By Eli Gershon

ISBN-10: 1447150694

ISBN-13: 9781447150695

Complicated issues up to speed and Estimation of State-Multiplicative Noisy platforms starts off with an creation and huge literature survey. The textual content proceeds to hide the sector of H∞ time-delay linear structures the place the problems of balance and L2−gain are provided and solved for nominal and unsure stochastic platforms, through the input-output method. It offers recommendations to the issues of state-feedback, filtering, and measurement-feedback keep an eye on for those structures, for either the continual- and the discrete-time settings. within the continuous-time area, the issues of reduced-order and preview monitoring keep an eye on also are awarded and solved. the second one a part of the monograph matters non-linear stochastic country- multiplicative platforms and covers the problems of balance, regulate and estimation of the structures within the H∞ experience, for either continuous-time and discrete-time circumstances. The publication additionally describes designated subject matters akin to stochastic switched structures with live time and peak-to-peak filtering of nonlinear stochastic structures. The reader is brought to 6 useful engineering- orientated examples of noisy state-multiplicative regulate and filtering difficulties for linear and nonlinear platforms. The e-book is rounded out via a three-part appendix containing stochastic instruments invaluable for a formal appreciation of the textual content: a simple advent to stochastic keep watch over techniques, elements of linear matrix inequality optimization, and MATLAB codes for fixing the L2-gain and state-feedback regulate difficulties of stochastic switched structures with dwell-time. complicated themes on top of things and Estimation of State-Multiplicative Noisy structures could be of curiosity to engineers engaged up to the mark structures examine and improvement, to graduate scholars focusing on stochastic keep an eye on idea, and to utilized mathematicians drawn to regulate difficulties. The reader is predicted to have a few acquaintance with stochastic regulate conception and state-space-based optimum keep watch over thought and techniques for linear and nonlinear systems.

Table of Contents

Cover

Advanced themes up to speed and Estimation of State-Multiplicative Noisy Systems

ISBN 9781447150695 ISBN 9781447150701

Preface

Contents

1 Introduction

1.1 Stochastic State-Multiplicative Time hold up Systems
1.2 The Input-Output technique for behind schedule Systems
1.2.1 Continuous-Time Case
1.2.2 Discrete-Time Case
1.3 Non Linear keep an eye on of Stochastic State-Multiplicative Systems
1.3.1 The Continuous-Time Case
1.3.2 Stability
1.3.3 Dissipative Stochastic Systems
1.3.4 The Discrete-Time-Time Case
1.3.5 Stability
1.3.6 Dissipative Discrete-Time Nonlinear Stochastic Systems
1.4 Stochastic procedures - brief Survey
1.5 suggest sq. Calculus
1.6 White Noise Sequences and Wiener Process
1.6.1 Wiener Process
1.6.2 White Noise Sequences
1.7 Stochastic Differential Equations
1.8 Ito Lemma
1.9 Nomenclature
1.10 Abbreviations

2 Time hold up structures - H-infinity keep an eye on and General-Type Filtering

2.1 Introduction
2.2 challenge formula and Preliminaries
2.2.1 The Nominal Case
2.2.2 The powerful Case - Norm-Bounded doubtful Systems
2.2.3 The strong Case - Polytopic doubtful Systems
2.3 balance Criterion
2.3.1 The Nominal Case - Stability
2.3.2 powerful balance - The Norm-Bounded Case
2.3.3 powerful balance - The Polytopic Case
2.4 Bounded genuine Lemma
2.4.1 BRL for behind schedule State-Multiplicative platforms - The Norm-Bounded Case
2.4.2 BRL - The Polytopic Case
2.5 Stochastic State-Feedback Control
2.5.1 State-Feedback keep an eye on - The Nominal Case
2.5.2 strong State-Feedback keep watch over - The Norm-Bounded Case
2.5.3 powerful Polytopic State-Feedback Control
2.5.4 instance - State-Feedback Control
2.6 Stochastic Filtering for not on time Systems
2.6.1 Stochastic Filtering - The Nominal Case
2.6.2 powerful Filtering - The Norm-Bounded Case
2.6.3 strong Polytopic Stochastic Filtering
2.6.4 instance - Filtering
2.7 Stochastic Output-Feedback keep an eye on for behind schedule Systems
2.7.1 Stochastic Output-Feedback regulate - The Nominal Case
2.7.2 instance - Output-Feedback Control
2.7.3 strong Stochastic Output-Feedback keep watch over - The Norm-Bounded Case
2.7.4 strong Polytopic Stochastic Output-Feedback Control
2.8 Static Output-Feedback Control
2.9 powerful Polytopic Static Output-Feedback Control
2.10 Conclusions

3 Reduced-Order H-infinity Output-Feedback Control

3.1 Introduction
3.2 challenge Formulation
3.3 The not on time Stochastic Reduced-Order H keep watch over 8
3.4 Conclusions

4 monitoring regulate with Preview

4.1 Introduction
4.2 challenge Formulation
4.3 balance of the behind schedule monitoring System
4.4 The State-Feedback Tracking
4.5 Example
4.6 Conclusions
4.7 Appendix

