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提出了一种新的不确定性机器人跟踪控制策略 .文中基于计算转矩控制结构 ,采用了函数链网络实现一个神经网络补偿器 ,并叠加一个鲁棒控制项 ,以补偿模型的不确定性部分 .另外 ,还考虑了神经网络逼近误差非一致有界的情形 ,设计了自适应的鲁棒控制项 .算法可保证跟踪误差及神经网络权估计最终一致有界 .与其它有关基于计算转矩控制的方法相比 ,该算法既不需要测量关节角加速度 ,也不要求惯性矩阵已知 .理论和仿真均证明了算法的可靠性和有效性
A new tracking control strategy of uncertain robot is proposed.In this paper, a neural network compensator based on the computational torque control structure is implemented by using a function chain network, and a robust control term is added to compensate for the uncertain part of the model In addition, the non-uniform boundedness of the approximation error of neural network is also considered and an adaptive robust control term is designed.The algorithm can guarantee that the tracking error and the neural network weight estimation are uniformly bounded.Compared with other algorithms based on computational torque control , The algorithm does not need to measure the angular acceleration of the joint and does not require the known inertia matrix.Theoretical and simulation prove that the algorithm is reliable and effective