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Learning Feed-Forward Control for a Two-Link Rigid Robot Arm

Nguyen Duy Cuong 1 and Tran Xuan Minh 2
1. Electronics Faculty, Thai Nguyen University of Technology, Thai Nguyen City, Vietnam
2. Electrical Faculty, Thai Nguyen University of Technology, Thai Nguyen City, Vietnam
Abstract—This paper introduces a control structure which consists of a Proportional Derivative (PD) controller and a Neural Network (NN)-based Learning Feed-Forward Controller (LFFC) to a Two-Link Rigid Robot Arm. An on-line B-spline neural network is used because of its local weight-updating characteristic, which has the advantages of fast convergence speed and low computation complexity. The torque applied to each link is defined using the Euler-Lagrange equation. The controller design takes into account the troubles caused by inertial loading, coupling reaction forces between joints, and gravity loading effects. This control structure can be directly applied to different robots within the same class with different lengths and masses. Simulation results are presented to demonstrate the robustness of our proposed controller under serve changes of the system parameters. 

Index Terms—neural network (NN), learning feed-forward control (LFFC), two-link rigid robot arm

Cite: Nguyen Duy Cuong and Tran Xuan Minh, "Learning Feed-Forward Control for a Two-Link Rigid Robot Arm," International Journal of Electronics and Electrical Engineering, Vol. 3, No. 4, pp. 279-284, August 2015. doi: 10.12720/ijeee.3.4.279-284
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