非线性主动悬架系统自适应最优控制
DOI:
作者:
作者单位:

1.昆明理工大学;2.太原理工大学;3.中船集团有限公司705研究所昆明分部

作者简介:

通讯作者:

中图分类号:

TP273

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Adaptive Optimal Control for Nonlinear Active Suspension Systems
Author:
Affiliation:

1.Kunming University of Science and Technology;2.Taiyuan University of Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    针对非线性主动悬架系统多性能指标综合优化问题, 提出一类自适应最优控制方法. 首先, 通过引入一阶低通滤波操作, 利用系统输入输出构建结构简单且调节参数少的一类未知非线性动态估计器, 在线估计系统未知非线性动态. 其次, 构建包含乘驾舒适度、悬架行程空间及输入能耗的性能指标函数. 采用单层神经网络对最优性能指标函数进行在线逼近, 并得到新的哈密尔顿函数. 为实现在线求解, 构建一类新的基于参数估计误差信息的自适应律, 在线更新神经网络权值并计算最优控制律. 最后, 理论分析闭环系统稳定性和收敛性, 并通过专业软件Carsim 与Matlab/Simulink搭建的联合仿真平台, 给出对比仿真结构验证了所提方法可有效解决主动悬架系统多目标性能优化控制问题, 提升主动悬架系统综合性能.

    Abstract:

    This paper proposes an adaptive optimal control method for active suspension systems subject to uncertain dynamics and multiple performance indices. An unknown system dynamics estimator, which merely uses the control input and output and has only one tuning parameter, is first constructed to reconstruct unknown system dynamics. Furthermore, a novel cost function concerning the ride comfort, suspension stroke and control input is established, which aims at achieving a compromise between the performance indices. Then, a single layer neural network (NN) is used to estimate the optimal cost function, by which the Hamiltonian function can be derived. To obtain the online solution, a novel adaptive law driven by the parameter estimation error is developed to update the unknown NN weights and calculate the optimal control action. Theoretical analysis is carried out to prove the stability and convergence of the closed-loop system. Finally, simulation results based on the vehicle simulation software, Carsim and Matlab/Simulink, are presented to demonstrate that the proposed adaptive optimal control method can make a trade-off between the performance indices and improve the overall suspension performance.

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历史
  • 收稿日期:2021-05-07
  • 最后修改日期:2021-08-30
  • 录用日期:2021-09-10
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