 The Information Filter

(11)

2.3.1 Information Space

(4)

2.3.2 Information Filter Derivation

(2)

2.3.3 Filter Characteristics

(1)

2.3.4 An Example of Linear Estimation

(3)

2.3.5 Comparison of the Kalman and Information Filters

(2)

2.4 The Extended Kalman Filter (EKF)

(6)

2.4.1 Nonlinear State Space

(1)

2.4.2 EKF Derivation

(4)

2.4.3 Summary of the EKF Algorithm

(1)

2.5 The Extended Information Filter (EIF)

(5)

2.5.1 Nonlinear Information Space

(1)

2.5.2 EIF Derivation

(3)

2.5.3 Summary of the EIF Algorithm

(1)

2.5.4 Filter Characteristics

(1)

2.6 Examples of Estimation in Nonlinear Systems

(9)

2.6.1 Nonlinear State Evolution and Linear Observations

(2)

2.6.2 Linear State Evolution with Nonlinear Observations

(2)

2.6.3 Nonlinear State Evolution with Nonlinear Observations

(3)

2.6.4 Comparison of the EKF and EIF

(2)

2.7 Summary

(2)

3 Decentralized Estimation for Multisensor Systems

(26)

3.1 Introduction

(1)

3.2 Multisensor Systems

(8)

3.2.1 Sensor Classification and Selection

(3)

3.2.2 Positions of Sensors in a Data Acquisition System

(1)

3.2.3 The Advantages of Multisensor Systems

(1)

3.2.4 Data Fusion Methods

(1)

3.2.5 Fusion Architectures

(2)

3.3 Decentralized Systems

(4)

3.3.1 The Case for Decentralization

(2)

3.3.2 Survey of Decentralized Systems

(2)

3.4 Decentralized Estimators

(9)

3.4.1 Decentralizing the Observer

(1)

3.4.2 The Decentralized Information Filter (DIF)

(3)

3.4.3 The Decentralized Kalman Filter (DKF)

(2)

3.4.4 The Decentralized Extended Information Filter (DEIF)

(2)

3.4.5 The Decentralized Extended Kalman Filter (DEKF)

(1)

3.5 The Limitations of Fully Connected Decentralization

(2)

3.6 Summary

(2)

4 Scalable Decentralized Estimation

(40)

4.1 Introduction

(2)

4.1.1 Model Distribution

(1)

4.1.2 Nodal Transformation Determination

(1)

4.2 An Extended Example

(13)

4.2.1 Unscaled Individual States

(3)

4.2.2 Proportionally Dependent States

(2)

4.2.3 Linear Combination of States

(5)

4.2.4 Generalizing the Concept

(1)

4.2.5 Choice of Transformation Matrices

(1)

4.2.6 Distribution of Models

(2)

4.3 The Moore-Penrose Generalized Inverse: T(+)

(5)

4.3.1 Properties and Theorems of T(+)

(3)

4.3.2 Computation of T(+)

100

(1)

4.4 Generalized Internodal Transformation

101

(7)

4.4.1 State Space Internodal Transformation: V(ji)(k)

101

(5)

4.4.2 Information Space Internodal Transformation: T(ji)(k)

106

(2)

4.5 Special Cases of T(ji)(k)

108

(4)

4.5.1 Scaled Orthonormal T(i)(k) and T(j)(k)

108

(1)

4.5.2 Diagonal I(+)(j)(z(j)(k))

109

(1)

4.5.3 Nonsingular and Diagonal I(+)(j)(Z(j)(k))

109

(1)

4.5.4 Row Orthonormal C(j)(k) and Nonsingular R(j)(k)

110

(1)

4.5.5 Row Orthonormal T(i)(k) and T(j)(k)

111

(1)

4.5.6 Reconstruction of Global Variables

111

(1)

4.6 Distributed and Decentralized Filters

112

(7)

4.6.1 The Distributed and Decentralized Kalman Filter (DDKF)

112

(2)

4.6.2 The Distributed and Decentralized Information Filter (DDIF)

114

(2)

