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Theory and computation of disturbance invariant sets for discrete-time linear systems

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  • Ilya Kolmanovsky
  • Elmer G. Gilbert

Abstract

This paper considers the characterization and computation of invariant sets for discrete-time, time-invariant, linear systems with disturbance inputs whose values are confined to a specified compact set but are otherwise unknown. The emphasis is on determining maximal disturbance-invariant sets X that belong to a specified subset Γ of the state space. Such d-invariant sets have important applications in control problems where there are pointwise-in-time state constraints of the form χ ( t ) ∈ Γ . One purpose of the paper is to unite and extend in a rigorous way disparate results from the prior literature. In addition there are entirely new results. Specific contributions include: exploitation of the Pontryagin set difference to clarify conceptual matters and simplify mathematical developments, special properties of maximal invariant sets and conditions for their finite determination, algorithms for generating concrete representations of maximal invariant sets, practical computational questions, extension of the main results to general Lyapunov stable systems, applications of the computational techniques to the bounding of state and output response. Results on Lyapunov stable systems are applied to the implementation of a logic-based, nonlinear multimode regulator. For plants with disturbance inputs and state-control constraints it enlarges the constraint-admissible domain of attraction. Numerical examples illustrate the various theoretical and computational results.

Suggested Citation

  • Ilya Kolmanovsky & Elmer G. Gilbert, 1998. "Theory and computation of disturbance invariant sets for discrete-time linear systems," Mathematical Problems in Engineering, Hindawi, vol. 4, pages 1-51, January.
  • Handle: RePEc:hin:jnlmpe:934097
    DOI: 10.1155/S1024123X98000866
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    Cited by:

    1. Andreas Ernst & Lars Grüne & Janosch Rieger, 2023. "A linear programming approach to approximating the infinite time reachable set of strictly stable linear control systems," Journal of Global Optimization, Springer, vol. 86(2), pages 521-543, June.
    2. Jiao, Feixiang & Zou, Yuan & Zhang, Xudong & Zhang, Bin, 2022. "Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station," Energy, Elsevier, vol. 247(C).

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