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Adaptive Control for Uncertain Model of Omni-directional Mobile Robot Based on Radial Basis Function Neural Network

Duyen Ha Thi Kim, Tien Ngo Manh*, Cuong Nguyen Manh, Nhan Duc Nguyen, Dung Pham Tien, Manh Tran Van, and Minh Phan Xuan
International Journal of Control, Automation, and Systems, vol. 19, no. 4, pp.1715-1727, 2021

Abstract : The paper proposes the method to deal with control problems of unmodeled components of the four-wheeled Omni-directional mobile robot. It is commonly challenging to design a model-based control scheme to achieve smooth movement in the tracking process due to the unknown elements in the mathematical model of the robot or external disturbances. Our main contribution focuses on designing an adaptive controller based on neural networks with online weight updating laws and Fuzzy logic to guarantee the high accuracy of the robot’s movement when the unknown factors adversely affect the robot control. At the initial step, a Dynamic Surface Control plays a role as a core of the controller for the robot system. Then, with the ability to estimate the appropriate value for uncertain nonlinear parts, a Radial Basis Function Neural Network is designed. Finally, a Fuzzy law is to utilize control parameters in each period to increase the adaptive behavior of the system. The stability and convergence of the system are proven by the Lyapunov’s stability theory. The simulation results illustrate the validity and the efficiency of the proposed control algorithm when the system is lack of robot model’s information.

Keyword : Dynamic surface control (DSC), four-wheeled omni robot (FWOR), radial basis function neural network (RBFNN), tracking.

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