Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks
Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks
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DOI：
10.1109/tfuzz.2006.879982
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发表时间：
2006-12
影响因子：
11.9
通讯作者：
C.-K. Lin
中科院分区：
文献类型：
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作者：
C.-K. Lin
提出了一种基于模糊小波网络的自适应非奇异终端滑模（NTSM）机器人跟踪控制器设计方法。与基于线性超平面的滑模控制相比，终端滑模控制器可以提供更快的收敛速度和更高的控制精度。因此，终端滑模控制器结合模糊小波网络，它可以准确地逼近未知的动态机器人系统，通过使用自适应学习算法，是一个有吸引力的机器人控制方法。此外，该学习算法可以在线调整模糊小波基函数的伸缩和平移参数以及隐层输出权值。因此，鲁棒控制律被用来消除不确定性，包括不可避免的逼近误差所造成的有限数量的模糊小波基函数。所提出的控制器不需要先验知识的机器人的动态和离线学习阶段。此外，闭环机器人系统的跟踪性能和稳定性可以保证李雅普诺夫理论。最后，通过对六连杆机器人的仿真比较，说明了基于模糊小波网络的控制方法的有效性
This paper presents an adaptive nonsingular terminal sliding mode (NTSM) tracking control design for robotic systems using fuzzy wavelet networks. Compared with linear hyperplane-based sliding control, terminal sliding mode controller can provide faster convergence and higher precision control. Therefore, a terminal sliding controller combined with the fuzzy wavelet network, which can accurately approximate unknown dynamics of robotic systems by using an adaptive learning algorithm, is an attractive control approach for robots. In addition, the proposed learning algorithm can on-line tune parameters of dilation and translation of fuzzy wavelet basis functions and hidden-to-output weights. Therefore, a robust control law is used to eliminate uncertainties including the inevitable approximation errors resulted from the finite number of fuzzy wavelet basis functions. The proposed controller requires no prior knowledge about the dynamics of the robot and no off-line learning phase. Moreover, both tracking performance and stability of the closed-loop robotic system can be guaranteed by Lyapunov theory. Finally, the effectiveness of the fuzzy wavelet network-based control approach is illustrated through comparative simulations on a six-link robot manipulator