Comparative Study of Some Adaptive Fuzzy Algorithms for Manipulator Control
The problem of manipulator control is a highly
complex problem of controlling a system which is multi-input, multioutput,
non-linear and time variant. In this paper some adaptive
fuzzy, and a new hybrid fuzzy control algorithm have been
comparatively evaluated through simulations, for manipulator
control. The adaptive fuzzy controllers consist of self-organizing,
self-tuning, and coarse/fine adaptive fuzzy schemes. These
controllers are tested for different trajectories and for varying
manipulator parameters through simulations. Various performance
indices like the RMS error, steady state error and maximum error are
used for comparison. It is observed that the self-organizing fuzzy
controller gives the best performance. The proposed hybrid fuzzy
plus integral error controller also performs remarkably well, given its
simple structure.
[1] Luh, J. Y. S., ÔÇÿConventional Controller Design for Industrial Robots - A
Tutorial-, IEEE Transactions on Systems, Man, and Cybernetics, 1983,
Vol. SM- 13, no. 3, Pages: 298-316.
[2] Hsia, T., ÔÇÿAdaptive control of robot manipulators-A review-,
Proceedings IEEE International Conference on Robotics and
Automation, April 1986, Vol. 3, Pages: 183 - 189.
[3] Tosunoglu, S., Tesar, D., ÔÇÿState of the Art in Adaptive control of robotic
systems-, IEEE Transactions on Aerospace and Electronic Systems,
Sept. 1988, Vol. 24, Issue: 5, Pages: 552 - 561.
[4] Er, M.J., ÔÇÿRecent developments and futuristic trends in robot
manipulator control-, Proceedings IEEE Asia-Pacific Workshop on
Advances in Motion Control, July 1993 Pages: 106 - 111.
[5] Banerjee, S., Peng Yung Woo, ÔÇÿFuzzy logic control of robot
manipulator-, Proceedings Second IEEE Conference on Control
Applications, Sept. 1993, Vol.1, Pages:87 - 88.
[6] Zhou, J., Coiffet, P., ÔÇÿFuzzy Control of Robots-, Proceedings IEEE
International Conference on Fuzzy Systems, March 1992, Pages: 1357 -
1364.
[7] Byung Kook Yoo, Woon Chul Ham, ÔÇÿAdaptive Control of Robot
Manipulator Using Fuzzy Compensator-, IEEE Transactions on Fuzzy
Systems, April 2000, Vol. 8, Issue: 2, Pages:186 - 199.
[8] Purwar, S., Kar, I.N., Jha, A.N., ÔÇÿAdaptive control of robot manipulators
using fuzzy logic systems under actuator constraints-, Proceedings of
IEEE International Conference on Fuzzy Systems, 25-29 July 2004, Vol
3, Pages: 1449 - 1454.
[9] Nagrath, I.J., Pahade Paras Shripal, Chand, A., ÔÇÿDevelopment and
implementation of intelligent control strategy for robotic manipulator-
IEEE/IAS International Conference on Industrial Automation and
Control, 1995, Pages: 215 - 220.
[10] Kazemian, H.B., ÔÇÿThe Self Organizing Fuzzy PID Controller-, IEEE
World Congress on Computational Intelligence., 4-9 May 1998, Vol. 1,
Pages:319 - 324.
[11] Koh, K.C., Cho, H.S., Kim, S.K., Jeong, I.S., ÔÇÿApplication of a selforganizing
fuzzy control to the joint control of a Puma-760 robot-,
Proceeding of IEEE International Workshop on Intelligent Robots and
Systems, 3-6 July 1990, vol.1, Pages: 537 - 542.
[12] Jantzen J., ÔÇÿThe Self-Organizing Fuzzy Controller-, Tech. report no 98-H
869 (soc), Technical University of Denmark, 19 Aug 1998.
[13] Llama, M.A., Kelly, R., Santibanez, V., ÔÇÿControl of Robot Manipulators
via fuzzy self tuning-, IEEE Transactions on Systems, Man and
Cybernetics, Feb. 2000, Part B, , Vol. 30 , Issue: 1 ,Pages:143 - 150.
[14] Chatterjee, A., Watanabe, K., ÔÇÿAn adaptive fuzzy strategy for motion
control of robot manipulators- ACM Journal of Soft Computing, March
2005, Vol. 9, Issue: 3, Pages: 185 - 193.
[15] Mudi, R.K., Pal, N.R., ÔÇÿA robust self-tuning scheme for PI- and PD-type
fuzzy controllers-, IEEE Transactions on Fuzzy Systems, Feb. 1999,
Vol. 7, Issue: 1, Pages: 2 - 16.
[16] Lin, C., Mon, Y., ÔÇÿHybrid adaptive fuzzy controllers with application to
robotic systems-, Fuzzy sets and systems, 2003, Vol. 139, Pages: 151-
165.
[17] Sun, Y., Joo, M., ÔÇÿHybrid Fuzzy Control of Robotics Systems-, IEEE
Transactions on Fuzzy Systems, Dec. 2004, Vol. 12, Issue 6, Pages: 755
- 765.
[1] Luh, J. Y. S., ÔÇÿConventional Controller Design for Industrial Robots - A
Tutorial-, IEEE Transactions on Systems, Man, and Cybernetics, 1983,
Vol. SM- 13, no. 3, Pages: 298-316.
