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Variance-Constrained Multi-Objective Stochastic Control and Filtering.

By: Contributor(s): Series: Wiley Series in Dynamics and Control of Electromechanical Systems SerPublisher: New York : John Wiley & Sons, Incorporated, 2015Copyright date: ©2015Edition: 1st edDescription: 1 online resource (320 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781118929476
Subject(s): Genre/Form: Additional physical formats: Print version:: Variance-Constrained Multi-Objective Stochastic Control and FilteringDDC classification:
  • 629.801/51923
LOC classification:
  • TJ213 -- .M255 2015eb
Online resources:
Contents:
Cover -- Title Page -- Copyright -- Contents -- Preface -- Series Preface -- Acknowledgements -- List of Abbreviations -- List of Figures -- Chapter 1 Introduction -- 1.1 Analysis and Synthesis of Nonlinear Stochastic Systems -- 1.1.1 Nonlinear Systems -- 1.1.2 Stochastic Systems -- 1.2 Multi-Objective Control and Filtering with Variance Constraints -- 1.2.1 Covariance Control Theory -- 1.2.2 Multiple Performance Requirements -- 1.2.3 Design Techniques for Nonlinear Stochastic Systems with Variance Constraints -- 1.2.4 A Special Case of Multi-Objective Design: Mixed H2/H∞ Control/Filtering -- 1.3 Outline -- Chapter 2 Robust H∞ Control with Variance Constraints -- 2.1 Problem Formulation -- 2.2 Stability, H∞ Performance, and Variance Analysis -- 2.2.1 Stability and H∞ Performance Analysis -- 2.2.2 Variance Analysis -- 2.3 Robust Controller Design -- 2.4 Numerical Example -- 2.5 Summary -- Chapter 3 Robust Mixed H2/H∞ Filtering -- 3.1 System Description and Problem Formulation -- 3.2 Algebraic Characterizations for Robust H2/H∞ Filtering -- 3.2.1 Robust H2 Filtering -- 3.2.2 Robust H∞ Filtering -- 3.3 Robust H2/H∞ Filter Design Techniques -- 3.4 An Illustrative Example -- 3.5 Summary -- Chapter 4 Robust Variance-Constrained Filtering with Missing Measurements -- 4.1 Problem Formulation -- 4.2 Stability and Variance Analysis -- 4.3 Robust Filter Design -- 4.4 Numerical Example -- 4.5 Summary -- Chapter 5 Robust Fault-Tolerant Control with Variance Constraints -- 5.1 Problem Formulation -- 5.2 Stability and Variance Analysis -- 5.3 Robust Controller Design -- 5.4 Numerical Example -- 5.5 Summary -- Chapter 6 Robust H2 Sliding Mode Control -- 6.1 The System Model -- 6.2 Robust H2 Sliding Mode Control -- 6.2.1 Switching Surface -- 6.2.2 Performances of the Sliding Motion -- 6.2.3 Computational Algorithm -- 6.3 Sliding Mode Controller.
6.4 Numerical Example -- 6.5 Summary -- Chapter 7 Variance-Constrained Dissipative Control with Degraded Measurements -- 7.1 Problem Formulation -- 7.2 Stability, Dissipativity, and Variance Analysis -- 7.3 Observer-Based Controller Design -- 7.3.1 Solvability of the Multi-Objective Control Problem -- 7.3.2 Computational Algorithm -- 7.4 Numerical Example -- 7.5 Summary -- Chapter 8 Variance-Constrained H∞ Control with Multiplicative Noises -- 8.1 Problem Formulation -- 8.2 Stability, H∞ Performance, and Variance Analysis -- 8.2.1 Stability -- 8.2.2 H∞ Performance -- 8.2.3 Variance Analysis -- 8.3 Robust State Feedback Controller Design -- 8.4 Numerical Example -- 8.5 Summary -- Chapter 9 Robust H∞ Control with Variance Constraints: the Finite-Horizon Case -- 9.1 Problem Formulation -- 9.2 Performance Analysis -- 9.2.1 H∞ Performance -- 9.2.2 Variance Analysis -- 9.3 Robust Finite-Horizon Controller Design -- 9.4 Numerical Example -- 9.5 Summary -- Chapter 10 Error Variance-Constrained H∞ Filtering with Degraded Measurements: The Finite-Horizon Case -- 10.1 Problem Formulation -- 10.2 Performance Analysis -- 10.2.1 H∞ Performance Analysis -- 10.2.2 System Covariance Analysis -- 10.3 Robust Filter Design -- 10.4 Numerical Example -- 10.5 Summary -- Chapter 11 Mixed H2/H∞ Control with Randomly Occurring Nonlinearities: The Finite-Horizon Case -- 11.1 Problem Formulation -- 11.2 H∞ Performance -- 11.3 Mixed H2/H∞ Controller Design -- 11.3.1 State-Feedback Controller Design -- 11.3.2 Computational Algorithm -- 11.4 Numerical Example -- 11.5 Summary -- Chapter 12 Mixed H2/H∞ Control with Markovian Jump Parameters and Probabilistic Sensor Failures: The Finite-Horizon Case -- 12.1 Problem Formulation -- 12.2 H∞ Performance -- 12.3 Mixed H2/H∞ Controller Design -- 12.3.1 Controller Design -- 12.3.2 Computational Algorithm -- 12.4 Numerical Example.
