面向模型未知的自由漂浮空间机械臂自适应神经鲁棒控制

Adaptive neural robust control for free-floatingspace manipulator facingunknown model

针对自由漂浮空间机械臂精确数学模型难以获得,外界干扰及载荷变化也会造成动力学模型的参数变化这一问题,提出一种不依赖模型的神经网络自适应鲁棒控制方法.针对自由漂浮空间机械臂精确模型的动力学模型,设计线性PID控制器,该控制器能够实现控制系统稳定性.然而实际工程中,空间机械臂数学模型难以精确获得,利用神经网络控制器来补偿机械臂动力学模型,设计网络权值的自适应学习律实现在线实时调整,避免对数学模型的依赖.设计自适应鲁棒控制器来抑制外界扰动和补偿逼近误差,提高系统鲁棒性和控制精度.基于Lyapunov理论,证明了闭环系统的稳定性.仿真试验验证了所提控制方法的有效性,对于自由漂浮空间机器人研究具有重要意义.

In order to solve the problem that the precise mathematical model of free-floating space manipulators is difficult to obtain and the parameters of the dynamic model will change due to the external disturbance and load change,a neural network adaptive robust control method without relying on the model is proposed.Aiming at the dynamics model of free-floating space manipulator,a linear PID controller is designed,which can realize the stability of the control system.However,in practical engineering,it is difficult to accurately obtain the mathematical model of the space manipulators.The neural network controller is used to compensate the dynamic model of the manipulators,and the adaptive learning law of the network weight is designed to realize online real-time adjustment,so as to avoid the dependence on the mathematical model.An adaptive robust controller is designed to suppress the disturbance and compensate the approximation error to improve the system robustness and control accuracy.Based on Lyapunov theory,the stability of the closed-loop system is proved.The simulation results verify the effectiveness of the proposed control method,which is of great significance for the research of free-floating space robots.