Abstract: A control law based on Function Sliding Mode Controller (FSMC) was proposed for trajectory tracking control of manipulator. The uncertainties of the system were achieved from dynamic model and sliding mode function. Then RBF neural network was used to approach uncertainties of the system. Because of the approximation error of neural network, especially at the initial phase, the function sliding mode controller and robust compensator were designed for error compensation of neural network. The function sliding mode controller was capable of overcoming chattering problem of common Sliding Mode Control (SMC), and improved the tracking ability of the system. The global stability of closed loop system was proved based on Lyapunov theory, the effectiveness of proposed control approach was also demonstrated by simulation results.

Key words: manipulator, function sliding mode, neural network, trajectory tracking, sliding mode control

摘要： 提出一种基于函数滑模控制器(FSMC)的控制策略,用于不确定机械手的轨迹跟踪控制。首先，由动力学模型和滑模函数得到系统的不确定项；然后，利用RBF神经网络逼近系统不确定项,由于神经网络逼近存在误差,而且在初始阶段误差较大,设计函数滑模控制器和鲁棒补偿项对神经网络逼近误差进行补偿,以克服普通滑模控制器容易引起的抖振问题,同时提高系统的跟踪控制性能。基于李亚普诺夫理论证明了闭环系统的全局稳定性,仿真实验也验证了方法的有效性。

关键词: 机械手, 函数滑模, 神经网络, 轨迹跟踪, 滑模控制