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This paper mainly investigates the stabilization problem of Takagi-Sugeno (T-S) fuzzy asynchronous Boolean control networks (ABCNs) under aperiodic sample-data state-feedback control. First, the system has been converted into a discrete time-delay system by using the theory of the semi-tensor product for matrices. After that, algebraic forms of the augmented ABCNs are obtained. Second, the stabilization of T-S fuzzy ABCNs with fixed time delay is researched. Based on that, noise is further considered in the time delay of ABCNs and the fixed time delay is altered to the unfixed case. Then, sufficient and necessary conditions for proposed stabilizations are derived by using different approaches. Ultimately, examples are provided to demonstrate the effectiveness and superiority of achieved results.

In this paper, the issue of exponential stabilization and sampled-data controller design for Takagi–Sugeno fuzzy large-scale networked control systems is studied by using the reduction-based ordinary differential equation prediction method. For the problem that matrices cannot be multiplied directly during the process of designing the sampled-data controller in this paper, a matrix dimensional transformation method is proposed. Firstly, a type of two-sided mode-dependent loop-based Lyapunov–Krasovskii functional is constructed, which compensates for the large delay and makes fuller use of the information in sampled-data interval. Secondly, the proposed method is used to give the design scheme of an aperiodic sampled-data controller, and furthermore, an iterative algorithm to verify the effectiveness of the requested control gains is provided. Finally, two coupled vehicle pendulum systems and two-area interconnected power systems are applied to demonstrate the efficiency of the presented approach.

We aim to further study the global stability of Boolean control networks (BCNs) under aperiodic sampled-data control (ASDC). According to our previous work, it is known that a BCN under ASDC can be transformed into a switched Boolean network (SBN), and further global stability of the BCN under ASDC can be obtained by studying the global stability of the transformed SBN. Unfortunately, since the major idea of our previous work is to use stable subsystems to offset the state divergence caused by unstable subsystems, the SBN considered has at least one stable subsystem. The central thought in this paper is that switching behavior also has good stabilization; i.e., the SBN can also be stable with appropriate switching laws designed, even if all subsystems are unstable. This is completely different from that in our previous work. Specifically, for this case, the dwell time (DT) should be limited within a pair of upper and lower bounds. By means of the discretized Lyapunov function and DT, a sufficient condition for global stability is obtained. Finally, the above results are demonstrated by a biological example.

This paper studies the aperiodic sampled-data (SD) control anti-synchronization issue of chaotic nonlinear systems under the effects of input saturation. At first, to describe the simultaneous existence of the aperiodic SD pattern and the input saturation, a nonlinear closed-loop system model is established. Then, to make the anti-synchronization analysis, a relaxed sampling-interval-dependent Lyapunov functional (RSIDLF) is constructed for the resulting closed-loop system. Thereinto, the positive definiteness requirement of the RSIDLF is abandoned. Due to the indefiniteness of RSIDLF, the discrete-time Lyapunov method (DTLM) then is used to guarantee the local stability of the trivial solutions of the modeled nonlinear system. Furthermore, two convex optimization schemes are proposed to expand the allowable initial area (AIA) and maximize the upper bound of the sampling period (UBSP). Finally, two examples of nonlinear systems are provided to illustrate the superiority of the RSIDLF method over the previous methods in expanding the AIA and enlarging the UBSP.

Output feedback stabilization problem for a class of networked switched planar systems with aperiodic sampled data is investigated, and the closed-loop system is made converge to a size-adjustable set including the equilibrium. First, state feedback control laws are systematically constructed by the so-called adding a power integrator technique, a special backstepping-like method. Then, switched observers are constructed by using the aperiodic sampled data, which are sampled based on a deliberately designed event-triggered mechanism that can exclude Zeno behavior and guarantee the existence of a positive minimal inter-event time, transmitted by network and held by a zero-order holder. Combining the reconstructed state and the sampled output information, output feedback control strategy can be carried out. Simulations are provided to demonstrate the effectiveness of the proposed method.

This work investigates the bipartite synchronization issue of a network of nonlinear dynamic systems with aperiodic data-sampling, and the underlying topology follows a changeable signed graph. In order to achieve the control objective, some event-triggered protocols are developed based on sampled data, and some theorems are derived, which elucidate how the system dynamics, network topology, triggering parameters and sampling periods decide synchronization, affirming the critical impact of the average value of the algebraic connectivity of an induced unsigned graph over certain intervals. Unlike most existing works on periodic event-triggered control, the triggering mechanism herein is based on aperiodic sampled data, which thus provides more flexibility for practical use. Meanwhile, the topology is allowed to alter either discretely or continuously, exhibiting good applicability. Finally, some numerical examples are given to validate the theoretical results and demonstrate the performance of our design.

This paper is concerned with the problem of H ∞ fuzzy filtering for continuous nonlinear stochastic systems which can be approximated by the Takagi–Sugeno (T–S) fuzzy systems and the input is aperiodically sampled. We proposed the fuzzy parameters dependent filtering to estimate the state variables for nonlinear stochastic systems. In general, it is difficult to solve the filtering parameters under the hybrid modeling technique of the sampled-data. And the discrete feedback property under the input-delay technique of the sampled-data always disappears. Therefore, we introduce the improved hybrid modeling technique to keep the discrete feedback property of the closed-loop systems. Next, the improved time-varying Lyapunov function method is adopted to analyze the remodeled hybrid systems. Then the sufficient conditions of mean-squared exponential stability and H ∞ performance of the filtering error systems are obtained and the parameters of the fuzzy filtering can be solved. The proposed improved hybrid modeling technique can be widely applied to practically address the H ∞ filtering design problem. Finally, a practical example of the balancing problem about inverted pendulum is used to show the effectiveness of the theoretical results.

This paper is concerned with stability analysis of sampled-data systems with non-uniform sampling patterns. The stability problem is solved based on an augmented looped Lyapunov functional with Wirtinger inequality approach. The effectiveness of this approach depends on proper selecting looped Lyapunov functional parts and less conservative inequality techniques. The method developed here is an extension of previous results by using novel augmented functional and Wirtinger inequality. Numerical examples are given to illustrate the result.