Adaptive trajectory tracking neural network control with robust compensator for robot manipulators
Adaptive trajectory tracking neural network control with robust compensator for robot manipulators
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DOI：
10.1007/s00521-015-1873-4
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发表时间：
2016-02
影响因子：
6
通讯作者：
P. Cuong;W. Nan
中科院分区：
文献类型：
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作者：
P. Cuong;W. Nan
提出了一种基于径向基函数（RBF）的自适应轨迹跟踪神经网络控制方法，用于具有鲁棒补偿器的多连杆机器人操作臂，以实现高精度的位置跟踪。设计一个合适的控制方案，以实现精确的轨迹跟踪和良好的控制性能的难点之一是要保证控制系统的稳定性和鲁棒性，由于摩擦力，外部干扰误差，参数变化。针对这一问题，将RBF网络应用于机器人关节位置控制。径向基函数网络由于其快速的学习算法和更好的逼近能力，在这类问题中表现出了很大的希望。自适应RBF网络能有效地提高系统在大不确定性情况下的控制性能。利用反向传播算法和李雅普诺夫稳定性定理，推导出网络参数的自适应调整律，保证了整个系统的稳定性和权值自适应的收敛性。在该控制方案中，鲁棒补偿器作为辅助控制器，以保证在各种环境下的稳定性和鲁棒性，如质量变化，外部干扰，建模不确定性。最后，通过与自适应模糊和小波网络控制方法的仿真和实验结果对比，验证了所提出的控制方法的有效性。
This paper presents an adaptive trajectory tracking neural network control using radial basis function (RBF) for ann-link robot manipulator with robust compensator to achieve the high-precision position tracking. One of the difficulties in designing a suitable control scheme which can achieve accurate trajectory tracking and good control performance is to guarantee the stability and robustness of control system, due to friction forces, external disturbances error, and parameter variations. To deal with this problem, the RBF network is investigated to the joint position control of ann-link robot manipulator. The RBF network is one approach which has shown a great promise in this sort of problems because of its fast learning algorithm and better approximation capabilities. The adaptive RBF network can effectively improve the control performance against large uncertainty of the system. The adaptive turning laws of network parameters are derived using the back-propagation algorithm and the Lyapunov stability theorem, so that the stability of the entire system and the convergence of the weight adaptation are guaranteed. In this control scheme, a robust compensator plays as an auxiliary controller to guarantee the stability and robustness under various environments such as the mass variation, the external disturbances, and modeling uncertainties. Finally, the simulation and experimental results in comparison with adaptive fuzzy and wavelet network control method are provided to verify the effectiveness of the proposed control methodology.