Research on LQR control of active suspension based on Improved Grey Wolf Optimization algorithm

In order to improve the riding comfort of the vehicle, a four DOF half-vehicle active suspension model is established, and a Linear Quadratic Regulator (LQR) controller is designed to improve the performance of the active suspension. In addition, aiming at the difficulty of selecting the weight coefficient in LQR controller and the problem of human subjectivity in the selection process, a strategy based on improved grey wolf optimization (IGWO) algorithm is proposed to optimize the weight coefficient of LQR. Aiming at the problem that the original GWO is easy to fall into local optimal solution, the nonlinear improvement of the convergence factor is made to better balance the global search ability and local search ability of the proposed algorithm. The simulation results show that compared with passive suspension and traditional LQR active suspension, the active suspension controlled by IGWO-LQR can greatly reduce the impact of road surface changes on the vehicle body, and the performance of vehicle suspension has been further improved, which proves the superiority of the proposed method.