Abstract
The design and control for active suspension is of great significance for improving the vehicle performance, which requires considering simultaneously three indexes including ride comfort, packaging requirements and road adaptability. To find optimal suspension parameters and provide a better tradeoff among these three performances, this paper presents a novel multi-objective particle swarm optimization (MPSO) algorithm for the suspension design. The mathematical model of quarter-car suspension is first established, and it integrates the hydraulic servo actuator model. Further a model predictive controller is designed for the suspension by using the control strategies of multi-step forecast, rolling optimization and online correction of predictive control theory. To use vehicle body acceleration, tire deflection and suspension stroke to represent the above three performances respectively, a multi-objective optimization model is constructed to optimize the suspension stiffness and damping coefficients. The MPSO algorithm includes extra crossover operations, which are applied to find the Pareto optimal set. The rule to update the Pareto pool is that the newly selected solutions must have two better performances compared with at least one already existed in the Pareto pool, which ensures that each solution is non-dominated within the Pareto set. Finally, numerical simulations on a vehicle-type example are done under B-level road surface excitation. Simulation results show that the optimized suspension can effectively reduce the vertical vibrations and improve the road adaptability. The model predictive controller also shows high robustness with vehicle under null load, half load and full load. Therefore, the proposed MPSO algorithm provides a new valuable reference for the multi-objective optimization of active suspension control.
Highlights
Suspension system is one of the crucial parts relative to vehicle ride comfort and road adaptability
A well-designed vehicle suspension is able to isolate the disturbance from road excitation, and guarantees better driving smoothness and road adaptability
The performance index of vehicle suspension needs to combine these three performance measures using a single objective optimization by assigning adjustable weights to the three performance terms or using the multi-objective optimization based on Pareto solutions
Summary
Suspension system is one of the crucial parts relative to vehicle ride comfort and road adaptability. MULTI-OBJECTIVE OPTIMIZATION OF ACTIVE SUSPENSION PREDICTIVE CONTROL BASED ON IMPROVED PSO ALGORITHM. Literature [15] proposed a new model predictive control algorithm for semi-active air suspension with a multi-mode switchable damper. The performance index of vehicle suspension needs to combine these three performance measures using a single objective optimization by assigning adjustable weights to the three performance terms or using the multi-objective optimization based on Pareto solutions. The latter can provide a Pareto set including many Pareto solutions, which ensures the designer to have more options depending on their requirements.
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