A Low-Complexity Adaptive Performance Control Scheme for Unknown Nonlinear Systems Subject to Input Saturation

This paper proposes a novel low-complexity adaptive performance control framework for uncertain nonlinear systems subject to input saturation. The proposed approach dynamically adjusts prescribed performance bounds online using a simple, low-complexity adaptation mechanism, eliminating the need for...

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Bibliographic Details
Published inIEEE control systems letters Vol. 9; p. 1
Main Authors Gkesoulis, Athanasios K., Bechlioulis, Charalampos P.
Format Journal Article
LanguageEnglish
Published IEEE 2025
Subjects
Adaptive control
Constrained control
Control systems
Neural networks
Nonlinear systems
Robots
Robust control
Simulation
System dynamics
Training
Trajectory
Tuning
Uncertain systems
Uncertainty
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Summary:This paper proposes a novel low-complexity adaptive performance control framework for uncertain nonlinear systems subject to input saturation. The proposed approach dynamically adjusts prescribed performance bounds online using a simple, low-complexity adaptation mechanism, eliminating the need for complex gain tuning and divisions with error signals. Explicit closed-form analytical bounds for tracking errors and input feasibility conditions are provided, ensuring both stability and prescribed performance despite input limitations. Simulation results clearly demonstrate the effectiveness, simplicity, and applicability of the proposed control strategy.
ISSN:2475-1456
2475-1456
DOI:10.1109/LCSYS.2025.3597302