A much-awaited guide to real-world problems in modern control and estimation This combined text and reference deals with the design of modern control systems. It is the first book in this rapidly growing field to approach optimal control and optimal estimation from a strictly pragmatic standpoint. Sidestepping the realm of theoretical mathematics, An Engineering Approach to Optimal Control and Estimation Theory offers realistic and workable solutions that can be put to immediate use by electrical and mechanical engineers in aerospace and in many other applications. The author draws on his extensive experience in research and development from industry, government, and academia to present systematic, accessible coverage of all important topics, including: * All basic mathematics needed to apply subsequent information * A historical perspective on the evolution of modern control and estimation theory * All major concepts relevant to the design of modern control systems--from the Kalman filter, to linear regulators, to decentralized Kalman filters * Practical examples useful in applying the principles under discussion * Problems at the end of each chapter * Carefully selected references from the vast number of books published on this subject * Appendixes reviewing matrix algebra that is central to modern control theory, as well as matrix subroutines, useful to both students and practicing engineers Optimal Control and Estimation Theory Optimal control and optimal estimation have seen tremendous growth over the past three decades, owing to major advances in aerospace and other types of engineering. Optimal control and estimation theory is crucial to the design of modern control systems; for instance, navigation, mobile robotics, or automated vehicles and aircraft. It ensures that variables such as temperatures or pressure are kept in check, regardless of the disturbance the system undergoes. Despite the proliferation of books on the subject, most of the material published in this area is highly theoretical, and approaches the subject in a ''theorem-proof'' fashion, which is more appropriate to mathematics than to an engineering text. As its title suggests, An Engineering Approach to Optimal Control and Estimation Theory provides a practical and accessible guide, focusing on applications and implementation, and answering real-world questions faced by control engineers. In its highly organized overview of all areas, the book examines the design of modern optimal controllers requiring the selection of a performance criterion, demonstrates optimization of linear systems with bounded controls and limited control effort, and considers nonlinearities and their effect on various types of signals. Covering all the basics, the book deals with the evolution of optimal control and estimation theory, and presents the necessary mathematical background needed for this study. It also lists references and problems, and supplies appendixes for those wishing to delve into matrix algebra. Throughout, it offers opportunities for experimentation, while discussing analysis, various filtering methods, and many other pertinent topics. An Engineering Approach to Optimal Control and Estimation Theory is an invaluable, self-contained reference for practicing engineers, a useful text for graduate students and qualified senior undergraduates, and an important resource for anyone interested in the future of modern control technology.
Author(s): George M. Siouris
Publisher: Wiley-Interscience
Year: 1996
Language: English
Pages: 407
tables......Page 0
table1......Page 1
To Karin......Page 2
table1......Page 3
image1......Page 4
table1......Page 5
ORGANIZATION OF THE TEXT......Page 6
ACKNOWLEDGMENTS......Page 7
INTRODUCTION AND HISTORICAL......Page 8
page9......Page 9
page10......Page 10
image2......Page 11
image2......Page 12
image2......Page 13
image2......Page 14
image2......Page 15
image2......Page 16
image1......Page 17
image1......Page 18
image2......Page 19
image2......Page 20
image2......Page 21
image2......Page 22
image2......Page 23
image2......Page 24
image2......Page 25
image3......Page 26
image2......Page 27
image2......Page 28
image2......Page 29
image3......Page 30
image3......Page 31
image2......Page 32
image2......Page 33
image2......Page 34
image3......Page 35
image2......Page 36
image2......Page 37
image2......Page 38
image2......Page 39
image2......Page 40
image2......Page 41
image2......Page 42
image2......Page 43
image2......Page 44
image2......Page 45
image2......Page 46
image2......Page 47
image2......Page 48
image2......Page 49
image2......Page 50
image2......Page 51
image2......Page 52
image2......Page 53
table1......Page 54
image2......Page 55
image2......Page 56
image2......Page 57
image2......Page 58
image2......Page 59
image2......Page 60
image2......Page 61
image2......Page 62
4.3 THE DISCRETE-TIME KALMAN FILTER......Page 63
image3......Page 64
image2......Page 65
image2......Page 66
image2......Page 67
image2......Page 68
image2......Page 69
image2......Page 70
image2......Page 71
image2......Page 72
image2......Page 73
image2......Page 74
image2......Page 75
image1......Page 76
image2......Page 77
image2......Page 78
image2......Page 79
image2......Page 80
image2......Page 81
image2......Page 82
image2......Page 83
image2......Page 84
image2......Page 85
image1......Page 86
image2......Page 87
image2......Page 88
image2......Page 89
image2......Page 90
image3......Page 91
image3......Page 92
4.7 THE U-D COVARIANCE ALGORITHM IN KALMAN FILTERS......Page 93
image2......Page 94
image2......Page 95
image1......Page 96
page17......Page 97
image1......Page 98
image2......Page 99
image2......Page 100
image1......Page 101
page22......Page 102
image2......Page 103
image2......Page 104
image2......Page 105
image2......Page 106
image2......Page 107
image1......Page 108
image2......Page 109
image2......Page 110
image2......Page 111
image2......Page 112
image2......Page 113
image2......Page 114
image2......Page 115
image2......Page 116
image1......Page 117
image1......Page 118
image2......Page 119
image2......Page 120
image2......Page 121
image2......Page 122
image2......Page 123
image2......Page 124
image2......Page 125
image2......Page 126
image2......Page 127
image2......Page 128
image2......Page 129
image2......Page 130
image2......Page 131
image2......Page 132
image2......Page 133
image2......Page 134
image2......Page 135
image1......Page 136
image2......Page 137
image2......Page 138
image2......Page 139
image2......Page 140
image2......Page 141
image2......Page 142
image2......Page 143
image2......Page 144
image2......Page 145
image1......Page 146
image2......Page 147
image2......Page 148
image2......Page 149
image2......Page 150
image2......Page 151
image2......Page 152
image2......Page 153
image2......Page 154
image2......Page 155
image2......Page 156
image2......Page 157
image2......Page 158
image2......Page 159
image2......Page 160
image2......Page 161
image2......Page 162
image2......Page 163
image2......Page 164
image1......Page 165
image2......Page 166
image2......Page 167
image1......Page 168
image2......Page 169
image1......Page 170
image2......Page 171
image3......Page 172
image2......Page 173
image2......Page 174
image2......Page 175
image2......Page 176
image2......Page 177
image2......Page 178
image2......Page 179
image2......Page 180
image1......Page 181
image1......Page 182
image2......Page 183
image2......Page 184
image2......Page 185
image2......Page 186
image2......Page 187
image2......Page 188
image2......Page 189
table1......Page 190
page46......Page 191
page47......Page 192
image1......Page 193
image2......Page 194
image2......Page 195
image2......Page 196
image2......Page 197
image2......Page 198
image2......Page 199
image2......Page 200
image2......Page 201
image1......Page 202
MATRIX LIBRARIES......Page 203
page59......Page 204
25 KI = K - N......Page 205
GO TO 40......Page 206
page62......Page 207
REFERENCES......Page 208
table1......Page 209
page65......Page 210
page66......Page 211
