Trajectory Tracking Control of an Industrial Robot Manipulator Using Fuzzy SMC with RBFNN

Ayça Gökhan Ak, Galip Cansever, Akın Delibaşı
2.451 783


One of the main problems associated with Sliding Mode Control (SMC) is that a whole knowledge of the system dynamics and system parameters is required to compute the equivalent control. Neural networks are popular tools for computing the equivalent control. In fuzzy SMC with Radial Basis Function Neural Network (RBFNN), a Lyapunov function is selected for the design of the SMC and RBFNN is proposed to compute the equivalent control. The weights of the RBFNN are adjusted according to an adaptive algorithm. Fuzzy logic is used to adjust the gain of the corrective control of the SMC. Proposed control method and a PID controller are implemented on an industrial robot manipulator (Manutec-r15). Experimental results indicate that the proposed method is a good candidate for trajectory control applications of robot manipulators.


Neural Network; Fuzzy Logic; Sliding Mode Control; Robot Control

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