Development of increasingly efficient production methods is a competiveness driving factor for any company. Today, many of these improvements include the integration of technology-based solutions into processes traditionally operated by humans. In this context, the present work aims to report the controller performance of a prototype developed for semi-automatic sewing stations. This project was fostered by “Factory Play”, a Portuguese company that produces inflatable structures, under the technical supervision of the Polytechnic Institute of Bragança. At the present time, the sewing station travel speed is regulated by an embedded PID controller that has been previously tuned using classical methods. However, even if the overall performance is currently acceptable, additional experiments were made regarding the use of evolutionary based algorithms to attain a better dynamic response and flexibility. This article present the results obtained using those methods where it is possible to confirm that the use of evolutionary algorithm will simplify the design process while consistently leading to a suitable solution.
If the inline PDF is not rendering correctly, you can download the PDF file here.
 J. P. Coelho, P. Santos, T. M. Pinho, J. Boaventura-Cunha, and J. Oliveira, “Instrumentation and control of an industrial sewing station”, 2018 13th APCA International Conference on Control and Soft Computing, doi: 10.1109/controlo.2018.8514280.
 M. Gen and R. Cheng, Genetic algorithms and engineering optimization. John Wiley & Sons, Inc., 2000
 D. Floreano and C. Mattiussi, Bio-Inspired Artificial Intelligence: Theories, Methods and Technologies, 1st. Cambridge, MA, U.S.A.: The MIT Press, 2008, ISBN:9780262062718
 Goldberg, D. E. (1989). Genetic Algorithms in search, optimization and machine learning, Addison-Wesley.
 Holland, J.H. (1975). Adaptation in natural artificial systems, Ann Arbor, MI: University of Michigan Press.
 Kennedy, J. and R.C. Eberhart (1995). Particle Swarm Optimization, Proc. Of the 1995 IEEE Int. Conf. On Neural Networks, pp. 1942-1948. IEEE Service Center, Piscataway, NJ.
 K. E. Parsopoulos and M. N. Vrahatis, Particle Swarm Optimization and Intelligence: Advances and Applications, 1st. Hershey, PA, U.S.A.: Information Science Reference, IGI Global, 2010
 De Moura Oliveira, P.B. and A. H. Jones (1998). Genetic Design of Two Degrees-of-Freedom PID Controllers for Set-Point Tracking and Disturbance Rejection, Controlo’98, pp. 111-118, Coimbra, Portugal.
 Chou P., Hwang T. (2004) Design of PID Controllers Using Genetic Algorithms Approach for Low Damping, Slow Response Plants. In: Yin FL., Wang J., Guo C. (eds) Advances in Neural Networks. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg
 X. Meng B. Song (2007), Fast Genetic Algorithms Used for PID Parameter Optimization, Proceedings of the IEEE International Conference on Automation and Logistics, Jinan, China.
 Perng, J-Wi and Hsieh, S-C (2019), Design of Digital PID Control Systems Based on Sensitivity Analysis and Genetic Algorithms, International Journal of Control, Automation and Systems, vol 17, 4, p.p. 1838—1846.
 Yang Luo, Hui Li, and Mingyong Shen (2006). Speed control of BLDCM for industrial sewing machine based on dSPACE. In 2006 International Conference on Mechatronics and Automation. IEEE.
 C. H. Houpis and G. B. Lamont, Digital Control Systems: Theory, Hardware, Software, 2nd. The McGraw-Hill Companies, Inc., 1992, ISBN: 0070305005.
 J. V. József, D. Drótos, J. Turán, J. Végh (2012). Processors, FPGAs, SOCs, trends and questions. Carpathian Journal of Electronic and Computer Engineering 5, 149-152.