Modeling and Adaptive Self-Tuning MVC Control of PAM Manipulator Using Online Observer Optimized with Modified Genetic Algorithm

DOI: 10.4236/eng.2011.32016   PDF   HTML     5,371 Downloads   9,220 Views   Citations


In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Model-based observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.

Share and Cite:

H. Anh and N. Nam, "Modeling and Adaptive Self-Tuning MVC Control of PAM Manipulator Using Online Observer Optimized with Modified Genetic Algorithm," Engineering, Vol. 3 No. 2, 2011, pp. 130-143. doi: 10.4236/eng.2011.32016.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] D. G. Caldwell, G. A. Medrano-Cerda and M. Goodwin, “Control of Pneumatic Muscle Actuators,” IEEE Control Systems Magazine, Vol. 1, No. 1, 1995, pp. 40-48. doi:10. 1109/37.341863
[2] D. W. Repperger, C. A. Phillips, et al., “Power/Energy Metrics for Controller Evaluation of Actuators Similar to Biological Systems,” Mechatronics, Vol. 15, No. 4, May 2005, pp. 459-469. doi:10.1016/j.mechatronics.2004.10. 002
[3] D. W. Repperger, C. A. Phillips, D. C. Johnson, R. D. Harmon and K. Johnson, “A Study of Pneumatic Muscle Tech-nology for Possible Assistance in Mobility,” Proceedings of 19th Annual International Conference IEEE En-gineering in Medicine and Biology Society, Chicago, 1997, pp. 1884-1887.
[4] B. J. Ruthenberg, N. A. Wa-sylewski and J. E. Beard, “An Experimental Device for Investigating the Force and Power Requirements of a Powered Gait Orthosis,” Journal of Rehabilitation Re-search & Development, Vol. 34, No. 2, 1997, pp. 203-213.
[5] G. R. Johnson and M. A. Buckley, “Develop-ment of a New Motorized Upper Limb Orthotic System (MULOS),” Proceedings of Rehabilitation Engineering Society North America, Pittsburgh, 1997, pp. 399-401.
[6] G. Colombo, M. Joerg, R. Schreier and V. Di-etz, “Treadmill Training of Paraplegic Patients Using a Robotic Orthosis,” Journal of Rehabilitation Research & Development, Vol. 37, No. 6, 2000, pp. 693-700.
[7] B. Tondu and P. Lopez, “Modeling and Control of McKib-ben Pneumatic Artificial Muscle Robot Actuators,” IEEE Control Systems Magazine, Vol. 20, No. 2, 2000, pp. 15-38. doi:10.1109/37.833638
[8] C. P. Chou and B. Hannaford, “Measurement and Modeling of McKibben Pneumatic Artificial Muscles,” IEEE Transaction on Robotics and Automation, Vol. 12, No. 1, 1996, pp. 90-102. doi:10.1109/70.481753
[9] D. B. Reynolds, D. W. Repperger, C. A. Phillips and G. Bandry, “Modeling the Dynamic Characteristics of Pneumatic Muscle,” Annals of Biomedical Engineering, Vol. 31, 2003, pp. 310-317. doi:10.1114/1.1554921
[10] K. K. Ahn and T. D. C. Thanh, “Intelligent Phase Plane Switching Control of Pneumatic Artificial Muscle (PAM) Manipulators with Magneto-Rheological Brake,” Mechatronics, Vol. 16, No. 2, March 2006, pp. 85-95. doi: 10.1016/j.mechatronics.2005.10.001
[11] K. K. Ahn and H. P. H. Anh, “System Modeling and Identification of the Two-Link Pneumatic Artificial Muscle (PAM) Manipulator Optimized with Genetic Algorithm,” Pro-ceedings 2006 of IEEE-ICASE International Conference, Busan, 2006, pp. 4744-4749.
[12] K. K. Ahn and H. P. H. Anh, “Identification of the Pneumatic Artificial Muscle Manipulators by MGA-Based Nonlinear NARX Fuzzy Model,” International Journal of Mechatronics, Vol. 19, No. 1, February 2009, pp. 106-133. doi:10.1016/j.mechatronics.2008.06. 004
