Quantized output feedback control for networked control systems
Information Sciences: an International Journal
Brief paper: Optimal linear estimation for systems with multiple packet dropouts
Automatica (Journal of IFAC)
H∞ filtering of networked discrete-time systems with random packet losses
Information Sciences: an International Journal
Networked data fusion with packet losses and variable delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust filtering with randomly varying sensor delay: the finite-horizon case
IEEE Transactions on Circuits and Systems Part I: Regular Papers
IEEE Transactions on Signal Processing
Networked H∞ filtering for linear discrete-time systems
Information Sciences: an International Journal
IEEE Transactions on Signal Processing
State estimation with asynchronous multi-rate multi-smart sensors
Information Sciences: an International Journal
Brief paper: Optimal estimation of linear discrete-time systems with stochastic parameters
Automatica (Journal of IFAC)
IEEE Transactions on Signal Processing
Data-driven predictive control for networked control systems
Information Sciences: an International Journal
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In networked control systems, random delays and packet dropouts are inevitable during data transmissions due to the limited communication capacity. The phenomena of random delays and packet dropouts of both sides from a sensor to an estimator and from a controller to an actuator are modeled via employing two groups of Bernoulli random variables. By defining some new variables, the original system with random delays and packet dropouts is equivalently transformed into a stochastic parameterized system. The optimal linear filter, predictor and smoother in the linear minimum variance sense are presented via the orthogonality projection approach. They depend on the probabilities of the stochastic parameters. The solutions to the optimal linear estimators are given by three equations including a Riccati, a Lyapunov and a simple difference. The stability of the proposed estimators is analyzed. At last, a sufficient condition for the existence of the steady-state estimators is given for time-invariant systems. Two examples are provided to verify the effectiveness of the proposed algorithms.