Advanced Topics in Control and Estimation of by Eli Gershon

By Eli Gershon

Complicated themes up to the mark and Estimation of State-Multiplicative Noisy structures starts off with an creation and broad 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 awarded and solved for nominal and unsure stochastic structures, through the input-output process. It offers strategies to the issues of state-feedback, filtering, and measurement-feedback regulate 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 regulate also are awarded and solved. the second one a part of the monograph matters non-linear stochastic nation- multiplicative platforms and covers the problems of balance, regulate and estimation of the platforms within the H∞ experience, for either continuous-time and discrete-time circumstances. The ebook additionally describes precise subject matters similar to stochastic switched structures with live time and peak-to-peak filtering of nonlinear stochastic platforms. The reader is brought to 6 sensible engineering- orientated examples of noisy state-multiplicative keep an eye on and filtering difficulties for linear and nonlinear platforms. The publication is rounded out by means of a three-part appendix containing stochastic instruments useful for a formal appreciation of the textual content: a easy advent to stochastic regulate procedures, points 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 up to speed and Estimation of State-Multiplicative Noisy structures could be of curiosity to engineers engaged on top of things platforms learn and improvement, to graduate scholars focusing on stochastic keep watch over concept, and to utilized mathematicians attracted to regulate difficulties. The reader is anticipated to have a few acquaintance with stochastic keep watch over idea and state-space-based optimum regulate idea and techniques for linear and nonlinear systems.

Table of Contents

Cover

Advanced issues on top of things 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 process for not on time Systems
1.2.1 Continuous-Time Case
1.2.2 Discrete-Time Case
1.3 Non Linear regulate 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 tactics - 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 platforms - H-infinity keep watch over 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 strong balance - The Norm-Bounded Case
2.3.3 strong balance - The Polytopic Case
2.4 Bounded actual Lemma
2.4.1 BRL for not on time State-Multiplicative structures - The Norm-Bounded Case
2.4.2 BRL - The Polytopic Case
2.5 Stochastic State-Feedback Control
2.5.1 State-Feedback keep watch over - The Nominal Case
2.5.2 powerful State-Feedback keep watch over - The Norm-Bounded Case
2.5.3 strong 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 strong Filtering - The Norm-Bounded Case
2.6.3 strong Polytopic Stochastic Filtering
2.6.4 instance - Filtering
2.7 Stochastic Output-Feedback keep watch over for not on time Systems
2.7.1 Stochastic Output-Feedback regulate - The Nominal Case
2.7.2 instance - Output-Feedback Control
2.7.3 powerful Stochastic Output-Feedback keep an eye on - 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 regulate 8
3.4 Conclusions

4 monitoring keep an eye on 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 regulate 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 genuine Lemma
5.4.1 instance - BRL
5.5 State-Feedback Control
5.5.1 instance - powerful 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 platforms - 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 advent 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 keep watch over 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 regulate 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 keep watch over of Stochastic Switched structures with stay 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 strong L-infinity-Induced regulate 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 regulate of Switched Systems
13.4 Non Linear platforms: size Output-Feedback Control
13.5 Discrete-Time Non Linear structures: l2-Gain
13.6 L-infinity keep an eye on and Estimation

A Appendix: Stochastic keep an eye on procedures - simple Concepts

B The LMI Optimization Method

C Stochastic Switching with reside Time - Matlab Scripts

References

Index

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

Sample text

When σ 2 = 1 we say that the Wiener process β(t) and the corresponding white noise ˙ process β(t) are standard. Back to Langevin’s equation, we may realize that it can also be written in terms of differentials as dx(t) = f (x(t), t)dt + g(x(t), t)dβ(t). This equation is, in fact, interpreted by t x(t) − x(t0 ) = t f (x(τ, τ )dτ + t0 g(x(τ, τ )dβ(τ ), t0 where the first term is a Lebesgue-Stieltges integral and the second term is an Ito integral with respect to the Wiener process β(t). 8 Ito Lemma 17 n−1 gti [β(ti+1 ) − β(ti )], where a = t0 < t1 < ...

The present chapter is organized as follows: We first investigate the stability of nominal delayed systems using the input–output approach and we then treat the robust stability problem for the resulting nonretarded systems that encounter either norm-bounded or polytopic type parameter uncertainties. We continue by deriving bounds on the L2 −gain of the uncertain delayed systems. Following the latter derivation, we obtain solutions to the problems of: state-feedback control, general-type filtering, and full-order measurement control for nominal systems and for uncertain ones.

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 . 38) Γ2T = [E0T 0 0 0 0 [ f hE0T ] 0 0 0 0 0 0], ¯ 0P + H ¯ 2 Yˆ ] 0 0 0 0 0 0 0 0 0 0 0], Γ¯2 = [[H Γ3T = [E1T 0 0 0 0 [ f hE1T ] 0 0 0 0 0 0], ¯ 1 P 0 0 0 0 0 0 0 0 0].

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