Sampled-data filtering with error covariance assignment

Wang, Zidong; Huang, Biao; Pei-jun, Huo

doi:10.1109/78.905899

Cited by 37 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Later, the results in Reference 18 have been improved by References 19 and 5 in which the conservatism has been decreased via reducing some of the restrictive conditions. By resorting to the discretization approach, some optimal filtering results for sampled‐data systems have been available in References 20–22, where the constructed filters are all discrete. Another quintessential method that is quite effective in dealing with optimal filtering of continuous systems associated with sampled measurements, is to introduce finite jumps into the continuous dynamics.…”

Section: Introductionmentioning

confidence: 99%

Optimal sequential fusion estimation for sampled‐data systems with random delays and multiplicative noise

Wang,

Han,

et al. 2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article is concerned with the optimal sequential fusion filter design issue for a class of sampled‐data systems subject to random transmission delays and multiplicative noise. The considered delay is modeled as a multistate Markov chain, while the multiplicative noise corrupting the observation measurements is assumed be a white noise sequence with known statistical properties. First, by reorganizing the original measurements, the solvability of the addressed problem is cast into the feasibility of an optimal estimation issue for the hybrid Markovian jump system without delays. Then, an optimal sequential fusion filter is constructed by utilizing a dynamic model with finite jumps on basis of the arrival order of measurements from different sensors, guaranteeing estimation performances both at sampling instants and within the sampling intervals. Moreover, the desired estimator gains can be computed via solving a series of coupled differential Riccati equations with jumps and coupled differential Lyapunov equations. Further, a stationary sequential fusion filter is achieved with the guarantee of the exponentially mean‐square stability. Finally, two simulation examples are provided to verify the validity of the proposed algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal sequential fusion estimation for sampled‐data systems with random delays and multiplicative noise

Wang,

Han,

et al. 2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…Such control systems involve both continuous-time and discrete-time signals in continuous-time framework are referred to as sampled-data systems. Considerable research efforts have been made on various aspects of sampled-data systems, such as control systems [10][11][12] and filtering problems [13][14][15]. It is worth mentioning that little progress has been made to design controllers for uncertain sampled-data systems to make the output to track the reference signal without steady-state error, although it is of both theoretical significance and practical importance.…”

Section: Introductionmentioning

confidence: 99%

Reliable Robust Sampled-Data H∞ Output Tracking Control with Application to Flight Control

Wang

Zhou

Wang

et al. 2014

ITC

View full text Add to dashboard Cite

This paper is concerned with the problem of robust H ∞ output tracking control for uncertain sampleddata systems with probabilistic actuator failures. By assuming that each actuator fault takes values randomly in a finite set, a new actuator-failure-mode is proposed. Lyapunov-Krasovskii functional combined with the input delay approach as well as the free-weighting matrix approach are employed to establish the H ∞ performance, and the controller design is cast into a convex optimization problem with linear matrix inequality (LMI) constraints. The designed reliable controller can guarantee that the output of the closed-loop sampled-data system tracks the reference signal without steady-state error. An airship model is considered in this paper and its simulation results are given.

show abstract

Variance‐constrained robust estimation for uncertain systems with multiple packet dropouts

Wang

Guo

Yue

2011

Optim Control Appl Methods

View full text Add to dashboard Cite

SUMMARYThis paper deals with variance-constrained filtering problem in networked control systems (NCSs) with multiple packet dropouts. The system is subject to time-invariant norm-bounded parameter uncertainties in both the state and measurement matrices. Based on a model of multiple packet dropouts, the consecutive packet losses rate is transformed into a stochastic parameter in the system representation. The problem addressed is to design a linear filter such that, for all admissible parameter uncertainties and consecutive packet dropouts, the error state of the filtering process is mean square bounded, and the steady-state variance of the estimation error for each state is not more than the individual prescribed upper bound. The conditions for the existence of a desired filter are obtained in terms of the solutions of two algebraic matrix inequalities, based on which an explicit expression of the robust filter is derived. Finally, an algorithm for solving the coupling inequalities, which is not optimal but practical, is provided and applied to some numerical examples. Copyright

show abstract

Sampled-data filtering with error covariance assignment

Cited by 37 publications

References 16 publications

Optimal sequential fusion estimation for sampled‐data systems with random delays and multiplicative noise

Optimal sequential fusion estimation for sampled‐data systems with random delays and multiplicative noise

Reliable Robust Sampled-Data H∞ Output Tracking Control with Application to Flight Control

Variance‐constrained robust estimation for uncertain systems with multiple packet dropouts

Contact Info

Product

Resources

About