Adaptive Takagi-Sugeno (T-S) fuzzy control was a real-time control method without off-line input and output data, but the approximate error between this T-S fuzzy model and the actual system model was rarely considered in the existing methods. This problem can lead to the degradation of controller accuracy in practical engineering. In order to solve this problem, the mathematical expression of the upper bound for the approximate error between adaptive T-S fuzzy logic system and the actual system was derived for the first time under any number of rules. This achievement was the key to the design of robust fuzzy controller based on Lyapunov synthesis method under finite number of rules, and this controller can achieve predictability of accuracy. Compared with existing T-S fuzzy control methods, the proposed method can realize the self-adjusting accuracy control for the unknown-structure system without the off-line input and output data. The unknown non-affine control system simulation and 3-DOF robotic arm experiment were carried out to verify the effectiveness of proposed method.Note to Practitioners—Due to the wide application of T-S fuzzy model in practice control system, such as the robot control, the unmanned vehicle control and so on, the actual control accuracy of T-S fuzzy controller was concerned by many scholars. However, the actual precision degradation of the controller was a difficult problem in on-line T-S fuzzy control. In order to solve this problem, a novel adaptive T-S fuzzy control method was proposed in this paper. In practical application, T-S fuzzy model often had a large approximate error with the actual model because of structure uncertainty, and this approximate error was the key factor affecting the actual accuracy of T-S fuzzy controller. Hence, the mathematical expression of the upper bound for the approximate error between adaptive T-S fuzzy logic system and the actual system was derived for the first time under any number of rules. Based on this mathematical expression, we can design a self-adjusting accuracy robust fuzzy controller based on Lyapunov synthesis method. 3-DOF robotic arm experiment verified that the proposed method can achieve the predefined control error with or without the feedforward of robot dynamics. 3-DOF robotic arm comparison experiments verified that the advantages of proposed method.

This article studies the input-to-state stability (ISS) problem of networked control systems (NCSs) subject to both Denial-of-Service (DoS) and unknown deception attacks (DAs). A neural network (NN)-based resilient event-triggered control protocol (RETCP) is first presented to mitigate resource constraints and the adverse effects of cyber attacks, where the NN technology is leveraged to neutralize and approximate the malicious data injected by unknown DAs. Then, we develop a new predictor to compensate for lost signals of NCSs during the DoS threats, so that the NCSs can further tolerate more unfavorable DoS and unknown DAs. It is shown that the resulting NCSs with the designed novel NN-based controller can achieve ISS under the complex attacks. Finally, experimental evaluations are conducted for an uncrewed ground vehicle (UGV) to verify efficacy of the proposed intelligent control protocols.

This article investigates the issue of asynchronous event-triggered secure control for Markov jump systems (MJSs) under aperiodic denial-of-service (DoS) attacks. A memory-based mode-dependent resilient event-triggering scheme (MMRETS) based on aperiodically sampled data is designed to avoid Zeno behavior and continuous monitor, which has the advantages in mitigating the transmission times and tolerating the DoS attacks. Meanwhile, the asynchronous circumstance between the system and corresponding event-triggered controller in the absence of DoS attacks is characterized via a hidden Markov model (HMM). Subsequently, by using a novel Lyapunov functional and an iterative approach, sufficient conditions are obtained to guarantee the${\mathcal {H}}_{\infty }$performance of the resultant Markov closed-loop system. Finally, a practical truck-trailer model example is provided to illustrate the efficacy and benefits of the proposed methods.

This article studies the input-to-state stability (ISS) problem of cyber–physical systems (CPSs) subject to unknown disturbance and malicious denial-of-service (DoS) attacks. A resilient event-triggering mechanism is first proposed to reduce unnecessary network resource consumption. Then, a novel predictive control protocol is put forward to skillfully compensate for lost signals of CPSs during the case of DoS attacks. It is shown that the resulting closed-loop CPSs can achieve ISS under the proposed control update protocol. One of the key advantages of our proposed control protocol is that the missing information can be compensated, and the CPSs thus, can tolerate more unfavorable DoS attacks. Moreover, an intuitive minimum interevent time is provided so as to exclude the Zeno execution of control signals. Finally, a simulation example on unmanned ground vehicle is presented to verify the effectiveness of the proposed control protocols.

