پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 11 |
| تعداد شماره‌ها | 210 |
| تعداد مقالات | 2,098 |
| تعداد مشاهده مقاله | 2,877,904 |
| تعداد دریافت فایل اصل مقاله | 2,085,801 |
Optimizing control motion of a human arm With PSO-PID controller | ||
| Journal of Computational & Applied Research in Mechanical Engineering (JCARME) | ||
| مقاله 3، دوره 7، شماره 1 - شماره پیاپی 13، آبان 2017، صفحه 23-34 اصل مقاله (911.22 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jcarme.2017.645 | ||
| نویسندگان | ||
| Mohammad Hossein Bayati Chaleshtari* 1؛ Elahe Norouzi2؛ Habib Ahmadi3 | ||
| 1Shahrood University of Technology | ||
| 2MSc. Student, Amirkabir University of Technology, Biomedical Engineering Department, Tehran, Iran | ||
| 3Assistant Professor,Mechanical Engineering Department,Shahrood University of Technology,Shahrood, Iran | ||
| تاریخ دریافت: 28 بهمن 1394، تاریخ بازنگری: 28 آبان 1395، تاریخ پذیرش: 23 آذر 1395 | ||
| چکیده | ||
| Functional electrical stimulation (FES) is the most commonly used system for restoring function after spinal cord injury (SCI). In this study, we used a model consists of a joint, two links with one degree of freedom, and two muscles as flexor and extensor of the joint, which simulated in MATLAB using SimMechanics and Simulink Toolboxes. The muscle model is based on Zajac musculotendon actuator and composed of a nonlinear recruitment curve, a nonlinear activation-frequency relationship, calcium dynamics, fatigue/recovery model, an additional constant time delay, force-length and force-velocity factors. In this study, we used a classic controller for regulating the elbow joint angle; a Proportional- Integral- Derivative controller. First, we tuned the PID coefficients with trial and error, and then a particle swarm optimization algorithm was used to optimize them. The important features of this algorithm include flexibility, simplicity, short solution time, and the ability to avoid local optimums. This PSO -PID controller uses particle swarm optimization algorithm to get the required pulse width for stimulating the biceps to reach the elbow joint to the desired angle. The fitness function was defined as sum square of error. The results for PSO -PID controller show faster response for reaching the range of the set point than the PID controller tuned by trial and error. However the PSO -PID is much better in terms of the rise time and the settling time, the PID tuned by trial and error has no overshoot. The time to reach the zero steady state error is half in PSO -PID in comparison to PID tuned by trial and error. | ||
| کلیدواژه‌ها | ||
| Functional Electrical Stimulation (FES)؛ PID Controller؛ Particle Swarm Optimization Algorithm؛ Transverse Plane | ||
| مراجع | ||
|
[1] C. C. L. Lynch, and M. R. Popovic, “Closed-Loop Control for FES: Past Work and Future Directions”, 10th Annual Conference of the International FES Society, Ottawa, Canada, pp. 56-59, (2005).
[2] S. Bin Mohamed Ibrahim, “The PID Controller Design Using Particle swarm optimization Algorithm”, PhD thesis, University of Southern Queensland, Toowoomba, Australia, pp.132-137, (2005).
[3] D. Blana, E.K. Chadwick, A. Bogert and R. F. Kirsch, “Feedback Control for a High Level Upper Extremity Neuroprosthesis”, ASB 29th Annual Meeting, Cleveland, Ohio, pp. 28-33, (2005).
[4] C. L. Lynch and M. R. Popovic, “Functional Electrical Stimulation”, IEEE Control Systems MaGAzine, Cleveland, Ohio, (2008).
[5] D. Blana, R. F. Kirsch and E. K. Chadwick, “Combined Feed forward and Feedback Control of a Redundant, Nonlinear, Dynamic Musculoskeletal System”, International Federation for Medical and Biological Engineering, Vol. 47, No. 5, pp. 533-542, (2009).
[6] M. Ferrarin, F. Palazzo, R. Riener and J. Quintern, “Model-Based Control of FESInduced Single Joint Movements”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 9, No. 3. pp. 78-88, (2001).
[7] V. M. Zatsiorsky, “Kinetics of Human Motion”, Human Kinetics, New Zealand, pp. 265-350, (2002).
[8] D. Zhang, and W. T. Ang, “Tremor Suppression of Elbow Joint via Functional Electrical Stimulation: A Simulation Study”, Proceeding of the 2006 IEEE, International Conference on Automation Science and Engineering, Beijing, China, pp.76-81 (2006).
