This article studies the stability issue and provides the hierarchical stability conditions for generalized neural networks (GNNs) embedded with interval variant delay (delay’s differential is unidentified). First, by transforming the state vectors with integral in the generalized free-matrix-based integral inequalities (GFIIs) into the multiple integral state vectors, the Lyapunov-Krasovskii functional (LKF) with hierarchy is put up based on these multiple integrals. Then, in the treatment of the LKF derivative, the GFIIs are utilized to estimate the delay related integrals of the quadratic product items. For the LKF differential, it is obtained as the delay function with the$2N-1$degree. Next, to set up the linear matrix inequality (LMI) forms and solve the nonlinear items injected by the GFIIs, the novel matrix-based negative conditions (NCs) for odd degree polynomials are put forward. Finally, the superiority of the proposed stability conditions with hierarchy is illustrated by several numerical examples.

In this article, the stability analysis for generalized neural networks (GNNs) with a time-varying delay is investigated. About the delay, the differential has only an upper boundary or cannot be obtained. For the both two types of delayed GNNs, up to now, the second-order integral inequalities have been the highest-order integral inequalities utilized to derive the stability conditions. To establish the stability conditions on the basis of the high-order integral inequalities, two challenging issues are required to be resolved. One is the formulation of the Lyapunov-Krasovskii functional (LKF), the other is the high-degree polynomial negative conditions (NCs). By transforming the integrals in N-order generalized free-matrix-based integral inequalities (GFIIs) into the multiple integrals, the hierarchical LKFs are constructed by adopting these multiple integrals. Then, the novel modified matrix polynomial NCs are presented for the 2N-1 degree delay polynomials in the LKF differentials. Thus, the hierarchical linear matrix inequalities (LMIs) are set up and the nonlinear problems caused by the GFIIs are solved at the same time. Eventually, the superiority of the provided hierarchical stability criteria is demonstrated by several numeric examples.

In this article, the dynamic event-triggered control scheme is given to achieve the consensus of a class of nonlinear multiagent systems with switching topologies and random packet loss. Different from the existing works of modeling topology switching in a random way, the persistent dwell-time switching rule is utilized to depict the scenario of topology alterations. To cope with the problem of redundant data transmission, the information interaction between neighboring agents is determined by the up-front design triggering condition. Instead of the conventional static threshold parameter (TP) in the triggering condition, the TP in this article can be adjusted dynamically. This means that the update frequency of the controller can be further optimized. The imperfect matching phenomenon between premise variables about the fuzzy system and controller is also tackled. Moreover, in theory, the packet loss is modeled as a Markov process. Eventually, an application example about the truck-trailer model is presented to express the practicability of the proposed method.

This article investigates the neural-network-based (NN-based) set-membership filtering issue for nonlinear systems. In order to lighten the network transmission burden and avoid data collisions, the weighted try-once-discard (WTOD) protocol is employed to regulate the signal transmission process, which provides higher transmission priority to the most needed data. Considering the data discarding problem of the WTOD protocol, a novel event-triggered compensation mechanism is proposed to compensate the measurement output processed by the WTOD protocol, thereby improving the filtering performance. Next, considering the nonlinear dynamics of the system and the unknown-but-bounded (UBB) noise interference, an NN-based set-membership filter is designed to solve the state estimation problem. In a unified set-membership framework, an neural-network (NN) weight adaptive tuning law and a state estimation algorithm are designed. Sufficient conditions are derived for the existence of the adaptive NN parameters and the NN-based set-membership filter, and two optimization problems are put forward to seek the optimal NN parameters and filtering parameters that make the filter performance optimal. Finally, illustrative examples demonstrate the effectiveness of the proposed compensation mechanism and filtering algorithm.

This article investigates the tracking problem of event-triggered prescribed performance fuzzy fault-tolerant control (FTC) for unknown Euler–Lagrange systems with actuator faults and external disturbances. First, the barrier Lyapunov functions (BLFs) and prescribed performance functions are synthesized to guarantee that the tracking errors satisfy the preset transient performance. Different from existing prescribed performance control methods, which require the initial values of the tracking errors to be within the prescribed performance functions, an error transformation method is introduced to ensure that the tracking errors with any bounded initial values can enter the preset boundaries within a preset time. Then, considering the unavailability of system parameters, the fuzzy logic systems are used to approximate unknown parameters of the system. What is more, to solve the problem of limited communication and computing resources in practical systems, an improved event-triggered control (ETC) scheme is proposed, which can reduce the communication and computation burden without satisfying the input-to-state stability assumption. Meanwhile, the Zeno phenomenon can be avoided. Furthermore, the effects of actuator faults and the event-triggered mechanism are handled by Nussbaum gain technology. Finally, the superiority of the proposed control algorithm is verified by simulation results.

This article addresses the exponential synchronization problem for a class of fuzzy inertial memsirtor-based neural networks with mixed time-varying delays. First, the inertial items are described as second-order systems and transformed into first-order systems by utilizing a appropriate variable substitution. Then, the fuzzy state-feedback control strategy and fuzzy adaptive control strategy are designed to ensure the exponential synchronization under the framework of Filippov solutions. The exponential synchronization algebraic conditions are obtained by choosing a proper Lyapunov–Krasovskii functional. Finally, two numerical simulations are provided to validate the effectiveness and benefit of the proposed results.

This article considers the problem of asynchronous fault detection filter (FDF) design for Takagi–Sugeno (T–S) fuzzy singular systems via dynamic event-triggered scheme. A mode-dependent dynamic event-triggered scheme is adopted to alleviate the communication load. Besides, a hidden Markov model is introduced to describe the asynchronous phenomenon between the system and the FDF. First, some sufficient criteria are established to ensure that the residual system is stochastically admissible with a certain$H_\infty$performance. Second, solvability criteria are presented to codesign the desired FDF gains and the event-triggered matrices. Finally, the correctness of the proposed method is shown by two examples.

By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output‐error‐like system with autoregressive moving average noise from input–output data. The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels. The simulation test results indicate that the proposed algorithms are effective.

This paper investigates the flight control of a quadrotor subject to the model uncertainties and external disturbances. We propose a linear quadratic regulation (LQR) tracking algorithm. However, the designed LQR controller is hard to be implemented because of the existing noises in the measured states. A modified extended Kalman filter (EKF) is then designed for the online estimation of the position, velocity and motor dynamics by using the measured outputs. From the experimental testing results, it is shown that the proposed EKF-based LQR control method solves the tracking problem of the quadrotor with less tracking errors than only using the LQR method.

Scale‐invariant feature transform (SIFT) operator is a widely used algorithm to detect local features in images, yet applying this algorithm for motion‐blurred image matching is difficult and inefficient. To resolve this issue, this study presents a motion‐blurred invariant SIFT algorithm that is based on sample matching in an image deformation reconstructed space. First, the motion‐blurred equation is deduced and its controlling parameters to reconstruct the deformation space are discretised. Second, the authors matched samples in the motion‐blurred space with the blurred image to identify maximal matching points and optimal parameters. In order to improve the searching efficiency, the authors used a univariate search technique combined with a variable step hill‐climbing method to determine the optimal matching. Together, the experimental results show that this improved algorithm has superior matching performance for motion‐blurred images.