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This article studies an event-based two-step transmission mechanism (TSTM) in the control design for networked T–S fuzzy systems. The transmission task is achieved in two steps. Consecutive triggering packets are relabeled in the first step by applying a traditional event-triggered mechanism (ETM). Then a probabilistic approach is employed to determine which packet is a real release packet (RRP) in the second step. This event-based TSTM is particularly suitable for scenarios in which traditional ETMs are unable to determine which packets are redundant. By discarding most of the unnecessary data packets, especially when the system is tending toward stability, the burden on the network bandwidth is reduced. To establish a control strategy for T–S fuzzy-based nonlinear systems with random uncertainties, a new timing analysis technique is proposed. Additionally, the necessary conditions for a nonlinear system’s mean-square asymptotic stability (MSAS) are derived. Finally, two practical applications demonstrate the effectiveness of the suggested transmission mechanism in networked T–S fuzzy systems.

This article is devoted to asynchronous networked filtering against sensor failures for a class of two-dimensional (2-D) Bernoulli jump systems. The purpose is to design a fault-tolerant reduced-order asynchronous (FTROA) filter that ensures asymptotic mean-square stability and $\mathcal {H}_{2}$ disturbance-attenuation performance for the resulting filtering error system, characterized by a bi-mode-jumping property. A hidden Bernoulli model is adopted to characterize asynchronous mode switching between the original 2-D system and the designed filter. A sufficient condition on the stability and $\mathcal {H}_{2}$ performance is proposed. Based on the condition, a method of the needed FTROA filter is developed in terms of linear matrix inequalities by introducing an order-dependent slack matrix. Considering the calculation complexity, an alternative filter design is further presented by resorting to suitable matrix splittings and the projection theorem. Compared to the former, the latter is shown to be capable of significantly mitigating the computational burden while maintaining the desired performance. Finally, an actual example concerning the metal rolling process is provided to verify the effectiveness of the presented filter design methods.

In this study, we investigated the optimal tracking performance (OTP) of feedback control systems with limited bandwidth and colored noise in a fading channel. For the steady state of the feedback control systems, an equivalent average channel (EAC) model was developed by retaining the effects of the first and second moments of the multiplicative channel output, and on the basis of the coprime decomposition, all-pass factorization, and Youla parameterization of controllers, exact expressions for the OTP were derived by designing two compensators. The expressions quantitatively show the relationship between the OTP and inherent features of the plant. Specifically, the directions and locations of unstable poles (UPs) and nonminimum phase (NMP) zeros adversely affect the tracking performance. Furthermore, the bandwidth limitation and the presence of colored noise also degrade the tracking performance. Finally, our conclusions were verified by considering a numerical arithmetic example.

This article presents a discussion on the adaptive fuzzy fully distributed consensus problem of heterogeneous switched nonlinear multiagent systems (SNMASs). The considered agents contain both first- and second-order dynamics. Fuzzy logic systems are introduced to approximate the unknown nonlinear terms of the SNMASs. Influenced by the systems' own factors or the external environment, the subsystems of agents may be unstabilizable and existing control strategies may not be able to ensure the stability of the systems. Therefore, it is necessary to provide a new consensus protocol to address this problem. In this article, a fully distributed consensus protocol is provided that includes an auxiliary system and a series of state-dependent switching laws. To ensure the stability of whole SNMASs and realize the consensus objective, convex combination technology and the single Lyapunov function method are adopted. Finally, the effectiveness of the proposed scheme is verified through simulation results.

This brief investigates the stability of switched nonlinear systems with input saturation and multiple unknown switching control directions using a novel Nussbaum design. The conventional Nussbaum design cannot be applied here directly because the multiple Nussbaum-type functions may cancel each other. To address the challenges posed by the unknown control directions, a novel variants of the Nussbaum-type function tailored to mitigate this limitation is introduced herein. The proposed control approach accounts for the input saturation constraints, while the system stability is verified using the average dwell time method. The approach is numerically validated on a jerk circuit system, demonstrating the effectiveness of the proposed design.

This article investigates the synchronization control problem of fuzzy complex dynamic networks (FCDNs) with diversified network attacks. By making full use of the weighted historically released packets, a novel anti-attack memory-based adaptive event-triggered protocol (AETP) and the corresponding anti-attack fuzzy memory-based controller are introduced by taking the sporadic denial-of-service (DoS) and deceptive attacks into consideration concurrently. Compared with the conventional event-triggered protocols and AETPs, the developed anti-attack memory-based AETP cannot only effectually reduce the frequency of event triggers, but also enhance the transient performance of FCDNs. In addition, in view of a new sampling-instant-dependent piecewise Lyapunov functional, less conservative criteria are induced to guarantee that the stochastically exponential synchronization of FCDNs is accomplished in the case where the deceptive and sporadic DoS attacks coexist. Finally, the merits and validity of the theoretical results are illustrated through numerical examples.

