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Computationally efficient nonlinear model predictive controller using parallel particle swarm optimization

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Języki publikacji
EN
Abstrakty
EN
As nonlinear optimization techniques are computationally expensive, their usage in the real-time era is constrained. So this is the main challenge for researchers to develop a fast algorithm that is used in real-time computations. This work proposes a fast nonlinear model predictive control approach based on particle swarm optimization for nonlinear optimization with constraints. The suggested algorithm divide and conquer technique improves computing speed and disturbance rejection capability, demonstrating its suitability for real-time applications. The performance of this approach under constraints is validated using a highly nonlinear fast and dynamic real-time inverted pendulum system. The solution presented through work is computationally feasible for smaller sampling times and it gives promising results compared to the state of art PSO algorithm
Rocznik
Strony
art. no. e140696
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
  • Government College of Engineering, Karad-415124, Maharashtra, India
  • Walchand College of Engineering, Sangli-416415, Maharashtra, India
Bibliografia
  • [1] E.F. Camacho and C. Bordons, “Nonlinear model predictive control: An introductory review,” Lecture Notes in Control and Information Sciences, vol. 358, pp. 1–16, 2007.
  • [2] L. Magni, D.M. Raimondo, and F. Allgöwer, “Nonlinear model predictive control,” Lecture Notes in Control and Information Sciences, vol. 384, 2009.
  • [3] F. Manenti, “Considerations on nonlinear model predictive control techniques,” Comput. Chem. Eng., vol. 35, no. 11, pp. 2491–2509, 2011.
  • [4] M. Cannon, D. Ng, and B. Kouvaritakis, “Successive linearization nmpc for a class of stochastic nonlinear systems,” in Nonlinear Model Predictive Control. Springer, 2009, pp. 249–262, doi: 10.1007/978-3-642-01094-1_20.
  • [5] S.P. Diwan and S.S. Deshpande, “Nonlinear model predictive controller for the real-time control of fast dynamic system,” in 2019 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2019, pp. 289–294, doi: 10.1109/ICCES45898.2019.9002380.
  • [6] M. Cannon, “Efficient nonlinear model predictive control algorithms,” Annu. Rev. Control, vol. 28, no. 2, pp. 229–237, 2004, doi: 10.1016/j.arcontrol.2004.05.001.
  • [7] Y. Chen, M. Bruschetta, D. Cuccato, and A. Beghi, “An adaptive partial sensitivity updating scheme for fast nonlinear model predictive control,” IEEE Trans. Autom. Control, vol. 64, no. 7, pp. 2712–2726, 2018, doi: 10.1109/TAC.2018.2867916.
  • [8] L.Wirsching, H.J. Ferreau, H.G. Bock, and M. Diehl, “An online active set strategy for fast adjoint based nonlinear model predictive control,” IFAC Proc. Vol., vol. 40, no. 12, pp. 234–239, 2007, doi: 10.3182/20070822-3-za-2920.00039.
  • [9] M. Diehl, A. Walther, H.G. Bock, and E. Kostina, “An adjoint-based sqp algorithm with quasi-newton jacobian updates for inequality constrained optimization,” Optim. Methods Software, vol. 25, no. 4, pp. 531–552, 2010, doi: 10.1080/10556780903027500.
  • [10] X. Feng, S. Di Cairano, and R. Quirynen, “Inexact adjoint-based sqp algorithm for real-time stochastic nonlinear mpc,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 6529–6535, 2020, doi: 10.1016/j.ifacol.2020.12.068.
  • [11] K. Antoniewicz and K. Rafal, “Model predictive current control method for four-leg three-level converter operating as shunt active power filter and grid connected inverter,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 65, no. 5, 2017, doi: 10.1515/bpasts-2017-0065.
  • [12] M. Ławryńczuk and R. Nebeluk, “Computationally efficient nonlinear model predictive control using the l1 cost-function,” Sensors, vol. 21, no. 17, p. 5835, 2021, doi: 10.3390/s21175835.
  • [13] Z. Tang and Y. Yang, “Two-stage particle swarm optimization based nonlinear model predictive control method for reheating furnace process,” ISIJ Int., vol. 54, no. 8, pp. 1836–1842, 2014, doi: 10.2355/isijinternational.54.1836.
  • [14] F. Xu, H. Chen, X. Gong, and Q. Mei, “Fast nonlinear model predictive control on fpga using particle swarm optimization,” IEEE Trans. Ind. Electron., vol. 63, no. 1, pp. 310–321, 2015, doi: 10.1109/TIE.2015.2464171.
  • [15] J. Ziętkiewicz, “Pso-based nonlinear predictive control for unmanned bicycle robot stabilization,” Studia z Automatyki i Informatyki, 2017.
  • [16] B. Chopard and M. Tomassini, “Particle swarm optimization,” in An Introduction to Metaheuristics for Optimization. Springer, 2018, pp. 97–102, doi: 10.1007/978-3-319-93073-2_6.
  • [17] S. Kiranyaz, T. Ince, and M. Gabbouj, “Particle swarm optimization,” in Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Springer, 2014, pp. 45–82, doi: 10.1007/978-3-642-37846-1_3.
  • [18] S. Sengupta, S. Basak, and R.A. Peters, “Particle swarm optimization: A survey of historical and recent developments with hybridization perspectives,” Mach. Learn. Knowl. Extr., vol. 1, no. 1, pp. 157–191, 2019. doi: 10.3390/make1010010.
  • [19] J. Zietkiewicz, P. Kozierski, and W. Giernacki, “Particle swarm optimisation in nonlinear model predictive control; comprehensive simulation study for two selected problems,” Int. J. Control, vol. 94, no. 10, pp. 2623–2639, 2021, doi: 10.1080/00207179.2020.1727957.
  • [20] J.F. Schutte, Applications of parallel global optimization to mechanics problems. University of Florida, 2005.
  • [21] G. Venter and J. Sobieszczanski-Sobieski, “Parallel particle swarm optimization algorithm accelerated by asynchronous evaluations,” J. Aerosp. Comput. Inf. Commun., vol. 3, no. 3, pp. 123–137, 2006.
  • [22] K.J. Åström and K. Furuta, “Swinging up a pendulum by energy control,” Automatica, vol. 36, no. 2, pp. 287–295, 2000, doi: 10.1016/S0005-1098(99)00140_5.
  • [23] M.F. Hamza, H.J. Yap, I.A. Choudhury, A.I. Isa, A.Y. Zimit, and T. Kumbasar, “Current development on using rotary inverted pendulum as a benchmark for testing linear and nonlinear control algorithms,” Mech. Syst. Signal Process., vol. 116, pp. 347–369, 2019, doi: 10.1016/j.ymssp.2018.06.054.
  • [24] Quanser, “Srv02 – et rotary servo and rotary pendulum model,” Instructor Manual, 2012.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f9d01e20-087d-4a93-bfe7-424cc44f36fe
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