5 H-infinity keep watch over and Estimation of Retarded Linear Discrete-Time Systems

5.1 Introduction
5.2 challenge Formulation
5.3 Mean-Square Exponential Stability
5.3.1 instance - Stability
5.4 The Bounded actual Lemma
5.4.1 instance - BRL
5.5 State-Feedback Control
5.5.1 instance - strong State-Feedback
5.6 behind schedule Filtering
5.6.1 instance - Filtering
5.7 Conclusions

6 H-infinity-Like regulate for Nonlinear Stochastic Syste8 ms

6.1 Introduction
6.2 Stochastic H-infinity SF Control
6.3 The In.nite-Time Horizon Case: A Stabilizing Controller
6.3.1 Example
6.4 Norm-Bounded Uncertainty within the desk bound Case
6.4.1 Example
6.5 Conclusions

7 Non Linear structures - H-infinity-Type Estimation

7.1 Introduction
7.2 Stochastic H-infinity Estimation
7.2.1 Stability
7.3 Norm-Bounded Uncertainty
7.3.1 Example
7.4 Conclusions

8 Non Linear structures - dimension Output-Feedback Control

8.1 creation and challenge Formulation
8.2 Stochastic H-infinity OF Control
8.2.1 Example
8.2.2 The Case of Nonzero G2
8.3 Norm-Bounded Uncertainty
8.4 In.nite-Time Horizon Case: A Stabilizing H Controller 8
8.5 Conclusions

9 l2-Gain and powerful SF regulate of Discrete-Time NL Stochastic Systems

9.1 Introduction
9.2 Su.cient stipulations for l2-Gain= .:ASpecial Case
9.3 Norm-Bounded Uncertainty
9.4 Conclusions

10 H-infinity Output-Feedback keep an eye on of Discrete-Time Systems

10.1 Su.cient stipulations for l2-Gain= .:ASpecial Case
10.1.1 Example
10.2 The OF Case
10.2.1 Example
10.3 Conclusions

11 H-infinity regulate of Stochastic Switched structures with reside Time

11.1 Introduction
11.2 challenge Formulation
11.3 Stochastic Stability
11.4 Stochastic L2-Gain
11.5 H-infinity State-Feedback Control
11.6 instance - Stochastic L2-Gain Bound
11.7 Conclusions

12 powerful L-infinity-Induced keep an eye on and Filtering

12.1 Introduction
12.2 challenge formula and Preliminaries
12.3 balance and P2P Norm sure of Multiplicative Noisy Systems
12.4 P2P State-Feedback Control
12.5 P2P Filtering
12.6 Conclusions

13 Applications

13.1 Reduced-Order Control
13.2 Terrain Following Control
13.3 State-Feedback keep watch over of Switched Systems
13.4 Non Linear platforms: size Output-Feedback Control
13.5 Discrete-Time Non Linear platforms: l2-Gain
13.6 L-infinity regulate and Estimation

A Appendix: Stochastic regulate techniques - uncomplicated Concepts

B The LMI Optimization Method

C Stochastic Switching with stay Time - Matlab Scripts

References

Index

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Extra resources for Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems

Example text

56) involves a search for two scalar variables: α and f . One may start by line searching for α, taking a fixed value for f , that leads to a stabilizing controller of minimum γ. Once such a controller is obtained, standard optimization techniques can be used, say Matlab function ”fminsearch”, which seek the combination of the two scalar parameters that bring γ to a local minimum. 2 Example – Output-Feedback Control We bring a stationary modified version of an example which is taken from the field of guidance control ([136], see also [53], Chapter 11).

33) where Υˆ11 = B2 Yˆ + Yˆ T B2T + mp + mTp + P AT0 + A0 P + Υˆ15 = T f h[P A0 1 ¯ 1−d Rp , + mTp + Yˆ T B2T ], T Υˆ16 = P C1T + Yˆ T D12 , Υˆ25 = T f h[P A1 − mTp ]. 7. 3). 33). In the latter case the matrices P > 0, R state-feedback gain is given by: K = Yˆ P −1 . 10). 8. 3). 37). In the latter case the state-feedback gain is given by: K = Yˆ P −1 . 5 Stochastic State-Feedback Control 35 where Υˆ11 = B2 Yˆ + Yˆ T B2T + mp + mTp + P AT0 + A0 P + 1 ¯ 1−d Rp , Υˆ12 = A1 P − mp , Υˆ15 = T f h[P A0 + mTp + Yˆ T B2T ], T Υˆ16 = P C1T + Yˆ T D12 , ¯0P + H ¯ 2 Yˆ ]T , Υˆ1,10 = [H Υˆ25 = T f h[P A1 − mTp ], ¯T, Υˆ2,12 = P H 1 ˜1 = h f ¯1 , ˜2 = h f ¯2 .

16), τ (t) is an unknown time-delay which satisfies: 0 ≤ τ (t) ≤ h, τ˙ (t) ≤ d < 1. 17) In order to solve the above problem, we introduce the following operators: Δ (Δ1 g)(t) = g(t − τ (t)), Δ (Δ2 g)(t) = t t−τ (t) g(s)ds. 18) In what follows we use the fact that the induced L2 -norm of Δ1 is bounded 1 by √1−d , and, similarly to [77], the fact that the induced L2 -norm of Δ2 is bounded by h. 19) where t Γβ = m t Gx(s)dβ(s), and Γν = m t−τ Hw1 (s)dν(s), t−τ and where w1 (t) = (Δ1 x)(t), and w2 (t) = (Δ2 y¯)(t).

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