H∞ filtering with randomly occurring sensor saturations and missing measurements

Abstract

In this paper, the H∞ filtering problem is investigated for a class of nonlinear systems with randomly occurring incomplete information. The considered incomplete information includes both the sensor saturations and the missing measurements. A new phenomenon of sensor saturation, namely, randomly occurring sensor saturation (ROSS), is put forward in order to better reflect the reality in a networked environment such as sensor networks. A novel sensor model is then established to account for both the ROSS and missing measurement in a unified representation by using two sets of Bernoulli distributed white sequences with known conditional probabilities. Based on this sensor model, a regional H∞ filter with a certain ellipsoid constraint is designed such that the filtering error dynamics is locally mean-square asymptotically stable and the H∞-norm requirement is satisfied. Note that the regional l2 gain filtering feature is specifically developed for the random saturation nonlinearity. The characterization of the desired filter gains is derived in terms of the solution to a convex optimization problem that can be easily solved by using the semi-definite programme method. Finally, a simulation example is employed to show the effectiveness of the filtering scheme proposed in this paper.

Event-Triggered Fault Estimation for Stochastic Systems over Multi-Hop Relay Networks with Randomly Occurring Sensor Nonlinearities and Packet Dropouts

“…Linear and nonlinear estimation problems were tackled for missing measurements in [22], where the nonlinear function of sensor was modeled as a sector-bound condition. A robust filter was designed in [23] against the sensor saturation and the packet losses such that the filtering error dynamics was mean-square stable and the performance index was satisfied. The problem of asynchronous filtering was addressed in [24] for stochastic Markov jump systems with probabilistic occurring sensor nonlinearities.…”

Optimal filtering for uncertain systems with stochastic nonlinearities, correlated noises and missing measurements

“…Apart from missing measurements, the ltering problems with deterministic nonlinearities have been extensively investigated, and various kinds of algorithms have been developed, such as H ∞ ltering [22,23], setvalued ltering [24] and robust extended Kalman ltering [25 -27]. In recent years, the so-called stochastic nonlinearities, caused by intermittent network congestion, random failure of components or sudden environment change, have become a popular research issue in the elds of control and state estimation [25,28,29].…”

Finite-horizon estimation of randomly occurring faults for a class of nonlinear time-varying systems

“…If not properly coped with, the ROP would unavoidably deteriorate the operating efficiency or even cause the instability of the plant. So far, a series of results have been reported in the literature concerning the filtering and control problems in the presence of ROP, for example, randomly occurring nonlinearities [4,18], randomly occurring uncertainties [10], randomly occurring sensor delays [4] and randomly occurring sensor saturations [23]. Nevertheless, an important class of networkinduced phenomena, namely, randomly occurring faults (ROFs), have been largely overlooked despite their practical significance in networked control systems.…”