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

Sampled-Data State Estimation for LSTM

This article first introduces a sampled-data state estimator design method for continuous-time long short-term memory (LSTM) neural networks with irregularly sampled output. To this end, the structure of the LSTM is addressed to obtain its dynamic equation. As a result, the LSTM neural network is modeled as a continuous-time linear parameter-varying system that is dependent on the gate units. For this system, the sampled-data Luenberger- and Arcak-type state estimator design methods are presented in terms of linear matrix inequalities (LMIs) by using the properties of the gate units. Lastly, the proposed method not only provides a numerical example for analyzing absolute stability but also demonstrates it in practice by applying a pre-trained behavior generation model of a robot manipulator.

Memory-based consensus control for multi-agent systems with time-varying topology and communication constraints

This paper focuses on the design of event-triggered observer-based heterogeneous memory controllers for leader-following multi-agent systems with time-varying topology. In order to save limited on-board resources, a novel adaptive event-triggered strategy based on the nonlinear transformation law of the estimation error is proposed in this paper, which can effectively reduce some unnecessary data transmission due to small fluctuations after the estimation error converges. Then, a more general topology structure described by an interval type-2 fuzzy model is adopted, which contains both nonlinear time-varying law and uncertain parameters. Taking into account the differences in the interactions between various agents, the heterogeneous fuzzy-dependent controllers with past state measurements are constructed to further improve consensus performance. Moreover, some sufficient conditions are derived to solve the designed observers and controllers while ensuring that the desired consensus of the multi-agent systems can be achieved. Finally, two examples are given to illustrate the superiority and effectiveness of the proposed event-triggered strategy, and also to verify that introducing past state measurements into the controller helps enhance the consensus control performance.

Zonotope-Based Distributed Set-Membership Fusion Estimation for Artificial Neural Networks Under the Dynamic Event-Triggered Mechanism

This article is concerned with the distributed set-membership fusion estimation problem for a class of artificial neural networks (ANNs), where the dynamic event-triggered mechanism (ETM) is utilized to schedule the signal transmission from sensors to local estimators to save resource consumption and avoid data congestion. The main purpose of this article is to design a distributed set-membership fusion estimation algorithm that ensures the global estimation error resides in a zonotope at each time instant and, meanwhile, the radius of the zonotope is ultimately bounded. By means of the zonotope properties and the linear matrix inequality (LMI) technique, the zonotope restraining the prediction error is first calculated to improve the prediction accuracy and subsequently, the zonotope enclosing the local estimation error is derived to enhance the estimation performance. By taking into account the side-effect of the order reduction technique (utilized in designing the local estimation algorithm) of the zonotope, a sufficient condition is derived to guarantee the ultimate boundedness of the radius of the zonotope that encompasses the local estimation error. Furthermore, parameters of the local estimators are obtained via solutions to certain bilinear matrix inequalities. Moreover, the zonotope-based distributed fusion estimator is obtained through minimizing certain upper bound of the radius of the zonotope (that contains the global estimation error) according to the matrix-weighted fusion rule. Finally, the effectiveness of the proposed distributed fusion estimation method is illustrated via a numerical example.

Optimized Distributed Filtering for Saturated Systems With Amplify-and-Forward Relays Over Sensor Networks: A Dynamic Event-Triggered Approach

In this article, the optimized distributed filtering problem is studied for a class of saturated systems with amplify-and-forward (AF) relays via a dynamic event-triggered mechanism (DETM). The AF relays are located in the channels between sensors and filters to prolong the transmission distance of signals, where the transmission powers of sensors and relays can be described by a sequence of random variables with a known probability distribution. With the purpose of alleviating the communication burden and preventing data collision, the DETM is used to schedule the transmission cases of nodes by dynamically adjusting the triggered threshold according to the practical requirements. An upper bound matrix (UBM) of the filtering error (FE) covariance is first provided under the sense of variance constraint and the proper filter gain is further constructed via minimizing the proposed UBM. In addition, the boundedness evaluation regarding the trace of the UBM is provided. Finally, simulation experiments are used to illustrate the usefulness of the developed distributed recursive filtering scheme.

Hybrid Adjusting Variables-Dependent Event-Based Finite-Time State Estimation for Two-Time-Scale Markov Jump Complex Networks

This article investigates the problem of dynamic event-triggered finite-time H∞ state estimation for a class of discrete-time nonlinear two-time-scale Markov jump complex networks. A hybrid adjusting variables-dependent dynamic event-triggered mechanism (DETM) is proposed to regulate the releases of measurement outputs of a node to a remote state estimator. Such a DETM contains both an additive dynamically adjusting variable (DAV) and a multiplicative adaptively adjusting variable. The aim is to design a DETM-based mode-dependent state estimator, which guarantees that the resultant error dynamics is stochastically finite-time bounded with H∞ performance. By constructing a mode-dependent Lyapunov function with multiple DAVs and a singular perturbation parameter associated with time scales, a matrix-inequalities-based sufficient condition is derived, the feasible solutions of which facilitate the design of the parameters of the state estimator. The validity of the designed state estimator and the superiority of the devised DETM are verified by two examples. It is verified that the devised DETM is capable of saving network resources and simultaneously improving the estimation performance.

Event-Triggered Resilient Filtering With the Interval Type Uncertainty for Markov Jump Systems

The problem of event-triggered resilient filtering for Markov jump systems is investigated in this article. The hidden Markov model is used to characterize asynchronous constraints between the filters and the systems. Gain uncertainties of the resilient filter are the interval type in this article, which is more accurate than the norm-bounded type to model the uncertain phenomenon. The number of linear matrix inequalities constraints can be decreased significantly by separating the vertices of the uncertain interval, so that the difficulty of calculation and calculation time can be reduced. Moreover, the event-triggered scheme is applied to depress the consumption of network resources. In order to find a balance between reducing bandwidth consumed and improving system performance, the threshold parameter is designed as a diagonal matrix in the event-triggered scheme. Utilizing the convex optimization method, the sufficient conditions are derived to guarantee that the filtering error systems are stochastically stable and satisfy the extended dissipation performance. Finally, a single-link robot arm system is delivered to certify the effectiveness and advantages of the proposed method.

A new event-triggered distributed state estimation approach for one-sided Lipschitz nonlinear discrete-time systems and its application to wireless sensor networks

This article proposes the design of a distributed state estimator for a class of one-sided Lipschitz nonlinear systems over wireless sensor networks. The suggested estimation scheme utilizes the one-sided Lipschitz constraint in conjunction with quadratic inner-boundedness, which makes it applicable to a broader class of nonlinear systems. The proposed estimator design is evaluated under a conventional event-triggered mechanism both in the absence and presence of external perturbations. Furthermore, a novel event-triggering condition is introduced that ensures error convergence to the origin in the absence of external perturbations. It is further established that the inclusion of new triggering condition reduces the estimation error upper bounds in the presence of external disturbances and noises. Sufficient conditions for boundedness of estimation errors are derived for each case, and matrix inequalities are developed for the calculation of estimator gains. Finally, a numerical example is considered to illustrate the efficacy of the proposed estimator.