12.5 Summary -- Chapter 13 Robust Variance-Constrained H∞ Control with Randomly Occurring Sensor Failures: The Finite-Horizon Case -- 13.1 Problem Formulation -- 13.2 H∞ and Covariance Performance Analysis -- 13.2.1 H∞ Performance -- 13.2.2 Covariance Analysis -- 13.3 Robust Finite-Horizon Controller Design -- 13.3.1 Controller Design -- 13.3.2 Computational Algorithm -- 13.4 Numerical Example -- 13.5 Summary -- Chapter 14 Mixed H2/H∞ Control with Actuator Failures: the Finite-Horizon Case -- 14.1 Problem Formulation -- 14.2 H∞ Performance -- 14.3 Multi-Objective Controller Design -- 14.3.1 Controller Design -- 14.3.2 Computational Algorithm -- 14.4 Numerical Example -- 14.5 Summary -- Chapter 15 Conclusions and Future Topics -- 15.1 Concluding Remarks -- 15.2 Future Research -- References -- Index -- EULA.
Summary: Unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomena Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performance Includes case studies of several nonlinear stochastic systems Investigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturations Considers both time-invariant systems and time-varying systems Exploits newly developed techniques to handle the emerging mathematical and computational challenges.
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Cover -- Title Page -- Copyright -- Contents -- Preface -- Series Preface -- Acknowledgements -- List of Abbreviations -- List of Figures -- Chapter 1 Introduction -- 1.1 Analysis and Synthesis of Nonlinear Stochastic Systems -- 1.1.1 Nonlinear Systems -- 1.1.2 Stochastic Systems -- 1.2 Multi-Objective Control and Filtering with Variance Constraints -- 1.2.1 Covariance Control Theory -- 1.2.2 Multiple Performance Requirements -- 1.2.3 Design Techniques for Nonlinear Stochastic Systems with Variance Constraints -- 1.2.4 A Special Case of Multi-Objective Design: Mixed H2/H∞ Control/Filtering -- 1.3 Outline -- Chapter 2 Robust H∞ Control with Variance Constraints -- 2.1 Problem Formulation -- 2.2 Stability, H∞ Performance, and Variance Analysis -- 2.2.1 Stability and H∞ Performance Analysis -- 2.2.2 Variance Analysis -- 2.3 Robust Controller Design -- 2.4 Numerical Example -- 2.5 Summary -- Chapter 3 Robust Mixed H2/H∞ Filtering -- 3.1 System Description and Problem Formulation -- 3.2 Algebraic Characterizations for Robust H2/H∞ Filtering -- 3.2.1 Robust H2 Filtering -- 3.2.2 Robust H∞ Filtering -- 3.3 Robust H2/H∞ Filter Design Techniques -- 3.4 An Illustrative Example -- 3.5 Summary -- Chapter 4 Robust Variance-Constrained Filtering with Missing Measurements -- 4.1 Problem Formulation -- 4.2 Stability and Variance Analysis -- 4.3 Robust Filter Design -- 4.4 Numerical Example -- 4.5 Summary -- Chapter 5 Robust Fault-Tolerant Control with Variance Constraints -- 5.1 Problem Formulation -- 5.2 Stability and Variance Analysis -- 5.3 Robust Controller Design -- 5.4 Numerical Example -- 5.5 Summary -- Chapter 6 Robust H2 Sliding Mode Control -- 6.1 The System Model -- 6.2 Robust H2 Sliding Mode Control -- 6.2.1 Switching Surface -- 6.2.2 Performances of the Sliding Motion -- 6.2.3 Computational Algorithm -- 6.3 Sliding Mode Controller.

