Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot

ABSTRACT

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.

KEYWORDS

Manipulator, Optimal Structure, Adaptive Controller, Genetic Algorithm, Neural Networks, Particle Swarm Optimization

Manipulator, Optimal Structure, Adaptive Controller, Genetic Algorithm, Neural Networks, Particle Swarm Optimization

Cite this paper

nullR. Rajendra and D. Pratihar, "Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot,"*Intelligent Control and Automation*, Vol. 2 No. 4, 2011, pp. 430-449. doi: 10.4236/ica.2011.24050.

nullR. Rajendra and D. Pratihar, "Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot,"

References

[1] Z. Qu, “Global Stability of Trajectory Tracking of Robot under PD Control,” Dynamics and Control, Vol. 4, No. 1, 1994, pp. 59-71. doi:10.1007/BF02115739

[2] W. Homsup and J. N. Anderson, “PD Control Performance of Robotic Mechanisms,” Proceedings of American Control Conference, Minneapolis, 10-12 June 1987, pp. 472-475.

[3] R. Kelly and R. Salgado, “PD Control with Computed Feed-Forward of Robot Manipulators: A Design Procedure,” IEEE Transactions on Robotics and Automation, Vol. 10, No. 4, 1994, pp. 566-571. doi:10.1109/70.313108

[4] P. R. Ouyang and W. J. Zhang, “A Novel Evolutionary PD Control and Application for Trajectory Tracking,” Proceedings of 7th International Conference on Control, Automation, Robotics and Vision, Singapore, 2-5 December 2002, pp. 1337-1342.

[5] T. Ravichandran, D. Wang and G. Heppler, “Simultaneous Plant-Control Design Optimization of a Two-Link Planar Manipulator,” Mechatronics, Vol. 16, No. 3-4, 2006, pp. 233-242. doi:10.1016/j.mechatronics.2005.09.008

[6] D. K. Pratihar, “Soft Computing,” Narosa Publications, New Delhi, 2008

[7] T. Ozaki, T. Suzuki, T. Furuhashi, S. Okuma and Y. Uchikawa, “Trajectory Control of Robotic Manipulators Using Neural Networks,” IEEE Transactions on Industrial Electronics, Vol. 38, No. 3, 1991, pp. 195-202. doi:10.1109/41.87587

[8] J. J. Craig, “Adaptive Control of Mechanical Manipulators,” Addison-Wesley, Reading, 1988.

[9] M. B. Ghalia and A. T. Alouani, “A Robust Trajectory Tracking Control of Industrial Robot Manipulators Using Fuzzy Logic,” Proceedings of 27th South Eastern Symposium on System Theory (SSST’95), Mississippi, 12-14 March 1995, pp. 268-271.

[10] A. Rueda and W. Pedrycz, “A Hierarchical Fuzzy-Neural-PD Controller for Robot Manipulators,” Proceedings of IEEE Conference on Computational Intelligence, Orlando, 26-29 June 1994, pp. 673-677.

[11] J. H. Park and H. Asada, “Concurrent Design Optimization of Mechanical Structure and Control for High Speed Robots,” Journal of Dynamic Systems, Measurements, and Control, Vol. 116, No. 3, 1994, pp. 344-356. doi:10.1115/1.2899229

[12] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks, Perth, 27 November-1 December 1995, pp. 1942-1948. doi:10.1109/ICNN.1995.488968

[13] Y. Shi and R. C. Eberhart, “A Modified Particle Swarm Optimizer,” Proceedings of IEEE International Conference on Evolutionary Computation, IEEE Press, Piscataway, 1998, pp. 69-73.

[14] M. Clerc and J. Kennedy, “The Particle Swarm-Explosion, Stability and Convergence in a Multi-Dimensional Complex Space,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, 2002, pp. 58-78. doi:10.1109/4235.985692

[15] Q. Chen, G. Guo and C. Li, “An Improved PSO Algorithm to Optimize BP Neural Network,” Proceedings of 5th International Conference on Natural Computation, Tianjian, 14-16 August 2009, pp. 357-360.

