Multi-Strategy Advanced Snake Optimizer-Based Optimal Feedback Control of Half Vehicle Suspension

To solve the problem of tuning the weight coefficient matrices of Linear Quadratic Regulator (LQR) for vehicle active suspension system, a multi-strategy Advanced Snake Optimizer-Linear Quadratic Regulator (ASO-LQR) is proposed based on the Snake Optimizer (SO). Firstly, the dynamic model of four-freedom-degree half vehicle active suspension is established, and the LQR is set up. Secondly, the multi-strategy Advanced Snake Optimizer (ASO) is designed to address the problems of slow convergence speed and low optimization accuracy of the SO. Wherein the quality of initial snake swarm is improved through the good-point set and oppositional learning; the algorithm’s capabilities of global exploration and local exploitation are enhanced by utilizing the adaptive oscillating weight; the crossover-mutation operator of Genetic Algorithm (GA) for parallel search of solution space as well as the Levy Flight perturbation of optimal solutions is introduced to avoid local optima. Then, the ASO is combined with the LQR by a fitness function including suspension performance indexes to reduce system vibration. Finally, compared with other algorithms, such as SO, GA, Adaptive Particle Swarm Optimization (APSO), et al., the proposed ASO has higher optimization accuracy and faster convergence speed. On the above basis, through the simulation tests of C-class road, bump road and speed generalization on the C-class road, the ASO-LQR-controlled active suspension (ASO-LQR-ASS) can improve vibration damping performance more effectively.