[13] J. Lilly, “Adaptive Tracking for Pneumatic Muscle Actuators in Bicep and Tricep Configurations,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 11, No. 3, 2003, pp. 56-63. doi:10.1109/ TNSRE.2003.816870
[14] G. A. Cerda, C. J. Bowler and D. G. Caldwell, “Adaptive Position Control of An-tagonistic Pneumatic Muscle Actuators,” Proceedings of IEEE International Conference on Intelligent Robots and Systems, Pittsburgh, Vol. 1, 5-9 August 1995, pp. 378-383.
[15] R. Q. van der Linde, “Design, Analysis, and Control of a Low Power Joint for Walking Robot by Phasic Activation of McKibben Muscle,” IEEE Transac-tions on Robotics and Automation, Vol. 15, No. 4, 1999, pp. 599-604. doi:10.1109/70.781963
[16] D. G. Cald-well, G. A. Medrano-Cerda and M. Goodwin, “Braided Pneumatic Muscle Actuator Control of a Multi-Jointed Manipulator,” Proceedings of IEEE Systems, Man, and Cybernetics Conference, Le Touquet, Vol. 1, 17-20 Oc-tober 1993.
[17] T. Hesselroth, K. Sarkar, P. Van der Smagt and K. Schulten, “Neural Network Control of a Pneumatic Robot Arm,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 24, No. 1, 1994, pp. 28-38. doi:10.1109/21. 259683
[18] D. W. Repperger, C. A. Phillips and M. Krier, “Controller Design Involving Gain Scheduling for a Large Scale Pneumatic Muscle Actuator,” Proceedings of IEEE Conference on Control Applications, Kohala Coast, 22-27 August 1999, pp. 234-241.
[19] S. W. Chan, J. Lilly, D. W. Repperger and J. E. Berlin, “Fuzzy PD+I Learning Control for a Pneumatic Muscle,” Proceedings of 2003 IEEE International Conference on Fuzzy Systems, St. Louis, 9 July 2003, pp. 278-283.
[20] X. Chang and J. H. Lilly, “Tracking Control of a Pneumatic Muscle by an Evolutionary Fuzzy Controller,” Intelligent Automation and Soft Computing, Vol. 9, No. 3, 2003, pp. 227-244.
[21] P. Carbonell, Z. P. Jiang and D. W. Repperger, “A Fuzzy Back-Stepping Controller for a Pneumatic Muscle Actuator System,” Proceedings of IEEE International Symposium on Intelligent Control, Mexico City, 5-7 September 2001, pp. 353-358.
[22] M. Hamerlain, “An Anthropomorphic Robot Arm Driven by Artificial Muscles Using a Variable Structure Control,” Proceedings of IEEE International Conference Intelligent Robots Systems, Pittsburgh, Vol. 1, 5-9 August 1995, pp. 550-555.
[23] D. W. Repperger, K. R. Johnson and C. A. Phillips, “A VSC Position Tracking System Involving a Large Scale Pneumatic Muscle Actuator,” Proceedings of IEEE 37th Conference Decision Control, Tampa, Vol. 4, 16-18 December 1998, pp. 4302-4307.
[24] J. J. Slotine and S. Sastry, “Tracking Control of Nonlinear System Using Sliding Surface with Application to Robot Manipulators,” International Journal of Control, Vol. 38, No. 2, 1993, pp. 465-492. doi:10.1080/0020717 8308933088
[25] K. Osuka, T. Kimura and T. Ono, “H? Control of a Certain Nonlinear Actuator,” Proceedings of IEEE Conference on Decision Control, Honolulu, 5-7 December 1990, pp. 370-371.
[26] Z. Li, B. Wittenmark and R. J. Evans, “Minimum Variance Prediction for Linear Time-Varying Systems,” Automatica, Vol. 33, No. 4, 1998, pp. 607-618. doi:10. 1016/S0005-1098(96)00210-5

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.