Accurate lane detection is critical for autonomous driving safety. In recent years, anchor-based detection methods have made significant progress. However, existing frameworks struggle in complex scenarios such as nighttime or dazzle light environments. Additionally, these methods exhibit limited geometric modeling and extrapolation capabilities for curvature variations in curved lanes. To tackle these challenges, we propose LaneMind, an innovative framework that combines human visual perception principles with advanced geometric modeling. Our approach features a dual-path architecture with cross-path attention mechanism, enabling simultaneous local feature extraction and global structure modeling. The network outputs confidence heatmap, followed by a skeleton-guided regression module that extracts medial-axis skeletons from high-probability lane regions to precisely localize lanes while maintaining topological continuity. Experimental results demonstrate that LaneMind achieves competitive performance across various benchmarks, particularly excelling in challenging curved lane scenarios and adverse lighting conditions. The framework’s robust performance and accurate detection quality highlight its potential for real-world autonomous driving applications.

This article addresses the issue of adaptive event-triggered and quantized control for a category of uncertain nonlinear systems, utilizing a prescribed-time (PT) control framework. We begin by introducing a dynamic event-triggering mechanism and a dynamic event-driven quantizer to develop a discrete control framework, without assuming the constraint of input-to-state stability (ISS). The aperiodic discrete control method can effectively improve the data transmission efficiency of the networked control system. Then, according to the adaptive parameter estimation, a novel PT event-triggered adaptive controller and a PT sampled and quantized adaptive controller are proposed. Compared with the backstepping control method, the designed "one-step-controller" decreases the computational loads of the virtual controllers. Moreover, the global PT stability of the nonlinear system is assured, and the Zeno phenomenon of the event-triggered sampling does not happen. Finally, the practicability and availability of the designed control method are validated via a numerical system and a manipulator system.

The problem of differentially private distributed Nash equilibrium seeking for aggregative games with unknown nonlinear players under unbalanced directed graphs is investigated in this article. We utilize an auxiliary variable to eliminate the influence of the unbalancedness caused by the topological structure. The consensus protocol is employed to estimate the aggregate function. To protect players' sensitive information, Laplace noise is added on shared messages among players to perturb the raw information. By combining with the heavy-ball method, Nesterov gradient descent strategy and the consensus protocol, a momentum-based auxiliary system is designed to generate the Nash equilibrium signals with differential privacy guarantee with the help of the fixed gradient step size. A weakening factor is adopted to ensure the almost sure convergence of the designed auxiliary system in the presence of noise. Then, we analyze the linear convergence of the auxiliary system to the Nash equilibrium by using the linear systems inequalities. The rigorous differential privacy with a finite cumulative privacy budget without requiring a tradeoff between the convergence accuracy and differential privacy is also proven. In addition, an adaptive fuzzy method is used to deal with the unknown nonlinear dynamics, and the controller is designed to drive the actions of the players to the neighborhood of Nash equilibrium with the aid of auxiliary system. Finally, we present a numerical example to present the effectiveness of the proposed algorithm.

This article focuses on the problem of predefined-time adaptive output-feedback tracking control with prescribed performance for a quadrotor unmanned aerial vehicle (QUAV). Fuzzy logic systems (FLSs) are utilized to identify the unknown nonlinear dynamics of QUAV, and a fuzzy state observer is devised to estimate immeasurable states. By using the command filter, the problem of “explosion of complexity” is successfully averted, meanwhile the influence of filtered error is eliminated by way of the fractional power error compensation mechanism. The issue of singularity is effectively tackled by the hyperbolic tangent function's property and L'Hospital's rule. A predefined-time performance function is inserted into the control scheme to ensure that the tracking errors are restricted to the preassigned performance bounds. It is strictly proven that the closed-loop system is practically predefined-time stable, and the position and attitude tracking errors are driven into a small region around zero in a predefined time. Finally, a comparative simulation example is provided to show the validity and superiority of the proposed predefined-time adaptive control algorithm.

