32 research outputs found

    Network-Based Event-Triggered Control for Singular Systems With Quantizations

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    Co-design of output feedback laws and event-triggering conditions for linear systems

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    We present a procedure to simultaneously design the output feedback law and the event-triggering condition to stabilize linear systems. The closed-loop system is shown to satisfy a global asymptotic stability property and the existence of a strictly positive minimum amount of time between two transmissions is guaranteed. The event-triggered controller is obtained by solving linear matrix inequalities (LMIs). We then exploit the flexibility of the method to maximize the guaranteed minimum amount of time between two transmissions. Finally, we provide a (heuristic) method to reduce the amount of transmissions, which is supported by numerical simulations

    Event-triggered proportional-derivative control for nonlinear network systems with a novel event-triggering scheme: Differential of triggered state consideration

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    This article proposes event-triggered proportional-derivative control for a class of nonlinear network control systems. For derivative action of the proposed proportional-derivative control, a novel event-triggering scheme is devised together with the control that considers a differential of a triggered state. The class of the nonlinear network systems is represented as a Lur'e system to consider various nonlinear cases. Time varying transmission delay is considered which can be defined by lower and upper delay bounds. The proposed proportional-derivative control is designed by Lyapunov-Krasovskii stability analysis, and the design condition is presented by linear matrix inequalities. The proposed event-triggered proportional-derivative control and event-triggering condition are verified with numerical simulation. ? 2017 The Author(s).111Ysciescopu

    Event-Triggered Static Output Feedback Simultaneous H∞ Control for a Collection of Networked Control Systems

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    This chapter considers the design of event-triggered static output feedback simultaneous H∞ controllers for a collection of networked control systems (NCSs). It is shown that conventional point-to-point wiring delayed static output feedback simultaneous H∞ controllers can be obtained by solving linear matrix inequalities (LMIs) with a linear matrix equality (LME) constraint. Based on an obtained simultaneous H∞ controller, an L2-gain event-triggered transmission policy is proposed for reducing the network usage. An illustrative example is presented to verify the obtained theoretical results

    Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information

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    The paper investigates the state estimation problem for a class of recurrent neural networks with sampled-data information and time-varying delays. The main purpose is to estimate the neuron states through output sampled measurement; a novel event-triggered scheme is proposed, which can lead to a significant reduction of the information communication burden in the network; the feature of this scheme is that whether or not the sampled data should be transmitted is determined by the current sampled data and the error between the current sampled data and the latest transmitted data. By using a delayed-input approach, the error dynamic system is equivalent to a dynamic system with two different time-varying delays. Based on the Lyapunov-krasovskii functional approach, a state estimator of the considered neural networks can be achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. Finally, a numerical example is provided to show the effectiveness of the proposed event-triggered scheme

    Dual-terminal event triggered control for cyber-physical systems under false data injection attacks

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    summary:This paper deals with the problem of security-based dynamic output feedback control of cyber-physical systems (CPSs) with the dual-terminal event triggered mechanisms (DT-ETM) under false data injection (FDI) attacks. Considering the limited attack energy, FDI attacks taking place in transmission channels are modeled as extra bounded disturbances for the resulting closed-loop system, thus enabling HH_{\infty} performance analysis with a suitable ϱ\varrho attenuation level. Then two buffers at the controller and actuator sides are skillfully introduced to cope with the different transmission delays in such a way to facilitate the subsequent security analysis. Next, a dynamic output feedback security control (DOFSC) model based on the DT-ETM schemes under FDI attacks is well constructed. Furthermore, novel criteria for stability analysis and robust stabilization are carefully derived by exploiting Lyapunov-Krasovskii theory and LMIs technique. Finally, an illustrative example is provided to show the effectiveness of the proposed method

    Mobile robot localization under stochastic communication protocol

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    summary:In this paper, the mobile robot localization problem is investigated under the stochastic communication protocol (SCP). In the mobile robot localization system, the measurement data including the distance and the azimuth are received by multiple sensors equipped on the robot. In order to relieve the network burden caused by network congestion, the SCP is introduced to schedule the transmission of the measurement data received by multiple sensors. The aim of this paper is to find a solution to the robot localization problem by designing a time-varying filter for the mobile robot such that the filtering error dynamics satisfies the HH_{\infty} performance requirement over a finite horizon. First, a Markov chain is introduced to model the transmission of measurement data. Then, by utilizing the stochastic analysis technique and completing square approach, the gain matrices of the desired filter are designed in term of a solution to two coupled backward recursive Riccati equations. Finally, the effectiveness of the proposed filter design scheme is shown in an experimental platform

    Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey

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    summary:Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research
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