In this article, we propose a full-state constraints asymptotic stability control for nonlinear multiple-input-multiple-output (MIMO) systems. This algorithm addresses the full-state constraints problem by restricting the constraint intervals to only the system states, thus compensating for the limitations of barrier Lyapunov functions. Additionally, it ensures the asymptotic stability of the closed-loop system in the presence of unknown functions. In contrast to neural network and fuzzy approximation algorithms, our proposed algorithm facilitates the convergence of system states to the origin without relying on assumptions about unknown functions. This leads to a reduction in conservatism compared to these algorithms. Furthermore, a simulation example is provided to demonstrate the effectiveness and superiority of the proposed algorithm.

In this paper, fixed‐time synchronization of nonlinear stochastic coupling multilayer neural networks is studied. The neural subnets in the multilayer networks are delay Cohen–Grossberg neural networks (DCGNNs). To overcome uncertain factors, we designed an adaptive delay‐dependent controller in synchronization. To describe constraints of communication and other related problems in networks, which are due to limitations for bit rates and bandwidths in communication channels, an adaptive fixed‐time control strategy is purposed by introducing quantization signal input. A theoretical framework about fixed‐time synchronization in multilayer delay Cohen–Grossberg neural networks (MDCGNNs) is established. We find that fixed settling time is related to the scale of MDCGNNs, characteristics of the designed controller parameters, and level of quantization. Finally, the effective of the theoretical framework is validated in an example.

This paper proposes a new full-state constraints (FSCs) control scheme for stabilizing nonlinear systems with unknown functions. This scheme solves the "explosion of terms (EOT)" problem of backstepping using a lemma and introduces fuzzy control to approximate the unknown functions. This ensures that all signals are semi-globally uniformly ultimately bounded (SGUUB), and the system state converges to a neighborhood of the origin. In order to get a smaller neighborhood and higher accuracy, a new time-varying constraint function is proposed to solve the FSCs. This method can directly design the constraint functions according to actual needs, and it is easy to implement, thus avoiding the shortcomings of the barrier Lyapunov functions (BLFs) and the mapping constraint functions. And it affects the steady-state performance of the states. Therefore, the constraint functions can be constructed to make the states approach a smaller neighborhood, thus making the steady-state performance error of the states is smaller. Thirdly, the algorithm is applied to Chua's circuit system, which verifies its validity.

In this article, a control algorithm is proposed to solve the global stabilization control problem of multiple input multiple output (MIMO) nonlinear systems with unknown function vectors (UFVs). Firstly, a Lemma dealing with UFVs is proposed. Then, combined with the backstepping method, the controller is designed such that all signals of the closed‐loop system are globally stable. Compared with the approximation method, the algorithm in this article solves the global stabilization control problem. Compared with the assumptions of UFVs, the algorithm in this article reduces the conservatism problem. At the same time, the algorithm in this article also solves the “explosion of terms” problem of backstepping. Compared with the methods to solve this problem: dynamic surface control (DSC) and direct fuzzy control, the algorithm in this article can make all signals of the closed‐loop system converge to the origin. Finally, the algorithm is applied to the model of spacecraft with modified Rodrigues parameters, and the simulation results show the effectiveness of this algorithm.

The implementation of fixed-time synchronization is a challenging problem for dynamic networks with derivative coupling. When there are derivative coupling in multilayer heterogeneous dynamic networks, it is difficult to obtain the fixed-time stable synchronization criteria via using the conventional Lyapunov function. To overcome these difficulty and challenge, we choose a special Lyapunov function to solve the fixed-time stable synchronization criteria. To eliminate the differential term in fixed time controller and reduce the difficulty of the design for controller, different from the comprehensive method used in lots of literatures, we use analysis method to design a fixed time control strategy. To be closer to reality, we consider multilayer neural networks with stochastic disturbances and nonlinear connections. When designing the controller, considering the actual communication constraints, we introduce quantization into the designed controller. Under the theoretical framework, we find that the upper limit for function of synchronization time is related to the quantization intensity, parameters of the designed Lyapunov function, parameters of the controller and a maximum eigenvalue related to the structure of multilayer Cohen–Grossberg neural networks. Finally, an secure communication algorithm based on the synchronization scheme is designed. The secure communication algorithm can be achieved before 11.0027. This shows that the derived theoretical framework is effective.

Synchronization is an essential factor of multiplex dynamic networks. We study synchronization among multiplex delay networks with random switching nonlinear coupling under random disturbances. Different from the existing research works, this paper focuses on multiplex delay nonlinear coupling networks with Bernoulli random switching topologies. To overcome the time-varying dynamic characteristics of the multiplex networks and describe the limitation in signal transmission process, an adaptive delay-dependent quantitative tracking control policy is presented. To achieve synchronization among the multiplex switched delay networks in a limited time, finite time control technology is introduced into the design of the adaptive quantitative controller in the policy. The theoretical framework about the stochastic synchronization among the multiplex switched nonlinear coupling networks is derived. We find that the setting time is related to quantization level, parameters of controllers and Lyapunov functions. Finally, an example is presented to show the effective for the theoretical framework.

