Fixed‐Time Adaptive Learning Control for Output‐Constrained Nonlinear Strict‐Feedback Systems

ABSTRACT

This paper investigates the adaptive dynamics learning and control problem for a class of nonlinear strict‐feedback systems subject to output constraints. First, an output constraint transformation framework is introduced to equivalently convert the constrained system into an unconstrained form. Based on this framework, an adaptive fixed‐time filtered learning control strategy is developed, which guarantees closed‐loop stability while strictly enforcing the prescribed output constraints. Next, under the persistent excitation condition, a radial basis function neural network (RBFNN) is employed to accurately approximate the unknown nonlinear dynamics. Furthermore, the learned dynamic information is incorporated into the control law design to construct a learning‐based controller, thereby effectively improving the transient performance and robustness of the system. Rigorous Lyapunov analysis is conducted to prove the uniform boundedness of all closed‐loop signals and the strict satisfaction of the output constraints. Finally, simulation results are presented to demonstrate the effectiveness and superiority of the proposed adaptive constrained dynamics learning and control approach.