It is challenging to ensure car driving comfort and safety under denial-of-service (DoS) jamming attacks. For this reason, this article proposes a resilient-based and attack-intensity adaptive event-triggered control scheme to study the$\mathcal{H}_{\infty}$controlled networked active quarter-vehicle suspension systems (QVSSs), which reduces the frequency of controller updates and provides robust resistance to DoS attacks. It is shown that if the tendency of DoS attack intensity increases, more communication signals can be transmitted to the controller. Conversely, if the tendency of DoS attack intensity decreases, fewer communication signals are being utilized. Using the novel two-side looped-functional method and some technical lemmas, sufficient conditions for the exponential stability and$\mathcal{H}_{\infty}$control performance of QVSSs are obtained. Simulation results indicate that the suggested control approach enhances driving comfort and safety while reducing the data transmission burden.

In this article, the dynamic event-triggered control problem of memristive neural networks (MNNs) under multiple cyber-attacks is considered. A novel dynamic event-triggering scheme (DETS) and the corresponding event-triggered controller are proposed by taking into consideration both denial-of-service and deception attacks (DoS-DAs). Then, a key lemma is established to show that the dynamic event-triggered controller can be used to solve the globally stochastically exponential stability (GSES) issue of concerned MNN under multiple cyber-attacks. Meanwhile, a novel Lyapunov functional is proposed based on the actual sampling pattern. It is shown that under our proposed dynamic event-triggered controller and Lyapunov functional, the concerned MNN can achieve GSES in the presence of DoS-DAs. In addition, our results include relevant results on event-triggered control of MNN with static event-triggering scheme (SETS) or without cyber-attacks as special cases. The effectiveness of the proposed event-triggered controller under multiple cyber-attacks is illustrated by a simulation example.

Addressing for the consensus control of multi-agent system (MAS) under the conditions that the directed communication topologies are switching and system nonlinearities are completely unknown, a global consensus control with fully distributed manner is proposed combining with fuzzy logic systems (FLS). FLS are used to approximate the unknown disturbances to enhance system robustness. To deal with the switching topologies of MAS, a reconstructing mechanism using a novel piecewise differentiable function is firstly proposed for the state and consensus errors of agents, which renders the state and consensus errors zero at each switching time instant and facilitates to the control design based on barrier functions, against the unexpected controller action at switching time instant. Incorporating with this reconstructing mechanism, a novel distributed controllers is designed, which is featured with low-complexity structure, fully distributed manner and adaptively reconstructing ability. These technical attempts contribute to some new results. Firstly, the global consensus of MAS with switching directed topologies and unknown nonlinearities is firstly achieved. Secondly, the consensus errors can be explicitly regulated to a prespecified arbitrary small residual set in the sense that the range of the set is the predefined functions given by designer. Finally, simulation results demonstrate the claims.

This article considers the dynamic event-triggered$\mathcal {H}_{\infty }$filtering problem for networked control systems (NCSs) under multiple cyber-attacks. By taking into account both denial-of-service (DoS) and deception attacks (DAs), a novel dynamic event-triggering mechanism (DETM) and the corresponding event-triggered filter are presented. The proposed DETM further reduces data transmissions and also guarantees the exclusion of Zeno behavior. By resorting to a piecewise Lyapunov functional (PLF), sufficient conditions are derived to ensure that the resulting filtering error system is globally stochastically exponentially stable with the$\mathcal {H}_{\infty }$performance in the presence of both DoS and DAs. A simulation example of a tunnel diode circuit system is displayed to illustrate the validity of the proposed event-triggered filter under multiple cyber-attacks.

This article investigates the resilient dynamic event-triggered control issue of interval type-2 T-S fuzzy systems (IT2 T-S FSs) under denial-of-service (DoS) and deception attacks. A novel resilient dynamic event-triggering mechanism (DETM) and the corresponding fuzzy dynamic event-triggered controller are proposed to solve the stochastically exponential stability (SES) problem of concerned IT2 T-S FSs in the presence of DoS and deception attacks. Meanwhile, an aperiodic-sampling-based piecewise loop Lyapunov functional is constructed. It is shown that the resulting closed-loop IT2 T-S FSs can achieve SES, and under the piecewise loop Lyapunov functional, some relaxed conditions are derived. In addition, the derived results can apply to IT2 T-S FSs subject to DoS attacks, static event-triggered control, or dynamic periodic-sampling-based event-triggered control. Finally, the effectiveness of the achieved results is verified by a numerical simulation on one-link manipulator.

