Abstract
AbstractAs the convergence rate of the conventional fuzzy neural network control (FNC) algorithm for a vehicle anti-lock braking system is slow, an improved ant colony optimization fuzzy neural network control (ACO-FNC) algorithm for ABS is proposed, and the control object of ACO-FNC is slip rate. The simulation model of single-wheel ABS is established. According to the comparison of the results of the conventional FNC algorithm and ACO-FNC algorithm, the performance of ACO-FNC algorithm in convergence speed, slip ratio control quality and braking distance is better than FNC algorithm.KeywordsAnt Colony Optimization(ACO)fuzzy neural networkcontrolAnti-locked Braking System(ABS)slip rate
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