This paper addresses the dynamic event-triggered (DET) state and fault interval estimation issue for networked switched systems under asynchronous switching scenarios with persistent dwell time (PDT) restriction. To promote a trade-off between performance and communication efficiency, an improved discrete-time DET mechanism with double dynamic variables is proposed, in which the added dynamic variable facilitates more efficient resource saving. By introducing mode-dependent auxiliary variables, new switched intermediate estimators are designed for interval estimation, which not only are free from the estimator matching condition, but also increase the degree of design freedom. Furthermore, considering the general asynchronism between the systems and the estimators induced by the DET mechanism, a zonotope-based iterative procedure is provided to construct mode-dependent zonotopes involving state and faults. By means of the analysis in the asynchronous switching scenarios with PDT restriction, less conservative feasibility conditions are formulated to optimize the design parameters. In addition, estimator-mode-dependent radius functions are constructed to promote the settlement of L∞performance problem for the radii of the zonotopes. Finally, the feasibility and superiority of the designed scheme are confirmed by an application example.

This paper is concerned with the problem of the optimalH∞fuzzy proportional-integral-derivative (PID) controller design for Takagi-Sugeno fuzzy systems subjected to infinite-distributed time delays and weighted try-once-discard (WTOD) scheduling effects. To avoid data collisions in communication networks, an improved WTOD communication protocol is proposed, which offers higher transmission precedence to the most needed data node. According to non-parallel distributed compensation scheme, a novel yet easy-to-implement fuzzy PID controller is devised, in which the integral term of the fuzzy PID controller equipped with a limited time window can be utilized for mitigating computational burden. Subsequently, considering the feasible regions of membership functions (MFs) of the fuzzy PID controller, in the case of guaranteeing stability of explored systems, a novel MFs online iterative learning strategy employing an adaptive momentum estimation algorithm is first provided for networked fuzzy systems. The proposed algorithm addresses the limitation of designing fuzzy controller MFs based on designers’ experience. On the basis of the provided MFs online iterative learning approach, MFs of the fuzzy PID controller are renewed in real-time for achieving betterH∞performance, namely, disturbance attenuation capacity for the explored systems. Ultimately, the feasibility of the developed control strategy is illustrated by means of some simulation results.

This paper is concerned with the adaptive dynamic event-triggered (DET)H∞control for a class of networked nonlinear systems with non-uniform sampling under the Takagi-Sugeno (T-S) fuzzy model. To address the problems of high energy consumption in sensors and the high occupancy rate of limited bandwidth, an improved adaptive DET communication mechanism with non-uniform sampling is proposed, in which the interval dynamic variable is constructed with an adaptive law. Unlike existing works, an online iterative asynchronous premise reconstruction (APR) technique is devised to tackle the challenges caused by the mismatch of premise variables. Based on the adaptive DET mechanism and the online iterative APR method, switched-like fuzzy controllers are designed, which can be switched at different triggering instances. Furthermore, to ensure stability and achieve the desired control performance, new triggering instants-dependent Lyapunov functions are construct- ed in accordance with the idea of event interval partitioning. Fi- nally, the practicability of the proposed strategy is demonstrated using two practical examples.

In human pose estimation, formulating keypoint localization as a classification task over discretized coordinate grids has proven effective. Essentially, the 2D features of the keypoints are reduced to 1D coordinate representations. This process leads to the loss of spatial constraints among keypoints and increases the difficulty for the model to capture their structural relationships. To address this issue, we propose an enhanced query attention mechanism constrained by bidirectional graphs. The core idea is to establish the topological constraints on the 1D coordinate representations. First, two fundamental connection directions of the skeleton are defined and encoded as a pair of adjacency matrices to enhance the feature interaction capability of the graph convolutional network (GCN). Second, a GCN-guided multi-scale feature fusion framework is designed to effectively combine multi-scale visual features with structural priors, thereby enhancing the representation of keypoint spatial distributions. Finally, a dual-gate module is incorporated into a GCN-guided attention unit to construct a structured query matrix constrained by the bidirectional skeleton graphs, which helps filter out spurious joint interactions and emphasize plausible ones. Extensive experiments on Tai Chi Chuan-Pose, Animal-Pose, AP-10K, MPII, COCO, and COCO-WholeBody datasets demonstrate that the proposed method outperforms existing methods in terms of both accuracy and robustness, particularly in balancing precise local keypoint localization with global pose consistency.

