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基于迭代学习和神经网络的机械臂变长度误差跟踪
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Abstract:
本文考察了在任意初始状态下不确定性机械臂的轨迹跟踪问题。首先，根据预设期望轨迹、随机变量概率分布函数和虚拟误差变量，建立误差动力学系统。其次，运用虚拟控制信号和迭代学习分别补偿没有运行的区间和处理随机变化的迭代长度。然后，通过自适应神经网络逼近机械臂的不确定性和外部干扰，并通过复合能量函数证明了跟踪算法的可行性。最后，通过一个仿真例子表明了本文算法的有效性。
In this paper, trajectory tracking of uncertain manipulator under arbitrary initial conditions is in-vestigated. Firstly, the error dynamics is established according to the preset expected trajectory, the probability distribution function of random variables and the virtual error variable. Secondly, the virtual control signal and iterative learning are used to compensate the interval without opera-tion and the iteration length with random changes. Then, the uncertainty and external disturbance of the manipulator arms are approximated by an adaptive neural network, and the practicability of the tracking algorithm is inspected by a composite energy function. Finally, the simulation example is certified to display the validity of the proposed algorithm.
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