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All Journal Journal of Robotics and Control (JRC)
Quang, Hiep Do
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

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Reinforcement Learning-Based Trajectory Control for Mecanum Robot with Mass Eccentricity Considerations Nguyen, Minh Dong; Ngo, Manh Tien; Quang, Hiep Do; Phuong, Nam Dao
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22148

Abstract

This article presents a robust optimal tracking control approach for a Four Mecanum Wheeled Robot (FMWR) using an online actor-critic reinforcement learning (RL) algorithm to address the challenge of precise trajectory tracking problem in the presence of mass eccentricity and friction uncertainty. In order to handle these obstacles, a detailed dynamics model is derived using Lagrange’s equation, and the Hamilton–Jacobi–Bellman (HJB) equation is solved by iteration algorithm with policy evaluation and improvement. The training laws of optimal control law and value function are proposed after minimizing the modified Hamiltonian function. Moreover, to handle the time-varying property of tracking error model, a transform is given with the addition of time derivative term. Simulation Studies demonstrate the approach’s effectiveness, significantly improving trajectory tracking accuracy and robustness against disturbances. This research contributes to mobile robotics by enhancing control precision and reliability in dynamic environments.
Reinforcement Learning-Based Trajectory Control for Mecanum Robot with Mass Eccentricity Considerations Nguyen, Minh Dong; Ngo, Manh Tien; Quang, Hiep Do; Phuong, Nam Dao
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22148

Abstract

This article presents a robust optimal tracking control approach for a Four Mecanum Wheeled Robot (FMWR) using an online actor-critic reinforcement learning (RL) algorithm to address the challenge of precise trajectory tracking problem in the presence of mass eccentricity and friction uncertainty. In order to handle these obstacles, a detailed dynamics model is derived using Lagrange’s equation, and the Hamilton–Jacobi–Bellman (HJB) equation is solved by iteration algorithm with policy evaluation and improvement. The training laws of optimal control law and value function are proposed after minimizing the modified Hamiltonian function. Moreover, to handle the time-varying property of tracking error model, a transform is given with the addition of time derivative term. Simulation Studies demonstrate the approach’s effectiveness, significantly improving trajectory tracking accuracy and robustness against disturbances. This research contributes to mobile robotics by enhancing control precision and reliability in dynamic environments.
Co-Authors Ngo, Manh Tien Nguyen, Minh Dong Phuong, Nam Dao