4.6.3 The Distributed and Decentralized Extended Kalman Filter (DDEKF)

116

(1)

4.6.4 The Distributed and Decentralized Extended Information Filter (DDEIF)

117

(2)

4.7 Summary

119

(2)

5 Scalable Decentralized Control

121

(20)

5.1 Introduction

121

(1)

5.2 Optimal Stochastic Control

121

(7)

5.2.1 Stochastic Control Problem

122

(1)

5.2.2 Optimal Stochastic Solution

123

(3)

5.2.3 Nonlinear Stochastic Control

126

(1)

5.2.4 Centralized Control

127

(1)

5.3 Decentralized Multisensor Based Control

128

(7)

5.3.1 Fully Connected Decentralized Control

129

(2)

5.3.2 Distribution of Control Models

131

(1)

5.3.3 Distributed and Decentralized Control

132

(2)

5.3.4 System Characteristics

134

(1)

5.4 Simulation Example

135

(5)

5.4.1 Continuous Time Models

135

(2)

5.4.2 Discrete Time Global Models

137

(1)

5.4.3 Nodal Transformation Matrices

138

(1)

5.4.4 Local Discrete Time Models

139

(1)

5.5 Summary

140

(1)

6 Multisensor Applications: A Wheeled Mobile Robot

141

(42)

6.1 Introduction

141

(1)

6.2 Wheeled Mobile Robot (WMR) Modeling

142

(7)

6.2.1 Plane Motion Kinematics

143

(2)

6.2.2 Decentralized Kinematics

145

(4)

6.3 Decentralized WMR Control

149

(11)

6.3.1 General WMR System Models

150

(2)

6.3.2 Specific WMR Implementation Models

152

(6)

6.3.3 Driven and Steered Unit (DSU) Control

158

(1)

6.3.4 Application of Internodal Transformation

159

(1)

6.4 Hardware Design and Construction

160

(7)

6.4.1 WMR Modules

161

(2)

6.4.2 A Complete Modular Vehicle

163

(2)

6.4.3 Transputer Architecture

165

(2)

6.5 Software Development

167

(7)

6.5.1 Nodal Program (Communicating Control Process)

168

(5)

6.5.2 Configuration Program (Decentralized Control)

173

(1)

6.6 On-Vehicle Software

174

(7)

6.6.1 Nodal Software

174

(2)

6.6.2 Decentralized Motor Control

176

(1)

6.6.3 WMR Trajectory Generation

176

(5)

6.7 Summary

181

(2)

7 Results and Performance Analysis

183

(26)

7.1 Introduction

183

(1)

7.2 System Performance Criteria

183

(3)

7.2.1 Estimation Criteria

184

(1)

7.2.2 Control Criteria

185

(1)

7.3 Simulation Results

186

(3)

7.3.1 Innovations

186

(1)

7.3.2 State Estimates

187

(1)

7.3.3 Information Estimates and Control

188

(1)

7.4 WMR Experimental Results

189

(15)

7.4.1 Trajectory Tracking

190

(2)

7.4.2 Innovations and Estimated Control Errors

192

(12)

7.5 Discussion of Results

204

(3)

7.5.1 Local DSU Innovations

204

(2)

7.5.2 Wheel Estimated Control Errors

206

(1)

7.5.3 WMR Body Estimates

206

(1)

7.6 Summary

207

(2)

8 Conclusions and Future Research

209

(8)

8.1 Introduction

209

(1)

8.2 Summary of Contributions

209

(2)

8.2.1 Decentralized Estimation

210

(1)

8.2.2 Decentralized Control

210

(1)

8.2.3 Applications

210

(1)

8.3 Research Appraisal

211

(2)

8.3.1 Decentralized Estimation

211

(2)

8.3.2 Decentralized Control

213

(1)

8.4 Future Research Directions

213

(4)

8.4.1 Theory

214

(1)

8.4.2 Applications

215

(2)

Bibliography

217

(10)

Index

227

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Decentralized Estimation and Control for Multisensor Systems

Summary

Table of Contents

Supplemental Materials