Prescribed Performance Asymptotic Tracking Control for Uncertain Non‐Linear Systems With Full‐State Constraints and Input Saturation

ABSTRACT In this paper, an extension of asymptotic tracking control is proposed for a class of high‐order uncertain non‐linear systems that consider prescribed performance, state constraints, and input saturation. To address the conflict between prescribed performance boundaries, input saturation, and full‐state constraints, a novel adaptive tube‐based prescribed performance boundary and a new barrier Lyapunov function are introduced. By integrating adaptive control and backstepping techniques, an adaptive neural network controller is developed. The incorporation of command filtering techniques helps avoid the “feasibility assumptions” of the virtual controller and the “computational complexity” issues arising from backstepping techniques. Additionally, two auxiliary systems are introduced to compensate for filtering errors and mitigate errors induced by input saturation. Stability analysis demonstrates that all signals are bounded, the tracking error converges asymptotically under prescribed performance constraints, and no violations of state constraints occur. Through comparative simulations, the effectiveness of the proposed approach is validated.