基于路面识别的主动馈能悬架多目标控制与优化

针对主动悬架减振性能和馈能特性在不同等级路面适应性较差的问题，建立了非线性电磁主动悬架模型; 考虑车辆在行驶过程中悬架簧上质量存在不确定性，提出了一种主动悬架自适应滑模控制器; 基于不同路面下悬架动力学响应数据，采用自适应模糊神经网络算法识别路面等级，确定控制器目标系数，实现了主动悬架安全性和舒适性之间的协调; 研究了电磁主动悬架馈能特性及其切换控制策略，在此基础上，考虑电磁主动悬架安全性、舒适性和节能性的矛盾关系，采用多目标粒子群优化(MOPSO)，以悬架动力学性能和馈能特性为设计目标综合优化控制器和悬架结构参数，并通过模糊集理论对多目标优化后的Pareto解集进行最优解选取。研究结果表明:模糊神经网络对不同等级路面下非线性电磁主动悬架的最大识别误差能够控制在10%以内，满足识别准确性要求; 在C级路面条件下，优化后的主动悬架与传统被动悬架相比，簧上质量振动加速度减小了35.3%，轮胎动行程增大了7.7%，但可以控制在10%的安全范围内; 与原主动悬架相比，优化后悬架簧上质量振动加速度减小了10.5%，馈能效率增大了1.7%，优化后自适应滑模控制器能够更好地协调悬架馈能特性和减振特性; 建立的非线性电磁主动悬架模型可实现不同路面等级下悬架系统安全性、舒适性和节能性的综合最优。 

Multi-objective control and optimization of active energy-regenerative suspension based on road recognition

For the problem that the vibration reduction performance and energy-regenerative characteristics of active suspension are less adaptable under different road classes, a nonlinear electromagnetic active suspension model was constructed. Considering the suspension sprung mass uncertainty during vehicle driving, an adaptive sliding mode controller of active suspension was proposed. An adaptive fuzzy neural network and the dynamics data of suspension under different roads were used to recognize road classes and determine the objective coefficient of the controller. Then, the coordination between safety and comfort of active suspension was realized. The energy-regeneration characteristics and switch control strategies of electromagnetic active suspension were studied. On this basis, the suspension dynamic performance and energy-regeneration characteristic were taken as the design objectives, and the contradictory relationships between the safety, comfort, and energy efficiency of electromagnetic active suspension were considered to comprehensively optimize the controller and suspension structure parameters through the multi-objective particle swarm optimization (MOPSO). The optimal solution was acquired from the Pareto solution set after the multi-objective optimization according to the fuzzy set theory. Research result reveals that the fuzzy neural network gives a maximum recognition error within 10% for various road classes when the nonlinear electromagnetic active suspension is employed. Thus, it meets the requirement of recognition accuracy. For C-class roads, the vibration acceleration of sprung mass of optimized active suspension reduces by 35.3% compared with the traditional passive suspension. The tire dynamic displacement increases by 7.7%, but it is still within 10%, ensuring safety. Compared with the original active suspension, the optimized suspension has 10.5% less sprung mass vibration acceleration and 1.7% higher energy-regeneration efficiency. The optimized adaptive sliding mode controller can better balance the energy-regeneration and vibration reduction characteristics of suspension. The established nonlinear electromagnetic active suspension model can realize the comprehensive optimization of safety, comfort, and energy efficiency of suspension system under different road classes. 5 tabs, 9 figs, 30 refs.