Event-triggered State Estimation Research Articles

Estimation and Control of Positive Complex Networks Using Linear Programming

This paper focuses on event-triggered state estimation and control of positive complex networks. An event-triggered condition is provided for discrete-time complex networks by which an event-based state estimator and an estimator-based controller are designed through matrix decomposition technology. Thus, the system is converted to an interval uncertain system. The positivity and the L1-gain stability of complex networks are ensured by resorting to a co-positive Lyapunov function. All conditions are solvable in terms of linear programming. Finally, the effectiveness of the proposed state estimator and controller are verified by a numerical example. The main contributions of this paper are as follows: (i) A positive complex network framework is constructed based on an event-triggered strategy, (ii) a new state estimator and an estimator-based controller are proposed, and (iii) a simple analysis and design approach consisting of a co-positive Lyapunov function and linear programming is presented for positive complex networks.

Finite-time decentralized event-triggered state estimation for coupled neural networks under unreliable Markovian network against mixed cyberattacks

Abstract This article investigates the issue of finite-time state estimation in coupled neural networks under random mixed cyberattacks, in which the Markov process is used to model the mixed cyberattacks. To optimize the utilization of channel resources, a decentralized event-triggered mechanism is adopted during the information transmission. By establishing the augmentation system and constructing the Lyapunov function, sufficient conditions are obtained for the system to be finite-time bounded and satisfy the $H_{\infty}$ performance index. Then, under these conditions, a suitable state estimator gain is obtained. Finally, the feasibility of the method is verified by a given illustrative example.

Dynamic Event-Triggered State Estimation for Power Harmonics With Quantization Effects: A Zonotopic Set-Membership Approach

Dynamic Event-Triggered State Estimation for Power Harmonics With Quantization Effects: A Zonotopic Set-Membership Approach

Dynamic event-triggered state estimation for discrete-time delayed switched neural networks with constrained bit rate

In this paper, a class of discrete-time delayed switched neural networks with dynamic event-triggered mechanism (DETM) and constrained bit rate is considered. In order to reduce the transmission frequency and alleviate the unnecessary resource loss between sensor and estimator, a DETM is proposed. The data transmission from sensor to estimator is realized through constrained bit rate channel. Therefore, in order to reflect the bandwidth allocation rules of accessible neurone nodes, a bit rate constraint model is introduced and an encoding-decoding mechanism is developed. This paper is concerned with the strategy of average dwell time (ADT) and linear matrix inequality, then sufficient conditions for the exponential ultimate boundedness of switched neural networks with DETM and constrained bit rate are proposed. Finally, an example is given to prove the effectiveness of the results.

Observer‐based dual event‐triggered control for non‐weighted L2$$ {\mathcal{L}}_2 $$‐gain of switched linear systems

AbstractAs everyone knows, strict mode‐dependent event‐triggered mechanism (MDETM) is rarely studied for switched systems due to the difficulty in settling down the relationship between the dwell time (DT) and triggered intervals. This problem is satisfactorily solved by considering the exponential stabilization via observer‐based dual MDETMs in switched linear systems. The dual MDETMs on the sensor‐controller (S‐C) channel and controller‐actuator (C‐A) channel are designed respectively according to mode‐dependent average dwell time (MDADT). The MDETMs on the C‐A channel are generated based on the MDETMs on the S‐C channel, which makes the new control technique strict mode‐dependent and reduces conservatism as much as possible. Furthermore, when there exists external disturbance, a non‐weighted ‐gain with better performance than existing results is given. By studying an augmented system, exponential stabilization is achieved through a new event‐triggered state estimation (ETSE) and dual MDETMs. The observer gain, controller gain, and non‐weighted ‐gain are derived through an effective algorithm. Finally, two numerical examples are given to illustrate the effectiveness of theoretical research.

Event-Triggered State Estimation and Control for Networked Nonlinear Systems Under Dynamic Sparse Attacks

Event-Triggered State Estimation and Control for Networked Nonlinear Systems Under Dynamic Sparse Attacks

Event-Triggered State and Disturbance Estimation for Lipschitz Nonlinear Systems With Unknown Time-Varying Delays.

We consider the event-triggered state and disturbance simultaneous estimation problem for Lipschitz nonlinear systems with an unknown time-varying delay in the state vector. For the first time, state and disturbance can be robustly estimated by using an event-triggered state observer. Our method uses only information of the output vector when an event-triggered condition is satisfied. This contrasts with previous methods of simultaneous state and disturbance estimation based on augmented state observers where the information of the output vector was assumed to be always continuously available. This salient feature, thus, lessens the stress on communication resources while can still maintain an acceptable estimation performance. First, to solve the new problem of event-triggered state and disturbance estimation, and to tackle unknown time-varying delays, we propose a novel event-triggered state observer and establish a sufficient condition for its existence. Then to overcome some technical difficulties in synthesizing observer parameters, we introduce some algebraic transformations and use inequalities, such as the Cauchy matrix inequality and the Schur complement lemma to establish a convex optimization problem in which observer parameters and optimal disturbance attenuation levels can be systematically derived. Finally, we demonstrate the applicability of the method by using two numerical examples.