This article provides a novel framework for the concurrent development of asynchronous observers and controllers for discrete-time nonlinear semi-Markov jump singularly perturbed systems subjected to mismatched modes, states, and premise variables between controlled systems and observer-based controllers. Aiming at characterizing nonlinearity with parameter uncertainty, the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy technique is implemented in system modeling. Meanwhile, it is supposed that both observer and controller modes can just be acquired via a hidden Markov mode detector in the first attempt. Then, following the concept of non-parallel distribution compensation (non-PDC), the observer-based IT2 fuzzy asynchronous controllers are constructed with observers and controllers sharing the same fuzzy membership function but different from that in systems, which improves the designed flexibility. In accordance with semi-Markov kernel approach and the Lyapunov function contingent upon both system modes and sojourn times, sufficient criteria are established for the functioning of expected mode-dependent IT2 fuzzy observers and controllers such that the σ-mean-square stability for the resulting nonlinear augmented semi-Markov jump singularly perturbed systems comprised of the controlled systems and observation error systems is guaranteed. Furthermore, from the perspective of fuzzy processing, parameters and relaxation matrices that comply with fuzzy rules are added to ensure system stability while further reducing the conservatism of conditions. Ultimately, a circuit model and comparison examples are shown to substantiate the necessity and superiority of the suggested technique.

This article addresses the finite-time neural predefined performance control (PPC) issue for state-constrained nonlinear systems (NSs) with exogenous disturbances. By integrating the predefined-time performance function (PTPF) and the conventional barrier Lyapunov function (BLF), a new set of time-varying BLFs is designed to constrain the error variables. This establishes conditions for satisfying full-state constraints while ensuring that the tracking error meets the predefined performance indicators (PPIs) within a predefined time. Additionally, the incorporation of the nonlinear disturbance observer technique (NDOT) in the control design significantly enhances the ability of the system to reject disturbances and improves overall robustness. Leveraging recursive design based on dynamic surface control (DSC), a finite-time neural adaptive PPC strategy is devised to ensure that the closed-loop system is semi-globally practically finite-time stable (SPFS) and achieves the desired PPIs. Finally, the simulation results of two practical examples validate the efficacy and viability of the proposed approach.

This work aims at the adaptive fuzzy fault-tolerant control (FTC) issue of nonlinear singular systems subject to external disturbances. First, we decompose the nonlinear singular system into two coupled subsystems: 1) differential subsystem and 2) algebraic subsystem. Then, an error system is denoted through designing state and disturbance observers, and the regularization and impulse-free conditions are derived. Combined with the state and disturbance observers, a novel output feedback-based adaptive fault-tolerant controller with the multi-input fault compensation technique and fuzzy approximation method is proposed, and the boundedness of the nonlinear singular system is guaranteed. Finally, the designed control scheme is utilized to a numerical system and a nonlinear circuit to validate the effectiveness and feasibility.

The work addresses the issue of observer-based control for networked nonlinear systems suffering from denial-of-service (DoS) attacks which are modeled in a novel persistent dwell time (PDT) switched form. The interval type-2 Takagi-Sugeno fuzzy model is adopted to accurately characterize the nonlinear feature in investigated systems and effectively capture uncertain parameters via membership functions. It should be noticed that a new framework comprising a special PDT switching strategy is proposed for distributed DoS attacks with energy limitation. Accordingly, the controlled system is converted into a complex and constrained PDT-switched system. Subsequently, based on weak multi-Lyapunov functions, membership-function-dependent sufficient criteria are offered for judging the globally uniformly exponential stability of the researched closed-loop system, from which the gains of the observer and the controller can be computed. Finally, an algorithm generating PDT switching sequences for distributed DoS attacks is proposed, and simulations are given to substantiate the usability of the developed approach.

