This paper considers multiparty synchronization (MS) for coupled memristive neural networks (CMNNs) with a time delay. Some T-S type If-Then fuzzy rules are also introduced into the CMNNs. An event-triggered controller (ETC) is designed to achieve the MS in finite time, which avoids continuous control signals. Along with Lyapunov theory, differential inclusion theory, and inequalities, some criteria can be obtained to achieve the finite-time MS (FTMS), and the setting time (ST) of the FTMS can be calculated. By jointly considering ST, control inputs, error networks, and synchronization conditions, an optimization model is provided to get an optimal ETC (OETC). Further, the particle swarm optimization (PSO) algorithm is utilized to solve the optimization model. Thus, it gives a method to choose control gains and parameters of an event-triggered function (ETF). Finally, two examples are given to verify the theoretical results. Especially, two comparative experiments are proposed to demonstrate that the OETC can save more control energy and reduce the number of triggered times.

This paper presents a method for state interval estimation and fault detection in lane-keeping systems for intelligent connected vehicles, utilizing a linear parameter-varying model and set-membership estimation approach. First, the inherently nonlinear vehicle dynamics model is transformed into a linear parameter-varying model. By retaining local nonlinear terms, the number of subsystems is reduced, thus decreasing the complexity of the method. The design flexibility of the observer is improved through specific matrix transformations. Using the L _∞ technique, the conservatism of nonlinear term constraints is reduced while maintaining performance. The observer gain matrix solution is reformulated as a linear matrix inequality problem without increasing computational complexity. Based on the interval estimation results, a fault detection logic is proposed that eliminates the need for manually set thresholds. Simulation results confirm the effectiveness and feasibility of the proposed method.

The adaptive optimal tracking control problem for n-link flexible-joint (FJ) robots is investigated in this article. The command filter is employed to eliminate the “explosion of complexity” (EOC) issue, while simultaneously removing the effect of filtered errors through the construction of fractional power error compensation signals. By means of fuzzy logic systems, the unknown nonlinear dynamics of FJ robots are identified. Building on this framework, an actor–critic structure is introduced to simultaneously optimize feedforward and feedback signals. It is proven that the closed-loop system is practical predefined-time stable, and the tracking errors of FJ robots converge to a small neighborhood of the origin within a predefined time. Finally, the effectiveness and superiority of the proposed algorithm are verified through a two-link FJ robots simulation example.

This article discusses how second-order nonlinear multi-agent systems achieve bipartite consensus in finite-time using intermittent event-triggered control strategies. Due to the problem arising from antagonistic links between neighboring nodes of second-order multi-agents, the finite-time bipartite consensus analysis is investigated, which is more challenging than that with only cooperative communication links. Accounting for limitations of communication bandwidths, an intermittent framework is proposed first. By utilizing algebraic graph theories and gauge transformations, sufficient conditions are given to achieve finite-time consensus in signed second-order multi-agent systems. Second, a hybrid control mechanism combining intermittent control with event-triggered control is developed. Therefore, some finite-time synchronization conditions of second-order multi-agents on signed graphs are further studied. The proposed protocol reduces the updating frequency of the controller, which saves communication resources a lot. Finally, the Zeno behavior is excluded and some numerical examples are performed to show the effectiveness of the theoretical analysis.

The finite-time cluster synchronization (FTCS) of fractional-order complex-valued (FOCV) neural network has attracted wide attention. It is inconvenient and difficult to decompose complex-valued neural networks into real parts and imaginary parts. This paper addresses the FTCS of coupled memristive neural networks (CMNNs), which are FOCV systems with a time delay. A controller is designed with a complex-valued sign function to achieve FTCS using a non-decomposition approach, which eliminates the need to separate the complex-valued system into its real and imaginary components. By applying fractional-order stability theory, some conditions are derived for FTCS based on the proposed controller. The settling time, related to the system’s initial values, can be computed using the Mittag–Leffler function. We further investigate the optimization of control parameters by formulating an optimization model, which is solved using particle swarm optimization (PSO) to determine the optimal control parameters. Finally, a numerical example and a comparative experiment are both provided to verify the theoretical results and optimization method.