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<p>To achieve the more effective and robust trajectory tracking performance for the omnidirectional mobile robotic chassis (OMRC), a control system is designed by combining the linear active disturbance rejection control (LADRC) and proportional-integral-derivative (PID) methods, while a novel modified fruit fly optimization algorithm (Le-OFFO) is proposed to achieve the optimal parameters for the designed controller. The PID controller is utilized to the outer loop for trajectory tracking and LADRC controller is applied to the inner loop for the control of torque of each motor. A fitness function is established to evaluate the cost of control system, with several metrics and constraints taking into consideration. The proposed Le-OFFO algorithm combines the levy flight and the opposition-based learning (OBL) operators, in which the levy flight can help to escape from local optimum and the OBL-based mutation operator can enhance the exploration ability. The optimal parameters combination for the designed control system can be achieved by using the Le-OFFO to minimize the objective function. Numerous results show that the Le-OFFO has better performance of convergence speed and exploitation capability compared with other optimization algorithms in solving the controller parameter tuning problem. In addition, the effectiveness of the optimized LADRC controller is validated by results of experiments when compared with basic PID controller and original ADRC controller.</p>

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A modified fruit fly optimization algorithm to active disturbance rejection control parameters tuning for trajectory tracking of omnidirectional mobile robotic chassis

  • 期刊论文

  • 发表日期：

    2025年5月1日

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  • Xiangyin Zhang,
  • Weihuan Wu,
  • Xiuzhi Li

摘要

To achieve the more effective and robust trajectory tracking performance for the omnidirectional mobile robotic chassis (OMRC), a control system is designed by combining the linear active disturbance rejection control (LADRC) and proportional-integral-derivative (PID) methods, while a novel modified fruit fly optimization algorithm (Le-OFFO) is proposed to achieve the optimal parameters for the designed controller. The PID controller is utilized to the outer loop for trajectory tracking and LADRC controller is applied to the inner loop for the control of torque of each motor. A fitness function is established to evaluate the cost of control system, with several metrics and constraints taking into consideration. The proposed Le-OFFO algorithm combines the levy flight and the opposition-based learning (OBL) operators, in which the levy flight can help to escape from local optimum and the OBL-based mutation operator can enhance the exploration ability. The optimal parameters combination for the designed control system can be achieved by using the Le-OFFO to minimize the objective function. Numerous results show that the Le-OFFO has better performance of convergence speed and exploitation capability compared with other optimization algorithms in solving the controller parameter tuning problem. In addition, the effectiveness of the optimized LADRC controller is validated by results of experiments when compared with basic PID controller and original ADRC controller.