This paper aims at proposing a dynamic aperiodic discrete mechanism based on event-based triggering and quantization for nonlinear systems considering time delay and disturbances. First, a dynamic event-based device for nonlinear systems subject to time delay is proposed with a state observer. The stability of the system is confirmed with an output-feedback control scheme and the Lyapunov-Krasovskii functional (LKF) technique. Then, a state observer and a disturbance observer are constructed with a new coordinate transformation for the nonlinear systems with external disturbances. A combined dynamic event-based sampling and quantization method is designed for the estimated states and disturbances. With the sampled and quantized estimated variables, an output-feedback control algorithm is presented to assure the boundedness of the closed-loop nonlinear systems with the LKF technique. Moreover, the Zeno behavior is avoided with the designed dynamic aperiodic discrete mechanisms. Finally, two examples are provided to present the feasibility of the designed event-based and quantized control method.

In this article,we investigate the distributed Nash equilibrium seeking problem for games with unknown nonlinear players under both event-triggered communication and switching topologies. To be specific, the fuzzy adaptive method is utilized to approximate the unknown nonlinear functions included in the players' dynamics. Under switching topologies, the fully distributed control strategies based on gradient-like optimization method and leader-consensus protocols are developed to solve the distributed Nash equilibrium seeking problem. A novel event-triggered communication protocol based on the local information from neighboring players is designed under switching topologies to reduce the communication burden. With the help of Lyapunov stability theory, it is proved that under the developed control strategies, the players' actions can be driven to the small neighborhood of the Nash equilibrium and the Zeno behavior can be avoided. The effectiveness of the developed algorithms is demonstrated through a numerical example.

This paper studies the exponential synchronization problem of a class of fuzzy memristive inertial neural networks (FMINNs) with time-varying delays. Considering the controller gain fluctuation and transmission delay, nonfragile memory sampled-data control is firstly employed to solve the synchronization of FMINNs. This work does not use the variable conversion technique, but directly performs efficient analysis of the system. An improved fuzzy membership functions-dependent Lyapunov-Krasovskii functional with two-sided looped-functional is designed, which is based on the entire sampling period, and includes the current states and delayed states information. Then, a fuzzy sampled-data controller with a switching topology is designed, and synchronization criteria are established, which take the excitatory and inhibitory of memristive synaptic weights into account. Finally, the effectiveness of the proposed method and the practicability of the addressed model are verified through numerical results.

This article studies the fuzzy-based fixed-time attitude control problem for quadrotor unmanned aerial vehicle under full-state constraints. The backstepping method is utilized to design the fixed-time attitude controller. To avoid the singularity problem, a new switching function is designed in the controller design. Barrier Lyapunov functions are employed to ensure that the system states always satisfy the constraints, and fuzzy logic systems (FLSs) are introduced to approximate the unknown nonlinear functions of the system model. It is proved that the tracking errors converge to a small region around the origin in fixed time. To validate the effectiveness of the method, software-in-loop simulation and practical flight experiments are carried out with the PX4 platform.

This article addresses the event-triggered adaptive tracking control issue for a series of uncertain nonlinear systems subject to unknown disturbances. By designing a prescribed-performance controller, the tracking error of the closed-loop system can be bounded in a predetermined allowable range. At the same time, the fixed-time adaptive control scheme combined with fuzzy logic systems can assure that the nonlinear system can be stable in a fixed time without limiting the initial condition. Then, a dynamic event-triggered mechanism is designed to sample the input signal. Through the rigorous proof, we have obtained that the zeno behavior can be effectively avoided. By a numerical example and a robotic arm system, we can conclude that the presented dynamic event-triggered prescribed-performance control technology is feasible, and the simulation verification system can converge within a fixed time frame.

This paper studies the problem of adaptive fuzzy tracking for a class of nonstrict feedback stochastic systems with input delay and asymmetric state constraints. Input delay is addressed based on the Pade approximation and introducing an intermediate variable, then the control problem for the original systems is transformed into one for non-delay system. For state constraints in the system, this paper proposes nonlinear state dependent function (NSDF) instead of barrier Lyapunov function (BLF), which removes the feasibility conditions. By fuzzy logic system (FLS) and variable separation technology, this paper effectively solves algebraic rings created by nonstrict feedback structures. A fuzzy adaptive controller is proposed to ensure that all variables in the system are bounded in probability and the asymmetric state constraints are well kept all the time. Finally, simulation examples confirm the effectiveness of control strategy.

