Moving Horizon Estimation of Networked Nonlinear Systems With Random Access Protocol

Abstract

This work is concerned with the moving horizon (MH) estimation issue for a type of networked nonlinear systems (NNSs) with the so-called random access (RA) protocol scheduling effects. To handle the signal transmissions between sensor nodes and the MH estimator, a constrained communication channel is employed whose channel constraints implies that, at each time instant, only one sensor node is permitted to access the communication channel and then send its measurement data. The RA protocol, whose scheduling behavior is characterized by a discrete-time Markov chain (DTMC), is utilized to orchestrate the access sequence of sensor nodes. By extending the robust MH estimation method, a novel nonlinear MH estimation scheme and the corresponding approximate MH estimation scheme are developed to cope with the state estimation task. Subsequently, some sufficient conditions are established to guarantee that the estimation error is exponentially ultimately bounded in mean square. Based on that, the main results are further specialized to linear systems with the RA protocol scheduling. Finally, two numerical examples and the corresponding figures are provided to verify the effectiveness/correctness of the developed MH estimation scheme and approximate MH estimation scheme.

Non‐fragile set‐membership estimation for sensor‐saturated memristive neural networks via weighted try‐once‐discard protocol

“…(i) A non‐fragile estimation scheme has been designed that can provide a robust estimation mechanism against the various interferences; (ii) a sufficient condition has been proposed to guarantee that the estimation error of concerned estimation for SSMNNs has been confined in an optimal ellipsoid; (iii) the estimator gain matrices can be obtained based on the feasible solutions to some recursive matrix inequalities and (iv) some comparisons have been provided to demonstrate the validity of the developed non‐fragile set‐membership estimation method. Further extensions include the discussions on the set‐membership estimation based on adaptive mechanism as in [42] and the moving‐horizon estimation problem for MNNs under different protocol scheduling effects as in [43–45].…”

Bias compensation‐based parameter and state estimation for a class of time‐delay non‐linear state‐space models

“…Parameter estimation, state estimation and filtering are important in the area of control. Recently, Zou et al presented the moving horizon estimation algorithms for networked time‐delay systems under round‐robin protocol [30] and for networked non‐linear systems with random access protocol [31], considering unknown inputs under dynamic quantisation effects [32]. In practice, there is a need for estimation of a class of non‐linear time‐delay state‐space model with coloured noise.…”

Moving horizon estimation with multirate measurements and correlated noises

“…[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Among others, the MHE (also called receding-horizon estimation) is one of the mostly applied algorithms dealing with complex nonlinear and/or constrained systems in industrial applications. 31 In a large number of real-world systems, it is quite common that different measurement signals are sampled with different rates, and the resulting multirate measurements are justified by the following two main reasons. The first reason is the limits of the measurement capabilities of physical sensors.…”

“…Typical state estimation schemes include, but are not limited to, the recursive state estimation, state estimation, set‐membership estimation and moving horizon estimation (MHE) approaches 16‐30 . Among others, the MHE (also called receding‐horizon estimation) is one of the mostly applied algorithms dealing with complex nonlinear and/or constrained systems in industrial applications 31 …”