The adaptive fuzzy distributed optimization problem of nonlinear multiagent systems with unknown nonlinearities and uncertain disturbances is explored in this article. The unknown nonlinearities in the system model are determined by incorporating fuzzy logic systems. Rather than using the existing known gradient values of local objective functions, this article further takes into account the measured gradient values that are based on the output information. The bounded estimation method and well-defined smooth functions are used to account for the effects of uncertainties. To ensure that the output can asymptotically converge to the optimal solution, a new adaptive fuzzy distributed optimization controller is proposed using the Lyapunov function method and the adaptive backstepping technique. To demonstrate the effectiveness of the developed distributed optimization technique, two practical examples are used.

A predefined-time adaptive consensus control strategy is developed for a class of multi-agent systems containing unknown nonlinearity. The unknown dynamics and switching topologies are simultaneously considered to adapt to actual scenarios. The time required for tracking error convergence can be easily adjusted using the proposed time-varying decay functions. An efficient method is proposed to determine the expected convergence time. Subsequently, the predefined time is adjustable by regulating the parameters of the time-varying functions (TVFs). The neural network (NN) approximation technique is used to address the issue of unknown nonlinear dynamics through predefined-time consensus control. The Lyapunov stability theory testifies that the predefined-time tracking error signals are bounded and convergent. The feasibility and effectiveness of the proposed predefined-time consensus control scheme are demonstrated through the simulation results.

This article explores observer-based input-to-state stabilization for Markov jump systems with both dynamic input and output quantization, as well as multimode injection attacks (IAs). The Markov chain of the plant, together with the IAs described by two stochastic processes following the categorical distribution, constitutes a pair of hidden Markov models. A sufficient condition for mean-square input-to-state stability of the feedback control system is proposed utilizing a Lyapunov-type function dependent on both the mode and exponential decay rate, the S-procedure, as well as several stochastic analysis tools. Then, a two-stage approach, with which the required controller and observer gains and dynamic scaling factors can be determined successively, is developed. The scaling factors are constructed as piecewise functions, excluding the possibility of singularities involved in previous dynamic quantized control approaches. Under the zero initial condition, a greedy backtracking suboptimization algorithm is further put forward, offering an estimate of the minimum permissible upper bound of the mean-square closed-loop state for a bounded disturbance input, given a fixed exponential decay rate. Finally, a vertical lift aircraft model is applied to validate the proposed quantized observer-based control approach and suboptimization algorithm.

This work examines the distributed leader-following consensus problem of feedforward nonlinear delayed multiagent systems involving directed switching topologies. In contrast to the existing studies, we focus on time delays acting on the outputs of feedforward nonlinear systems, and we permit that the partial topology dissatisfy the directed spanning tree condition. In the cases, we present a novel output feedback-based general switched cascade compensation control method that addresses the above-mentioned problem. First, we put forward a distributed switched cascade compensator by introducing multiple equations, and we design the delay-dependent distributed output feedback controller with the compensator. Subsequently, when the control parameters-dependent linear matrix inequality is met and the switching signal of the topologies obeys a general switching law, we prove that the established controller can render that the follower’s state asymptotically tracks the leader’s state by employing an appropriate Lyapunov–Krasovskii functional. The given algorithm allows output delays to be arbitrarily large and increases the switching frequency of the topologies. A numerical simulation is presented to demonstrate the practicability of our proposed strategy.

This article presents a positivity analysis and exponential stabilization design for singular switched positive systems (SSPSs) with actuator saturation under a mode-dependent minimum dwell time (MDMDT) constraint. First, a necessary and sufficient positivity criterion is proposed for SSPSs using the singular value decomposition approach. Then, an exponential stability condition is provided for the closed-loop SSPSs via the mode-dependent state-feedback control using a novel discretized linear copositive Lyapunov function technique. Furthermore, by applying the matrix decomposition technique to the controller gain matrix and controller auxiliary gain matrix, an effective mode-dependent design scheme for a saturation controller in the solvable linear programming (LP) form is proposed for the SSPSs. For singular positive systems, an effective saturation control scheme in the solvable LP form can be obtained accordingly. Finally, three examples are provided to validate the results.

Local nonlinear fuzzy systems are useful for control solutions due to their ability to handle unmeasurable/inexact premise variables and reduce computational complexity. However, their estimation problems still require further development. This article addresses this issue by investigating set-membership estimation for a class of discrete-time local nonlinear uncertain Takagi–Sugeno fuzzy systems with guaranteed performance. We propose a new observer architecture that solves partially decouplable unknown inputs and converts nonlinear error dynamics into a linear parameter-varying type. Using the systematic $\ell _\infty$-technique, the design conditions in the form of linear matrix inequalities ensure stability and output performance of the state estimation. We also derive a straightforward and effective zonotopic analysis method, considering the fuzzy and local nonlinear context, for less conservative results without using any specific interval set computation. Furthermore, a fast fault detection logic is proposed as an application of the set-membership estimation. Finally, we demonstrate the feasibility and advantages of our approach through three compelling examples, showcasing its efficacy in different scenarios.