柔性关节空间机器人基于神经网络的自适应反演控制

讨论了载体位姿无控情况下,漂浮基柔性关节空间机器人的关节运动控制器设计问题。利用系统动量、动量矩守恒关系及拉格朗日法,建立了空间机器人的动力学模型。为实现系统关节运动控制目标,借助于神经网络函数逼近技术,提出了一种柔性关节空间机器人的自适应反演控制方案。所提控制方案无需预知系统各惯性参数的真实信息,且可避免对载体位置相关量进行实时地测量与反馈,因此较适于实际应用。数值仿真结果表明：上述控制方案可使系统各柔性关节的振动较小,并能够有效地控制空间机器人完成所期望的关节运动。

ADAPTIVE BACKSTEPPING CONTROL OF FLEXIBLE-JOINT SPACE ROBOT BASED ON NEURAL NETWORK

The design problem of a joint motion controller for a flexible-joint space robot with an uncontrolled base is discussed. With the linear and angular momentum conservation of the system and a Lagrangian method, the dynamic model of a space robot is established. To realize the control target of joint motion, an adaptive backstepping control scheme for a flexible-joint space robot is proposed via the function approximation technique of neural network. The presented control scheme need not know the information of system parameters, and avoids the measurements and feedback of the position-related variables of the base. Thusly, it is more suitable for practical applications. Numerical simulation results show that the control scheme proposed can obtain the smaller vibration of flexible joints and effectively control the space robot system to accomplish the desired joint motion.