6.4 Numerical Example -- 6.5 Summary -- Chapter 7 Variance-Constrained Dissipative Control with Degraded Measurements -- 7.1 Problem Formulation -- 7.2 Stability, Dissipativity, and Variance Analysis -- 7.3 Observer-Based Controller Design -- 7.3.1 Solvability of the Multi-Objective Control Problem -- 7.3.2 Computational Algorithm -- 7.4 Numerical Example -- 7.5 Summary -- Chapter 8 Variance-Constrained H∞ Control with Multiplicative Noises -- 8.1 Problem Formulation -- 8.2 Stability, H∞ Performance, and Variance Analysis -- 8.2.1 Stability -- 8.2.2 H∞ Performance -- 8.2.3 Variance Analysis -- 8.3 Robust State Feedback Controller Design -- 8.4 Numerical Example -- 8.5 Summary -- Chapter 9 Robust H∞ Control with Variance Constraints: the Finite-Horizon Case -- 9.1 Problem Formulation -- 9.2 Performance Analysis -- 9.2.1 H∞ Performance -- 9.2.2 Variance Analysis -- 9.3 Robust Finite-Horizon Controller Design -- 9.4 Numerical Example -- 9.5 Summary -- Chapter 10 Error Variance-Constrained H∞ Filtering with Degraded Measurements: The Finite-Horizon Case -- 10.1 Problem Formulation -- 10.2 Performance Analysis -- 10.2.1 H∞ Performance Analysis -- 10.2.2 System Covariance Analysis -- 10.3 Robust Filter Design -- 10.4 Numerical Example -- 10.5 Summary -- Chapter 11 Mixed H2/H∞ Control with Randomly Occurring Nonlinearities: The Finite-Horizon Case -- 11.1 Problem Formulation -- 11.2 H∞ Performance -- 11.3 Mixed H2/H∞ Controller Design -- 11.3.1 State-Feedback Controller Design -- 11.3.2 Computational Algorithm -- 11.4 Numerical Example -- 11.5 Summary -- Chapter 12 Mixed H2/H∞ Control with Markovian Jump Parameters and Probabilistic Sensor Failures: The Finite-Horizon Case -- 12.1 Problem Formulation -- 12.2 H∞ Performance -- 12.3 Mixed H2/H∞ Controller Design -- 12.3.1 Controller Design -- 12.3.2 Computational Algorithm -- 12.4 Numerical Example.

12.5 Summary -- Chapter 13 Robust Variance-Constrained H∞ Control with Randomly Occurring Sensor Failures: The Finite-Horizon Case -- 13.1 Problem Formulation -- 13.2 H∞ and Covariance Performance Analysis -- 13.2.1 H∞ Performance -- 13.2.2 Covariance Analysis -- 13.3 Robust Finite-Horizon Controller Design -- 13.3.1 Controller Design -- 13.3.2 Computational Algorithm -- 13.4 Numerical Example -- 13.5 Summary -- Chapter 14 Mixed H2/H∞ Control with Actuator Failures: the Finite-Horizon Case -- 14.1 Problem Formulation -- 14.2 H∞ Performance -- 14.3 Multi-Objective Controller Design -- 14.3.1 Controller Design -- 14.3.2 Computational Algorithm -- 14.4 Numerical Example -- 14.5 Summary -- Chapter 15 Conclusions and Future Topics -- 15.1 Concluding Remarks -- 15.2 Future Research -- References -- Index -- EULA.

Unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomena Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performance Includes case studies of several nonlinear stochastic systems Investigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturations Considers both time-invariant systems and time-varying systems Exploits newly developed techniques to handle the emerging mathematical and computational challenges.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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