[16] M. Han and L. Jiang, “Endpoint Prediction Model of Basic Oxygen Furnace Steelmaking Based on PSO-ICA and RBF Neural Network,” Proceedings of International Conference on Intelligent Control and Information Processing, Dalian, 13-15 August 2010, pp. 388-393. doi:10.1109/ICICIP.2010.5565236

[17] M. Braik, A. Sheta and A. Arieqat, “A Comparison between GAs and PSO in Training ANN to Model the TE Chemical Process Reactor,” Proceedings of the AISB Symposium on Swarm Intelligence Algorithms and Applications, Aberdeen, 1-4 April 2008, pp. 25-31.

[18] A. Abe and K. Komuro, “Trajectory Planning for Saving Energy of a Flexible Manipulator Using Soft Computing Methods,” Proceedings of International Conference on Control, Automation and Systems, Gyeonggi-Do, 27-30 October 2010, pp. 1462-1467.

[19] K. S. Fu, R. C. Gonzalez and C. S. G. Lee, “Robotics: Control, Sensing, Vision, and Intelligence,” McGraw-Hill Inc., Boston, 1987.

[20] J. Nishii, K. Ogawa and R. Suzuki, “The Optimal Gait Pattern in Hexapods Based on Energetic Efficiency,” Proceedings 3rd International Symposium on Artificial Life and Robotics, Oita, 19-21 January 1998, pp. 106-109.

[21] F. P. Beer, R. E. Johnston Jr. and J. T. Dewolf, “Strength of Materials,” Tata-McGraw-Hill Publishing Company Limited, New Delhi, 2004.

[22] J. R. Davis, “ASM Specialty Handbook Al and Al Alloys,” ASM International Materials Part, Ohio, 1993.

[23] E. G. Dieter, “ASM Handbook, Material Selection and De- sign,” Vol. 20, ASM International Materials Park, Ohio, 1997.

[24] W. F. Gale and T. C. Totemeier, “Smithhells Metal Reference,” 8th Edition, Butterworth-Heinemann, Waltham, 2004.

[25] E. T. George and S. Mackenzie, “Handbook of Aluminum. Vol. 1. Physical Metallurgy and Processes,” Marcel Dekker Inc., New York, 2003.

[26] S. L. Semiatin, “ASM Handbook Forging and Forming,” Vol. 14, ASM International Materials Park, Ohio, 1998.

[27] J. E. Temple, “Handbook of Structural Design in Al Alloys,” Temple Press, Brooks, 1953.

[28] C. Wu, 2010. www.efunda.com

[29] J. J. Craig, “Introduction to Robotics: Mechanics and Control,” Pearson Education, Upper Saddle River, 2006.

[30] M. K. Spong, S. Hutchinson and M. Vidyasagar, “Robot Modeling and Control,” John Wiley& Sons, Inc., New York, 2006.

[31] M. Akar and I. Temiz, “Motion Controller Design for the Speed Control of DC Servomotor,” International Journal of Applied Mathematics and Informatics, Vol. 4, No. 1, 2007, pp. 131-137.

[32] K. Deb and R. B. Agrawal, “Simulated Binary Crossover for Continuous Search Space,” Complex Systems, Vol. 9, No. 2, 1995, pp. 115-148.

[33] J. H. Holland, “Adaptation in Natural and Artificial Systems,” The University of Michigan Press, Ann Arbor, 1975.

[34] D. E. Goldberg, “Genetic Algorithm in Search Optimization, and Machine Learning,” Addison-Wesley, Reading, 1989.

[35] R. C. Eberhert, P. Simpson and R. Dobbins, “Computational Intelligence PC Tools: Dalian,” Chapter 6, Academic Press, San Diego, 1996, pp. 212-226.

[36] Y. Shi and R. C. Eberhart, “Empirical Study of Particle Swarm Optimization,” Proceedings of IEEE International Congress on Evolutionary Computation, Washington DC, 6-9 July 1999, pp. 101-106.

