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Research Articles

Fast nonlinear model predictive controller using parallel PSO based on divide and conquer approach

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Pages 2230-2239 | Received 09 Sep 2021, Accepted 05 Jun 2022, Published online: 22 Jun 2022

References

  • Abualigah, L., Elaziz, M. A., Khasawneh, A. M., Alshinwan, M., Ibrahim, R. A., M. A. Al-qaness, Mirjalili, S., Sumari, P., & Gandomi, A. H. (2022). Meta-heuristic optimisation algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results. Neural Computing and Applications, 34(6), 4081–4110. https://doi.org/10.1007/s00521-021-06747-4
  • Antoniewicz, K., & Rafal, K. (2017). Model predictive current control method for four-leg three-level converter operating as shunt active power filter and grid connected inverter. Bulletin of the Polish Academy of Sciences. Technical Sciences, 65(5), 601–607. https://doi.org/10.1515/bpasts-2017-0065
  • Åström, K. J., & Furuta, K. (2000). Swinging up a pendulum by energy control. Automatica, 36(2), 287–295. https://doi.org/10.1016/S1474-6670(17)57951-3
  • Camacho, E. F., & Bordons, C. (2007). Nonlinear model predictive control. In Model predictive control (pp. 249–288). Springer.
  • Cannon, M. (2004). Efficient nonlinear model predictive control algorithms. Annual Reviews in Control, 28(2), 229–237. https://doi.org/10.1016/j.arcontrol.2004.05.001
  • Cannon, M., Ng, D., & Kouvaritakis, B. (2009). Successive linearization NMPC for a class of stochastic nonlinear systems. In Nonlinear model predictive control (pp. 249–262). Springer.
  • Chen, Y., Bruschetta, M., Cuccato, D., & Beghi, A. (2018). An adaptive partial sensitivity updating scheme for fast nonlinear model predictive control. IEEE Transactions on Automatic Control, 64(7), 2712–2726. https://doi.org/10.1109/TAC.2018.2867916
  • Chopard, B., & Tomassini, M. (2018). Particle swarm optimization. In An introduction to metaheuristics for optimization (pp. 97–102). Springer.
  • Diehl, M., Walther, A., Bock, H. G., & Kostina, E. (2010). An adjoint-based SQP algorithm with quasi-Newton Jacobian updates for inequality constrained optimization. Optimization Methods & Software, 25(4), 531–552. https://doi.org/10.1080/10556780903027500
  • Diwan, S. P., & Deshpande, S. S. (2019). Nonlinear model predictive controller for the real-time control of fast dynamic system. In International conference on communication and electronics systems (ICCES) (pp. 289–294). IEEE.
  • Du, X., Htet, K. K. K., & Tan, K. K. (2016). Development of a genetic-algorithm-based nonlinear model predictive control scheme on velocity and steering of autonomous vehicles. IEEE Transactions on Industrial Electronics, 63(11), 6970–6977. https://doi.org/10.1109/TIE.2016.2585079
  • Feng, X., Di Cairano, S., & Quirynen, R. (2020). Inexact adjoint-based SQP algorithm for real-time stochastic nonlinear MPC. IFAC-PapersOnLine, 53(2), 6529–6535. https://doi.org/10.1016/j.ifacol.2020.12.068
  • Hamza, M. F., Yap, H. J., Choudhury, I. A., Isa, A. I., Zimit, A. Y., & Kumbasar, T. (2019). Current development on using rotary inverted pendulum as a benchmark for testing linear and nonlinear control algorithms. Mechanical Systems and Signal Processing, 116, 347–369. https://doi.org/10.1016/j.ymssp.2018.06.054
  • Kiranyaz, S., Ince, T., & Gabbouj, M. (2014). Particle swarm optimization. In Multidimensional particle swarm optimization for machine learning and pattern recognition (pp. 45–82). Springer.
  • Lalwani, S., Sharma, H., Satapathy, S. C., Deep, K., & Bansal, J. C. (2019). A survey on parallel particle swarm optimization algorithms. Arabian Journal for Science and Engineering, 44(4), 2899–2923. https://doi.org/10.1007/s13369-018-03713-6