In this article, the issue of exponential stability (ES) is investigated for a class of switched stochastic neural networks (SSNNs) with proportional delay (PD). The key feature of PD is an unbounded time-varying delay. By considering the comparison principle and combining the extended formula for the variation of parameters, we conquer the difficulty in consideration of PD effects for such networks for the first time, where the subsystems addressed may be stable or unstable. New delay-dependent conditions with respect to the mean-square ES of systems are established by employing the average dwell-time (ADT) technique, stochastic analysis theory, and Lyapunov approach. It is shown that the acquired minimum average dwell time (MADT) is not only relevant to the stable subsystems (SSs) and unstable subsystems (USs) but also dependent on the decay ratio (DR), increasing ratio (IR), as well as PD. Finally, the availability of the derived results under an average dwell-time-switched regulation (ADTSR) is illustrated through two numerical simulation examples.

In this article, the issues of finite-time synchronization and finite-time adaptive synchronization for the impulsive memristive neural networks (IMNNs) with discontinuous activation functions (DAFs) and hybrid impulsive effects are probed into and elaborated on, where the stabilizing impulses (SIs), inactive impulses (IIs), and destabilizing impulses (DIs) are taken into account, respectively. Not resembling several earlier works, a more extensive range of impulses in the context of impulsive effects has been analyzed without using the known average impulsive interval strategy (AIIS). In light of the theories of differential inclusions and set-valued map, as well as impulsive control, new sufficient criteria with respect to the estimated settling time for synchronization of the related IMNNs are established using two types of switching control approaches, which sufficiently utilize information from not only the SIs, DIs, and DAFs but also the impulse sequences. Two simulation experiments are presented to the efficiency of the proposed results.

In this research, the fuzzy adaptive event-triggered control (FAETC) issue is addressed for uncertain nonlinear networked control systems with network-induced delays (NIDs) and external disturbance. In order to effectively capture parameter uncertainties, the interval type-2 (IT-2) Takagi-Sugeno (T-S) fuzzy model is utilized to represent such a system. Considering the fact that the controller is fuzzy and the threshold can promptly update its state according to the current and latest sampled signals (SSs), it becomes quite challenging to solve the dissipative stabilization problem (DSP) with the existing schemes. Then, a novel FAETC protocol is put forward to reduce the utilization of communication resources while maintaining the desired control performance. By employing the fuzzy-logic technique and the looped Lyapunov functional (LLF) approach, sufficient conditions related to the relationship between the stabilization and desired dissipative performance for the resulting system are formulated. A numerical example is used to validate the feasibility of our attained results.

In this article, the fuzzy pinning adaptive event-triggered control (FPAETC) problem related to complex networked systems is investigated. In light of the fact that the underlying controllers are fuzzy pinning, and the threshold parameters can flexibly update their states based on the latest and current sampled signals rather than transmitted signals, a novel state-dependent FPAETC protocol is devised to reduce the frequency of event-triggering and save more communication resources. Moreover, a more general time-dependent AETC protocol is proposed compared to the existing related results. Then, in combination with the graph theory, looped Lyapunov functional method, and fuzzy logic pinning technique, some relaxed criteria that establish the relationship between the synchronization and desired$\mathcal {H}_{\infty }$performance for the considered system are derived. Simulation results validate the superiority of derived theoretical results.

In this article, an investigation about the issue of fuzzy event-triggered asynchronous dissipative control for T–S fuzzy Markov jump systems (FMJSs) with unknown transition probabilities and nonuniform sampling is conducted. First of all, a mode-dependent looped Lyapunov–Krasovskii functional (LKF) is proposed, which takes full use of the available information not only from sawtooth structure characteristics but also from the inner sampling dynamics. Meanwhile, a hidden Markov chain is employed to depict the mismatch between the original system and the state-dependent fuzzy event-triggered controller. Then, based on the LKF methodology, matrix inequality techniques, and the reciprocally convex combination protocol, some relaxed criteria with respect to the stochastic stable of the considered system and the desired dissipative performance are derived, simultaneously. A numerical experiment is given to illustrate the significance of the theoretical results.