This paper addresses the problem of H infinity admissibilization for time-varying delayed non-linear singular impulsive jump systems based on memory fuzzy state-feedback control. New criteria to guarantee the H infinity admissibility for the fuzzy time-varying delayed singu-lar impulsive jump systems are obtained by establishing the improved timer-dependent Lyapunov-Krasovskii functional. The singular value decomposition technique is utilized to eliminate internal impulses, and the memory fuzzy state-feedback controller is successfully designed by using parallel distribution compensation technique to inhibit the effects of ex-ternal unstable impulses and time-varying delays. The gains of the desired memory fuzzy state-feedback controller are obtained through solving linear matrix inequalities. Finally, the rightness and validity of the obtained results are demonstrated by two simulation ex-amples containing the bio-economic system.(c) 2023 Elsevier Inc. All rights reserved.

In this paper, an adaptive fuzzy output feedback control scheme is proposed for a class of unknown nonlinear systems with sensor attacks. The fuzzy logic systems are employed to approximate the uncertain nonlinearities, and the backstepping technique is implemented to construct controllers. The unknown output feedback coefficient is handled by using a Nussbaum function in the first step of backstepping design, eventually developing an adaptive controller to accommodate sensor attacks. Compared to the current control schemes for nonlinear systems under sensor attacks, the benefit of the proposed adaptive controller is that it can apply to nonlinear systems with both uncertainty and unmeasurable states. Finally, two examples validate the efficacy of the proposed control scheme.

This paper researches robust H_∞ asynchron-ous fault detection for uncertain singular Markov jump systems with time-varying delays based on hidden Marko-v model strategy. The aim is to implement asynchronous fault detection for uncertain singular Markov jump system and realize stochastic admissibility with H_∞ performance level for augmented uncertain singular Markov jump fault detection system. By applying singular value decomposition method and free weighting matrix technique, modified admissibility conditions are addressed based on Lyapunov stability theory. Robust fault detection problem is translated into H_∞ filter design in this work. A hidden Markov model is used to describe a kind of asynchronous phenomenon produced by original system’s modes and fault detection filter’s modes, and the desired filter gains are obtained by solving linear matrix inequalities. Finally, a numerical example and a direct current motor system are used to verify the effectiveness of this approach.

This paper investigates the deconvolution filter for Lur'e time-varying delays singular Markovian jump systems. Firstly, by establishing mode-dependent Lyapunov–Krasovskill functional and considering sector bounded conditions, stochastic stability conditions and H∞ performance index are obtained for Lur'e singular Markovian jump systems. Secondly, both regularity and impulse-freeness are acquired by singular value decomposition technique. Thirdly, deconvolution filter is realized by virtue of linear matrix inequalities. Ultimately, the utility of this present method is validated by a numerical example and an oil catalytic cracking process.

The finite-time synchronization issue of reaction-diffusion memristive neural networks (RDMNNs) is studied in this paper. To better synchronize the parameter-varying drive and response systems, an innovative gain-scheduled integral sliding mode control scheme is proposed, where the 2n controller gains can be scheduled and an integral switching surface function that contains a discontinuous term is involved. Moreover, by constructing a novel Lyapunov-Krasovskii functional and combining reciprocally convex combination (RCC) method, a less conservative finite-time synchronization criterion for RDMNNs is derived in the form of linear matrix inequalities (LMIs). Finally, three numerical simulations are exploited to illustrate the effectiveness, superiority and practicability of this paper.

In this paper, an adaptive neural control scheme is proposed for a class of unknown nonlinear systems with unknown sensor hysteresis. The radial basis function neural networks are employed to approximate the unknown nonlinearities and the backstepping technique is implemented to construct controllers. The difficulty of the control design lies in that the genuine states of the system are not available for feedback, which is caused by sensor hysteresis. The proposed control scheme eventually ensures the practical finite-time stability of the closed-loop system, which is proved by the Lyapunov theory. A numerical simulation example is included to verify the effectiveness of the developed approach.

