Nonlinear Filtering Methods and Applications

✍ Scribed by Chandra, Kumar Pakki Bharani; Gu, Da-Wei

• Publisher: Imprint: Springer, Springer International Publishing
• Year: 2019
• Tongue: English
• Leaves: 197
• Edition: 1st ed. 2019
• Category: Library

✦ Synopsis

This book gives readers in-depth know-how on methods of state estimation for nonlinear control systems. It starts with an introduction to dynamic control systems and system states and a brief description of the Kalman filter. In the following chapters, various state estimation techniques for nonlinear systems are discussed, including the extended, unscented and cubature Kalman filters, etc. The cubature Kalman filter and its variants are introduced in particular detail because of their efficiency and their ability to deal with systems with Gaussian and/or non-Gaussian noise. The book also discusses information-filter and square-root-filtering algorithms, useful for state estimation in some real-time control system design problems. A number of case studies are included in the book to illustrate the application of various nonlinear filtering algorithms. Nonlinear Filtering is written for academic and industrial researchers, engineers and research students who are interested in nonlinear control systems analysis and design. The chief features of the book include: dedicated coverage of recently developed nonlinear, Jacobian-free, filtering algorithms; examples illustrating the use of nonlinear filtering algorithms in real-world applications; detailed derivation and complete algorithms for nonlinear filtering methods help readers to a fundamental understanding and easier coding of those algorithms; and MATLABʼ codes associated with case-study applications can be downloaded from the Springer Extra Materials website.;Linear and Nonlinear Control Systems -- State Estimation and Prediction -- Linear Estimation Techniques -- Jacobian-Based Filters -- Unscented Kalman Filters -- Cubature Kalman Filters -- Variants of Cubature Kalman Filters -- Robustness Consideration of Filtering Algorithms.

✦ Table of Contents

Preface......Page 6
Contents......Page 8
List of Figures......Page 11
List of Tables......Page 15
List of Algorithms......Page 16
1.1 Introductory Remarks......Page 17
1.2 Linear and Nonlinear Control Systems......Page 18
1.3 Control System Design and System States......Page 20
1.4 Kalman Filter and Further Developments......Page 22
1.5 What is in This Book......Page 23
References......Page 25
2.1 Introductory Remarks......Page 28
2.2 Mathematical Preliminary......Page 29
2.3 Desired Properties of State Estimators......Page 36
2.4 Least Square Estimator and Luenberger State Observer......Page 37
2.5 Luenberger State Observer for a DC Motor......Page 40
References......Page 42
3.1 Introductory Remarks......Page 44
3.2.1 Process and Measurement Models......Page 45
3.2.2 Derivation of the Kalman Filter......Page 46
3.3 Kalman Information Filter......Page 49
3.4 Discrete-Time calHinfty Filter......Page 51
3.5.1 Quadruple-Tank System......Page 54
3.5.2 Sliding Mode Control of the Quadruple-Tank System......Page 58
3.5.3 Combined Schemes: Simulations and Results......Page 62
3.6 Summary......Page 70
References......Page 71
4.2 Extended Kalman Filter......Page 73
4.3 Extended Information Filter......Page 75
4.4 Extended calHinfty Filter......Page 78
4.5 A DC Motor Case Study......Page 79
4.6 Nonlinear Transformation and the Effects of Linearisation......Page 81
4.7 Summary......Page 86
References......Page 87
5.2 CKF Theory......Page 88
5.2.2 Measurement Update......Page 89
5.2.3 Cubature Rules......Page 90
5.3 Cubature Transformation......Page 92
5.3.1 Polar to Cartesian Coordinate Transformation—Cubature Transformation......Page 94
5.4 Study on a Brushless DC Motor......Page 96
5.4.1 BLDC Motor Dynamics......Page 97
5.4.2 Back EMFs and PID Controller......Page 98
5.4.4 BLDC Motor Experiments......Page 102
5.5 Summary......Page 108
References......Page 109
6.2 Cubature Information Filters......Page 110
6.2.1 Information Filters......Page 111
6.2.2 Extended Information Filter......Page 112
6.2.3 Cubature Information Filter......Page 113
6.2.4 CIF in Multi-sensor State Estimation......Page 115
6.3.1 calHinfty Filters......Page 117
6.3.2 Extended calHinfty Information Filter......Page 118
6.3.3 Cubature calHinfty Filter......Page 120
6.3.4 Cubature calHinfty Information Filter......Page 122
6.4.1 Square-Root Extended Kalman Filter......Page 125
6.4.2 Square-Root Extended Information Filter......Page 126
6.4.3 Square-Root Extended calHinfty Filter......Page 127
6.4.4 Square-Root Cubature Kalman Filter......Page 129
6.4.5 Square-Root Cubature Information Filter......Page 131
6.4.6 Square-Root Cubature calHinfty Filter......Page 133
6.4.7 Square-Root Cubature calHinfty Information Filter......Page 137
6.5.1 PMSM Model......Page 142
6.5.2 State Estimation Using SREIF and SRCIF......Page 143
6.5.3 State Estimation Using SRCKF and SRCcalHinftyF......Page 145
6.5.4 State Estimation with Multi-sensors Using SREIF, SRCIF and SRCcalHinftyIF......Page 148
6.6.1 The Model......Page 156
6.6.2 State Estimation in the Presence of Non-Gaussian Noises......Page 157
6.6.3 State Estimation with Near-Perfect Measurements......Page 158
References......Page 160
7.2 Unscented Kalman Filter......Page 162
7.2.1 Unscented Transformation......Page 163
7.2.2 Unscented Kalman Filter......Page 166
7.3 State-Dependent Riccati Equation (SDRE) Observers......Page 168
7.4 SDRE Information Filter......Page 170
7.4.1 SDREIF in Multi-sensor State Estimation......Page 171
7.5 PMSM Case Revisited......Page 173
7.6.1 Uncertainties and Robustness Requirement......Page 176
7.6.2 Compensation of Missing Sensory Data......Page 177
7.6.3 Selection of Linear Prediction Coefficients and Orders......Page 181
7.6.4 A Case Study......Page 185
References......Page 193
Index......Page 196

✦ Subjects

Automatic control;Image processing;Signal processing;Speech processing systems