Comparison of Distributed PD-like and Importancebased Fuzzy Logic Controllers for a Two-link Rigid-flexible Manipulator

Abstract

Fuzzy logic control presents a computationally efficient and robust alternative to conventional controllers. While experts can easily design fuzzy logic controllers (FLCs) for many applications, some systems such as multilink flexible manipulators, which have many variables and complex behavior, offer challenges to fuzzy logic control. In this paper we present two distributed controllers for a two-link rigid-flexible manipulator that moves in a vertical plane where the gravity field is active. The first distributed controller, which is based on observing the performance of the manipulator, uses three PD-like FLCs: the first two FLCs control joint angles and joint angular velocities while the third controls the tip vibration. The second distributed controller is based on evaluating the importance degrees of the output variables of the system. Variables with the same rank of high importance degrees are grouped together, while variables with low importance degrees may be deleted to simplify the design of the controller. The fuzzy rules in the two proposed structures are selected to mimic the performance of comparable linear controllers. The parameters in both FLCs are tuned using nonlinear programming to obtain better performance. The two distributed FLCs are simulated and compared. The robustness of both tuned distributed FLCs is tested by varying the joint trajectories and angular velocities. The effect of changing the payload on the robustness of the two controllers is also considered.

Keywords

Flexible manipulator fuzzy logic controller distributed importance-based fuzzy logic controller distributed PD-like fuzzy logic controller

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References

Arciniegas, J. , Eltimsahy, A. , and Cios, K. , 1993, “Fuzzy inference and the control of flexible robotic manipulators” , in Proceedings of the IEEE International Symposium on Intelligent Control, Chicago, IL, pp. 250–254 .

Chung, F. L. and Duan, J. C. , 2000, “On multistage fuzzy neural network modeling” , IEEE Transactions on Fuzzy Systems 8(2), 125–142 .

Green A. and Sasiadek, J. Z. , 2001, “Fuzzy and optimal control of a two-link flexible manipulator” , in Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Como, Italy, pp. 116– 1174 .

Jang, J. S. , 1993, “ANFIS: adaptive-network-based fuzzy inference system” , IEEE Transactions on Systems, Man and Cybernetics 23, 665–684 .

Kubica, E. and Wang, D. , 1999, “A two-stage fuzzy controller for a flexible single link robot” , International Journal of Robotics and Automation 14, 9–14 .

Lin, Y. and Lee, T. , 1993, “An investigation of fuzzy logic control of flexible robots” , Robotica 11, 363–371 .

Miller, A. J. , 1990, Subset Selection in Regression, Chapman and Hall, London .

Moudgal, V. G. , Kwong, W. A. , Passino, K. M. , and Yurkovich, S. , 1995, “Fuzzy learning control for a flexible-link robot” , IEEE Transactions on Fuzzy Systems 3, 199–210 .

Nelder, J. and Mead, R. , 1965, “A simplex method for function minimization” , Computer Journal 7, 308–313 .

10.

Oke, G. and Istefanopulos, Y. , 2001, “Gradient-descent based trajectory planning for regulation of a two-link flexible robotic arm” , in Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Como, Italy, pp. 948–952 .

11.

Seber, G. A. F. and Wild, C. J. , 1989, Nonlinear Regression, Wiley, New York .

12.

Shi, Z., 2003, “Distributed importance-based fuzzy logic controllers for flexible manipulators”, Ph.D. Dissertation, University of Nevada, NV.

13.

Shi, L. Z. and Trabia, M. , 2000, “Comparison of two distributed fuzzy logic controller for flexible-link manipulators” , ASME, Dynamic System and Control Division 69-1, 443–452 .

14.

Siddique, M. N. H. and Tokhi, M. O. , 2002, “GA-based neuro-fuzzy controller for flexible-link manipulator” , in Proceedings of the International Conference on Control Applications, Glasgow, UK, pp. 2673–2678 .

15.

Trabia, M. , 1998, “Tuning of distributed fuzzy logic controller for a flexible-link robot” , Vibration and Noise Control 97, 57–66 .

16.

Trabia, M. and Shi, L.Z. , 2001, “Design and tuning of a distributed fuzzy logic controller for flexible-link manipulators” , Journal of Intelligent and Fuzzy Systems 10(4), 215–233 .

17.

Yoo, B. , Jeong, S. , and Ham, W. , 1996, “Hybrid control of flexible manipulator based on fuzzy relations” , in Proceedings of the IEEE International Conference on Fuzzy Systems, New Orleans, LA, pp. 817–823 .