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In this paper, a novel constrained switching rule, called “time-triggered logical switching” (TTLS) is considered for switched logical dynamic systems (SLDSs). Compared with the general time-triggered switching, the activation mode of TTLS cannot be arbitrary and is pre-allocated according to the logic operation, which is more practical. The TTLS is described as an LDS, and according to the characteristics of LDS, the stability analysis of SLDSs with TTLS is converted into the stability analysis of SLDSs under the logical switching cycle sequences. Firstly, based on the equivalent algebraic form of SLDSs with TTLS, combining the Lyapunov theory of LDS with average dwell-time method, several sufficient conditions are put forward for ensuring the point stability of the considered SLDSs. Then, by defining the switching cycle invariant subset and constructing a new system, the set stability analysis of the original system is transformed into the point stability analysis of the new system, and further, the obtained results for the point stability analysis are applied to the set stability analysis. At last, the validity of obtained results is illustrated by simulation on gene and protein signaling activity patterns. [ABSTRACT FROM AUTHOR]

This paper considers weakly and strongly input-to-state stability (ISS) of a discrete-time time-varying delayed system with synchronous impulses and switches. By employing the uniformly asymptotically stable function and mode-dependent dwell time conditions, ISS criteria are derived using the Krasovskii and Razumikhin techniques. The time differences of the Krasovskii functionals or the Razumikhin functions can be mode-dependent and time-varying, which release constraints of some existing results. As subsequent results, we apply the derived ISS criteria to the multi-agent systems (MASs). Finally, two examples considering the quasi-consensus of MASs are given to verify the established results. [ABSTRACT FROM AUTHOR]

This paper adopts a quantization-dependent Lyapunov function to study the output feedback stabilization problem of a switched system subject to output event-triggered sampling and quantization. First, a new event-triggered mechanism related to quantization parameters is proposed, which can obtain a trade-off between the event-triggered frequency and quantization density on the premise of ensuring asymptotic stability of the switched system. Moreover, sufficient conditions are obtained to ensure asymptotic stability of the switched system by a quantization-dependent Lyapunov function. Lastly, through a numerical simulation and PWM-driven boost converter system, we demonstrate the availability of the proposed quantization-dependent Lyapunov function and event-triggered mechanism methods based on quantization parameters, respectively. [ABSTRACT FROM AUTHOR]

This paper investigates the state estimation for switched memristive neural networks with nonhomogeneous sojourn probabilities. Essentially different from most current literature, a novel switching law is developed to depict the dynamic behavior of switched memristive neural networks, in which the sojourn probabilities of each subsystem are assumed to be nonhomogeneous, and a higher-level deterministic switching signal is proposed to regulate proper feedback switching information by means of the average dwell time approach. Meanwhile, to alleviate the constraint network bandwidth resource efficiently, a dynamic event-triggered mechanism with a novel threshold parameter is proposed in determining if the current data should be released or not. By resorting to the Lyapunov functional technique and the stochastic analysis strategy, some sufficient conditions are addressed to ensure the stochastic stability of the augmented switched memristive neural networks. In the end, the effectiveness and superiority of the developed results are verified by a numerical example. [ABSTRACT FROM AUTHOR]

Aiming at a class of switched uncertain nonlinear strict-feedback systems under the action of average dwell time switching signal, this paper proposes a novel adaptive neural network output feedback tracking control based on the consideration of the full state constraints. The controller is proposed based on neural networks. One of the key characteristics of the system discussed is that the state variables cannot be measured and the system states need to be kept within the constraint ranges. For the sake of estimating the unmeasured states, the observer is constructed. In order to ensure all states which are within the time-varying boundary, the tangent barrier Lyapunov function (BLF-Tan) is selected in the design process. The boundedness of the closed-loop signals with average dwell time is guaranteed by the designed controllers and all the states limit in their constrained sets. It has been proved that the output tracking error converge to a small neighborhood of zero. In addition, the significance of the presented control strategy is verified and tested by a simulation example. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of simultaneous fault detection and control (SFDC) for Markov jump systems (MJSs) over sensor networks. In order to save the bandwidth of the network, round-Robin protocol is adopted for the communication between the sensors and fault detection filter. First, to fully utilize the data of sensor networks, a new distributed system is developed especially for SFDC, and a residual system, which integrates the MJSs with the distributed SFDC system, is also established. Then, by constructing a mode-dependent Lyapunov functional, the conditions for distributed SFDC are derived, which can make the residual system asymptotically mean-square stable and meanwhile satisfy the prescribed H∞ performance requirements. Finally, comparison analyses are made by examples to illustrate the effectiveness and superiority of the proposed new design approach. [ABSTRACT FROM AUTHOR]