Event-triggered state and fault simultaneous estimation for nonlinear systems with time delays

This paper addresses the problem of event-triggered robust state and fault simultaneous estimation for nonlinear time-delay systems subject to actuator and sensor unknown disturbances. Based on a fault decomposition technique and some basic mathematical transformations, we obtain an augmented system where the state vector consists of the variable of the original system and the fault. Then a novel event-triggered state observer for the augmented system is proposed to robustly estimate the variable of the original system and the fault. We next established a sufficient condition for the existence of such an observer. We translated it into a linear matrix inequality (LMI), which can be effectively solved using the MATLAB LMI Control Toolbox. Finally, an illustrative example is applied to test the proposed method.

Event-Triggered State Estimation for Networked Switched Systems: An Output Predictor Approach

In this article, we investigate the event-triggered state estimation for networked switched systems with (a)synchronous switching. A new multimode output predictor-based sampled-data observer and an event-triggered mechanism are proposed. Between event-triggered sampling times, an output predictor is utilized to compensate for the impact of sampling, and at the sampling instants, the predicted output is reset with the updated sampled output. Two scenarios are considered: the observer's switching signal is synchronous with the system's switching signal, and the switching signals among them are asynchronous, and frequent switching is allowed during interevent intervals. Average dwell time and linear matrix inequalities techniques are employed to guarantee the global uniform exponential convergence of the proposed observer. Moreover, Zeno phenomenon can be ruled out by proving the existence of a positive lower bound of interevent intervals. Finally, the effectiveness of the proposed approach is illustrated by a circuit system and an academic example.

Event-triggered state estimation for complex networks under deception attacks: a partial-nodes-based approach

This paper addresses the issue of state estimation for a kind of complex network (CN) with distributed delays and random interference through output measurements. In the data transmission, the deception attacks are taken into account by resorting to a sequence of Bernoulli random variables with a given probability. Considering the complexity of the network, the fact that only partial output measurements are available in practical environments presents a new challenge. Therefore, the partial-nodes-based (PNB) state estimation problem is proposed. For the sake of data collision avoidance and energy saving, a general event-triggered scheme is adopted in the design of the estimator. A novel estimator is constructed to consider both cyber attacks and resource limitations, filling the gap in previous results on PNB state estimation. By using the Lyapunov method and several stochastic analysis techniques, a few sufficient conditions are derived to guarantee the desired security and convergency performance for the overall estimation error. The estimator gains are obtained by solving a set of matrix inequalities with nonlinear constraints. At last, two examples and simulations are presented to further show the efficiency of the proposed method.

Event-Triggered State Estimation for Uncertain Systems with Binary Encoding Transmission Scheme

This paper proposes an event-triggered state estimation method for parameter-uncertain systems with a binary encoding transmission scheme. Firstly, a binary encoding transmission scheme is introduced between the state estimator and the system to improve the efficiency of network communication. Secondly, an event-triggering mechanism (ETM) is designed to ensure the accuracy of state estimation and reduce the computational burden of the state estimator. At the event-triggered moments, considering the uncertainty of the system, the binary encoding transmission scheme, and the ETM, a moving horizon estimator (MHER) is designed using the robust least squares optimization method to obtain optimal state estimation. At the no-event-triggered moments, the state estimation of the system is computed based on an open-loop state estimator (OLER). Furthermore, stability analysis showed that the state estimation error of the proposed method is bounded. Finally, the practical value of the proposed in this paper is confirmed through numerical simulation.

Attack Detection for Networked Control Systems Using Event-Triggered Dynamic Watermarking

Dynamic watermarking schemes can enhance the cyber attack detection capability of networked control systems (NCSs). This paper presents a linear event-triggered solution to conventional dynamic watermarking (CDW) schemes. Firstly, the limitations of CDW schemes for event-triggered state estimation based NCSs are investigated. Secondly, a new event-triggered dynamic watermarking (ETDW) scheme is designed by treating watermarking as symmetric key encryption, based on the limit convergence theorem in probability. Its security property against the generalized replay attacks (GRAs) is also discussed in the form of bounded asymptotic attack power. Thirdly, finite sample ETDW tests are designed with matrix concentration inequalities. Finally, experimental results of a networked inverted pendulum system demonstrate the validity of our proposed scheme.

Dynamic Event-Triggered State Estimation for Markov Jump Neural Networks With Partially Unknown Probabilities.

This article focuses on the investigation of finite-time dissipative state estimation for Markov jump neural networks. First, in view of the subsistent phenomenon that the state estimator cannot capture the system modes synchronously, the hidden Markov model with partly unknown probabilities is introduced in this article to describe such asynchronization constraint. For the upper limit of network bandwidth and computing resources, a novel dynamic event-triggered transmission mechanism, whose threshold parameter is constructed as an adjustable diagonal matrix, is set between the estimator and the original system to avoid data collision and save energy. Then, with the assistance of Lyapunov techniques, an event-based asynchronous state estimator is designed to ensure that the resulting system is finite-time bounded with a prescribed dissipation performance index. Ultimately, the effectiveness of the proposed estimator design approach combining with a dynamic event-triggered transmission mechanism is demonstrated by a numerical example.