[37] J. J. Liang and P. N. Suganthan, “Defining a Standard for Particle Swarm Optimization,” Proceedings of Swarm Intelligence Symposium, Pasadena, 8-10 June 2005, pp. 124-129. doi:10.1109/SIS.2005.1501611

[38] J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, “Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions,” IEEE Transactions on Evolutionary Computation, Vol. 10, No. 3, 2006, pp. 281-295. doi:10.1109/TEVC.2005.857610

[1] Z. Qu, “Global Stability of Trajectory Tracking of Robot under PD Control,” Dynamics and Control, Vol. 4, No. 1, 1994, pp. 59-71. doi:10.1007/BF02115739

[2] W. Homsup and J. N. Anderson, “PD Control Performance of Robotic Mechanisms,” Proceedings of American Control Conference, Minneapolis, 10-12 June 1987, pp. 472-475.

[3] R. Kelly and R. Salgado, “PD Control with Computed Feed-Forward of Robot Manipulators: A Design Procedure,” IEEE Transactions on Robotics and Automation, Vol. 10, No. 4, 1994, pp. 566-571. doi:10.1109/70.313108

[4] P. R. Ouyang and W. J. Zhang, “A Novel Evolutionary PD Control and Application for Trajectory Tracking,” Proceedings of 7th International Conference on Control, Automation, Robotics and Vision, Singapore, 2-5 December 2002, pp. 1337-1342.

[5] T. Ravichandran, D. Wang and G. Heppler, “Simultaneous Plant-Control Design Optimization of a Two-Link Planar Manipulator,” Mechatronics, Vol. 16, No. 3-4, 2006, pp. 233-242. doi:10.1016/j.mechatronics.2005.09.008

[6] D. K. Pratihar, “Soft Computing,” Narosa Publications, New Delhi, 2008

[7] T. Ozaki, T. Suzuki, T. Furuhashi, S. Okuma and Y. Uchikawa, “Trajectory Control of Robotic Manipulators Using Neural Networks,” IEEE Transactions on Industrial Electronics, Vol. 38, No. 3, 1991, pp. 195-202. doi:10.1109/41.87587

[8] J. J. Craig, “Adaptive Control of Mechanical Manipulators,” Addison-Wesley, Reading, 1988.

[9] M. B. Ghalia and A. T. Alouani, “A Robust Trajectory Tracking Control of Industrial Robot Manipulators Using Fuzzy Logic,” Proceedings of 27th South Eastern Symposium on System Theory (SSST’95), Mississippi, 12-14 March 1995, pp. 268-271.

[10] A. Rueda and W. Pedrycz, “A Hierarchical Fuzzy-Neural-PD Controller for Robot Manipulators,” Proceedings of IEEE Conference on Computational Intelligence, Orlando, 26-29 June 1994, pp. 673-677.

[11] J. H. Park and H. Asada, “Concurrent Design Optimization of Mechanical Structure and Control for High Speed Robots,” Journal of Dynamic Systems, Measurements, and Control, Vol. 116, No. 3, 1994, pp. 344-356. doi:10.1115/1.2899229

[12] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks, Perth, 27 November-1 December 1995, pp. 1942-1948. doi:10.1109/ICNN.1995.488968

[13] Y. Shi and R. C. Eberhart, “A Modified Particle Swarm Optimizer,” Proceedings of IEEE International Conference on Evolutionary Computation, IEEE Press, Piscataway, 1998, pp. 69-73.

[14] M. Clerc and J. Kennedy, “The Particle Swarm-Explosion, Stability and Convergence in a Multi-Dimensional Complex Space,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, 2002, pp. 58-78. doi:10.1109/4235.985692

[15] Q. Chen, G. Guo and C. Li, “An Improved PSO Algorithm to Optimize BP Neural Network,” Proceedings of 5th International Conference on Natural Computation, Tianjian, 14-16 August 2009, pp. 357-360.

[16] M. Han and L. Jiang, “Endpoint Prediction Model of Basic Oxygen Furnace Steelmaking Based on PSO-ICA and RBF Neural Network,” Proceedings of International Conference on Intelligent Control and Information Processing, Dalian, 13-15 August 2010, pp. 388-393. doi:10.1109/ICICIP.2010.5565236

[17] M. Braik, A. Sheta and A. Arieqat, “A Comparison between GAs and PSO in Training ANN to Model the TE Chemical Process Reactor,” Proceedings of the AISB Symposium on Swarm Intelligence Algorithms and Applications, Aberdeen, 1-4 April 2008, pp. 25-31.

