The purpose of this paper is to improve the flexibility and tracking errors of the controllers-based neural networks (NNs) for mobile manipulator robot (MMR) in the presence of time-varying uncertainties.
The conventional backstepping force/motion control is developed by the wavelet fuzzy CMAC neural networks (WFCNNs) (for mobile-manipulator robot). The proposed WFCNNs are applied in the tracking-position-backstepping controller to deal with the uncertain dynamics of the controlled system. In addition, an adaptive robust compensator is proposed to eliminate the inevitable approximation errors, uncertain disturbances, and relax the requirement for prior knowledge of the controlled system. Besides, the position tracking controller, an adaptive robust constraint-force is also considered. The online-learning algorithms of the control parameters (WFCNNs, robust term and constraint-force controller) are obtained by using the Lyapunov stability theorem.
The design of the proposed method is determined by the Lyapunov theorem such that the stability and robustness of the control-system are guaranteed.
The WFCNNs are more the generalized networks that can overcome the constant out-weight problem of the conventional fuzzy cerebellar model articulation controller (FCMAC), or can converge faster, give smaller approximation errors and size of networks in comparison with FNNs/NNs. In addition, an intelligent-control system by inheriting the advantage of the conventional backstepping-control-system is proposed to achieve the high-position tracking for the MMR control system in the presence of uncertainties variation.
This work was supported by the National Natural Science Foundation of China (6117075; 60835004), the National High Technology Research and Development Program of China (863 Program) (2012AA111004; 2012AA112312).
Thang Mai, L. and Yao Wang, N. (2014), "Adaptive-WFCNNs-backstepping force/motion control system for mobile-manipulator robot", Kybernetes, Vol. 43 No. 2, pp. 281-306. https://doi.org/10.1108/K-11-2013-0258Download as .RIS
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