基于超平行空间集员滤波的非线性系统状态估计方法
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江南大学

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TP273

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国家高技术研究发展计划(863计划),国家自然科学基金项目(面上项目,重点项目,重大项目),中国博士后科学基金


Hyperparallel space set-membership filtering based state estimation algorithm for nonlinear system
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Jiangnan University

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    摘要:

    针对噪声有界但未知条件下的非线性系统状态估计问题,提出了基于超平行空间集员滤波算法,利用Stirling矩阵将模型进行一阶展开,基于凸差规划完成线性化误差定界,随后采用超平行空间表示误差边界和状态可行集,求解下一时刻预测状态可行集超平行体.在更新步将观测值分解为多个带, 融入观测值的线性化误差并将带依次与超平行体相交,得到该时刻的超平行空间描述下的状态可行集更新情况.本文所提算法避免了在求解线性化误差过程中外包误差集合带来的体积扩充,降低了非线性集员滤波算法的保守性,给出的仿真示例验证了算法的可行性和有效性.

    Abstract:

    For solving the problem of state estimation in nonlinear systems with bounded but unknown noise, a hyperparallel space set-membership filtering based state estimation algorithm is proposed. The Stirling matrix is used to expand the model into the one dimension, and the linearization error boundary is calculated based on convex difference programming. Then, the hyperparallel space is used to represent the error boundary and the feasible state set, before finding the predictive parallelotope incorporating true states in the next time. In the update step, the observation is decomposed into multiple stripe, and the updating feasible state set described by the hyperparallel space is obtained by integrating the linearization error of the observed values into stripe and intersecting them with the parallelotope in turn. The proposed algorithm avoids the volume increase caused by sets bounding remainder in the process of linearization error, thus can reduce the conservatism of nonlinear set-membership filtering algorithm. The simulation example shows the feasibility and effectiveness of the algorithm.

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历史
  • 收稿日期:2021-01-05
  • 最后修改日期:2021-06-23
  • 录用日期:2021-07-05
  • 在线发布日期: 2021-08-01
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