This article is concerned with the output feedback security tracking control of a class of disturbed second-order nonlinear systems against denial-of-service (DoS) attacks. Novel radial basis function neural network (RBFNN)-based finite-time state observers are developed to estimate the system’s unavailable states. Adaptive filters are proposed to suppress the influences of disturbances and RBFNN approximation errors. Then, an RBFNN-based security controller is designed to alleviate the effects of nonlinear dynamics and DoS attacks based on the signals of observers and filters. It is established that the uniformly ultimately bounded output tracking results of the system can be obtained by utilizing an RBFNN-based finite-time observation and filtering compensation control designs through Lyapunov stability analysis. Comparative simulations are employed to display the feasibility and superiority of the designed RBFNN-based observation and filtering compensation control schemes of a nonlinear autonomous marine system (AMS).

This paper addresses the robust time-constrained consensus control of a class of multi-agent systems (MASs) subject to nonlinear dynamics and external disturbances. Adaptive compensation technique is utilized to eliminate the negative effects of nonlinearities and disturbances. New distributed finite-time consensus control strategies are designed to ensure bounded consensus in MASs by leveraging adaptive compensation signals. The finite-time stability of the MAS is proved by utilizing the Lyapunov theorem. Finally, simulation outcomes involving multiple unmanned marine systems within a multi-agent framework confirm the efficacy of the proposed approach.

This article investigates the issue of observer-based fixed-time-synchronized tracking control for Euler-Lagrange (EL) systems with uncertain dynamics, bias-actuator faults and external disturbances. A novel fixed-time observer is proposed to reconstruct the actuator faults and system uncertainties, so that the observation error can reduce to zero within a fixed time. A fixed-time stable system with fast convergence rate is developed by using switching terms to design a newly sliding mode variable with the norm-normalized sign function. Then, on the basis of the reconstructed information from the fixed-time observer and the sliding mode variable, a robust control law is developed to realize fixed-time-synchronized stability of the EL system. It is concluded by Lyapunov stability theorem that the proposed method not only can guarantee that the boundary of convergence time is irrelevant of initial values of the system states, but also make all elements of the system tracking errors reach the origin simultaneously under the influence of actuator faults, external disturbances and uncertain dynamics. Finally, several comparative simulations are carried out to validate the developed observation and control schemes as well as their effectiveness.

In this article, we consider the cooperative tracking problem for a class of nonlinear multiagent systems (MASs) with unknown dynamics under denial-of-service (DoS) attacks. To solve such a problem, a hierarchical cooperative resilient learning method, which involves a distributed resilient observer and a decentralized learning controller, is introduced in this article. Due to the existence of communication layers in the hierarchical control architecture, it may lead to communication delays and DoS attacks. Motivated by this consideration, a resilient model-free adaptive control (MFAC) method is developed to withstand the influence of communication delays and DoS attacks. First, a virtual reference signal is designed for each agent to estimate the time-varying reference signal under DoS attacks. To facilitate the tracking of each agent, the virtual reference signal is discretized. Then, a decentralized MFAC algorithm is designed for each agent such that each agent can track the reference signal by only using the obtained local information. Finally, a simulation example is proposed to verify the effectiveness of the developed method.

This article is concerned with the output feedback security control of a class of high-order nonlinear-interconnected systems with denial-of-service (DoS) attacks, nonlinear dynamics, and exogenous disturbances. First, extreme learning machine (ELM) and adaptive techniques are adopted to approximate the unknown nonlinearities. Then, novel adaptive ELM-based nonlinear state observers with adaptive compensation functions are developed to estimate the unmeasurable states during DoS attacks under the influence of the disturbances. Further, by combining with the backstepping control and filtering techniques, adaptive ELM-based controllers are proposed to achieve uniformly ultimately bounded results based on the observation and adaption control signals under the influence of DoS attacks, nonlinear dynamics, and exogenous disturbances. Comparative studies are carried out to validate the effectiveness of the developed ELM-based adaptive observation and control strategies for two interconnected power systems.

