| 整车主动悬架系统分布式滚动时域一致性估计 |
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| 引用本文： | 宋秀兰,周文乐,徐晨辉,何德峰.整车主动悬架系统分布式滚动时域一致性估计[J].控制理论与应用,2023,40(8):1488-1496. |
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| 作者姓名： | 宋秀兰 周文乐 徐晨辉 何德峰 |
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| 作者单位： | 浙江工业大学 信息工程学院,浙江工业大学 信息工程学院,浙江工业大学 信息工程学院,浙江工业大学 信息工程学院 |
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| 基金项目： | 国家自然科学基金项目(62173303), 浙江省公益性技术应用研究项目(LGF22F030013) |
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| 摘 要： | 考虑整车主动悬架系统的约束状态估计问题,本文提出基于一致性原理的分布式滚动时域估计(DMHE)算法.首先,为了降低状态估计过程中的计算量,将整车主动悬架系统分解为若干降阶子系统.其次,为提高分布式状态估计效果,采用滚动时域估计(MHE)方法处理主动悬架系统的状态和噪声约束.考虑子系统与邻居估计状态的相关性,在采样间隔中执行多次一致性原理实现主动悬架系统状态的信息融合,进一步建立了算法的稳定性充分条件.最后,通过对比仿真实验验证算法的有效性和优越性.
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| 关 键 词： | 整车主动悬架 分布式状态估计 滚动时域估计 一致性 |
| 收稿时间： | 2022/4/5 0:00:00 |
| 修稿时间： | 2023/7/14 0:00:00 |
Distributed moving horizon consensus estimation of full-car active-suspension systems |
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| SONG Xiu-lan,ZHOU Wen-le,XU Chen-hui,HE De-feng.Distributed moving horizon consensus estimation of full-car active-suspension systems[J].Control Theory & Applications,2023,40(8):1488-1496. |
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| Authors: | SONG Xiu-lan ZHOU Wen-le XU Chen-hui HE De-feng |
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| Affiliation: | College of Information Engineering, Zhejiang University of Technology,College of Information Engineering, Zhejiang University of Technology,College of Information Engineering, Zhejiang University of Technology,College of Information Engineering, Zhejiang University of Technology |
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| Abstract: | A distributed moving horizon estimation (DMHE) algorithm based on the consensus principle was proposed for constrained state estimation of full-car active-suspension systems. Firstly, to reduce the calculation demand in the state estimation, the vehicle active suspension system is decomposed into several subsystems with reduced order. Secondly, to improve the effect of distributed state estimation, the moving horizon estimation (MHE) method is used to deal with the state and noise constraints of the active-suspension system. Considering the correlation between the subsystem and its neighbor estimation states, the information fusion of states of the active-suspension system is realized by applying the consensus principle several times in a sampling interval. The sufficient conditions for ensuring stability of the algorithm are further established. Finally, the effectiveness and the superiority of the algorithm are verified by some comparison simulation experiments. |
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| Keywords: | full-car active suspension distributed state estimation moving horizon estimation consensus |
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