This article is concerned with the local stabilization of neural networks (NNs) under intermittent sampled-data control (ISC) subject to actuator saturation. The issue is presented for two reasons: 1) the control input and the network bandwidth are always limited in practical engineering applications and 2) the existing analysis methods cannot handle the effect of the saturation nonlinearity and the ISC simultaneously. To overcome these difficulties, a work-interval-dependent Lyapunov functional is developed for the resulting closed-loop system, which is piecewise-defined, time-dependent, and also continuous. The main advantage of the proposed functional is that the information over the work interval is utilized. Based on the developed Lyapunov functional, the constraints on the basin of attraction (BoA) and the Lyapunov matrices are dropped. Then, using the generalized sector condition and the Lyapunov stability theory, two sufficient criteria for local exponential stability of the closed-loop system are developed. Moreover, two optimization strategies are put forward with the aim of enlarging the BoA and minimizing the actuator cost. Finally, two numerical examples are provided to exemplify the feasibility and reliability of the derived theoretical results.

This article studies the optimal synchronization of linear heterogeneous multiagent systems (MASs) with partial unknown knowledge of the system dynamics. The object is to realize system synchronization as well as minimize the performance index of each agent. A framework of heterogeneous multiagent graphical games is formulated first. In the graphical games, it is proved that the optimal control policy relying on the solution of the Hamilton-Jacobian-Bellmen (HJB) equation is not only in Nash equilibrium, but also the best response to fixed control policies of its neighbors. To solve the optimal control policy and the minimum value of the performance index, a model-based policy iteration (PI) algorithm is proposed. Then, according to the model-based algorithm, a data-based off-policy integral reinforcement learning (IRL) algorithm is put forward to handle the partially unknown system dynamics. Furthermore, a single-critic neural network (NN) structure is used to implement the data-based algorithm. Based on the data collected by the behavior policy of the data-based off-policy algorithm, the gradient descent method is used to train NNs to approach the ideal weights. In addition, it is proved that all the proposed algorithms are convergent, and the weight-tuning law of the single-critic NNs can promote optimal synchronization. Finally, a numerical example is proposed to show the effectiveness of the theoretical analysis.

In this article, a robust optimal output regulation framework for nonlinear systems with unknown parameters is proposed, in which a suboptimal feedforward–feedback controller is designed. Specifically, a novel internal model principle-based feedforward controller is designed to cope with the adverse effects of unknown parameters. Considering the system performance and control cost, an optimal-feedback controller is then designed via the adaptive dynamic programming method. It is proved that under the proposed suboptimal controller, all the signals of the closed-loop systems remain bounded and the tracking error is arbitrarily small. Furthermore, the predefined performance index is minimized. Finally, two simulation examples are given to verify the effectiveness of the proposed framework.

In this article, the sampled-data control (SDC) problem for nonlinear systems is investigated with the help of the homogeneous domination approach. Different from the existing results, the nonlinearities of the systems are nonlinearly growing in unmeasurable states. A novel technical lemma is proposed to estimate the state trajectories between two successive sampling points. Then the new homogeneous reduced-order observer and sampled-data controller with genuinely nonlinear structures are constructed by using the sampled information of output. After choosing appropriate sampling period, the proposed SDC scheme can stabilize the nonlinear systems. Finally, the effectiveness of the results is demonstrated by using two simulation examples and the superiority of the proposed method is shown by comparing the control performance with the existing results.

This study explores finite-time adaptive neural tracking control for output-constrained nonlinear systems. An improved command filter was utilized to simplify the controller, and a compensation system ensured that the filter error converged in finite time. To avoid singularities during the controller design process, a novel switch function was employed in the command filter, including a compensation system and virtual controller, which guaranteed the second-order derivability of the virtual controller. Furthermore, to reduce the communication burden, an improved Zeno-free event-triggered condition was introduced. The control strategy ensured that all the closed-loop system variables remained bounded and that the reference trajectory could be well-tracked in finite time. Finally, a simulation example was given to support our control strategy.

