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Approximation-free Adaptive Prescribed Performance Control for Unknown SISO Nonlinear Systems with Input Saturation

Panagiotis S. Trakas, Charalampos P. Bechlioulis

20222022 IEEE 61st Conference on Decision and Control (CDC)21 citationsDOI

Abstract

A universal approximation-free adaptive prescribed performance control scheme is designed for unknown SISO nonlinear systems with input saturation. The proposed control method introduces a compromising relaxation of output performance specifications depending on the input limitations. Given the conflicting nature between input and output constraints, the stability properties are inevitably local. In this respect, a sufficient stability condition for the closed-loop system is provided through theoretical analysis. Owing to the adopted prescribed performance control technique, the satisfaction of the aforementioned stability properties guarantees the desired trade-off between input and output constraints. Moreover, no hard calculations are needed, neither for the controller nor for the adaptive law, maintaining the complexity of the control algorithm relatively low. Finally, the proposed approach is clarified and verified by various simulation studies.

Topics & Concepts

Control theory (sociology)Nonlinear systemAdaptive controlController (irrigation)Stability (learning theory)Computer scienceRelaxation (psychology)Saturation (graph theory)Control (management)MathematicsPsychologyCombinatoricsQuantum mechanicsPhysicsSocial psychologyArtificial intelligenceAgronomyMachine learningBiologyAdaptive Control of Nonlinear SystemsAdvanced Control Systems OptimizationAdaptive Dynamic Programming Control
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