Event-triggered state estimation for cyber-physical systems with partially observed injection attacks

Event-triggered state estimation for cyber-physical systems with partially observed injection attacks

Dynamic event-triggered state estimation for time-delayed spatial-temporal networks under encoding-decoding scheme

This paper is concerned with the dynamic event-triggered state estimation problem for a class of spatial-temporal networks (STNs) with time-varying delays under an encoding-decoding strategy. For the sake of reducing the unnecessary resources wastes, we establish a dynamic event-triggered mechanism to determine whether the current measurement output data is transmitted to the filter, where the threshold is dynamically adjusted according to a certain rule. In order to enhance the robustness of signal transmission, an encoding-decoding strategy is exploited in the process of the data transmission. To be specific, the original signals encoded as a bit string are transmitted through binary symmetric channels with certain crossover probabilities and then restored by a decoder at the receiver. By constructing Lyapunov-Krasovskii functional, we obtain a sufficient condition to ensure that the estimation error system is exponential mean square ultimately bounded. Subsequently, the desired state estimator is designed in terms of the solution to a certain matrix inequality. Finally, a numerical example is shown to demonstrate that the proposed state estimator is valid for time-delayed STNs.

Dynamic Event-triggered State Estimation for Nonlinear Coupled Output Complex Networks Subject to Innovation Constraints

This letter investigates the recursive state estimation (RSE) problem for a class of coupled output complex networks via the dynamic event-triggered communication mechanism (DETCM) and innovation constraints (ICs). Firstly, a DETCM is employed to regulate the transmission sequences. Then, in order to improve the reliability of network communication, a saturation function is introduced to constrain the measurement outliers. A new RSE method is provided such that, for all output coupling, DETCM and ICs, an upper bound of state estimation error covariance (SEEC) is presented in a recursive form, whose trace can be minimized via parameterizing the state estimator gain matrix (SEGM). Moreover, the theoretical analysis is given to guarantee that the error dynamic is uniformly bounded. Finally, a simulation example is illustrated to show the effectiveness of the proposed RSE method.

Adaptive event-triggered state estimation for a class of stochastic complex networks subject to coding-decoding schemes and missing measurements

This paper is concerned with the adaptive event-triggered recursive state estimation (RSE) issue for a class of nonlinear complex dynamical networks (CDNs) with random coupling parameter, missing measurements (MMs) and coding-decoding-based communication mechanism (CDBCM). First of all, a random variable uniformly distributed in a fixed interval is adopted to model the varying topologies. Then, the Bernoulli random sequence with uncertain statistical properties is considered to characterize the phenomenon of MMs subject to the uncertain occurrence probability situation. Furthermore, in order to ensure the security and reliability of the shared network channel, the adaptive event-triggered scheduling strategy (AETSS) and CDBCM are both employed to govern the data transmission thereby enhancing the communication quality. The aim of this paper is to present an RSE scheme for a class of stochastic CDNs such that for all MMs, AETSS and CDBCM, the state estimation error covariance (SEEC) is given the SEEC upper bound (SEECUB) is derived. Then, the state estimator gain matrix (SEGM) is parameterized by means of optimizing the trace of SEECUB. Moreover, the monotonicity of the trace of SEECUB with respect to the available missing probability is clarified detailed. Finally, an illustrative simulation is executed for the purpose of verifying the validity of the proposed RSE scheme.

An adaptive gain based approach for event-triggered state estimation with unknown parameters and sensor nonlinearities over wireless sensor networks

The distributed state and parameter estimation problem is investigated in this paper for discrete-time nonlinear systems subject to sensor nonlinearities and stochastic disturbances over a wireless sensor network. A novel architecture for distributed state estimator is introduced that incorporates adaptive coupling gains to govern the information exchange between the sensor nodes under event-triggering mechanism. The aim of this paper is to provide a scalable structure for unknown parameter identification independent of sensor networks’ complexity. The boundedness of estimation error is ensured in the framework of uniformly ultimately bounded stability by developing an algebraic connectivity based criterion. The estimator gains including proposed coupling gains are then presented as solution to matrix inequalities. Finally, two simulation examples are presented to demonstrate the effectiveness of proposed estimation architecture.

Orbit Estimation for Spacecraft Based on Intermittent Measurements: An Event-Triggered UKF Approach

This article addresses a problem of spacecraft orbit estimation with measurement transmission restrictions through an event-triggered state estimation approach. Specifically, we consider the scenario that the ground station communicates with the spacecraft through multiple channels and each channel decides whether or not to transmit the measurements to the spacecraft based on separate deterministic event-triggering conditions. Considering $J_2$ perturbations, an event-triggered unscented Kalman filter-based state estimation algorithm is designed on the basis of a model of orbit dynamics and a measurement model of the ground station. The performance of the estimator is evaluated based on real-time two-line element data. The obtained results indicate that the proposed algorithm can achieve satisfactory estimation performance at reduced measurement transmission rates.

Adaptive event-triggered state estimation for large-scale systems subject to deception attacks

Adaptive event-triggered state estimation for large-scale systems subject to deception attacks