基于切换增益调节的神经滑模控制的机器人位置跟踪

针对机器人的位置轨迹跟踪问题，提出一种基于切换增益调节的神经网络滑模控制方法。首先设计基于机器人位置的滑模控制器模块，然后通过神经网络来调节滑模控制器中的切换增益，使得切换增益能随着外界干扰作用等不确定项的改变而改变，从而能实时地估计切换增益，解决传统滑模控制中的抖振问题，最后，以双臂机器人为对象，采用MATLAB仿真软件对该控制算法进行了验证。结果表明，与传统的滑模控制相比较，该方法能使机器人更好地跟踪期望的位置轨迹，并有效地减轻了抖振。

Robot Position Tracking Based on Neural Sliding Mode Control with Switching Gain Regulation

A neural network sliding mode control method based on switching gain regulation is proposed for the position tracking of robot. Firstly, a sliding mode controller was designed for the robot. Then, the switching gain of the sliding mode controller was regulated by neural network. The switching gain can be changed with the changing of uncertainties such as external interference. Thus, the switching gain can be estimated in real time and the chattering problem in the traditional sliding mode control can be solved. Finally, the control method was verified on a double-arm robot. The simulation results show that the robot can track the desired trajectory better and alleviate the chattering problem more effectively with the proposed neural network sliding mode control method than the traditional sliding mode control.