Optimal design of model predictive controller based on transient search optimization applied to robotic manipulators

Xingjia Li; Jinan Gu; Zedong Huang; Wenbo Wang; Jing Li; Xingjia Li; Jinan Gu; Zedong Huang; Wenbo Wang; Jing Li

doi:10.3934/mbe.2022436

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 9: 9371-9387. doi: 10.3934/mbe.2022436

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Optimal design of model predictive controller based on transient search optimization applied to robotic manipulators

1.
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
2.
School of Electronic Information and Electrical Engineering, Anyang Institute of Technology, Anyang 455000, China

Academic Editor: Yang Kuang

Received: 28 April 2022 Revised: 16 June 2022 Accepted: 23 June 2022 Published: 27 June 2022

Due to nonlinearity and uncertainty of the robotic manipulator, the design of the robot controller has a crucial impact on its performance of motion and trajectory tracking. In this paper, the linear parameter varying (LPV) - model predictive controller (MPC) of a two-link robot manipulator is established and then the controller's optimal parameters are determined via a newly developed meta-heuristic algorithm, transient search optimization (TSO). The proposed control method is verified by set point and nonlinear trajectory tracking. In the test of set-point tracking, the LPV-MPC scheme optimized by TSO has better performance compared to the computed torque controller (CTC) schemes tuned by TSO or other metaheuristic algorithms. In addition, good performances can also be observed in the tests of nonlinear trajectory tracking via the LPV-MPC scheme by TSO. Moreover, the robustness of the method to structural uncertainty is verified by setting a large system parameter deviation. Results reveal that we achieved some improvements in the optimization of MPC of the robot manipulator by employing the proposed method.
Citation: Xingjia Li, Jinan Gu, Zedong Huang, Wenbo Wang, Jing Li. Optimal design of model predictive controller based on transient search optimization applied to robotic manipulators[J]. Mathematical Biosciences and Engineering, 2022, 19(9): 9371-9387. doi: 10.3934/mbe.2022436

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Abstract

Due to nonlinearity and uncertainty of the robotic manipulator, the design of the robot controller has a crucial impact on its performance of motion and trajectory tracking. In this paper, the linear parameter varying (LPV) - model predictive controller (MPC) of a two-link robot manipulator is established and then the controller's optimal parameters are determined via a newly developed meta-heuristic algorithm, transient search optimization (TSO). The proposed control method is verified by set point and nonlinear trajectory tracking. In the test of set-point tracking, the LPV-MPC scheme optimized by TSO has better performance compared to the computed torque controller (CTC) schemes tuned by TSO or other metaheuristic algorithms. In addition, good performances can also be observed in the tests of nonlinear trajectory tracking via the LPV-MPC scheme by TSO. Moreover, the robustness of the method to structural uncertainty is verified by setting a large system parameter deviation. Results reveal that we achieved some improvements in the optimization of MPC of the robot manipulator by employing the proposed method.

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