An Anti-sideslip Path Tracking Control Method of Wheeled Mobile Robots

Bai, Guoxing; Meng, Yu; Gu, Qing; Wang, Guodong; Dong, Guoxin; Zhou, Lei

doi:10.1007/978-3-031-13822-5_22

Cited by 1 publication

(1 citation statement)

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“…In 2017, Wang et al proposed an MPC path tracking method for field vehicles [20]. Between 2019 and 2022, based on NMPC, Bai et al attempted to solve the problems faced in path tracking for a variety of mobile devices, such as tractortrailer vehicles [21], car-like vehicles [22][23][24], and differential wheeled robots [25,26]. In 2020, Yin et al designed an NMPC-based path tracking controller using discrete previewed points in the inertial system [27].…”

Section: Introductionmentioning

confidence: 99%

Path Tracking for Car-like Robots Based on Neural Networks with NMPC as Learning Samples

et al. 2022

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In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle the system constraints well, its real-time performance is poor. To solve this problem, a neural network control method with NMPC as the learning sample is proposed. The design process of this control method includes establishing the NMPC controller based on the time-varying local model, generating learning samples based on this NMPC controller, and training to obtain the neural network controller. The proposed controller is tested by a joint simulation of MATLAB and Carsim and compared with other controllers. According to the simulation results, the accuracy of the NN controller is close to that of the NMPC controller and far better than that of the Stanley controller. In all simulations, the absolute value of displacement error of the NN controller does not exceed 0.2854 m, and the absolute value of heading error does not exceed 0.2279 rad. In addition, the real-time performance of the NN controller is better than that of the NMPC controller. The maximum time cost and average time cost of the NN controller are, respectively, 40.91% and 22.37% smaller than those of the NMPC controller under the same conditions.

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