基于复合控制策略的柔性关节机械手轨迹跟踪研究

Research on composite control for flexible joint robot

目前常用的柔性关节机械手运动控制策略设计中,存在的主要问题是关节的加速度与跃度必须准确已知,这对测量带来了极大的困难,同时会对系统引入测量噪声。为解决该问题,提出了复合控制策略。将系统分为刚体模型与柔性模型的组合,对两个模型分别设计控制器以达到机械手关节轨迹跟踪的目的。以一个双臂柔性关节机械手轨迹跟踪为例,仿真结果表明,该复合控制策略可以实现柔性关节机械手的轨迹跟踪要求,具有一定的可行性。

Among numerous control schemes for flexible joint robots,the main problem is that the full state variable of acceleration and jerk must be known,which are difficult to measure,and the noise may be merged in the main signal.To solve this problem,a self adaptive composite control scheme is developed to control the flexible joint robots with modeling errors and subject to uncertain disturbances,which is based on considering the system as a low dimensional nominal rigid and a linear elastic subsystem.Using this approach,the controller consists of a slow and a fast term,the slow control is based on the well-known computed torque method and a RBF neural network based compensating controller.The neural network is trained on line based on lyapunov theory to compensate for the modeling uncertainties,thus its convergence is guaranteed.The fast term is designed to provide stiffness and damping for eliminating elastic deformation.Simulations are presented for a planner manipulator with two flexible joints,the trajectory tracking results are provided to demonstrate performance of the scheme.