Qinyuan Liu

Bio: Qinyuan Liu is an academic researcher from Tongji University. The author has contributed to research in topics: Wireless sensor network & Filter (signal processing). The author has an hindex of 14, co-authored 33 publications receiving 1101 citations. Previous affiliations of Qinyuan Liu include Tsinghua University.

Event-Based Recursive Distributed Filtering Over Wireless Sensor Networks

Abstract: In this technical note, the distributed filtering problem is investigated for a class of discrete time-varying systems with an event-based communication mechanism. Each intelligent sensor node transmits the data to its neighbors only when the local innovation violates a predetermined Send-on-Delta (SoD) data transmission condition. The aim of the proposed problem is to construct a distributed filter for each sensor node subject to sporadic communications over wireless networks. In terms of an event indicator variable, the triggering information is utilized so as to reduce the conservatism in the filter analysis. An upper bound for the filtering error covariance is obtained in form of Riccati-like difference equations by utilizing the inductive method. Subsequently, such an upper bound is minimized by appropriately designing the filter parameters iteratively, where a novel matrix simplification technique is developed to handle the challenges resulting from the sparseness of the sensor network topology and filter structure preserving issues. The effectiveness of the proposed strategy is illustrated by a numerical simulation.

A survey of event-based strategies on control and estimation

Abstract: The event-based strategies have recently received considerable research attention due primarily to their irreplaceable superiority in resource-constrained systems. Compared with the widely adopted time-driven schemes, such novel event-based schemes have advantages of improving the efficiency in resource utilization in many real applications. Event-based strategies represent an effective way of generating sporadic executions, where an execution is generated only when a specific event (e.g. a certain signal exceeds a prescribed threshold) arises. In this survey, we aim to summarize the results available in the literature on event-based strategies so as to promote the related research in this realm. The progress of the event-based design and analysis strategies is systematically reviewed in both control and estimation domains. Specifically, the event-based control strategies have been discussed for networked control systems, multi-agent systems and other systems, and the event-based estimation schemes have b...

REVIEW A survey of event-based strategies on control and estimation

Abstract: The event-based strategies have recently received considerable research attention due primarily to their irreplaceable superiority in resource-constrained systems. Compared with the widely adopted time-driven schemes, such novel event-based schemes have advantages of improving the efficiency in resource utilization in many real applications. Event-based strategies represent an effective way of generating sporadic executions, where an execution is generated only when a specific event (e.g. a certain signal exceeds a prescribed threshold) arises. In this survey, we aim to summarize the results available in the literature on event-based strategies so as to promote the related research in this realm. The progress of the event-based design and analysis strategies is systematically reviewed in both control and estimation domains. Specifically, the event-based control strategies have been discussed for networked control systems, multi-agent systems and other systems, and the event-based estimation schemes have been highlighted according to the send-on-delta and send-on-area concepts. Some potential future research directions are finally pointed out for event-based strategies.

On Kalman-Consensus Filtering With Random Link Failures Over Sensor Networks

Abstract: This paper is concerned with the distributed state estimation problem over wireless sensor networks. The communication links are unreliable that are subject to random link failures modeled as a set of independent Bernoulli processes. To estimate the plant state collaboratively, a Kalman-consensus filtering approach is developed where the sensors spread the local information obtained from the Kalman filtering algorithm by performing a consensus of the inverse covariance matrices at each time instant. Sufficient conditions for the stochastic boundedness of the Kalman-consensus filter are established. It is shown that the filtering performance is directly influenced by the network connectivity and the collective observability. A numerical example is illustrated to verify the proposed results.

Event-Based $H_{\infty}$ Consensus Control of Multi-Agent Systems With Relative Output Feedback: The Finite-Horizon Case

Abstract: In this technical note, the $H_{\infty}$ consensus control problem is investigated over a finite horizon for general discrete time-varying multi-agent systems subject to energy-bounded external disturbances. A decentralized estimation-based output feedback control protocol is put forward via the relative output measurements. A novel event-based mechanism is proposed for each intelligent agent to utilize the available information in order to decide when to broadcast messages and update control input. The aim of the problem addressed is to co-design the time-varying controller and estimator parameters such that the controlled multi-agent systems achieve consensus with a disturbance attenuation level $\gamma$ over a finite horizon $[0,T]$ . A constrained recursive Riccati difference equation approach is developed to derive the sufficient conditions under which the $H_{\infty}$ consensus performance is guaranteed in the framework of event-based scheme. Furthermore, the desired controller and estimator parameters can be iteratively computed by resorting to the Moore-Penrose pseudo inverse. Finally, the effectiveness of the developed event-based $H_{\infty}$ consensus control strategy is demonstrated in the numerical simulation.

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

Abstract: 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.

Recent Advances in Consensus of Multi-Agent Systems: A Brief Survey

Abstract: In this paper, we mainly review the topics in consensus and coordination of multi-agent systems, which have received a tremendous surge of interest and progressed rapidly in the past few years. Focusing on different kinds of constraints on the controller and the self-dynamics of each individual agent, as well as the coordination schemes, we categorize the recent results into the following directions: consensus with constraints, event-based consensus, consensus over signed networks, and consensus of heterogeneous agents. We also review some applications of the very well developed consensus algorithms to the topics such as economic dispatch problem in smart grid and k -means clustering algorithms.

An Overview and Deep Investigation on Sampled-Data-Based Event-Triggered Control and Filtering for Networked Systems

Abstract: This paper provides an overview and makes a deep investigation on sampled-data-based event-triggered control and filtering for networked systems. Compared with some existing event-triggered and self-triggered schemes, a sampled-data-based event-triggered scheme can ensure a positive minimum inter-event time and make it possible to jointly design suitable feedback controllers and event-triggered threshold parameters. Thus, more attention has been paid to the sampled-data-based event-triggered scheme. A deep investigation is first made on the sampled-data-based event-triggered scheme. Then, recent results on sampled-data-based event-triggered state feedback control, dynamic output feedback control, $H_\infty$ filtering for networked systems are surveyed and analyzed. An overview on sampled-data-based event-triggered consensus for distributed multiagent systems is given. Finally, some challenging issues are addressed to direct the future research.