Abstract Hydraulic-supports alignment is to keep the coal mining face in line and is heavily influenced by the various geological states. The experiences produced by the moving process are unbalanced, which leads to the agent not learning important knowledge from the rare samples. This paper is the first to introduce the reinforcement learning to the hydraulic-supports alignment, and establish the Markov optimal decision model by TD3 algorithm. Aiming at the imbalance issue of the experience, this paper proposes a segmented experience pool and three sampling replay mechanisms according to the characteristics of the moving process with various geological states. Experimental results show that the improved TD3, utilizing a segmented experience pool with three different replay mechanisms, could effectively identify the optimal moving policy and achieve significant convergence in cases involving both normal movement and insufficient movement of hydraulic-supports. In contrast, the TD3 performs inadequately and struggles to find the optimal policy.

This paper aims to the remote synchronous control for complex networks with amplify-and-forward (AF) relays. Firstly, the AF relay dynamic with random noise is integrated into the control signal transmission. Secondly, the complex networks incorporating an AF relay is converted into the sliding control mode (SMC), which robustly addresses unknown disturbances among the nodes to achieve synchronous states. Thirdly, an adaptive gain mechanism based on the equivalent value of sign function of SMC is proposed to estimate the disturbance amplitude, hence mitigating the chattering of SMC. Finally, the illustrative simulation demonstrated the effectiveness of proposed methodology.

This article is concerned with the design problem of an $H_{\infty }$ optimal fault detection (FD) filter for networked interval type-2 (IT2) fuzzy systems that are subjected to stochastic cyberattacks. To effectively reduce the utilization of constrained network resources, a new dynamically adjusted event-triggered weighted try-once-discard (DAET-WTOD) protocol is developed, in which two adaptive rules are constructed based on the measured output and the probability of denial-of-service (DoS) attacks. Furthermore, a fuzzy switched-like FD filter is designed with the purpose of detecting system fault signals, while simultaneously considering the DAET-WTOD protocol and stochastic cyberattacks. Subsequently, by utilizing an imperfect premise matching (IPM) scheme, an opposition-based learning adaptive differential evolution algorithm is proposed to deal with the networked IT2 fuzzy systems. This algorithm is capable of iteratively searching the membership function values of the fuzzy filter in real time, thereby achieving improved $H_{\infty }$ performance. Finally, some simulation results are provided to verify the feasibility and advantages of the proposed $H_{\infty }$ optimal FD technique.

This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.

This article presents the finite-frequency optimization fault detection (FD) strategy for Takagi-Sugeno (T-S) fuzzy systems. Under the imperfect premise matching (IPM) policy, a weighted fuzzy FD observer (WFFDO) with the$L_{\infty }/L_{2}$robustness performance and the finite-frequency$ H_{-}$fault sensitivity performance is first proposed, which signifies the residual signal is robust to the external interference and sensitive to potential faults. Some parameters and slack matrices are introduced to obtain more relaxed conditions of designing the WFFDO with mixed performance. Afterward, a new online membership functions (MFs) iterative learning algorithm with the exponential decay learning rate is proposed for the sake of updating the observer MFs in real-time such that optimal$L_{\infty }/L_{2}$performance can be achieved in this article. In addition, sufficient criterion is established so as to ensure the convergence of the structured mean squared error cost function by means of Lyapunov stability theory. Eventually, two simulation examples are given for illustrating the feasibility and superiority of the developed optimization FD technique.

This article studies the problem of adaptive event-triggered-based reduced-order fault detection (FD) filter design for a category of nonlinear systems with complex communication channel under interval type-2 (IT2) fuzzy-approximation technique. For the purpose of further saving communication resources, a new adaptive event-triggered mechanism equipped with more comprehensive system information is proposed, which can also ensure that the data transmission rate is not lower than the minimum limit predesigned in real time. Considering the complexity of communication network environment, a new data transmission mathematical model with fading measurements and network induced delays is constructed. In addition, to decrease the design complexity of FD systems, a reduced-order FD filter subject to the adaptive event-triggered mechanism as well as mismatched membership functions is devised. By means of the Lyapunov theory, the sufficient criteria are derived to ensure stochastic stability with$H_{\infty }$index for the FD systems. Finally, two practical examples are provided for the sake of verifying the effectiveness of the presented FD technique.

