Set-theoretic Approaches to the Aperiodic Control of Linear Systems
Florian D. Brunner
Cite this publication as
Florian D. Brunner, Set-theoretic Approaches to the Aperiodic Control of Linear Systems (2017), Logos Verlag, Berlin, ISBN: 9783832592578
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Descripción / Abstract
In this thesis, we employ set-theoretic properties of additively disturbed linear discrete-time systems to develop stabilizing aperiodically updated control laws for plants controlled over communication networks. In particular, we design event-triggered and self-triggered controllers with a priori guarantees on closed-loop characteristics such as stability, asymptotic bound, and average communication rate. Different models for the disturbances are taken into account, namely arbitrary disturbances of which only a bound in the form of a compact set is known and stochastic disturbances with known probability distribution. For setups with hard constraints on the states and inputs, we propose aperiodic schemes based on robust model predictive control methods. Both the full information (state-feedback) case, as well as the limited information (output-feedback) case are investigated. It is demonstrated that the proposed controllers achieve a considerable reduction in the required network usage with only moderate or non-existing deterioration of the closed-loop properties guaranteed by comparable controllers that transmit information at every point in time.
Índice
- BEGINN
- 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
- 2.5 Model predictive control
- 3 Linear Systems Perturbed by Bounded Disturbances
- 3.1 Preliminaries
- 3.2 Lyapunov-based approach
- 3.3 Set-based approach
- 3.4 Summary
- 4 Stochastic Threshold Design in Event-triggered Control
- 4.1 Threshold design for arbitrarily distributed disturbances
- 4.2 Stochastic thresholds for Gauian noise disturbances
- 4.3 Summary
- 5 Aperiodic Model Predictive Control of Constrained Linear Systems
- 5.1 Lyapunov-based approach
- 5.2 Mixed set{Lyapunov approach
- 5.3 Purely set-based approach
- 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
- 6.2 Event-triggered output-feedback control
- 6.3 Summary
- 7 Conclusions