Adaptive Neural Network Control for Uncertain Time-Varying State Constrained Robotics Systems
针对不确定多关节机器人系统，提出一种自适应神经网络控制器，通过非线性映射和时变障碍李雅普诺夫函数，确保系统状态在预设时变范围内有界，跟踪误差收敛到零附近。
In this paper, we design an adaptive neural network (NN) controller of uncertain n-joint robotic systems with time-varying state constraints. By proposing a nonlinear mapping, the robotic systems are transformed into the multiple-input, multiple-output systems. Compared with constant constraints, the time-varying state constraints are more general in the real systems. To overcome the design challenge, the time-varying barrier Lyapunov function is introduced to ensure that the states of the robotic systems are bounded within the predetermined time-varying range. The NN approximations are employed to approximate the uncertain parametric and unknown functions in the robotic systems. Based on the Lyapunov analysis, it can be proved that all signals of robotic systems are bounded; the tracking errors of system output converge on a small neighborhood of zero and the time-varying state constraints are never violated. Finally, a simulation example is performed to demonstrate the feasibility of the proposed approach.