Computationally efficient nonlinear model predictive controller using parallel particle swarm optimization

• Autorzy: Diwan Supriya P. , Deshpande Shraddha S.

Identyfikatory

DOI

10.24425/bpasts.2022.140696

• 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

Słowa kluczowe

EN

nonlinear model predictive control; particle swarm optimization; PSO; fast dynamic systems; rotary inverted pendulum; divide approach; conquer approach

PL

kontrola predykcyjna modelu nieliniowa; optymalizacja roju cząstek; PSO; system dynamiczny szybki; wahadło obrotowe odwrócone

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2022
• Tom: Vol. 70, nr 4
• Strony: art. no. e140696
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Diwan Supriya P.

• Government College of Engineering, Karad-415124, Maharashtra, India

autor

Deshpande Shraddha S.

• Walchand College of Engineering, Sangli-416415, Maharashtra, India

Bibliografia

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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).