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In this brief, the dynamic event-triggered control is investigated for a novel class of singularity-perturbed dynamic networks (SPDNs) with multi-time-scale. First, for a single agent, the two-time-scale characteristic described by a small singularity-perturbed parameter (SPP) is considered. Then, every agent is equipped with a personalized SPP $\varepsilon _{i}$ , which denotes the multi-time-scale characteristic of the SPDNs. Owing to the SPP $\varepsilon _{i}$ , the restriction in most the existing works that every agent must have the same SPP can be removed. Further, the networks with single-time-scale, two-time-scale, or multi-time-scale can all be modeled by the proposed SPDNs by selecting an appropriate value of $\varepsilon _{i}$. A state feedback controller based only on the slow dynamics information is considered. Besides, a dynamic event-triggered mechanism (ETM), where the threshold parameters are adjusted as the slow state fluctuations, is introduced to reduce the communication frequency reasonably. By means of Lyapunov stability theorem and a decoupled method, some stability conditions that are independent of the scale of the SPDNs are obtained. Finally, the effectiveness and advantages of the proposed method over existing ones are demonstrated through an electronic circuit system. [ABSTRACT FROM AUTHOR]

In this article, the consensus problem of linear multiagent systems (MASs) is investigated, where an event-triggered mechanism combined with sampler is introduced to rationally utilize the network bandwidth and the communication energy. Different from most existing results on the event-triggered consensus of MASs, a time-varying communication delay (CD) with a more general form is constructed in this article. With the help of a new class of Lyapunov functional and advanced inequalities, some novel criteria for achieving the asymptotic consensus of the considered MASs under different CD cases are obtained. For demonstrating the superiority of the proposed method from the conservatism and the practicability, a compared example and an application of spacecraft formation flying are proposed, respectively. [ABSTRACT FROM AUTHOR]

In this article, the event-triggered asynchronous control problem of nonlinear multiagent systems is investigated based on Takagi–Sugeno fuzzy models. In order to rationally utilize network resources and elaborately avoid unnecessary continuous monitoring, an event-triggered mechanism with a new dynamic threshold parameter and a sampler are considered, respectively. Besides, an asynchronous operation method is adopted to deal with the mismatched premise variables between the fuzzy system and fuzzy controller. Based on the Lyapunov stability theory and appropriate inequality, some sufficient criteria in the form of linear matrix inequalities are obtained to ensure the stability of the closed-loop system. Finally, an illustrative example is provided to demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the reliable extended passive control problem for semi-Markov jump Takagi-Sugeno (T–S) fuzzy systems based on an event-triggered mechanism (ETM). An asynchronous approach is utilized to cope with the fuzzy-basis-dependent membership functions in networked fuzzy systems. For fear of the waste of communication resources, an ETM to screen the transmitted data is adopted for T–S fuzzy semi-Markov jump systems under consideration. Owing to the use of Lyapunov stability theory and some novel integral inequalities based on auxiliary functions, some sufficient conditions are obtained to guarantee that the resulting closed-loop system not only meets a prescribed mixed $\mathcal {H} _{\infty }$ and passive performance but also is stochastically stable. Then, by using a skillful matrix decoupling approach, the gains of the fuzzy controller may be expressed specifically. Lastly, the superiority and availability of the developed design method are extensively described and validated via its application to a truck control model. [ABSTRACT FROM AUTHOR]

This paper gives attention to the issue of nonfragile state estimation for a class of Markov jump systems with repeated scalar nonlinearities and redundant channels. A mixed $$\mathcal {H}_{\infty }$$ and $$ l_{2}-l_{\infty }$$ performance index is introduced to optimize the state estimation performance. Assisted by the introduced random variables, this paper constructs a new type of communication models to give a description of multi-channel packet dropout. With the aid of the Lyapunov-Krasovskii function, a sufficient condition is derived, which assures the error dynamic is stochastically stable conforming with the prespecified mixed $$\mathcal {H} _{\infty }$$ and $$l_{2}-l_{\infty }$$ performance. Eventually, a numerical example is proposed to exactly reflect the applicability of this creative method. [ABSTRACT FROM AUTHOR]

