Design and simulation of vehicle controllers through genetic algorithms

Viloria, Amelec; Zelaya, Nelson Alberto Lizardo; Varela, Noel

doi:10.1016/j.procs.2020.07.064

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…The convergence criteria of this algorithm are met through selection, crossover, mutation, and iteration. Because of its simple structure, global search, and convenient implementation, the GA is widely used in controller parameter optimization [18], parameter identification [19], path optimization [20], and neural network optimization [21]. Feng et al used a genetic algorithm to search for the PID controller parameters of robotic excavators to obtain improved tracking performance [22].…”

Section: Introductionmentioning

confidence: 99%

Path Tracking Control of an Autonomous Tractor Using Improved Stanley Controller Optimized with Multiple-Population Genetic Algorithm

et al. 2022

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To improve the path tracking accuracy of autonomous tractors in operation, an improved Stanley controller (IMP-ST) is proposed in this paper. The controller was applied to a two-wheel tractor dynamics model. The parameters of the IMP-ST were optimized by multiple-population genetic algorithm (MPGA) to obtain better tracking performance. The main purpose of this paper is to implement path tracking control on an autonomous tractor. Thus, it is significant to study this field because of smart agricultural development. According to the turning strategy of tractors in field operations, five working routes for tractors were designed, including straight, U, Ω, acute-angle and obtuse-angle routes. Simulation tests were conducted to verify the effectiveness of the proposed IMP-ST in tractor path tracking for all routes. The lateral root-mean-square (RMS) error of the IMP-ST was reduced by up to 36.84% and 48.61% compared to the extended Stanley controller and the original Stanley controller, respectively. The simulation results indicate that the IMP-ST performed well in guiding the tractor to follow all planned working routes. In particular, for the U and Ω routes, the two most common turning methods in tractor field operations, the path tracking performance of the IMP-ST was improved by 41.72% and 48.61% compared to the ST, respectively. Comparing and analyzing the e-Ψ and β-γ phase plane of the three controllers, the results indicate that the IMP-ST has the best control stability.

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