Abstract:

An adaptive identification strategy was proposed based on the generalized regression neural network (GRNN) model and the sequential quadratic programming (SQP) algorithm to obtain and identify the key dynamic parameters of commercial vehicles in real time. A GRNN model was established and trained using the training data obtained via the SQP algorithm, with being enabled to adaptively identify key parameters according to the vehicle’s operating states. A co-simulation platform was built with integrating the TruckSim and the Matlab/Simulink to conduct simulation experiments under various driving conditions. The results show that compared with a fixed-parameters model, under the sine wave steering input condition, the maximum error of the vehicle’s sideslip angle is reduced by 73.9% than the TruckSim model with the maximum error of the roll angle being reduced by 76.7%. Meanwhile, these two errors are reduced by 98.0% and 63.1% under the double-lane change condition, respectively. Therefore, these results demonstrate the feasibility and effectiveness of the proposed method.

Key words: vehicle safety, commercial buses, SQP (sequential quadratic programming) algorithm, GRNN (general regression neural network) model, dynamics parameters, adaptive identification