An output-feedback-based event-triggered control issue of a class of uncertain nonlinear systems considering state quantization and input delay is investigated. In this study, by constructing the state observer and adaptive estimation function, a discrete adaptive control scheme is designed based on the dynamic sampled and quantized mechanism. With the aid of the Lyapunov-Krasovskii functional method and a stability criterion, the global stability of the time-delay nonlinear systems is ensured. Additionally, the Zeno behavior will not happen in the event-triggering. Finally, a numerical example and a practical example are presented to verify the effectiveness of the designed discrete control algorithm with input time-varying delay.

In this article, adaptive event-triggered finite-time control is explored for uncertain nonlinear systems with time delay. First, to handle the time-varying state delays, the Lyapunov-Krasovskii function is used. Fuzzy-logic systems are used to deal with the unknown nonlinearities of the system. Notice that compared to the reporting achievements, our proposed virtual control laws are derivable by using the novel switch function, which avoids "singularity hindrance" problem. Moreover, the dynamic event-triggered controller is designed to reduce the communication pressure and we prove that the controller is Zeno free. Our proposed control strategy ensures that the tracking error is arbitrarily small in finite time and all variables of the closed-loop system remain bounded. Finally, to show the effectiveness of our control strategy, the simulation results are given.

This work investigates the adaptive fixed-time disturbance rejection control issue for a class of time-delay nonlinear systems subject to event-triggered and quantized input signals. First, a disturbance observer is proposed to promote the controller design. Compared with the related disturbance observer design, the observer designed in this article can be used to construct the fixed-time control scheme. Then, combined with the backstepping technique and fuzzy logic systems, an adaptive fuzzy event-triggered and quantized fixed-time control scheme is designed. Moreover, to deal with the unknown time delays, the Lyapunov–Krasovskii functional method is utilized to achieve the system stability. The proposed control algorithm reduces the transmission burden of the data channel with a new combined event-based sampling and quantization and improves the robustness of the time-delay nonlinear system. Finally, a numerical example and a practical example are given to validate the feasibility of the control scheme.

This article studies a nonlinear disturbance observer (NDO)-based finite-time fuzzy adaptive dynamic surface control (DSC) of nonlinear systems (NSs) with external disturbances and preassigned performance indices. By constructing a type of finite-time preassigned-performance function (FTPF), the tracking error variable is confined within the boundaries of the FTPF such that the performance metrics, for instance, steady-state error, and convergence time, could be satisfied. Incorporating fuzzy adaptive control with the NDO technique, an effective composite NDO (CNDO) scheme is built to reckon the unknown composite disturbances, including unknown external disturbances and fuzzy approximation errors, which implies that fuzzy approaching errors can be further cut down. It is confirmed that the closed-loop system is semiglobally practically finite-time stable, as well as the tracking error and convergence time satisfy the predefined performance indices. In the end, the validity of the CNDO-based finite-time adaptive DSC scheme has been evidenced by a practical example model.

This article studies the adaptive fuzzy event-triggered fixed-time practical tracking control problem for flexible-joint robot system. Since the nonlinearities of the system are difficult to obtain, fuzzy logic systems are utilized. Second-order command filters are used to avoid the “explosion of complexity” problem. Moreover, a novel compensation system is proposed. The new error compensation system cannot only compensate for the error of the filter band, but also make the error converge in fixed time. By using backstepping technique, the virtual control laws and the adaptive law are designed. Notice that compared to the reporting achievements, our proposed virtual control laws are second-order derivable by using the novel switch function, which avoids “singularity hindrance” problem. To reduce communication pressure, the event-triggered controller is designed and Zeno behavior is avoided. Our proposed control strategy ensures that the tracking error can be arbitrarily small in fixed time and all variables of the closed-loop system remain bounded. Finally, simulation results are given to show the effectiveness of our control strategy.

