Distributed H ∞ state estimation for stochastic delayed 2-D systems with randomly varying nonlinearities over saturated sensor networks

In this paper, the distributed H ∞ state estimation problem is investigated for the two-dimensional (2-D) time-delay systems. The target plant is characterized by the generalized Fornasini-Marchesini 2-D equations where both stochastic disturbances and randomly varying nonlinearities (RVNs) are considered. The sensor measurement outputs are subject to saturation restrictions due to the physical limitations of the sensors. Based on the available measurement outputs from each individual sensor and its neighboring sensors, the main purpose of this paper is to design distributed state estimators such that not only the states of the target plant are estimated but also the prescribed H ∞ disturbance attenuation performance is guaranteed. By defining an energy-like function and utilizing the stochastic analysis as well as the inequality techniques, sufficient conditions are established under which the augmented estimation error system is globally asymptotically stable in the mean square and the prescribed H ∞ performance index is satisfied. Furthermore, the explicit expressions of the individual estimators are also derived. Finally, numerical example is exploited to demonstrate the effectiveness of the results obtained in this paper

Optimal Experiment Design for Dynamic System Identification.

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Stochastic Approximation Control of Time Varying Non- Minimum Phase Systems

No abstract available

Strong consistency of a class of recursive stochastic gradient algorithms

In this work, strong consistency results for a class of stochastic gradient algorithms are developed where the system is not necessarily minimum-phase or stable and is assumed to be of the CARMA form. The analysis uses only standard martingale results, and strong consistency is shown to hold under certain persistent excitation conditions

Globally convergent adaptive pole assignment for a class of non-minimum-phase stochastic systems

A key difficulty of the explicit approach to self-tuning control—both theoretically and computationally—is the need to solve a polynomial identity to generate the required controller coefficients. For systems with uncorrelated output noise, however, the identity has a simple solution, and in this paper the implications of this phenomenon are discussed in relation to self-tuning regulation. A suitable explicit algorithm is introduced, and it is shown that, under certain conditions, global stability and system identifiability can be established without recourse to sophisticated estimator management techniques

Stochastic Approximation Control of Time Varying Non- Minimum Phase Systems

No abstract available