IEEE Transactions on Automatic Control· 2016Q1
Event-Triggered Adaptive Control for a Class of Uncertain Nonlinear Systems
- 1,532citations
- Q1SCImago
- 2016year
Short summary
New adaptive control schemes for uncertain nonlinear systems eliminate the need for input-to-state stability (ISS) assumptions regarding measurement errors by designing the controller and triggering event simultaneously.
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Key points
- Developed event-triggered adaptive control for uncertain nonlinear systems without requiring globally Lipschitz nonlinearities.
- Eliminated the need for input-to-state stability (ISS) assumptions on measurement errors by co-designing controller and triggering event.
- Introduced fixed, relative, and a novel switching threshold triggering strategies.
- Guaranteed global boundedness of all closed-loop signals and exponential convergence of errors to an adjustable set.
AI-generated from the title and abstract; the full text is not read.
Abstract
In this technical note, the problem of event-trigger based adaptive control for a class of uncertain nonlinear systems is considered. The nonlinearities of the system are not required to be globally Lipschitz. Since the system contains unknown parameters, it is a difficult task to check the assumption of the input-to-state stability (ISS) with respect to the measurement errors, which is required in most existing literature. To solve this problem, we design both the adaptive controller and the triggering event at the same time such that the ISS assumption is no longer needed. In addition to presenting new design methodologies based on the fixed threshold strategy and relative threshold strategy, we also propose a new strategy named the switching threshold strategy. It is shown that the proposed control schemes guarantee that all the closed-loop signals are globally bounded and the tracking/stabilization error exponentially converges towards a compact set which is adjustable.
The authors' abstract, as published at the source. IEEE Transactions on Automatic Control, 2016 · DOI ↗
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Field: Control and Systems Engineering
Control and Systems EngineeringEngineering