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A hybrid fuzzy logic and neural network algorithm for robot motion control

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2 Author(s)
Shiuh-Jer Huang ; Dept. of Mech. Eng., Nat. Taiwan Inst. of Technol., Taipei, Taiwan ; Ruey-Jing Lian

Robotic manipulators are multivariable nonlinear coupling dynamic systems. Industrial robots were controlled by using a traditional controller, the control performance of which may change with respect to operating conditions. Since the robotic manipulators have complicated nonlinear mathematical models, control systems based on the system model are difficult to design. In this paper, a model-free hybrid fuzzy logic and neural network algorithm was proposed to control this multi-input/multi-output (MIMO) robotic system. First, a fuzzy logic controller was designed to control individual joints of this 4-degree-of-freedom (DOF) robot. Secondly, a coupling neural network controller was introduced to take care of the coupling effect among joints and refine the control performance of this robotic system. The experimental results showed that the application of this control strategy effectively improved the trajectory tracking precision

Published in:

IEEE Transactions on Industrial Electronics  (Volume:44 ,  Issue: 3 )