多关节机器人的模糊神经滑模控制

Fuzzy Neural Sliding Mode Control for Multi-link Robots

针对含有建模误差和不确定干扰的多关节机器人轨迹跟踪控制,提出了一种模糊神经滑模控制方法。该方法采用全局快速终端滑模面,保证了系统能够从任意初始状态在有限时间内到达滑模面和平衡点。采用模糊神经网络自适应地补偿系统的建模误差和外界干扰,保证了滑模控制在滑模面的运动。文中利用李亚普诺夫稳定性判据推导出了控制器的控制律和模糊神经网络的目标函数,通过模糊神经网络的在线学习,削弱了滑模控制的抖振。仿真结果表明了其有效性。

A fuzzy neural sliding mode controller is proposed for trajectory tracking control of multi-link robots with uncertain external disturbances and system model errors. This approach uses a global fast terminal sliding mode manifold, which guarantees that the controlled system can reach the sliding mode manifold and equilibrium point in finite time from any initial state. A fuzzy neural network is applied to learn the upper bound of system model errors and external disturbances, and enforce the sliding mode motion. The control law and the cost function of the fuzzy neural network are calculated by Lyapnov stability method. Chattering of the sliding mode control is reduced by the fuzzy neural network＇s learning. Simulation results verify the validity of the control scheme.