Event-triggered Transmissions Research Articles

Distributed Filtering for Complex Networks Under Multiple Event-Triggered Transmissions Within Node-Wise Communications

This paper focuses on the distributed filtering and fault estimation problems for a class of complex networks, where the communications between filters at different nodes are subject to dynamic event-triggered (DET) transmissions. A filter is constructed at each node by resorting to local measurements and information from neighboring nodes and thus the developed algorithm can be carried out distributedly. Different from the clock-driven signal transmissions in traditional distributed filtering schemes, the transmissions of both state estimates and the upper bounds of filtering error covariances (FECs) between the nodes are monitored by a multiple DET strategy to reduce unnecessary burdens in the links. Under DET transmissions, an upper bound of the FEC is obtained and then minimized via parameterizing the filter recursively. Novel sufficient conditions, which are dependent on locally available information, are provided to guarantee the uniform boundedness of the FEC at each node. The proposed method is used to solve the fault estimation problem in complex networks, where the estimation error is ensured to be exponentially bounded. Some illustrative examples are employed to show the effectiveness of our algorithm.

State Estimation with Event-Based Inputs Using Stochastic Triggers

Abstract Event-based communication and state estimation offer the potential to improve resource utilization in networked sensor and control systems significantly. Sensor nodes can trigger transmissions when data are deemed useful for the remote estimation units. To improve the estimation performance, the remote estimator can exploit the implicit information conveyed by the event trigger even if no transmission is triggered. The implicit information is typically incorporated into the measurement update of a remote Kalman filter. In this paper, event-triggered transmissions of input data are investigated that enter the prediction step of the remote estimator. By employing a stochastic trigger, the implicit input information remains Gaussian and can easily be incorporated into the remote Kalman filter. The proposed event-based scheme is evaluated in remote tracking scenarios, where system inputs are transmitted aperiodically.

UIO-based Fault Estimation for a Class of Time-varying Systems with Event-triggered Transmissions

This paper addresses the fault estimation problem for a class of discrete time-varying systems by resorting to the unknown input observer (UIO) technique. The system is assumed to be subject to unknown inputs and stochastic additive disturbances. The sensor can decide whether to send the current measurement to the filter according to the difference between the current measurement and the last transmitted one. A set of filters is obtained such that the effects of unknown inputs on the estimation results can be integrated into uncertainties with known bounds and each filter corresponds one possible fault. Upper bounds of the state and fault estimation error covariances are calculated in the simultaneous presence of the unknown inputs and event-triggered measurements, and then the filter gains are determined recursively to minimize the bounds. The residual matching method is employed to isolate the faults and the outputs of the filters can be used to realize fault estimation. The effectiveness of the proposed method is demonstrated by a simulation example.

Finite-horizon quantized [formula omitted] filter design for a class of time-varying systems under event-triggered transmissions

This paper is concerned with the finite-horizon quantized H∞ filter design problem for a class of time-varying systems with quantization effects and event-triggered measurement transmissions. A componentwise event-triggered transmission strategy is put forward to reduce the unnecessary communication burden for the purpose of energy efficiency. The transmitted measurements triggered according to prespecified events are quantized by a logarithmic quantizer. Special attention is paid to the design of the filter such that a prescribed H∞ performance can be guaranteed over a given finite horizon in the presence of nonlinearities, quantization effects and event-triggered transmissions. Two sets of Riccati difference equations are introduced to ensure the H∞ estimation performance of the designed filter. The filter design algorithm is recursive and thus suitable for online computation. A simulation example is illustrated to show the effectiveness of the proposed algorithm applied to the fault detection problem.

Event-based filtering for time-varying nonlinear systems subject to multiple missing measurements with uncertain missing probabilities

This paper is concerned with the recursive filtering problem for a class of time-varying nonlinear stochastic systems in the presence of event-triggered transmissions and multiple missing measurements with uncertain missing probabilities. The measurements from different sensors may undergo the missing phenomena, which are characterized by a set of mutually independent Bernoulli random variables and the missing probabilities could be uncertain. In addition, the event-triggered transmission mechanism is introduced to reduce the network communication burden, where the current measurement is transmitted to the remote filter only when it changes greatly compared with the previous one. The aim of this paper is to design a time-varying filter such that, in the presence of the multiple missing measurements, event-triggered transmission mechanism and stochastic nonlinearities, an upper bound of the filtering error covariance is obtained and then minimized by properly designing the filter gain. The explicit form of the filter gain is given in terms of the solutions to two recursive matrix equations. It is shown that the developed filtering scheme is of a recursive form applicable for the online computations. Finally, we provide two illustrative examples to demonstrate the feasibility and applicability of the developed event-triggered filtering scheme.

Event-triggered filtering and fault estimation for nonlinear systems with stochastic sensor saturations

ABSTRACTThis paper is concerned with the filtering problem for a class of nonlinear systems with stochastic sensor saturations and event-triggered measurement transmissions. An event-triggered transmission scheme is proposed with hope to ease the traffic burden and improve the energy efficiency. The measurements are subject to randomly occurring sensor saturations governed by Bernoulli-distributed sequences. Special effort is made to obtain an upper bound of the filtering error covariance in the presence of linearisation errors, stochastic sensor saturations as well as event-triggered transmissions. A filter is designed to minimise the obtained upper bound at each time step by solving two sets of Riccati-like matrix equations, and thus the recursive algorithm is suitable for online computation. Sufficient conditions are established under which the filtering error is exponentially bounded in mean square. The applicability of the presented method is demonstrated by dealing with the fault estimation problem. An illustrative example is exploited to show the effectiveness of the proposed algorithm.

Fault Detection and Diagnosis for a Class of Nonlinear Systems with Decentralized Event-triggered Transmissions

In this paper, the fault detection and diagnosis problems are considered for a class of discrete nonlinear systems with decentralized event-triggered measurement transmissions. Each sensor determines, according to certain triggering rules, whether to transmit the present measurement to remote filters based on only locally available information. A set of filters is designed where each filter aims to jointly estimate the system states and a specific possible fault. Upper bounds of the estimation error covariances are obtained in the simultaneous presence of the linearization errors and decentralized event-triggered transmissions, and then the filter gains are calculated to minimize such bounds. The filters are designed in a recursive way and thus the algorithm is applicable for online implementation. When a fault is detected, the filter with the least residual is regarded as the one corresponding to the actual fault and its output can be seen as the states and fault estimation. The effectiveness of the proposed method is illustrated by a simulation example.

Design and Verification of Hardware Building Blocks for High-Speed and Fault-Tolerant In-Vehicle Networks

This paper presents the design, implementation, and validation of a FlexRay transceiver and a SpaceWire (SpW) router and interface, which constitute the main hardware building blocks of the two in-vehicle communication standards. The FlexRay protocol features data rates up to 10 Mb/s and time- and event-triggered transmissions, along with scalable fault-tolerance support, and it is expected to become the standard network for X-by-wire and active safety automotive systems. However, collision avoidance and driver-assistance applications based on camera/radar sensors require data rates up to hundreds of megabits per second as well as fault tolerance, features that can hardly be covered by current or expected automotive standards. In this scenario, a promising technology seems to be the new SpW protocol, currently used in avionics and aerospace.