基于SQP和GRNN的商用客车动力学参数自适应辨识

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中图分类号：U461.4 文献标识码：A DOI： 10.3969/j.issn.1674-8484.2025.04.015

Abstract：Anadaptive identification strategy was proposed based on the generalized regression neural network （GRNN）model and the sequential quadratic programming （SQP）algorithm to obtainand identify the keydynamic parameters of commercial vehicles inrealtime.AGRNNmodel was established and trained using the training data obtainedviatheSQPalgorithm，withbeing enabledtoadaptivelyidentifykeyparametersaccording tothevehicle's operating states.A co-simulation platform was built with integrating the TruckSimand the Matlab/Simulink to conduct simulation experiments undervarious driving conditions.The results show that compared witha fixedparameters model，under the sine wave steering input condition，themaximumerrorof thevehicle'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 errorsare reduced by 98.0% and 63.1% under the double-lane change condition， respectively.Therefore，these results demonstrate the feasibilityand effectiveness of the proposed method.

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

由于商用客车载客量多、质心高，且多运营在城市道路环境下，一旦发生事故，造成的经济损失和人员伤亡会更加严重[1-3]。（剩余10626字）