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Optimization of Energy Consumption of Industrial Robots Using Classical PID and MPC Controllers

Author

Listed:
  • Rabab Benotsmane

    (Faculty of Mechanical Engineering and Informatics, University of Miskolc, Egyetemváros, H-3515 Miskolc, Hungary)

  • György Kovács

    (Faculty of Mechanical Engineering and Informatics, University of Miskolc, Egyetemváros, H-3515 Miskolc, Hungary)

Abstract

Industrial robots have a key role in the concept of Industry 4.0. On the one hand, these systems improve quality and productivity, but on the other hand, they require a huge amount of energy. Energy saving solutions have to be developed and applied to provide sustainable production. The purpose of this research is to develop the optimal control strategy for industrial robots in order to minimize energy consumption. Therefore, a case study was conducted for the development of two control strategies to be applied to the RV-2AJ Mitsubishi robot arm with 5 DOF, where the system is a nonlinear one. The first examined controller is the classical linear proportional integral derivative (PID) controller, while the second one is the linear model predictive control (MPC) controller. In our study, the performances of both the classical PID model and the linear MPC controller were compared. As a result, it was found that the MPC controller in the execution of the three defined reference trajectories [(1) curve motion, (2) N-shaped motion, and (3) circle motion] was always faster and required less energy consumption, whereas in terms of precision the PID succeeded in executing the trajectory more precisely than the MPC but with higher energy consumption. The main contribution of the research is that the performances of the two control strategies with regard to a complex dynamic system were compared in the case of the execution of three different trajectories. The evaluations show that the MPC controller is, on the one hand, more energy efficient; on the other hand, it provides a shorter cycle time compared to the PID controller.

Suggested Citation

  • Rabab Benotsmane & György Kovács, 2023. "Optimization of Energy Consumption of Industrial Robots Using Classical PID and MPC Controllers," Energies, MDPI, vol. 16(8), pages 1-28, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3499-:d:1125801
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    References listed on IDEAS

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    1. Ying He & Jiangping Mei & Zhiwei Fang & Fan Zhang & Yanqin Zhao, 2018. "Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-26, December.
    2. Kazuki Nonoyama & Ziang Liu & Tomofumi Fujiwara & Md Moktadir Alam & Tatsushi Nishi, 2022. "Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization," Energies, MDPI, vol. 15(6), pages 1-20, March.
    3. Rabab Benotsmane & László Dudás & György Kovács, 2021. "Newly Elaborated Hybrid Algorithm for Optimization of Robot Arm’s Trajectory in Order to Increase Efficiency and Provide Sustainability in Production," Sustainability, MDPI, vol. 13(15), pages 1-23, July.
    4. Rabab Benotsmane & József Vásárhelyi, 2022. "Towards Optimization of Energy Consumption of Tello Quad-Rotor with Mpc Model Implementation," Energies, MDPI, vol. 15(23), pages 1-25, December.
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    Cited by:

    1. Agnieszka Sękala & Tomasz Blaszczyk & Krzysztof Foit & Gabriel Kost, 2024. "Selected Issues, Methods, and Trends in the Energy Consumption of Industrial Robots," Energies, MDPI, vol. 17(3), pages 1-23, January.

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