This article addresses the problem of fast fixed-time tracking control for robotic manipulator systems subject to model uncertainties and disturbances. First, on the basis of a newly constructed fixed-time stable system, a novel faster nonsingular fixed-time sliding mode (FNFTSM) surface is developed to ensure a faster convergence rate, and the settling time of the proposed surface is independent of initial values of system states. Subsequently, an extreme learning machine (ELM) algorithm is utilized to suppress the negative influence of system uncertainties and disturbances. By incorporating fixed-time stable theory and the ELM learning technique, an adaptive fixed-time sliding mode control scheme without knowing any information of system parameters is synthesized, which can circumvent chattering phenomenon and ensure that the tracking errors converge to a small region in fixed time. Finally, the superior of the proposed control strategy is substantiated with comparison simulation results.

As one of the most widely used applications of the Internet of Things (IoT), intelligent transportation system (ITS) is of great significance for urban traffic planning, traffic control, and traffic guidance. However, widespread traffic congestion occurs with the increased number of vehicles. The traffic flow prediction is a good idea for traffic congestion. Therefore, many schemes have been proposed for accurate and real-time traffic flow prediction, but there still exist many issues, including low accuracy, weak adaptability and inferior real-time. Meanwhile, the complex spatial and temporal dependencies in traffic flow are still challenging. To address the above issues, we propose a novel spatial-temporal Chebyshev graph neural network model (ST-ChebNet) for traffic flow prediction to capture the spatial-temporal features, which can ensure accurate traffic flow prediction. Concretely, we first add a fully connected layer to fuse the features of traffic data into a new feature to generate a matrix, and then the long short-term memory (LSTM) model is adopted to learn traffic state changes for capturing the temporal dependencies. Then, we use the Chebyshev graph neural network (ChebNet) to learn the complex topological structures in the traffic network for capturing the spatial dependencies. Eventually, the spatial features and the temporal features are fused to guarantee the traffic flow prediction. The experiments show that ST-ChebNet can make accurate and real-time traffic flow prediction compared with other eight baseline methods on real-world traffic data sets PeMS.

This article is concerned with the robust adaptive fault-tolerant control (FTC) circuit designs for a class of continuous-time disturbed systems. A circuit realization method is investigated to convert the robust adaptive FTC control schemes into analog control circuits. An adaptive compensation control scheme against state-dependent and partially bounded actuator faults and disturbances is first developed to demonstrate the approach clearly, then its equivalent control circuits are implemented by using the circuit theory. Compared with simulation results achieved by MATLAB and professional circuit simulation software, the effectiveness of the proposed robust adaptive FTC circuits is validated by a rocket fairing system and a Chua's circuit system.

In this article, we consider the distributed fault-tolerant resilient consensus problem for heterogeneous multiagent systems (MASs) under both physical failures and network denial-of-service (DoS) attacks. Different from the existing consensus results, the dynamic model of the leader is unknown for all followers in this article. To learn this unknown dynamic model under the influence of DoS attacks, a distributed resilient learning algorithm is proposed by using the idea of data-driven. Based on the learned dynamic model of the leader, a distributed resilient estimator is designed for each agent to estimate the states of the leader. Then, a new adaptive fault-tolerant resilient controller is designed to resist the effect of physical failures and network DoS attacks. Moreover, it is shown that the consensus can be achieved with the proposed learning-based fault-tolerant resilient control method. Finally, a simulation example is provided to show the effectiveness of the proposed method.

This article addresses the problem of fault-tolerant consensus control of a general nonlinear multiagent system subject to actuator faults and disturbed and faulty networks. By using neural network (NN) and adaptive control techniques, estimations of unknown state-dependent boundaries of nonlinear dynamics and actuator faults, which can reflect the worst impacts on the system, are first developed. A novel NN-based adaptive observer is designed for the observation of faulty transformation signals in networks. On the basis of the NN-based observer and adaptive control strategies, fault-tolerant consensus control schemes are designed to guarantee the bounded consensus of the closed-loop multiagent system with disturbed and faulty networks and actuator faults. The validity of the proposed adaptively distributed consensus control schemes is demonstrated by a multiagent system composed of five nonlinear forced pendulums.

This article is concerned with the consensus formation control problem of a class of networked quadrotor aircraft partially bounded and state-dependent perturbations. A general distributed consensus error model is first developed to formulate the formation behavior of the networked quadrotor aircraft. Then, by using adaptive techniques, virtual position control strategies are proposed to eliminate the impacts of perturbations, so that the following quadrotor aircraft can boundedly track the desired position trajectory with a satisfying pattern. Furthermore, based on the designed virtual position control strategies, the attitude reference angles are constructed and adaptive attitude control strategies are further designed to guarantee that the attitude angles track the reference angles asymptotically. In terms of the asymptotic tracking results of attitude control systems, the bounded consensus formation results are obtained based on the Lyapunov stability theorem. Numerical simulations are carried out to verify the efficiency of the designed position formation as well as attitude tracking control strategies of the networked quadrotor aircraft.

