Set-Theoretic Approaches to the Aperiodic Control of Linear Systems.

Set-Theoretic Approaches to the Aperiodic Control of Linear Systems.

保存先:
書誌詳細
第一著者: Brunner, Florian D.
フォーマット: eBook
言語:English
出版事項: Berlin : Logos Verlag Berlin, 2017.
主題:
Information theory-Methodology.
Information theory > Methodology
オンライン･アクセス:Click for online access
目次:
  • Intro
  • 1 Introduction
  • 2 Background and Preliminaries
  • 2.1 Dynamical systems and stability
  • 2.2 Discrete-time linear systems
  • 2.3 Event-triggered and self-triggered control
  • 2.4 Set-valued estimation
  • 2.4.1 Set-valued estimates from linear estimator dynamics
  • 2.4.2 Set-valued moving-horizon estimation
  • 2.5 Model predictive control
  • 3 Linear Systems Perturbed by Bounded Disturbances
  • 3.1 Preliminaries
  • 3.2 Lyapunov-based approach
  • 3.2.1 Theoretical results
  • 3.2.2 Output feedback
  • 3.2.3 Computational aspects
  • 3.2.4 Numerical examples
  • 3.3 Set-based approach
  • 3.3.1 State feedback
  • 3.3.2 Analysis of given aperiodic schemes
  • 3.3.3 Output feedback
  • 3.3.4 Computational aspects
  • 3.3.5 Numerical examples
  • 3.4 Summary
  • 4 Stochastic Threshold Design in Event-triggered Control
  • 4.1 Threshold design for arbitrarily distributed disturbances
  • 4.1.1 Probability assigment
  • 4.1.2 Expected value assignment
  • 4.2 Stochastic thresholds for Gau{u02C7}ian noise disturbances
  • 4.2.1 State-Feedback
  • 4.2.2 Output feedback
  • 4.3 Summary
  • 5 Aperiodic Model Predictive Control of Constrained Linear Systems
  • 5.1 Lyapunov-based approach
  • 5.1.1 A Lyapunov function for robust MPC
  • 5.1.2 Relaxing the rate of decrease
  • 5.1.3 Aperiodic control algorithms
  • 5.1.4 Implementation
  • 5.2 Mixed set{Lyapunov approach
  • 5.2.1 Feasibility by value function decrease, stability by set-membership condition
  • 5.2.2 Aperiodic control algorithms
  • 5.2.3 Implementation
  • 5.3 Purely set-based approach
  • 5.3.1 Feasibility from set-membership conditions
  • 5.3.2 Aperiodic control algorithms
  • 5.3.3 Implementation
  • 5.4 Threshold-based event-triggered MPC: analysis and stochastic design
  • 5.5 Numerical example
  • 5.6 Summary
  • 6 Output-feedback Event-triggered Model Predictive Control.
  • 6.1 Set-valued moving horizon estimation in model predictive control
  • 6.1.1 General results
  • 6.1.2 Realization with set-valued moving horizon estimation
  • 6.1.3 Implementation
  • 6.1.4 Numerical example
  • 6.2 Event-triggered output-feedback control
  • 6.2.1 Closed-loop properties
  • 6.2.2 Implementation
  • 6.2.3 Numerical Examples
  • 6.2.4 Outlook: extension to self-triggered control
  • 6.3 Summary
  • 7 Conclusions.

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