This article deals with the distributed state estimation for mixed delays system under unknown attacks. A new multichannel random attack model is established for the first time, where network attacks are considered to exist in three channels: the target-to-sensor channel, the senor-to-sensor channel, and the sensor-to-estimator channel. In the above model, transmitted packets are allowed to be attacked multiple times simultaneously, and when they are successfully attacked, the transmitted information is modified. Besides, the topology of the sensor network is considered to change dynamically according to the Markov chain. Based on the newly established distributed estimation model, the estimation error system is proven to be asymptotically mean-square stable under a given${H_\infty }$antidisturbance index by using a Lyapunov theory and a stochastic analysis technique; then, the estimator parameter matrices are solved utilizing a linearization method. Finally, several simulation examples are listed to testify the effectiveness of the designed algorithm.

In this article, the optimal tracking control problem for a class of discrete time-delay systems with disturbance is studied using the information fusion control methodology. First, the disturbance estimation is regarded as a minimization problem, and an information fusion disturbance observer (IFDO) is designed. Second, combining the soft constraint requirement of the output tracking error contained in the performance index function, a fusion filter is constructed to estimate the system co-state and its information weight under system state and input delays. Finally, the obtained co-state and its information weight, the soft constraint requirement of the control input contained in the performance function, as well as the system delay dynamics, are all transformed into the information equations about the control input, and then the information fusion delay controller (IFDC) is directly obtained by fusing these information equations. As a result, the negative effects caused by disturbance and time delays can be effectively compensated at the same time. The convergence of the tracking errors of the closed-loop system is guaranteed by the Lyapunov stability theory. Numerical simulation results have been provided to demonstrate the effectiveness and robustness of the proposed control algorithm.

This article deals with H∞state estimation of neural networks with mixed delays. In order to make full use of delay information, novel delay-product Lyapunov-Krasovskii functional (LKF) by using parameterized delay interval is first constructed. Then, generalized free-weighting-matrix integral inequality is used to estimate the derivative of LKF to reduce the conservatism. Also, a more general activation function is further applied by combining with parameterized delay interval in order to obtain a more accurate estimator model. Finally, sufficient conditions are derived to confirm that the estimation error system is asymptotically stable with a prescribed H∞performance. Numerical examples are simulated to show the benefits of our proposed method.

This paper proposes a distributed model free adaptive iterative learning control (MFAILC) method for a class of unknown nonlinear multiagent systems to perform consensus tracking. Here, both fixed and iteration-varying topologies are considered and only a subset of followers can access the desired trajectory in each topology. To design the control protocol, the agent’s dynamic is first transformed into a dynamic linearization model along the iteration axis, and then a distributed MFAILC scheme is constructed to guarantee that all agents can track the desired trajectory. Through rigorous analysis, it is shown that under this novel distributed MFAILC scheme, the tracking errors of all agents are convergent along the iteration axis. The main merit of this design is that consensus tracking task can be achieved only utilizing the input/output data of the multiagent system. Three examples are given to validate the effectiveness of the proposed design.

This paper investigates the global consensus of multiagent systems with internal delays, communication delays, and directed topology. By defining a weighted average state of agents, the considered network is decoupled into several subsystems, then the consensus of the considered systems can be guaranteed by the asymptotic stability of several low dimensional linear delayed systems. Furthermore, new Lyapunov-Krasovskii functional is constructed by introducing multi-integral terms and new augmented vectors, and extended relaxed integral inequality combining Wirtinger integral inequality with convex combination approach is also employed to tackle with the derivatives of the functional. As a result, the less conservative stability criterion is derived, which guarantees the global consensus of the considered multiagent systems. Several numerical examples are provided to show the effectiveness of the proposed methods.

This paper investigates the local consensus issue for multiagent systems with nonlinear dynamics and time-varying delays. By defining a weighted average state of the agents and applying local linearization, we show that the local consensus of the agents in a directed communication network can be guaranteed by the asymptotic stability of several decoupled delayed systems. Then, by employing a novel Lyapunov-Krasovskii functional, proposing a new extended reciprocally convex approach and using some matrix analysis, we derive a sufficient condition for the local consensus in terms of linear matrix inequalities associated with the dynamics of the agents, the eigenvalues of Laplacian matrix, and the time-varying delay. Finally, two numerical examples are provided to show the effectiveness of the analytical results.