In this article, the problem of output feedback control for a class of stochastic nonlinear systems in the presence of nondifferentiable measurement function and input saturation is studied. A novel power-auxiliary system is introduced to handle the adverse effects of input saturation. What is more, the common growth assumptions of nonlinear terms can be eliminated by a key lemma. Then, an output feedback controller is constructed to ensure that all the signals in the closed-loop system are globally bounded almost surely. Finally, a simulation shows that the control strategy is effective.

The adaptive event-triggered-based neural network control is explored for switching nonlinear systems with nonstrict-feedback structure and time-varying delays in this article. First, the switching observer is designed to estimate the unmeasurable states. Due to the existence of time-varying input delay, a compensation system is introduced. The average dwell-time (ADT) scheme and the event-triggered controller are established. Furthermore, the semiglobal uniform ultimate boundedness (SGUUB) of all the variables in the closed-loop system is achieved and the Zeno behavior is avoided. Finally, the numerical simulation shows that our proposed control approach is effective.

In this article, the exponential synchronization of Markovian jump neural networks (MJNNs) with time-varying delays is investigated via stochastic sampling and looped-functional (LF) approach. For simplicity, it is assumed that there exist two sampling periods, which satisfies the Bernoulli distribution. To model the synchronization error system, two random variables that, respectively, describe the location of the input delays and the sampling periods are introduced. In order to reduce the conservativeness, a time-dependent looped-functional (TDLF) is designed, which takes full advantage of the available information of the sampling pattern. The Gronwall-Bellman inequalities and the discrete-time Lyapunov stability theory are utilized jointly to analyze the mean-square exponential stability of the error system. A less conservative exponential synchronization criterion is derived, based on which a mode-independent stochastic sampled-data controller (SSDC) is designed. Finally, the effectiveness of the proposed control strategy is demonstrated by a numerical example.

In this article, we investigate the distributed output optimization for general uncertain high-order nonlinear multiagent systems (MASs), where nonlinear functions are constrained by a linear growth condition. The dynamic gain approach is utilized to cope with the influence of the unknown optimal solution. First, the distributed optimal coordinators (DOCs) with an adjustable parameter are constructed to steer the generated signals converging to the optimal solution. By developing the iterative design strategy, the dynamic reference-tracking controllers are then designed so that the output of each agent follows the generated value of coordinators, respectively. It is proved that all states are globally bounded, as well as the tracking error between the outputs and optimal solution can be bounded in a finite time by an arbitrarily small constant. Simulation studies demonstrate the validness of the main idea.

In this article, global stabilization for a class of nonlinear uncertain time-delay systems with saturated input is considered. The saturated input is handled by a new auxiliary system with dynamic gain and the linear growth condition with unknown growth rates is removed. A novel adaptive observer-controller coupling design framework is put forward, under which the closed-loop system is globally bounded. Finally, a simulation shows that the proposed technique is effective.

This paper investigates the fixed-time stabilization problem for a class of nonlinear systems via event-triggered control. The event-triggered mechanism can be applied to the nonlinear system with the coexistence of low-order and high-order nonlinearities. In order to deal with these low-order and high-order terms as well as achieve the objective of fixed-time stabilization, the initial value of the system is divided into two cases, and then the event-triggered controller is designed, respectively. By switching control theory, it is proved that the nonlinear system is globally fixed-time stable under the designed controller and the Zeno behavior can be excluded. Finally, two simulations show that the proposed technology is effective.

This technical correspondence studies the consensus problem for second-order multiagent systems under network topologies with a directed spanning tree. Consensus analysis for systems with the distributed delayed proportional-integral (PI)-type controller is given. Crossing directions of the characteristic roots can be identified by a sufficient condition. If the rightward crossing condition holds, the delay margin can be obtained to guarantee that the systems reach consensus if and only if the time delay is less than the critical value. Otherwise, it is possible that the systems switch from consensus to nonconsensus and back to the consensus as the delay increases. Simulation examples are provided to demonstrate the theoretical analysis.

