Safe Adaptive Control [electronic resource] : Data-driven Stability Analysis and Robust Synthesis /
Stefanovic, Margareta.author.
Safonov, Michael G.author.
Safe Adaptive Control gives a formal and complete algorithm for assuring the stability of a switched control system when at least one of the available candidate controllers is stabilizing. The possibility of having an unstable switched system even in the presence of a stabilizing candidate controller is demonstrated by referring to several well-known adaptive control approaches, where the system goes unstable when a large mismatch between the unknown plant and the available models exists ("plant-model mismatch instability"). Sufficient conditions for this possibility to be avoided are formulated, and a "recipe" to be followed by the control system designer to guarantee stability and desired performance is provided. The problem is placed in a standard optimization setting. Unlike the finite controller sets considered elsewhere, the candidate controller set is allowed to be continuously parametrized so that it can deal with plants with a very large range of uncertainties.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
Introduction -- Definitions and Preliminary Facts -- Stability Results -- Generalization to the Time-varying Case -- Switched Adaptive Controller: Robust Synthesis -- Comparison with Other Switching Adaptive Schemes -- Conclusion.
Safe Adaptive Control gives a formal and complete algorithm for assuring the stability of a switched control system when at least one of the available candidate controllers is stabilizing. The possibility of having an unstable switched system even in the presence of a stabilizing candidate controller is demonstrated by referring to several well-known adaptive control approaches, where the system goes unstable when a large mismatch between the unknown plant and the available models exists ("plant-model mismatch instability"). Sufficient conditions for this possibility to be avoided are formulated, and a "recipe" to be followed by the control system designer to guarantee stability and desired performance is provided. The problem is placed in a standard optimization setting. Unlike the finite controller sets considered elsewhere, the candidate controller set is allowed to be continuously parametrized so that it can deal with plants with a very large range of uncertainties.
Control engineering.
System theory.
Mathematical optimization.
Artificial intelligence.
Control and Systems Theory.
Systems Theory, Control.
Optimization.
Artificial Intelligence.
Lecture notes in control and information sciences