Neural-Network-Based Terminal Sliding-Mode Control of Robotic Manipulators Including Actuator Dynamics
Liangyong Wang; Tianyou Chai; Lianfei Zhai
Industrial Electronics, IEEE Transactions on
Volume 56, Issue 9, Sept. 2009 Page(s):3296 - 3304
Digital Object Identifier 10.1109/TIE.2008.2011350
Summary:A neural-network-based terminal sliding-mode control (SMC) scheme is proposed for robotic manipulators including actuator dynamics. The proposed terminal SMC (TSMC) alleviates some main drawbacks (such as contradiction between control efforts in the transient and tracking errors in the steady state) in the linear SMC while maintains its robustness to the uncertainties. Moreover, an indirect method is developed to avoid the singularity problem in the initial TSMC. In the proposed control scheme, a radial basis function neural network (NN) is adopted to approximate the nonlinear dynamics of the robotic manipulator. Meanwhile, a robust control term is added to suppress the modeling error and estimate the error of the NN. Finite time convergence and stability of the closed loop system can be guaranteed by Lyapunov theory. Finally, the proposed control scheme is applied to a robotic manipulator. Experimental results confirm the validity of the proposed control scheme by comparing it with other control strategies.
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