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Reinforcement Q-learning PID Controller for a Restaurant Mobile Robot with Double Line-Sensors

Published: 07 March 2020 Publication History

Abstract

The wheeled mobile robots have been widely applied in daily applications for its simplicity, robustness, stability and low-cost of manufacturing. Typically, PID controller is implemented to drive a mobile robot. However, the performance of the robot is greatly depended on the PID control parameters are tuned and the robustness of the mechanical system. Since the analog controllers were replaced by digital controller, PID control parameters could be automatically tuned to adapt the variation of the system parameters and operating condition. In this manuscript, a mathematical model is developed for a line following mobile robotic system which is used as a restaurant serving robot. The serving robot is controlled by an adaptive PID controller using Q-learning algorithm. The simulation and experimental results are compared to verify the advantage of the adaptive PID controller over classical PID controller.

References

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Ahmed A. M., Ayman A. A., Farhan A. S.: Mechatronics design of a mobile robot system. I.J. Intelligent Systems and Applications. 03, 23--36 (2013).
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Nguyen, D.N., Cao, T.B., Pham, T.H., Vo, N.T., Dang, P.V and Le, H.N.: Design and Control of a Ball-balancing Robot. The 4th International Conference on Green Technology and Sustainable Development(GTSD), Ho Chi Minh City. 317--322 (2018).
[3]
Tran, N.H., Le, C., Vo, N.T. and Tran, X.T.: Fuzzy Self-tuning PID Controller for Position Response of Machining Table Driven by Hydraulic Motor. The First International Conference on Material, Machines and Methods for Sustainable Development (MMMS), Da Nang City. 481--488 (2018).
[4]
Carlucho, I., Paula, M. D., Villar, S. A. and Acosta, G. G.: Incremental Q--learning strategy for adaptive PID control of mobile robots. Expert Systems with Applications. 80, 183--199 (2017).
[5]
Vo, N.T., Dang, P.V., Ngo, T.N., Le, H.N., Do, L.H.T.: Restaurant Serving Robot with Double Line Sensors Following Approach. 2019 IEEE International Conference on Mechatronics and Automation (ICMA). 235--239 (2019).
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Watkins, C. J. C. H.: Technical Note Q,-Learning. Machine Learning, 8, 279--292 (1992).

Cited By

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  • (2024)Analysis of the parametric configuration impact on BallBot control performanceInternational Journal of Mechanical System Dynamics10.1002/msd2.12133Online publication date: 29-Nov-2024
  • (2021)Scheduling PID Attitude and Position Control Frequencies for Time-Optimal Quadrotor Waypoint Tracking under Unknown External DisturbancesSensors10.3390/s2201015022:1(150)Online publication date: 27-Dec-2021

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  1. Reinforcement Q-learning PID Controller for a Restaurant Mobile Robot with Double Line-Sensors

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    cover image ACM Other conferences
    ICMLSC '20: Proceedings of the 4th International Conference on Machine Learning and Soft Computing
    January 2020
    175 pages
    ISBN:9781450376310
    DOI:10.1145/3380688
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 07 March 2020

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    Author Tags

    1. Adaptive controller
    2. Mobile robot
    3. PID
    4. Q-learning

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    View all
    • (2024)Analysis of the parametric configuration impact on BallBot control performanceInternational Journal of Mechanical System Dynamics10.1002/msd2.12133Online publication date: 29-Nov-2024
    • (2021)Scheduling PID Attitude and Position Control Frequencies for Time-Optimal Quadrotor Waypoint Tracking under Unknown External DisturbancesSensors10.3390/s2201015022:1(150)Online publication date: 27-Dec-2021

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