Vehicle Attitude Control of Magnetorheological Semi-Active Suspension Based on Multi-Objective Intelligent Optimization Algorithm

A multi-objective intelligent optimization algorithm-based attitude control strategy for magnetorheological semi-active suspension is proposed to address the vehicle attitude imbalance generated during steering and braking. Firstly, the mechanical properties of the magnetorheological damper (MRD) are tested, and the parameters in the hyperbolic tangent model of the magnetorheological damper are identified through experiments. Secondly, a simulation model of the whole vehicle multi-degree-of-freedom vehicle dynamics including magnetorheological damper is established, and the whole-vehicle Linear Quadratic Regulator (LQR) controller is designed. Then, the optimization design model of the joint vehicle controller and vehicle dynamics is established to design the optimization fitness function oriented to the body attitude control performance, and the attitude optimal controller is calculated with the help of multi-objective intelligent optimization algorithm. Simulation results show that the proposed control method is able to improve the body roll angle, body pitch angle, and suspension dynamic deflection well on the basis of ensuring no deterioration in other performance indexes, ensuring good attitude control capability of the vehicle and verifying the feasibility of the control strategy.