采用神经元补偿器的机械手计算转矩控制算法

在本文中，我们提出了一种神经网络控制方法，以增 强在机械手控制中普遍使用的计算转矩控制结构．本算法将计算转矩控制结构，和神经网络 补偿结构有效地结合起来，使得在不增加控制结构复杂性的基础上，大大增强了控制的鲁棒 性．由于神经元补偿器具有很强的自适应性，因此在整个控制算法中无需事先精确了解机器 人的动力学参数和结构，而且在操作中变化的参数也能得到很好的补偿．这种算法还有一个 突出的优点就是，神经元补偿器作为前馈控制回路中的一个独立部分，使得整个控制系统能 够实现多速率采样控制结构．

COMPUTED-TORQUE CONTROL SCHEME FOR ROBOT MANIPULATORS WITH A NEURO-COMPENSATOR

In this paper, a novel control scheme is proposed to enhance the conventional computed torque control structure of robot manipulators. The control scheme is based on the combination of a classical computed torque control as a feed forward structure and a neural network as a compensation structure. The resulting control scheme has a simple structure with improved robustness. The neuro compensator has a good adaptability, accurate knowledge of both the robot dynamic parameters and structure are not required in advance, and large parameter variations during operation can also be compensated for. This special control algorithm also has the implementation advantage in that the neuro compensator is independent of the feed forward control loop and, therefore, a multi rate sampling structure for the whole control system can be applied.