基于自适应神经网络的柔性关节机械臂控制

柔性关节机械臂系统是一个非线性高阶系统,且其动力学方程难以精确地获得。因此,提出一种以关节驱动电机的输入电压为控制量的自适应神经网络控制器,用于控制多连杆柔性关节机械臂系统。所提控制方法通过对柔性关节机械臂模型解耦得到关节转角关于电压的方程,以电压为系统控制输入。设计神经网络控制器用于逼近最优控制输入,并设计鲁棒控制器补偿逼近误差。该控制方法不再涉及复杂的动力学方程,因此能简化计算。相比已有控制方法,所提出的控制策略更简单、响应更快且更有效。并以二连杆柔性关节机械臂为例进行了仿真研究,结果证明了所提出控制方法的有效性。

Control of Flexible-Joint Robots Based on Adaptive Neural Networks

The flexible-joint manipulator system is a nonlinear high-order system. It is difficult to obtain the kinetic equation accurately. A proposal has been made of an adaptive neural network controller based on the input voltage of the joint drive motor, with its application for the control of the multi-link flexible-joint manipulator system. By adopting the proposed control method, the equations of the joint angle can be obtained by decoupling the flexible joint manipulator model, with motor voltage the inputs of the manipulator system. A neural network controller is designed to approximate the optimal control input, and a robust controller is designed to compensate the approximation error. The control method does not involve complex dynamic equations, thus greatly simplifying the process of calculation. As a result, the proposed control strategy, compared with the existing control methods, is simpler, faster and more efficient. A simulation test has been carried out on the two-link flexible-joint manipulators, thus verifying the effectiveness of the proposed control method.