基于路面识别的非线性悬架系统自适应控制

针对非线性悬架系统，基于多目标布谷优化和路面识别算法，研究不同路面等级下悬架非线性系统特性，实现根据路面等级调整控制参数的目的.首先建立四分之一车辆模型，选取电流为优化变量，簧载质量加速度和轮胎动行程为优化目标;然后利用布谷优化算法求取不同路面下悬架最优参数，并利用路面识别方法得到当前路面等级，结合悬架性能需求实现悬架在不同路面下自适应调节.仿真结果表明:1)控制算法可根据不同路面情况自适应调整悬架参数，提高系统性能;2)相比于传统粒子群优化方法(PSO)，基于布谷优化算法得到的控制电流能提供更为理想的悬架系统性能.

Adaptive Control of the Nonlinear Suspension System Based on Road Estimation

To adjust the control parameters according to road levels and study the characteristics of suspension nonlinear parameters under different road levels， an algorithm was proposed based on cuckoo search optimization and road estimation. Firstly， a quarter nonlinear suspension model with nonlinear dampers and springs was created， which sprung mass acceleration and tire deflection were taken as the optimization objective and the current of nonlinear dampers was taken as the optimization variable. Then， a cuckoo search-based multi-objective optimization method was used to calculate the optimal control parameter， and a road estimation method was used to identify the road level to adaptively adjust the system performance according to road input. The simulation results showed that: 1) the road estimation and cuckoo search-based algorithm can adjust the control parameter adaptively according to road levels， and the proposed method can improve riding comfort when the tire keeps contacting the road surface; 2) compared with the particle swarm optimization(PSO)， the current calculated by cuckoo search can provide better suspension performances.