In this paper, the issue on cluster synchronization (CS) of complex networks (CNs) with parameter mismatches and time-varying delays (TVDs) is investigated. A memory pinning impulsive control (MPIC) approach, which is dependent not only on the current state and the history state but also on the number of nodes to be controlled at impulsive instants, is proposed for the first time. By resorting to the effective Lyapunov functional and impulsive differential inequalities, some criteria for achieving the CS of the concerned networks are established. Compared with the existing results, the network nodes are nonidentical and two MPIC cases are taken into account. Two numerical examples based on MATLAB software are presented to demonstrate the effectiveness of the proposed MPIC approach.

This paper deals with the issue of sampled-data stabilization for T-S fuzzy systems (TSFSs) with state delays and nonuniform sampling. First, a fuzzy membership function (FMFs)-dependent approach is proposed, which uses information not only on both the delayed state and actual sampling pattern but also on the FMFs. Second, the inner sampling interval is split into flexible terminals, and a novel FMFs-dependent Lyapunov-Krasovskii functional (LKF) is constructed. Meanwhile, a fuzzy sampled-data controller (FSDC) with a switched topology is designed to solve the tricky issue on the estimation of FMFs-dependent terms. Then, based on the LKF methodology, the extended Wirtinger's inequality, and an improved reciprocally convex combination strategy, some relaxed criteria with both a larger sampling period and upper bound of time delays for achieving the stabilization of TSFSs are derived. Two numerical examples are presented to demonstrate the superiority and applicability of the proposed scheme.

This paper studies the problem of synchronization for a class of Takagi–Sugeno (T–S) fuzzy complex networks, where the node dynamics may include partial coupling, diffusion coupling, discrete-time coupling, and delayed coupling. A fuzzy sampled-data control strategy that takes into account the time-delay effect is designed to solve the synchronization problem of such networks. Synchronization criteria are established for complex networks with the target node by constructing a modified time-dependent Lyapunov functional and using a mathematical induction approach. In contrast to most existing results, the constructed Lyapunov functional is neither necessarily positive on sampling intervals nor needlessly continuous at sampling instants. Moreover, a corresponding greater aperiodic sampling interval is obtained. Numerical simulation is presented to demonstrate the feasibility and validity of the proposed strategies.

This paper investigates the issue of sampled-data stabilization for Takagi-Sugeno fuzzy memristive neural networks (FMNNs) with time-varying delay. First, the concerned FMNNs are transformed into the tractable fuzzy NNs based on the excitatory and inhibitory of memristive synaptic weights using a new convex combination technique. Meanwhile, a switched fuzzy sampled-data controller is employed for the first time to tackle stability problems related to FMNNs. Then, the novel stabilization criteria of the FMNNs are established using the fuzzy membership functions (FMFs)-dependent Lyapunov-Krasovskii functional. This sufficiently utilizes information from not only the delayed state and the actual sampling pattern but also the FMFs. Two simulation examples are presented to demonstrate the feasibility and validity of the proposed method.

This paper studies the problems of stability and stabilization of a class of chaotic systems (CSs) with Takagi-Sugeno fuzzy model and nonuniform sampling. It designs a fuzzy sampled-data protocol based on a switched idea to tackle the stability issue of such systems. Novel criteria on stabilization of CSs are established by employing a fuzzy membership function (FMF)-dependent Lyapunov functional and the information of the time derivative of FMFs, which significantly utilize the available characteristics of the actual sampling pattern and FMFs simultaneously. Unlike the existing works, a larger sampling interval is obtained by this new approach. A simulation example on chaotic Rossler's system is employed to demonstrate the superiority and reduced conservatism of the proposed method.

This paper studies the problem of synchronization for a class of stochastic complex dynamical networks. It designs for the first time a distributed impulsive protocol based on pinning control that involves a constant signal transmission delay to tackle synchronization issues of such networks. Novel criteria on network synchronization are established by employing a time-dependent Lyapunov functional and a mathematical induction approach, where information on the state variables themselves and their neighbors is sufficiently utilized. Moreover, it is shown that the frequency of impulsive occurrence, impulsive input delays, stochastic perturbations, and the feedback control strength can significantly affect the synchronization performance. Numerical simulations are given to illustrate the effectiveness of the derived theoretical results.