[2] Hsia, T., ÔÇÿAdaptive control of robot manipulators-A review-,
Proceedings IEEE International Conference on Robotics and
Automation, April 1986, Vol. 3, Pages: 183 - 189.
[3] Tosunoglu, S., Tesar, D., ÔÇÿState of the Art in Adaptive control of robotic
systems-, IEEE Transactions on Aerospace and Electronic Systems,
Sept. 1988, Vol. 24, Issue: 5, Pages: 552 - 561.
[4] Er, M.J., ÔÇÿRecent developments and futuristic trends in robot
manipulator control-, Proceedings IEEE Asia-Pacific Workshop on
Advances in Motion Control, July 1993 Pages: 106 - 111.
[5] Banerjee, S., Peng Yung Woo, ÔÇÿFuzzy logic control of robot
manipulator-, Proceedings Second IEEE Conference on Control
Applications, Sept. 1993, Vol.1, Pages:87 - 88.
[6] Zhou, J., Coiffet, P., ÔÇÿFuzzy Control of Robots-, Proceedings IEEE
International Conference on Fuzzy Systems, March 1992, Pages: 1357 -
1364.
[7] Byung Kook Yoo, Woon Chul Ham, ÔÇÿAdaptive Control of Robot
Manipulator Using Fuzzy Compensator-, IEEE Transactions on Fuzzy
Systems, April 2000, Vol. 8, Issue: 2, Pages:186 - 199.
[8] Purwar, S., Kar, I.N., Jha, A.N., ÔÇÿAdaptive control of robot manipulators
using fuzzy logic systems under actuator constraints-, Proceedings of
IEEE International Conference on Fuzzy Systems, 25-29 July 2004, Vol
3, Pages: 1449 - 1454.
[9] Nagrath, I.J., Pahade Paras Shripal, Chand, A., ÔÇÿDevelopment and
implementation of intelligent control strategy for robotic manipulator-
IEEE/IAS International Conference on Industrial Automation and
Control, 1995, Pages: 215 - 220.
[10] Kazemian, H.B., ÔÇÿThe Self Organizing Fuzzy PID Controller-, IEEE
World Congress on Computational Intelligence., 4-9 May 1998, Vol. 1,
Pages:319 - 324.
[11] Koh, K.C., Cho, H.S., Kim, S.K., Jeong, I.S., ÔÇÿApplication of a selforganizing
fuzzy control to the joint control of a Puma-760 robot-,
Proceeding of IEEE International Workshop on Intelligent Robots and
Systems, 3-6 July 1990, vol.1, Pages: 537 - 542.
[12] Jantzen J., ÔÇÿThe Self-Organizing Fuzzy Controller-, Tech. report no 98-H
869 (soc), Technical University of Denmark, 19 Aug 1998.
[13] Llama, M.A., Kelly, R., Santibanez, V., ÔÇÿControl of Robot Manipulators
via fuzzy self tuning-, IEEE Transactions on Systems, Man and
Cybernetics, Feb. 2000, Part B, , Vol. 30 , Issue: 1 ,Pages:143 - 150.
[14] Chatterjee, A., Watanabe, K., ÔÇÿAn adaptive fuzzy strategy for motion
control of robot manipulators- ACM Journal of Soft Computing, March
2005, Vol. 9, Issue: 3, Pages: 185 - 193.
[15] Mudi, R.K., Pal, N.R., ÔÇÿA robust self-tuning scheme for PI- and PD-type
fuzzy controllers-, IEEE Transactions on Fuzzy Systems, Feb. 1999,
Vol. 7, Issue: 1, Pages: 2 - 16.
[16] Lin, C., Mon, Y., ÔÇÿHybrid adaptive fuzzy controllers with application to
robotic systems-, Fuzzy sets and systems, 2003, Vol. 139, Pages: 151-
165.
[17] Sun, Y., Joo, M., ÔÇÿHybrid Fuzzy Control of Robotics Systems-, IEEE
Transactions on Fuzzy Systems, Dec. 2004, Vol. 12, Issue 6, Pages: 755
- 765.
@article{"International Journal of Information, Control and Computer Sciences:55159", author = "Sudeept Mohan and Surekha Bhanot", title = "Comparative Study of Some Adaptive Fuzzy Algorithms for Manipulator Control", abstract = "The problem of manipulator control is a highly
complex problem of controlling a system which is multi-input, multioutput,
non-linear and time variant. In this paper some adaptive
fuzzy, and a new hybrid fuzzy control algorithm have been
comparatively evaluated through simulations, for manipulator
control. The adaptive fuzzy controllers consist of self-organizing,
self-tuning, and coarse/fine adaptive fuzzy schemes. These
controllers are tested for different trajectories and for varying
manipulator parameters through simulations. Various performance
indices like the RMS error, steady state error and maximum error are
used for comparison. It is observed that the self-organizing fuzzy
controller gives the best performance. The proposed hybrid fuzzy
plus integral error controller also performs remarkably well, given its
simple structure.", keywords = "Hybrid fuzzy, Self-organizing, Self-tuning,Trajectory tracking.", volume = "1", number = "12", pages = "3855-9", }