TAILIEUCHUNG - Adaptive-backstepping position control based on recurrent-fwnns for mobile manipulator robot

In this paper, we proposed an adaptive-backstepping position control system for mobile manipulator robot (MMR). By applying recurrent fuzzy wavelet neural networks (RFWNNs) in the position-backstepping controller, the unknown-dynamics problems of the MMR control system are relaxed. | Journal of Science and Technology 54 (3A) (2016) 23-38 ADAPTIVE-BACKSTEPPING POSITION CONTROL BASED ON RECURRENT-FWNNS FOR MOBILE MANIPULATOR ROBOT Mai Thang Long*, Tran Huu Toan Faculty of Electronics Technology, Industrial University of HCMC, 12 Nguyen Van Bao, Go Vap, Hochiminh * Email: maithanglong@ Received: 16 June 2016; Accepted for publication: 26 July 2016 ABSTRACT In this paper, we proposed an adaptive-backstepping position control system for mobile manipulator robot (MMR). By applying recurrent fuzzy wavelet neural networks (RFWNNs) in the position-backstepping controller, the unknown-dynamics problems of the MMR control system are relaxed. In addition, an adaptive-robust compensator is proposed to eliminate uncertainties that consist of approximation errors and uncertain disturbances. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. The effectiveness of the proposed method is verified by comparative simulation results. Keywords: backstepping controller, recurrent fuzzy wavelet, neural networks, adaptive robust control, mobile-manipulator robot. 1. INTRODUCTION The MMR has been applied in a variety of applications in industrial sectors, such as mining, outdoor exploration, and planetary sciences. The MMR structure consists of arms and a mobile platform with kinematic and dynamic constraints, which make it a highly coupled dynamic nonlinear system. Therefore, the traditional model control methods-based feedback techniques with the assumptions of known dynamics [1] are not easy to utilize in the MMR control system. The method using adaptive model-free controllers-based fuzzy/neural networks (NNs) is a useful tool to deal with the uncertain dynamics of the MMR [2]. With the selflearning characteristic, good approximation capability [3], the NNs have been applied successfully in robotic control applications [4, 5]. Fuzzy NNs (FNNs), the combination of the NNs and fuzzy techniques, contains .

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