论文标题
非线性设置会员滤波器通过共识-ADMM具有状态估计约束
Nonlinear Set Membership Filter with State Estimation Constraints via Consensus-ADMM
论文作者
论文摘要
本文考虑了未知但有界噪声的非线性动态系统的状态估计问题。 SET成员资格过滤器（SMF）是解决此问题的流行算法。在设定的成员资格设置中，我们研究了滤波器估计需要受线性或非线性平等约束的过滤问题。我们为非线性动态系统提出了基于乘数的共识交替方向方法（ADMM）算法。为了应对非线性的难度，不是线性化非线性系统，而是使用半无限编程方法（SIP）方法将非线性系统转换为线性系统，这使我们能够获得更准确的估计椭圆形。对于SIP的解决方案，提出了ADMM算法来处理状态估计约束，并且可以有效地求解算法的每种迭代。最后，提出的过滤器被应用于典型的数值示例，以证明其有效性。
This paper considers the state estimation problem for nonlinear dynamic systems with unknown but bounded noises. Set membership filter (SMF) is a popular algorithm to solve this problem. In the set membership setting, we investigate the filter problem where the state estimation requires to be constrained by a linear or nonlinear equality. We propose a consensus alternating direction method of multipliers (ADMM) based SMF algorithm for nonlinear dynamic systems. To deal with the difficulty of nonlinearity, instead of linearizing the nonlinear system, a semi-infinite programming (SIP) approach is used to transform the nonlinear system into a linear one, which allows us to obtain a more accurate estimation ellipsoid. For the solution of the SIP, an ADMM algorithm is proposed to handle the state estimation constraints, and each iteration of the algorithm can be solved efficiently. Finally, the proposed filter is applied to typical numerical examples to demonstrate its effectiveness.
