Robust Adaptive Neural Tracking Control for a Class of Perturbed Uncertain Nonlinear Systems With State Constraints
针对一类具有完全未知非线性、全状态硬约束和未知时变有界扰动的严格反馈不确定非线性系统，提出一种鲁棒自适应神经跟踪控制方法，利用积分障碍李雅普诺夫函数处理状态约束和未知控制增益，神经网络逼近未知函数，并设计自适应参数补偿逼近误差和扰动，保证闭环信号有界且状态不越界。
In this paper, we deal with the problem of tracking control for a class of uncertain nonlinear systems in strictfeedback form subject to completely unknown system nonlinearities, hard constraints on full states, and unknown time-varying bounded disturbances. Integral barrier Lyapunov functionals are constructed to handle the unknown affine control gains (g(·)) with state constraints simultaneously. This removes the need on the knowledge of control gains for control design and avoids the conservative step of transforming original state constraints into new bounds on tracking errors. Neural networks (NNs) are used to approximate the unknown continuous packaged functions. To enhance the robustness, adapting parameters are developed to compensate the unknown bounds on NNs approximations and external disturbances. Design parameters-dependent feasibility conditions are formulated as sufficient conditions for the existence of feasible design parameters to guarantee the state constraints, and an offline constrained optimization step is proposed to obtain the optimal design parameters prior to the implementation of the proposed control. It is proved that the proposed control can guarantee the semiglobal uniform ultimate boundedness of all signals in closed-loop system, all states are ensured to remain in the predefined constrained state space, and tracking error converges to an adjustable neighborhood of the origin by choosing appropriate design parameters. Simulations are performed to validate the proposed control.