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This article presents the design of a motion tracking controller for the Mecanum wheel mobile robot (MWMR) with uncertain parameters, subject to external disturbances and a sideways sliding wheel. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. Those are control algorithms developed on the Backstepping technical platform. A nonlinear kinematics algorithm uses a backstepping technique to track adaptive trajectory using a 2-layer neural network to improve the control quality of the nonlinear kinematics algorithm using a backstepping technique applied to MWMR. The Lyapunov criteria is used to assess the closed system’s stability. Simulation results with Matlab/Simulink reveal that the effectiveness of the proposed controller exhibits a steady state and position deviation of the right and left wheels is accordingly 0.00032 (m) and respectively 0.00008(m) and the angular velocity tracking error in the right and left wheels of the control method is 0.007 (rad/s) in the corridor environment when testing with PID-Backstepping NN controller compared with PID-Backstepping controller.

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Design of PID-Backstepping Neural Networks Controller for Mecanum Wheeled Autonomous Robot

  • 会议论文

  • 发表日期：

    2026年

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  • Vo Thu Hà,
  • Nguyen Thi Thanh,
  • Thân Thị Thương,
  • Nguyen Thi Hien,
  • Vo Quang Vinh

摘要

This article presents the design of a motion tracking controller for the Mecanum wheel mobile robot (MWMR) with uncertain parameters, subject to external disturbances and a sideways sliding wheel. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. Those are control algorithms developed on the Backstepping technical platform. A nonlinear kinematics algorithm uses a backstepping technique to track adaptive trajectory using a 2-layer neural network to improve the control quality of the nonlinear kinematics algorithm using a backstepping technique applied to MWMR. The Lyapunov criteria is used to assess the closed system’s stability. Simulation results with Matlab/Simulink reveal that the effectiveness of the proposed controller exhibits a steady state and position deviation of the right and left wheels is accordingly 0.00032 (m) and respectively 0.00008(m) and the angular velocity tracking error in the right and left wheels of the control method is 0.007 (rad/s) in the corridor environment when testing with PID-Backstepping NN controller compared with PID-Backstepping controller.