This article is concerned with the robust adaptive fault-tolerant consensus control and the circuital implementation problems for a class of homogeneous multiagent systems with external disturbances and actuator faults. A robust adaptive consensus control strategy is developed to automatically eliminate the effects of actuator bias and partial loss-of-control-effectiveness faults, and simultaneously specify the$L_{2}$performance of systems. The achievement of exponential consensus of the closed-loop disturbed and faulty multiagent system is provided on the basis of the Lyapunov stability theory. Furthermore, a physical implementation method is developed based on circuit theory to translate the proposed adaptive consensus control strategy into analog circuits. By using a professional tool for circuit simulations, effectiveness of the developed circuits is verified via a multiagent system composed by mobile robots with two independent driving wheels.

This article is concerned with the robust adaptive control circuit design for pulse wide modulation (PWM)-based dc–dc buck converters with load variations and exogenous disturbances. A robust adaptive perturbation rejection control strategy is first developed to suppress time-varying and state-dependent perturbations, which are composed of load variations and disturbances. Then, equivalent analog control circuits of the adaptive control strategy are implemented on the basis of the circuit theory. Bounded tracking of the closed-loop converter system in the presence of perturbations is achieved based on the Lyapunov stability theorem. Simulations and experimental results are provided to validate the efficiency of the proposed adaptive perturbation rejection control strategy in a dc–dc buck converter system.

This paper is concerned with the robust constrained control problem for a class of fault-tolerant time-varying systems against actuator faults and input amplitude saturation. An adaptive technique is proposed to compensate for the impacts of actuator bias faults and partial loss of control effectiveness, as well as to eliminate the effects of unknown time-varying parameters of the systems. An auxiliary system is developed to ensure that the actuator behaves within the limitation of the actuator amplitude. Based on the compensation strategies and auxiliary signals, a novel adaptive fault-tolerant constrained controller is constructed to guarantee the convergence of the system states into a small stability domain in the presence of actuator faults, actuator amplitude limitations, and unknown system parameters. An example of F-18 flight control systems is given to illustrate the proposed procedures and its effectiveness.

In this paper, the problem of fault tolerant tracking control for a linear time-invariant system subject to actuator faults and saturations is addressed. An auxiliary system is developed to ensure actuators behave within amplitude and rate limits under the influence of partial loss of control effectiveness. Based on the auxiliary system, a fault tolerant compensation controller is constructed to guarantee tracking errors to converge to a small region. Some relationships among tracking errors, command signals, actuator faults, amplitude and rate limits as well as controller parameters are comprehensively studied and explicitly illustrated with formulas. An example of rudder-roll damping control for a cruise keeping ship is included to illustrate the proposed procedures and their effectiveness.

In this paper, the non-fragile finite-time tracking control problem is addressed for a class of uncertain linear systems with controller multiplicative coefficient variations. An adaptive control strategy is constructed to ensure that the system tracks a time-varying target orbit. The relationship of the bound of tracking errors and the size of uncertainties and controller multiplicative coefficient variations is deeply investigated. On the basis of Lyapunov stability theory, it shows that the bounded tracking of resulting adaptive system can be reached within a finite time, and the tracking errors of the system can be reduced as small as desired by adjusting controller parameters. The effectiveness of the proposed design is illustrated via a decoupled longitudinal model of F-18 aircraft.

This paper deals with a class of complex networks with nonideal coupling networks, and addresses the problem of asymptotic synchronization of the complex network through designing adaptive pinning control and coupling adjustment strategies. A more general coupled nonlinearity is considered as perturbations of the network, while a serious faulty network named deteriorated network is also proposed to be further study. For the sake of eliminating these adverse impacts for synchronization, indirect adaptive schemes are designed to construct controllers and adjusters on pinned nodes and nonuniform couplings of un-pinned nodes, respectively. According to Lyapunov stability theory, the proposed adaptive strategies are successful in ensuring the achievement of asymptotic synchronization of the complex network even in the presence of perturbed and deteriorated networks. The proposed schemes are physically implemented by circuitries and tested by simulation on a Chua's circuit network.