Trajectory tracking for mobile robots based on fractional-order sliding mode and dynamic modeling

Introduction Trajectory tracking control is a core link to ensure the normal operation of robots. However, traditional trajectory tracking control methods have problems such as low operational efficiency and low control accuracy. The research proposes a trajectory tracking control model based on the fractional-order synovial membrane control algorithm and dynamic modeling to solve this problem. Methods The research adopts the fractional synovial control algorithm to design the trajectory tracking controller, and uses the particle swarm optimization algorithm and radial basis neural network to optimize the controller. Then, based on kinematic modeling and dynamic modeling, the main operating components and coupling relationships of the robot are analyzed to improve the accuracy of trajectory tracking control. Results The position error of the improved fractional synovial membrane controller designed in the research is stable within the range of [−1 × 10 −5 , 1 × 10 −5 ], which is smaller than that of the comparison demonstrating high accuracy. The rise time of the trajectory tracking control model proposed in the research is 0.06 seconds, which is less than that of the comparison model and has a relatively fast convergence speed. Discussion The experimental results show that the trajectory tracking control model proposed in the research has better effects in terms of accuracy and stability, as well as ascending speed. It can perform trajectory control more precisely and efficiently, maintaining the normal operation of the robot. In the future, the accuracy of the research and the ability of adaptive dynamic adjustment will be further enhanced to improve the practical application effect of the model.