In this article, a statistical similarity measure is introduced to quantify the similarity between two random vectors. The measure is, then, employed to develop a novel outlier-robust Kalman filtering framework. The approximation errors and the stability of the proposed filter are analyzed and discussed. To implement the filter, a fixed-point iterative algorithm and a separate iterative algorithm are given, and their local convergent conditions are also provided, and their comparisons have been made. In addition, selection of the similarity function is considered, and four exemplary similarity functions are established, from which the relations between our new method and existing outlier-robust Kalman filters are revealed. Simulation examples are used to illustrate the effectiveness and potential of the new filtering scheme.

This paper addresses the actuator fault estimation sliding mode observer design problem of linear continuous-time systems over digital communication channels. This problem frequently occurred in a network environment where data has to be quantized before being transmitted via digital communication channels. Traditional observers (linear Luenberger observer, Walcott-ZaK sliding mode observer) are not effective to solve this design issue since the effects of signal quantization will degrade estimation performances evidently. In this paper, a new descriptor sliding mode observer method is presented to overcome this difficult problem. It is shown that, if the quantizer density is larger than $\sqrt{2}$-1, the designed observer can compensate quantization errors completely, and the fault vector can be reconstructed despite of signal quantization. Finally, a simulation example with the F-404 aircraft engine model is proposed to demonstrate the effectiveness of the proposed robust digital observer design approach.

This paper is concerned with the fault estimation (FE) problem for continuous-time descriptor switched systems with unknown external disturbances, actuator, and sensor faults. In this paper, the multiplicative actuator fault is decoupled into the normal term and the fault term, where the actuator fault term and sensor faults can be extended into a new augmented vector. Then, a novel FE approach is developed for descriptor switched systems subject to the switching actions and state-inconsistence phenomena. By the parameter design, the proposed method can eliminate the effect of faults and disturbances, and achieve the accurate estimations of states and faults simultaneously. Finally, an example of circuit system is presented to show the effectiveness of the proposed method.

This article addresses the simultaneous actuator and sensor fault estimation (FE) problem for a class of Markovian jump systems (MJSs) with nondifferentiable actuator failures. In order to overcome the difficulties brought by the nondifferentiable actuator failures, we construct an extended vector composed of states, sensor faults, and disturbances, where the derivatives of actuator failures are not required in this augmented system. Then, two novel observer-based approaches are developed for the augmented descriptor system to cope with the FE problem. The first one is a reduced-order FE observer, where the actuator failures can be estimated by the algebraic input reconstruction strategy. The second one refers to an iterative learning observer (ILO) design method, which can obtain the accurate FE result by integrating the estimations in the iterative processes. The two proposed FE observer design methods can avoid the sliding surface switching problem produced by sliding-mode observers in the area of MJSs. Finally, a practical example of the F-404 aircraft engine system is presented to show the validity of the proposed FE observer design techniques.

In multi-autonomous underwater vehicle (multi-AUV) systems, the convergence rate is characterized by the pace of consistency of the key state information for each member. The topology with leader-follower architecture is designed as a combination of an undirected graph between followers and a digraph between leaders and followers. An overview of influences on convergence rate of the second-order consensus algorithm is elaborated in three aspects, along with the main contributions in this paper. Specifically, the explicit expression of the maximum convergence rate is established based on the root locus method, and then, the effects of control parameters on the convergence rate are analyzed. Moreover, the influences of network topologies on the convergence rate are investigated from the view of adjusting the existing connectivity, changing the weights on links, and utilizing hierarchical structure. The combination of consensus and filtering algorithm is also an approach to enhance the capacity of multi-AUV systems. In order to eliminate the accumulated errors in the process of dead reckoning, a collaborative navigation model is presented, and then, a localization approach based on consensus-unscented particle filter algorithm is proposed. Simulations results are provided to verify location performance under the assumption of Gaussian white noise in the systems. In addition, the influences of the topologies on positioning accuracy are explored.

A novel robust Rauch-Tung-Striebel smoothing framework is proposed based on a generalized Gaussian scale mixture (GGScM) distribution for a linear state-space model with heavy-tailed and/or skew noises. The state trajectory, mixing parameters and unknown distribution parameters are jointly inferred using the variational Bayesian approach. As such, a major contribution of this work is unifying results within the GGScM distribution framework. Simulation and experimental results demonstrate that the proposed smoother has better accuracy than existing smoothers.