To address the issue of reduced tracking accuracy caused by time-varying characteristic parameters during the path-tracking process of intelligent electric vehicles, this paper proposes a control strategy that integrates an adaptive linear quadratic regulator (IALQR) with a radial basis function neural network (RBFNN)-based adaptive feedforward control, effectively mitigating the impact of time-varying parameter variations. Based on linear quadratic regulator (LQR) theory, an adaptive linear quadratic regulator (ALQR) is designed, which employs an adaptive sliding mode observer (ASMO) to estimate tire lateral forces and incorporates the forgetting factor recursive least squares (FFRLS) method to dynamically adjust tire cornering stiffness. Furthermore, considering road curvature and changes in lateral tracking error, an adaptive feedforward controller based on an RBFNN is developed to generate the feedforward steering angle. Finally, a co-simulation platform is established using CarSim/Simulink. Under double-lane-change conditions, the proposed strategy demonstrates a 38.8% improvement in tracking accuracy compared with ALQR alone and an 83.9% enhancement over conventional LQR, validating the feasibility of this integrated control approach.
Chen et al. (Wed,) studied this question.
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