Licheng Wang

Probability-Dependent Static Output Feedback Control for Discrete-Time Nonlinear Stochastic Systems with Missing Measurements

This paper is devoted to the problems of gain-scheduled control for a class of discrete-time stochastic systems with infinite-distributed delays and missing measurements by utilizing probability-dependent Lyapunov functional. The missing-measurement phenomenon is assumed to occur in a random way, and the missing probability is time varying with securable upper and lower bounds that can be measured in real time. The purpose is to design a static output feedback controller with scheduled gains such that, for the admissible random missing measurements, time delays, and noises, the closed-loop system is exponentially mean-square stable. At last, a simulation example is exploited to illustrate the effectiveness of the proposed design procedures.

On Quantized H∞ Filtering for Multi-Rate Systems Under Stochastic Communication Protocols: The Finite-Horizon Case

Abstract In this paper, the finite-horizon H ∞ filtering problem is investigated for a class of linear discrete-time multi-rate systems with quantization effects under the stochastic communication protocol (SCP). The SCP is adopted to mitigate the undesirable data collision phenomenon resulting from the limited bandwidth of communication networks. Governed by a Markov chain, the SCP is employed to determine which sensor node should be granted the access right at each transmission instant. In order to cope with the difficulty caused by the asynchronous sampling, a lifting technique is utilized to convert the multi-rate system into a single-rate one with the identical slow sampling rate. The main purpose of the addressed problem is to design a set of time-varying filters for the multi-rate systems such that, for all admissible multi-sampling periods and quantization effects under the SCP, the H ∞ constraint is satisfied over a finite-horizon. By resorting to the complete square method and the Riccati difference equation (RDE) technique, sufficient conditions are established to ensure the existence of the desired filters. Then, the filter parameters are explicitly expressed in terms of the solution to two coupled backward RDEs. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed filter design algorithm.

Event-triggered variance-constrained finite-horizon state estimation for discrete-time systems with redundant channels

In this paper, the variance-constrained finite-horizon state estimation problem is studied for a class of discrete time-varying systems with an event-triggering mechanism. An event-based transmission scheme is employed during the data communication from the sensor to the estimator with hope to reduce the network burden and energy consumption. Furthermore, redundant channels are utilized to enable the data delivery service to be more reliable in a networked environment. Attention is fixed on the design of a time-varying state estimator such that, in the presence of external disturbances and probabilistic packet dropouts, the estimation error variance achieves the prescribed constraint over a finite-horizon. By means of the mathematical induction method, sufficient conditions are put forward to ensure the error variance is bounded within a prescribed upper bound at each sampling instant. A numerical example is provided to illustrate the usefulness of the proposed estimator design scheme.