[18] A. Abe and K. Komuro, “Trajectory Planning for Saving Energy of a Flexible Manipulator Using Soft Computing Methods,” Proceedings of International Conference on Control, Automation and Systems, Gyeonggi-Do, 27-30 October 2010, pp. 1462-1467.

[19] K. S. Fu, R. C. Gonzalez and C. S. G. Lee, “Robotics: Control, Sensing, Vision, and Intelligence,” McGraw-Hill Inc., Boston, 1987.

[20] J. Nishii, K. Ogawa and R. Suzuki, “The Optimal Gait Pattern in Hexapods Based on Energetic Efficiency,” Proceedings 3rd International Symposium on Artificial Life and Robotics, Oita, 19-21 January 1998, pp. 106-109.

[21] F. P. Beer, R. E. Johnston Jr. and J. T. Dewolf, “Strength of Materials,” Tata-McGraw-Hill Publishing Company Limited, New Delhi, 2004.

[22] J. R. Davis, “ASM Specialty Handbook Al and Al Alloys,” ASM International Materials Part, Ohio, 1993.

[23] E. G. Dieter, “ASM Handbook, Material Selection and De- sign,” Vol. 20, ASM International Materials Park, Ohio, 1997.

[24] W. F. Gale and T. C. Totemeier, “Smithhells Metal Reference,” 8th Edition, Butterworth-Heinemann, Waltham, 2004.

[25] E. T. George and S. Mackenzie, “Handbook of Aluminum. Vol. 1. Physical Metallurgy and Processes,” Marcel Dekker Inc., New York, 2003.

[26] S. L. Semiatin, “ASM Handbook Forging and Forming,” Vol. 14, ASM International Materials Park, Ohio, 1998.

[27] J. E. Temple, “Handbook of Structural Design in Al Alloys,” Temple Press, Brooks, 1953.

[28] C. Wu, 2010. www.efunda.com

[29] J. J. Craig, “Introduction to Robotics: Mechanics and Control,” Pearson Education, Upper Saddle River, 2006.

[30] M. K. Spong, S. Hutchinson and M. Vidyasagar, “Robot Modeling and Control,” John Wiley& Sons, Inc., New York, 2006.

[31] M. Akar and I. Temiz, “Motion Controller Design for the Speed Control of DC Servomotor,” International Journal of Applied Mathematics and Informatics, Vol. 4, No. 1, 2007, pp. 131-137.

[32] K. Deb and R. B. Agrawal, “Simulated Binary Crossover for Continuous Search Space,” Complex Systems, Vol. 9, No. 2, 1995, pp. 115-148.

[33] J. H. Holland, “Adaptation in Natural and Artificial Systems,” The University of Michigan Press, Ann Arbor, 1975.

[34] D. E. Goldberg, “Genetic Algorithm in Search Optimization, and Machine Learning,” Addison-Wesley, Reading, 1989.

[35] R. C. Eberhert, P. Simpson and R. Dobbins, “Computational Intelligence PC Tools: Dalian,” Chapter 6, Academic Press, San Diego, 1996, pp. 212-226.

[36] Y. Shi and R. C. Eberhart, “Empirical Study of Particle Swarm Optimization,” Proceedings of IEEE International Congress on Evolutionary Computation, Washington DC, 6-9 July 1999, pp. 101-106.

[37] J. J. Liang and P. N. Suganthan, “Defining a Standard for Particle Swarm Optimization,” Proceedings of Swarm Intelligence Symposium, Pasadena, 8-10 June 2005, pp. 124-129. doi:10.1109/SIS.2005.1501611

[38] J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, “Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions,” IEEE Transactions on Evolutionary Computation, Vol. 10, No. 3, 2006, pp. 281-295. doi:10.1109/TEVC.2005.857610