In this paper, the problem of reliable event-triggered H ∞ control is addressed for networked control systems by using retarded dynamic output feedback. The randomness of actuators failures is modeled by a stochastic variable in a Markov jump model framework. To guarantee the considered closed-loop system is stochastically stable with a prescribed H ∞ performance level, a Markov jump event-triggered retarded dynamic output feedback H ∞ controller is designed in this paper, which is the main purpose of our study. By using the stochastic analysis techniques and novel integral inequalities, some sufficient conditions for the solvability of the addressed problem are derived. Finally, an example using a satellite control system model is provided to explain the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the non-fragile mixed H ∞ and passive asynchronous state estimation problem for uncertain discrete-time Markov jump neural networks (MJNNs). Both the uncertainties of system and the sensor nonlinearity are considered to be randomly occurring which are governed by a set of Bernoulli distributed white sequences. Since inaccuracies or uncertainties may occur in the designed state estimator and the complete mode synchronization between plant and state estimator is hardly possible, a non-fragile asynchronous state estimator design method is presented. By using an optimize matrix decoupling approach and Lyapunov-Krasovskii methodology, some sufficient conditions for the existence of non-fragile mixed H ∞ and passive asynchronous state estimator are proposed. A numerical example is presented to demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the resilient finite-time H ∞ filtering problem for discrete-time uncertain Markov jump neural networks with packet dropouts is investigated. The purpose is to design a filter which is insensitive with respect to filter gain uncertainties subjects to an H ∞ performance level. The data packet dropouts phenomenon modeled by a stochastic Bernoulli distributed process is also considered. In terms of the linear matrix inequalities methodology, some sufficient conditions which guarantee that the filtering error system is finite-time bounded with a prescribed H ∞ performance level are established. Based on the conditions, an explicit expression for the desired filter is given. A numerical example is provided to illustrate the validness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a dissipativity-based state estimation methodology for static neural networks with time-varying delay. An Arcak-type observer is used to construct the estimation error system. To reduce the conservatism of observer design, a Lyapunov–Krasovskii functional (LKF) is adopted to fully exploit the available characteristics about activation function. In addition, a relaxed constraint condition is put forward to keep the whole LKF positive without requiring parts of involved matrices to be positive. By adopting the LKF and constraint condition, estimation conditions with a strict dissipative performance are obtained, which ensures the asymptotic stability of estimation error system. The computation of gain matrices about observer can be transformed into a convex optimization problem. Two examples are given to illustrate the validity and advantage of provided methodology. [ABSTRACT FROM AUTHOR]

This paper focuses on the energy-to-peak filtering issue for a class of singular semi-Markov jump systems with unideal measurements. Some network-induced phenomena, such as sensor nonlinearity and packet dropouts caused by the unideal measurements are considered. The occurrence of sensor nonlinearity is described in a random way and obeyed a Bernoulli distribution. In the framework of the Lyapunov–Krasovskii stability theory, some sufficient conditions are given to ensure that the considered error system is stochastically mean-square stable and guarantees an energy-to-peak (or called L 2 − L ∞ ) performance level. On the basis of these conditions, an available design method to the desired filter is proposed drawing support from an improved matrix decoupling approach. For showing the effectiveness and superiority of the proposed method, we finally provide two illustrated examples. [ABSTRACT FROM AUTHOR]

In this article, an extended state observer (ESO) design problem is investigated for uncertain nonlinear systems subject to limited network bandwidth. First, for rational information exchange scheduling, a dynamic event-triggered (DET) communication protocol is proposed. Different from the traditional static event-triggered strategies with fixed thresholds, an internal dynamic variable is introduced to be adaptively adjusted by a dual-directional regulating mechanism. Thus, more desirable tradeoff between observation performance and communication resource efficiency is achieved. Second, inspired by our early work on Takagi-Sugeno fuzzy ESO (TSFESO), a novel paradigm of event-triggered TSFESO is initially proposed. Third, under the DET mechanism, the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics. Finally, the effectiveness of the proposed method is verified by numerical examples. The nonlinear estimating efficiency and linear numerical tractability are integrated in TSFESO. In addition, a generalized ESO formulation is developed to allow some nonadditive uncertainties incompatible with total disturbance, such as improved event-triggered strategy, and thus, the application sphere of ESO is further expanded.

Plasmodium falciparum thymidylate kinase (PfTMK) is an essential enzyme for the growth of the organism because of its critical role in the de novo synthesis of deoxythymidine 5'-diphosphate (TDP), a precursor for TTP that is required for DNA replication and repair. The kinetics, thermodynamic parameters, and substrate binding properties of PfTMK for TMP, dGMP, ADP, and ATP were measured and characterized by steady-state kinetics and a combination of isothermal titration calorimetry, tryptophan fluorescence titration, and NMR. Mutational studies were performed to investigate residues that contribute to the unique ability of PfTMK to also utilize dGMP as a substrate. Isothermal titration calorimetry experiments revealed that dGMP binding exhibits a unique half-site binding mechanism. The occlusion of the empty site in the dGMP complex is supported by molecular mechanics calculations. Relaxation dispersion experiments show that the dGMP and enzyme complex is more dynamic at the dimer interface than the TMP complex on the μs-ms time scale. The unique properties of dGMP binding need to be considered in the design of guanosine-based PfTMK-specific inhibitors.