Sliding innovation filter (SIF) has recently been introduced as a robust strategy for estimation of linear systems. The SIF has been extended to nonlinear systems via analytical linearization. However, as the performance of the extended SIF (ESIF) degrades in the presence of severe nonlinearities, this article has initially developed a derivative-free cubature SIF (CSIF) that uses statistical linearization for the error propagation. In addition, the SIF gain has been reformed to incorporate the innovation covariance matrix, thus reducing the estimation error. Furthermore, the adaptive fading factor has been employed to strengthen the robustness and convergence properties of the CSIF against abrupt changes of state variables. Simulation results of the proposed estimation algorithm named the strong tracking innovation filter (STIF) have been compared to those of the ESIF and the CSIF in different conditions of the modeling error in the dynamic system and statistical characteristics of the system inputs. This comparison has demonstrated the superior performance of the STIF in terms of the convergence rate, estimation accuracy, and the chattering elimination. Integration of the STIF into a sliding mode controller for the concurrent estimation and control of a Mars lander has reconfirmed the robustness and accuracy of the proposed STIF.

中文翻译：

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强大的跟踪创新过滤器


滑动创新滤波器（SIF）最近被引入作为线性系统估计的稳健策略。 SIF 已通过分析线性化扩展到非线性系统。然而，由于扩展 SIF (ESIF) 的性能在存在严重非线性时会降低，因此本文最初开发了一种无导数立方 SIF (CSIF)，它使用统计线性化进行误差传播。此外，对SIF增益进行了改革，纳入新息协方差矩阵，从而减少了估计误差。此外，采用自适应衰落因子来增强CSIF针对状态变量突变的鲁棒性和收敛性。在动态系统建模误差和系统输入统计特性的不同条件下，将所提出的称为强跟踪创新滤波器（STIF）的估计算法的仿真结果与ESIF和CSIF的仿真结果进行了比较。这一比较证明了 STIF 在收敛速度、估计精度和抖振消除方面的优越性能。将 STIF 集成到滑模控制器中，用于火星着陆器的并行估计和控制，再次证实了所提出的 STIF 的鲁棒性和准确性。