Abstract:

Proposed in this paper is a rollover warning control strategy based on the genetic algorithm-optimized BP neural network ( GANN) for hydraulic in-wheel motor hybrid heavy truck.In the investigation,first,a rollover reference model of the heavy truck with three degrees of freedom was established,and the rollover warning indicator was selected.Next,based on the rollover reference model,an observer of rollover warning indicator was presented.Then,a new rollover warning algorithm named GANN-TTR was proposed by introducing genetic algorithm-optimized BP neural network to optimize the traditional TTR ( Time-To-Rollover) algorithm.Moreover,a system model was conducted on the Trucksim platform,a hydraulic system model was presented on the AMESim platform,and a rollover warning algorithm was achieved on the Matlab /Simulink platform.Finally,a co-simulation platform was constructed on the basis of Matlab /Simulink,AMESim and Trucksim platforms to simulate the truck under step steering and hook steering conditions,and the warning precision of the traditional TTR algorithm,the BP algorithm and the proposed GANN-TTR algorithm were compared.Simulated results show that,with the proposed GANN-TTR rollover warning algorithm,the warning precision effectively improves,and the minimum error between the revised warning curve obtained through vertical velocity and driver steering angle and the ideal warning curve is low to 5%.

Key words: heavy truck, rollover warning algorithm, warning precision, genetic algorithm, BP neural network