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

Year 2015, Volume: 28 Issue: 1, 141 - 148, 23.02.2015

Ayça Ak , Galip Cansever Akın Delibaşı

Abstract

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.

Keywords

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

References

• Abdelhameed, M., Adaptive Neural Network Based Controller for Robots, Mechatronics, 147-162, (1999).
• Yimin, Li., I-io, Y., and Chua, C., Model-Based PID Control of Constrained Robot in a Dynamic Environment with Uncertainty, IEEE International Conference on Control Applications, Alaska, (2000).
• Lee, MJ. and Choi, Y.K., An Adaptive Using Neurocontroller Manipulators, IEEE Transaction on Industrial Electronics, Vol.51, No:3, (2004). for Robot
• Cervantes, I. and Alvarez-Ramirez, J., On the PID Tracking Control of Robot Manipulators, Systems & Control Letters 42, 37-46, (2001).
• Wai, R., Tracking Control Based on Neural for Network Neurocomputing 51, 425-445, (2003). Robot Manipulator,
• Saad, M., Bigras, P., Dessaint, L. and Al-Haddad, K., Adaptive Robot Control Using Neural Networks, IEEE Transaction on Industrial Electronics, Vol.41, No: 2., (1994).
• Lee, M. and Choi, Y., An Adaptive Neurocontroller Using RBFN for Robot Manipulators, IEEE Transaction on Industrial Electronics, Vol.51, (2004).
• Sun, W. and Wang, Y., An Adaptive Fuzzy control for Robotic Manipulators, International Conference on Control, Automation, Robotics and Vision, Kunming China, 1952-1956, (2004).
• Choi, S. and Kim, J., A Fuzzy-Sliding Mode Controller for Robust Tracking of Robotic Manipulators, (1997). Vol.7, 199-216,
• Lin, C. and Mon, Y, Hybrid Adaptive Fuzzy Controllers with Application to Robotic Systems, Fuzzy sets and systems, 151-165, (2003).
• Guo, Y. and Woo, P., An Adaptive Fuzzy Sliding Modem Controller for Robotic Manipulators, IEEE Transactions on System, Man, and Cybernetics- Part A: Systems and Humans, Vol.33, 149-159, (2003).
• Ertugrul, M. and Kaynak, O., Neuro sliding mode control of robotic manipulators, Mechatronics 10, 239-263, (2000).
• Utkin, V.I., Variable Structure Systems with Sliding Modes. IEEE Transaction on Automatic Control AC-22, 212-222, (1977).
• Edwards, C. and Spurgeon, K., Sliding Mode Control. Taylor&Fransis Ltd. (1998).
• Tsai, C., Chung, H. and Yu, F., Neuro-Sliding Mode Control with Its Applications to Seesaw Systems. IEEE Transaction on Neural Networks, Vol.15, (2004).
• Bekit, B. W., Whidborne, J.F. and Seneviratne, L.D. Fuzzy Sliding Mode Control for a Robot Manipulator, Robotics and Automation, 320–325, (1997). Intelligence in
• Ham, F. M. and Kostanic, I., Neurocomputing for Science& Engineering. Mc Graw-Hill Inc. (2001).