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Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems

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  • Phong B. Dao

    (Department of Robotics and Mechatronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

Abstract

Multiagent control system (MACS) has become a promising solution for solving complex control problems. Using the advantages of MACS-based design approaches, a novel solution for advanced control of mechatronic systems has been developed in this paper. The study has aimed at integrating learning control into MACS. Specifically, learning feedforward control (LFFC) is implemented as a pattern for incorporation in MACS. The major novelty of this work is that the feedback control part is realized in a real-time periodic MACS, while the LFFC algorithm is done on-line, asynchronously, and in a separate non-real-time aperiodic MACS. As a result, a MACS-based LFFC design method has been developed. A second-order B-spline neural network (BSN) is used as a function approximator for LFFC whose input-output mapping can be adapted during control and is intended to become equal to the inverse model of the plant. To provide real-time features for the MACS-based LFFC system, the open robot control software (OROCOS) has been employed as development and runtime environment. A case study using a simulated linear motor in the presence of nonlinear cogging and friction force as well as mass variations is used to illustrate the proposed method. A MACS-based LFFC system has been designed and implemented for the simulated plant. The system consists of a setpoint generator, a feedback controller, and a time-index LFFC that can learn on-line. Simulation results have demonstrated the applicability of the design method.

Suggested Citation

  • Phong B. Dao, 2021. "Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems," Energies, MDPI, vol. 14(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:420-:d:479915
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    References listed on IDEAS

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    1. Abderraouf Maoudj & Brahim Bouzouia & Abdelfetah Hentout & Ahmed Kouider & Redouane Toumi, 2019. "Distributed multi-agent scheduling and control system for robotic flexible assembly cells," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1629-1644, April.
    2. Lu Liu & Siyuan Tian & Dingyu Xue & Tao Zhang & YangQuan Chen, 2019. "Industrial feedforward control technology: a review," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2819-2833, December.
    3. M’hammed Sahnoun & David Baudry & Navonil Mustafee & Anne Louis & Philip Andi Smart & Phil Godsiff & Belahcene Mazari, 2019. "Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2981-2997, December.
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    Cited by:

    1. Tomasz Winiarski & Szymon Jarocki & Dawid Seredyński, 2021. "Grasped Object Weight Compensation in Reference to Impedance Controlled Robots," Energies, MDPI, vol. 14(20), pages 1-15, October.

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