Abstract
This paper studies a moving horizon estimation approach to solve the constrained state estimation problem for uncertain networked systems with random packet loss. The system model error range is known, and the packet loss phenomena are modeled by a binary switching random sequence. Taking the model error, the packet loss, the system constraints, and the network transmission noise into account, a time-varying weight matrix is obtained by solving a least-square problem. Then, a robust moving horizon estimator is designed to estimate the system state by minimizing an optimization problem with an arrival cost function. The proposed estimator ensures that the optimal estimated state can be obtained in the worst case. Furthermore, the asymptotic convergence of the estimator is analyzed and some sufficient conditions for convergence are given. Finally, the validity of the proposed approach can be demonstrated by numerical simulations.
Highlights
Most networked control systems (NCSs) implement control strategies based on the state feedback
In [7], the problem of optimal H∞ filtering for network systems with multiple packet loss is studied by modeling the phenomena of packet loss as a Bernoulli distribution and transforming the probability of random packet loss into the stochastic parameters of the system
Based on the above analysis, it is assumed that the packet loss phenomena are modeled by a binary switching random sequence, and the system model error range is known. is paper designs a new Moving horizon estimation (MHE) estimator to solve the problem of state estimation for network systems with packet loss and system uncertainty. e proposed method retains the quadratic optimization part of the MHE problem, and the weight matrix of the optimization objective is set as a time-varying matrix and obtained by solving a least-square function
Summary
Most networked control systems (NCSs) implement control strategies based on the state feedback (see, e.g., [1, 2]). The H∞ filtering problem for linear discrete-time systems with time-varying norm-bounded parameter uncertainties is studied in [8], and an estimator is designed to guarantee the estimation error to be quadratic stable. Is paper designs a new MHE estimator to solve the problem of state estimation for network systems with packet loss and system uncertainty. E proposed method retains the quadratic optimization part of the MHE problem, and the weight matrix of the optimization objective is set as a time-varying matrix and obtained by solving a least-square function. E emphasis of this paper mainly includes the following points: (1) a new robust MHE problem is studied in a unified framework including random packet loss sum, model error, transmission noise, and system constraints. For a column vector x, the notation ‖x‖2Y stands for xTYx, where Y is a symmetric positive-semidefinite matrix. e notation A+ denotes the pseudoinverse of matrix A. e matrix diags1, . . . , sn is a block diagonal with blocks sn
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