This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Feedback systems: Input-output properties

Neural networks

Microgrids consist of multiple parallel-connected distributed generation (DG) units with coordinated control strategies, which are able to operate in both grid-connected and islanded modes Microgrids are attracting considerable attention since they can alleviate the stress of main transmission systems, reduce feeder losses, and improve system power quality When the islanded microgrids are concerned, it is important to maintain system stability and achieve load power sharing among the multiple parallel-connected DG units However, the poor active and reactive power sharing problems due to the influence of impedance mismatch of the DG feeders and the different ratings of the DG units are inevitable when the conventional droop control scheme is adopted Therefore, the adaptive/improved droop control, network-based control methods, and cost-based droop schemes are compared and summarized in this paper for active power sharing Moreover, nonlinear and unbalanced loads could further affect the reactive power sharing when regulating the active power, and it is difficult to share the reactive power accurately only by using the enhanced virtual impedance method Therefore, the hierarchical control strategies are utilized as supplements of the conventional droop controls and virtual impedance methods The improved hierarchical control approaches such as the algorithms based on graph theory, multi-agent system, the gain scheduling method, and predictive control have been proposed to achieve proper reactive power sharing for islanded microgrids and eliminate the effect of the communication delays on hierarchical control Finally, the future research trends on islanded microgrids are also discussed in this paper

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Review of Active and Reactive Power Sharing Strategies in Hierarchical Controlled Microgrids

Technical communique: Network-based feedback control for systems with mixed delays based on quantization and dropout compensation

This paper studies the problem of predictive output feedback control for networked control systems (NCSs) with random communication delays A networked-predictive-control scheme is employed to compensate for the network-induced delay Furthermore, the time-varying predictive controller with mixed random delays for networked systems is introduced Then, the system is formulated as a Markovian jump system New techniques are presented to deal with the distributed delay in the discrete-time domain Based on the analysis of closed-loop NCSs, the designed predictive time-varying output feedback controller can guarantee system stability Simulation example demonstrates the compensation for random communication delays and data loss in networked systems using the proposed predictive scheme

Predictive Output Feedback Control for Networked Control Systems

In this technical note, a new class of discrete-time networked nonlinear systems with mixed random delays and packet dropouts is introduced, and the H∞ filtering problem for such systems is investigated The mixed stochasitc time-delays consist of both discrete and infinite distributed delays and the packet dropout phenomenon occurs in a random way Furthermore, new techniques are presented to deal with the infinite distributed delay in the discrete-time domain Sufficient conditions for the existence of an admissible filter are established, which ensure the asymptotical stability as well as a prescribed H∞ performance Finally, examples are given to demonstrate the effectiveness of the proposed filter design scheme in this technical note

Filtering for Discrete-Time Networked Nonlinear Systems With Mixed Random Delays and Packet Dropouts

This brief addresses the stability analysis problem for stochastic neural networks (SNNs) with discrete interval and distributed time-varying delays. The interval time-varying delay is assumed to satisfy 0 < d1 ? d(t) ? d2 and is described as d(t) = d 1+h(t) with 0 ? h(t) ? d 2 - d 1. Based on the idea of partitioning the lower bound d 1, new delay-dependent stability criteria are presented by constructing a novel Lyapunov-Krasovskii functional, which can guarantee the new stability conditions to be less conservative than those in the literature. The obtained results are formulated in the form of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the effectiveness and less conservatism of the developed results.

Exponential Stability on Stochastic Neural Networks With Discrete Interval and Distributed Delays

In this paper, the problem of asymptotic stability for stochastic Hopfield neural networks (HNNs) with time delays is investigated. New delay-dependent stability criteria are presented by constructing a novel Lyapunov-Krasovskii functional. Moreover, the results are further extended to the delayed stochastic HNNs with parameter uncertainties. The main idea is based on the delay partitioning technique, which differs greatly from most existing results and reduces conservatism. Numerical examples are provided to illustrate the effectiveness and less conservativeness of the developed techniques.

Rongni Yang

Papers

Technical communique: Network-based feedback control for systems with mixed delays based on quantization and dropout compensation

Predictive Output Feedback Control for Networked Control Systems

Filtering for Discrete-Time Networked Nonlinear Systems With Mixed Random Delays and Packet Dropouts

Exponential Stability on Stochastic Neural Networks With Discrete Interval and Distributed Delays

Novel Robust Stability Criteria for Stochastic Hopfield Neural Networks With Time Delays