[9] A. Maleki and R. Shafaei, “Musculoskeletal Model of Arm for FES Research Studies”, 4th Cario International Biomedical Conference, University of Salford, Ireland UK, (2008).
[10] Negin Hesam-Shariati, “Control of Reanimation of Paralyzed Arm for Reaching Movement Using FES’’, M. Sc. Thesis, Bioelectrics, Amirkabir University of Technology, 183 pages, (2012).
[11] K. Kurosawa, R. Futami, T. Watanabe and N. Hoshimiya, “Joint Angle Control by FES Using a Feedback Error Learning Controller”, IEEE Transactions on Neural Systems and Rehabilitation *Corresponding author email address: mhbayati88@gmail.com Engineering, Vol. 13, No. 3, pp. 62-77, (2005).
[12] M. O. Ali, S. P. Koh, K. H. Chong, S. K. Tiong and Z.A. Obaid, "Genetic Algorithm Tuning Based PID Controller for Liquid-Level Tank System", Proceedings of the International Conference on Man- Machine Systems (ICoMMS), Kuala Lumpur, Malaysia, pp. 29-33 (2009).
[13] Eberhart R, and J. Kennedy, A New Optimizer Using Particle Swarm Theory. Proc of 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan. IEEE Service Center Piscataway NJ, pp. 39-43, (1995).
[14] J. Kennedy, R. Eberhart, Particle Swarm Optimization. Proc of IEEE International Conference on Neural Network, Perth, Australia, IEEE Service Center Piscataway NJ, pp. 1942-1948, (1995).
[15] M. Clerc, and J. Kennedy, The particle swarm-explosion stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, pp. 58-73, (2002).
[16] M. Dorigo, V. Maniezzo, and A. Colorni, Ant system: optimization by a colony of cooperationg agents. IEEE Transactions on Systems. Man, and Cybernetics-Part B, Vol. 26, No. 1, pp. 29-41, (1996).
[17] Y. Shi, and R. C. Eberhart, A modified particle swam optimizer. IEEE Word Congress on Computational Intelligence, pp. 69-73, (1998).
[18] Zheng Jianchao, Jie Jing, and Cui Zhihua., Particle swam optimization. Science Publishing Company of Beijing. Vol. 22, No. 3, pp. 94-112 (2004).
[19] R. C. Eberhart, and Y. Shi, Comparison between genetic algorithms and Particle Swarm Optimization. Porto V W, Saravanan N, Waagen D, et al. Evolutionary Programming VII. [S.l.]: Springer, pp. 611-616, (1998).
[20] R. C. Eberhart, and Y. Shi, Comparing inertia weights and constriction factors in Particle Swarm Optimization. Proceedings of the Congress on Evolutionary Computation, pp. 84-88, (2000).
[21] N. Higashi, and H. Iba, Particle Swarm Optimization with Gaussian mutation. Proceedings of the 2003 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, pp. 72-79, (2003).
[22] M. Clerc, The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Proceedings of the Congress on Evolutionary Computation. Piscataway, NJ: IEEE Service Center, pp. 1951-1957, (1999).
[23] A. Colorni, M. Dorigo, and V. Maniezzo, et al.; Distributed optimization by ant colonies. Proceedings of the 1st European Conference on Artificial Life, pp. 134- 142, (1991).
[24] P. J. Angeline, Using selection to improve Particle Swarm Optimization. Proceedings of the Congress on Evolutionary Computation. Piscataway. NJ: IEEE Press, pp. 84-89, (1999).
[25] Duan Haibin, Ant Colony Optimization theory and Application. Science Publishing Company of Beijing. Vol. 23, No. 1, pp. 82-93 (2005).
[26] Gao Ying, Xie Shengli, Particle swam optimization Algorithm based on simulated annealing (SA) approach .Computer Engineering and Application, Vol. 40, No. 1, pp. 47-49, (2004).
[27] Qinghai Bai, Analysis of Particle Swarm Optimization Algorithm ‘Journal of Computer and Information Science’, Vol. 3, No. 1, pp. 57-71, (2010).
[28] Chen Yonggang, Yang Fengjie, and Sun Jigui.; A new Particle swam optimization Algorithm. Journal of Jilin University, Vol. 24, No. 2, pp. 181-185, (2006). | ||
|
آمار تعداد مشاهده مقاله: 1,806 تعداد دریافت فایل اصل مقاله: 1,392 |
||