Adaptive LQR Path Tracking Control for 4WS Electric Vehicles Based on Genetic Algorithm

The four-wheel steering electric vehicles are considered as ideal autonomous vehicles, and the linear quadratic regulator (LQR) is widely adopted in path tracking control. The choice of $Q$ and $R$ matrices is an essential problem in LQR design. However, the weights of the LQR controller are typically designed based on empirical methods, which are cumbersome and inefficient when the scenario changes. This study proposes an adaptive LQR path tracking controller. The parameters of $Q$ and $R$ matrices are optimized through a new fitness function of the genetic algorithm and form an offline table. Finally, an online adaptive LQR controller is developed by selecting $Q$ and $R$ through looking up the table. The simulation results show the effectiveness of the proposed controller in improving tracking accuracy and vehicle stability simultaneously. The max lateral acceleration can be limited to 2.65m/s ² with high tracking accuracy at low speed. Moreover, it can still track the reference path at high speed.