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• W3082179968 startingPage "1857" @default.
• W3082179968 abstract "The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition-based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters." @default.
• W3082179968 created "2020-09-08" @default.
• W3082179968 creator A5010320097 @default.
• W3082179968 date "2020-08-27" @default.
• W3082179968 modified "2023-10-16" @default.
• W3082179968 title "Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm" @default.
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• W3082179968 doi "https://doi.org/10.1002/int.22275" @default.
• W3082179968 hasPublicationYear "2020" @default.
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