  • Magni, L., Raimondo, D. M., & Allgöwer, F. (2009). Nonlinear model predictive control. Lecture Notes in Control and Information Sciences, 384. https://doi.org/10.1007/978-3-642-01094-1
  • Manenti, F. (2011). Considerations on nonlinear model predictive control techniques. Computers & Chemical Engineering, 35(11), 2491–2509. https://doi.org/10.1016/j.compchemeng.2011.04.009
  • Nobahari, H., & Nasrollahi, S. (2019). A non-linear estimation and model predictive control algorithm based on ant colony optimization. Transactions of the Institute of Measurement and Control, 41(4), 1123–1138. https://doi.org/10.1177/0142331218798680
  • Qazani, M. R. C., Asadi, H., Arogbonlo, A., Rahimzadeh, G., Mohamed, S., Pedrammehr, S., Lim, C. P., & Nahavandi, S. (2021, October). Whale optimization algorithm for weight tuning of a model predictive control-based motion cueing algorithm. In 2021 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 1042–1048). IEEE.
  • Quanser (2012). SRV02-ET rotary servo and rotary pendulum model. Instructor Manual.
  • Sarailoo, M., Rahmani, Z., & Rezaie, B. (2015). A novel model predictive control scheme based on bees algorithm in a class of nonlinear systems: application to a three tank system. Neurocomputing, 152, 294–304. https://doi.org/10.1016/j.neucom.2014.10.066
  • Schutte, J. F. (2005). Applications of parallel global optimization to mechanics problems. University of Florida.
  • Sengupta, S., Basak, S., & Peters, R. A. (2019). Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives. Machine Learning and Knowledge Extraction, 1(1), 157–191. https://doi.org/10.3390/make1010010
  • Tang, Z., & Yang, Y. (2014). Two-stage particle swarm optimization-based nonlinear model predictive control method for reheating furnace process. ISIJ International, 54(8), 1836–1842. https://doi.org/10.2355/isijinternational.54.1836
  • Tian, Z., Ren, Y., & Wang, G. (2019). Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 41(1), 26–46. https://doi.org/10.1080/15567036.2018.1495782
  • Venter, G., & Sobieszczanski-Sobieski, J. (2006). Parallel particle swarm optimization algorithm accelerated by asynchronous evaluations. Journal of Aerospace Computing, Information, and Communication, 3(3), 123–137. https://doi.org/10.2514/1.17873
  • Wirsching, L., Ferreau, H. J., Bock, H. G., & Diehl, M. (2007). An online active set strategy for fast adjoint based nonlinear model predictive control. IFAC Proceedings Volumes, 40(12), 234–239. https://doi.org/10.3182/20070822-3-ZA-2920.00039
  • Xu, F., Chen, H., Gong, X., & Mei, Q. (2015). Fast nonlinear model predictive control on FPGA using particle swarm optimization. IEEE Transactions on Industrial Electronics, 63(1), 310–321. https://doi.org/10.1109/TIE.2015.2464171
  • Zhongda, T., Shujiang, L., Yanhong, W., & Xiangdong, W. (2018). SVM predictive control for calcination zone temperature in lime rotary kiln with improved PSO algorithm. Transactions of the Institute of Measurement and Control, 40(10), 3134–3146. https://doi.org/10.1177/0142331217716983
  • Zietkiewicz, J. (2017). PSO-based nonlinear predictive control for unmanned bicycle robot stabilization. Studia z Automatyki i Informatyki, 42, Article 14463. https://depot.ceon.pl/handle/123456789/13617
  • Zietkiewicz, J., Kozierski, P., & Giernacki, W. (2021). Particle swarm optimisation in nonlinear model predictive control; comprehensive simulation study for two selected problems. International Journal of Control, 94(10), 2623–2639. https://doi.org/10.1080/00207179.2020.1727957

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