This article investigates the fixed-time adaptive fuzzy output-feedback control for nontriangular structural nonlinear systems with multiple objective constraints, where the fuzzy logic systems (FLSs) are employed to construct a state observer to estimate the unavailable states. Firstly, a modified fractional-order filter (FOF) is adopted to handle the issue of singularity inherent in the constraint domain under the backstepping framework. Meanwhile, the error compensation signal is designed to eliminate the negative effect of filter errors. Secondly, a barrier Lyapunov function (BLF) is utilized to accommodate the multiple objective constraints. Furthermore, the event-triggered rule with a relative threshold is constructed to neutralize the communication burden. Following the fixed-time stability criterion, it is demonstrated that the proposed control scheme not only ensures that the tracking error is adjusted to a residual set within a fixed time, but also multiple objective functions are confined within the specified range. Finally, a simulation practical example is implemented to confirm the validity and potential of the theoretical results.

This article investigates the issue of hybrid-triggered control for fuzzy Markov jump system under input saturation. First, a hybrid-triggered control scheme is presented, which can improve network transmission efficiency and system performance. Second, by constructing a novel Lyapunov–Krasovskii functional which fully considers the whole sampling interval information from $$x(t_{k})$$ to $$x(t_{k+1})$$ , the stochastic stability conditions with less conservative are proposed using linear matrix inequality approach. Then, the desired mode-dependent controller is developed for the studied system. Finally, numerical examples are offered to verify the practicability of our results.

The units of networks, composed of systems, may converge to the same behavior in a fixed settling time, known as fixed‐time synchronization. In this paper, the problem of fixed‐time outer synchronization of double‐layered multiplex networks is studied. To synchronize the networks, delayed feedback controllers are designed. To guarantee the fixed‐time synchronization, some sufficient criteria are derived. The controllers and sufficient criteria can be applied to undirected and directed networks. The fixed settling time is related to controllers, the number of nodes for networks, and the dimension of node. Some numerical examples are provided to illustrate the effectiveness of our results.

This paper studies global output feedback stabilization (OFS) control for non-strict feedback nonlinear systems with unknown functions (UFs). In order to deal with the UFs problem, a Lemma is proposed. This Lemma not only avoids the disadvantages of approximation methods, but also avoids the Assumptions of UFs. Therefore, the algorithm in the paper reduces the conservatism. Then, a control algorithm is proposed to solve the global OFS control problem of closed-loop systems. At the same time, the “explosion of terms” problem of backstepping is also solved. Finally, the algorithm is applied to Duffing system and Chua’s oscillator system to verify the effectiveness of the algorithm.

This paper focuses on the mean-square exponentially (ME) admissibility and stabilization for neutral singular Markovian jump systems (NSMJSs) with mixed interval time-varying delays. With the aid of state decomposition approach, a state decomposition Lyapunov-Krasovskii functional (LKF) is founded and some mode-dependent conditions are given to guarantee the unforced NSMJSs to be ME admissible. On this basis, a mode-dependent state feedback controller is presented. By solving the decomposition components of state feedback control parameters, the designed state feedback controller is obtained. It should be pointed out that our results complement and improve the results of the existing literatures. By two number-based examples, the advantage and availability of our methods are presented. (C) 2021 Elsevier Inc. All rights reserved.

The problem of finite-time filtering for nonlinear Markovian jump systems subject to extended dissipativity with unknown transition rates and time-varying delays is investigated in this paper. Firstly, by constructing novel Lyapunov-Krasovskii functionals and utilizing delay partitioning method, the error system is proved to be stochastically finite-time bounded and extended dissipative. Secondly, in virtue of linear matrix inequalities approach, the desired mode-dependent filter is obtained. Finally, two simulations are illustrated for the purpose of demonstrating the less conservativeness and effectiveness of the proposed method.

The issue of asynchronous mixed H-infinity and passive control for Takagi-Sugeno fuzzy singular delayed Markovian jump system is investigated in this paper. The modes of designed fuzzy controller operate asynchronously with the modes of original system, which can be represented by a hidden Markovian model (HMM). By constructing a delay-dependent and mode-dependent stochastic Lyapunov-Krasovskii functional, new criteria are derived to guarantee that the fuzzy singular delayed Markovian jump system is stochastically admissible with a mixed H-infinity and passivity performance. Then, an asynchronous fuzzy controller is designed successfully via parallel distributed compensation technique and HMM principle based on these criteria. Finally, two simulation examples including a DC motor device are presented to verify the correctness and effectiveness of the derived results. (C) 2022 Elsevier Inc. All rights reserved.

In this paper, the global output regulation problem (ORP) for nonlinear output feedback affine systems with unknown functions (UFs) and unmeasured states is studied. Firstly, a Lemma is proposed to solve not only the global ORP of nonlinear systems, but also the ‘explosion of terms’ problem of backstepping. Secondly, the control scheme is designed based on backstepping method, which avoids an Assumption to solve the ORP. Therefore, the algorithm reduces the conservatism and increases the applicability. Finally, the algorithm is applied to Duffing system to verify the effectiveness of the algorithm.