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

Abstract

This letter 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.