输入受限下机器人关节神经网络自适应控制

Neural network adaptive control of the robot joint with limited input

针对机器人关节控制输入受限以及动力学模型中存在非线性摩擦、柔性变形和未知外部干扰力矩等问题,提出了一种基于径向基函数(radial basis function,RBF)神经网络的输入饱和指令滤波自适应控制方法。基于指令滤波反步法,采用饱和函数约束控制输入的幅值,使用RBF神经网络在线逼近未知干扰,并利用Lyapunov稳定性理论证明了闭环系统的所有误差最终一致有界。仿真结果表明,控制算法不仅使系统的控制输入幅值被严格约束在规定的范围之内,完成了对目标轨迹的高精度跟踪(跟踪误差约为±0.003 rad),而且还可抵抗外部阶跃干扰力矩和建模误差对控制系统的不良影响,保证系统的高精度与强鲁棒性,性能优于PID(propotional integral derivative)控制和普通指令滤波反步控制(command filter backstepping control,CFBC),对机器人关节在高精度领域应用与智能控制具有重要价值。

An adaptive control method of input saturation command filtering based on radial basis function(RBF)neural network was proposed to solve the problems of limited control input,nonlinear friction,flexible deformation and unknown disturbance torque in the dynamic model of the robot joint.Based on the instruction filter backstepping method,the saturation function was used to constrain the amplitude of control input and the RBF neural network was used to approach the unknown disturbances.All the errors of the closed-loop system were proved ultimately uniformly bounded by using the Lyapunov stability theory.The simulation results show that the proposed control algorithm not only makes the control input amplitude of the system strictly constrained within the specified range,but also completes the high-precision tracking of the target trajectory(the tracking error is about±0.003 rad).It can also resist the adverse effects of external step disturbance torque and modeling error on the control system,ensuring high accuracy and strong robustness of the system.The performance of the proposed control method is better than PID(propotional integral derivative)control and ordinary CFBC(command filter backstepping control)methods.It is of great value to the application and intelligent control of the robot joint in high precision field.