Fixed-time neural network composite learning control for uncertain nonlinear systems

A novel fixed-time neural network composite learning control (FNNCLC) scheme is proposed for nonlinear strict-feedback systems with unknown dynamics. The neural network (NN) is employed to handle system uncertainty. By utilizing tracking errors and prediction errors to update NN weights, accurate network learning is achieved under a weaker excitation condition termed interval excitation (IE) condition, instead of the typically required strict persistent excitation (PE) condition. Moreover, for the first time, a high order term and first order term of the prediction error are introduced to design composite learning adaptive laws, achieving the convergence of NN weights within fixed time. Additionally, a smooth fixed-time (FXT) dynamic surface control scheme is constructed without potential singularity problems, which mitigates complexity explosion by avoiding fractional power terms and complex switching strategies when formulating the control law. The stability of the proposed control scheme is analyzed by using Lyapunov technique. Simulation results demonstrate the effectiveness of the proposed controller.