This article addresses adaptive fuzzy event-triggered controller design for nonstrict nonlinear systems. First, for estimating the unmeasurable states, the high-gain fuzzy state observer is designed. By using backstepping technique, the adaptive fuzzy controller is designed. A new switching threshold event-triggered mechanism is given to decide when the controller needs to be updated and the Zeno behavior is avoided. Stability analysis shows that the tracking error can be arbitrarily small and all variables of the closed-loop system remain bounded. At last, the effectiveness of our control strategy is illustrated through simulation.

This article analyzes the consensus of first-order multiagent systems under the network topology with a directed spanning tree. A distributed PID controller with time delay is designed. D-parameterization approach is used and the crossing set consisting of frequencies such that at least one characteristic root is on the imaginary axis is identified. It is proven that the rightward crossings of the characteristic roots are always guaranteed. The exact delay margin is then determined. Numerical simulation is proposed to demonstrate the theoretical analysis.

This article studies the control problem for a class of stochastic nonlinear time-delay systems with uncertain output functions. Under the appropriate assumptions, a stabilization controller is explicitly constructed by applying the adding a power integrator method. Then, using the Lyapunov-Krasovskii functionals to address time-delay, it is proven that the designed controller can guarantee the closed-loop system to be globally asymptotically stable (GAS) in probability. Finally, two simulations show that the control strategy is effective and can be applied to the actual system.

This paper presents an auxiliary random series approach to model the effect of network induced problems, such as data losses and transmission delay subject to event-based communication scheme for nonlinear continuous time systems. T-S fuzzy model is employed to describe the nonlinear systems. In order to save the bandwidth and energy, we introduce the event-triggered mechanism to reduce the number of data for transmission and computation. Thus, it is necessary to consider the influence of data losses, data disorder, and transmission delay since the transmitted data packets become more important. Consequently, it is very complicated to analyze the performance of such networked system and one of the most difficult part, in the authors' opinion, is to construct the mathematical model of closed-loop systems. In this paper, we present an auxiliary random series approach to describe the data transmitted in the system, and therefore, the closed-loop systems can be obtained. Associated with a tailor-made Lyapunov-Krasovskii functional, the stability analysis is processed and a fuzzy controller is designed. Asynchronous membership functions are considered to obtain more relaxed stability conditions. To clarify the effectiveness of the proposed method, a cart-damper-spring system is employed for simulation.

In this article, the event-triggered-based adaptive neural network control problem is studied for a class of nonlinear time-delay systems with nonstrict-feedback structures and unknown control directions. First, a compensation system is introduced to handle the input delay and an observer is also designed to estimate the unmeasurable states. Then, by employing the neural networks and the variable separation approach, the adaptive backstepping method is applied to control the nonlinear systems with nonstrict-feedback structures. By codesigning the adaptive controller and the triggering mechanism, the input-to-state stability (ISS) assumption with respect to the measurement error is removed. Finally, it is shown that the proposed event-triggered adaptive controller can ensure the semiglobal boundedness of all the states in the closed-loop systems.

This paper investigates the cooperative output regulation problem of singular heterogeneous multiagent systems. General distributed observers are proposed for every agent obtaining the estimated state of the exosystem. The feedforward control technique and reduced-order approach are used to design distributed singular output feedback controllers and distributed normal output feedback controllers. The proposed cooperative dynamic controller is dependent on the plant parameters and the interaction topologies. A simulation example is provided to demonstrate the effectiveness of the proposed design method.

This paper studies the global asymptotic stability problem of a class of reaction–diffusion neural networks with time-varying delays. To overcome the difficulty caused by the partial differential term, a novel Lyapunov–Krasovskii functional is proposed, and a partial differential equation technique together with a linear operator approach are also applied to obtain the delay-dependent stability criteria, which are less conservative than the existing results. Finally, simulation examples are given to verify and illustrate the theoretical analysis.