关键词: 严格反馈非线性系统, 全驱动系统, 预设性能, 神经网络, 跟踪控制

Abstract: Abstract: Aiming at the problem of prescribed performance tracking control for uncertain strict-feedback nonlinear systems, this paper proposes a neural network tracking control method based on the fully-actuated system framework and improved prescribed perfo- rmance. The original system is converted into a high-order fully-actuated model through equivalent transformation, which significantly simplifies the controller structure and reduces the computational burden. In this paper, an improved performance function and its corre- sponding error transformation scheme are further proposed, and the constraint boundaries are employed to ensure the desired performa- nce of the system in terms of both steady-state and transient responses; meanwhile, the RBF neural network is utilized to perform linear approximation on the unknown nonlinear functions in the system, thereby enhancing the robustness of the system under model uncerta- inty. Finally, the Lyapunov method is adopted to prove the stability of the controller, ensuring that all signals of the closed-loop system are uniformly ultimately bounded. Simulation results verify the effectiveness of the proposed theoretical method, and the tracking error satisfies the prescribed performance constraints within the specified time.

Key words: strictly feedback nonlinear system, fully actuated system, prescribed performance, neural network, tracking control