In this paper, an event-triggered fuzzy security path following control problem is investigated for autonomous ground vehicles subject to aperiodic denial of service attacks. Firstly, a switched interval type-2 fuzzy model is established to depict the vehicle path following system, in which both the vehicle dynamic nonlinearities and the aperiodic denial of service attacks are well addressed. Secondly, to guarantee that the latest packets are sent out immediately at the end of the denial of service attacks, a novel attack-dependent event-triggered scheme is developed to improve the signal transmission efficiency and reduce the performance loss caused by denial of service attacks. Then, by constructing a piecewise Lyapunov function based on the average dwell time of the denial of service attacks, a security control method is proposed to guarantee the exponential stability and the path following performance of the switched fuzzy path following system. Finally, the superiority of the proposed control strategy is verified by experimental tests as compared with the current path following control methods.

This study proposes a novel distributed model predictive control (DMPC) approach for mixed vehicle platoons (MVPs), which achieves driving safety, asymptotic stability, and traffic wave dissipation simultaneously. The longitudinal dynamics model and safety constraints are first established for each vehicle. The MVPs are divided into several sub-platoons according to the distribution of connected-and-automated vehicles (CAVs) and human-driven vehicles (HDVs). Then, a compound controller to ensure the head-to-tail string stability of MVPs is constructed as a reference controller for the subsequent design of the double-layer distributed model predictive controller (DL-DMPC). To describe the behavior of human drivers, a car-following model specific to HDVs is developed. On the basis of the designed compound controller and the description of HDVs, the DL-DMPC is proposed. The first layer improves tracking performance and satisfies the state constraints of each CAV under different communication topologies through an optimization problem. The second layer utilizes controller-matching technology to ensure the asymptotic stability of vehicle platoons and dissipate traffic waves. With the above double-layer structure, the DL-DMPC can simultaneously address multiple tasks and is applicable in various communication topologies. Simulations and analyses based on the NGSIM dataset are conducted in various scenarios to validate the effectiveness of the developed approach.

To meet the growing demand of electrical dispatch, accurate prediction of a dynamic thermal rating (DTR) for transmission lines is crucial. However, the uncertainty of DTR caused by weather data imbalance constraints poses a risk to secure grid operation. To this end, a secure probabilistic interval prediction model is developed to tap potential DTR using bootstrap plus-guided time-series generative adversarial networks (TimeGAN) and spatiotemporal graph network (STGN), called BP-G2NN. The TimeGAN is used to augment the data to solve the weather data imbalance problem. And the STGN model is developed to dynamically strengthen the weight of the model to the key potential feature. In addition, the designed BP strategy restricts the frequency of maximum DTR exceeding the upper bound of prediction intervals and solves the inherent problem of quantile crossings. The simulation experiments using real data verify the validity of the model for DTR decisions.

This paper proposes a switched velocity-dependent path-following control method for autonomous ground vehicles under uncertain cornering stiffness and time-varying velocity. A switched polytopic linear-parameter-varying (LPV) model combining path-following and vehicle lateral dynamics is established, where the velocity-dependent parameters are divided into several switched regions. In each region, a trapezoidal polytope is adopted to describe the varying parameters. A global$H_{\infty }$performance analysis is carried out for the switched polytopic LPV vehicle system based on the average dwell time approach with multiple Lyapunov functions. Based on the analysis, a switched velocity-dependent controller is synthesized to achieve a desired path-following performance by regulating the steering angle of the front wheels. The proposed switched path-following control specializes in handling large range of varying velocity, and achieves superior path-following performance compared with the conventional gain-scheduling control. Simulations are carried out for typical road maneuvers to illustrate the advantages of the proposed method. The results are also applicable to other LPV analysis and synthesis conditions involving time-varying parameters of large varying ranges.

The problem of secure finite-horizon consensus control for discrete time-varying multiagent systems (MASs) with actuator saturation and cyber attacks is addressed in this article. A random attack model is first proposed to account for randomly occurring false data injection attacks and denial-of-service attacks, whose dynamics are governed by the random Markov process. The hybrid secure control scheme is developed to mitigate the influence of arbitrary cyber attacks on system performance. Specifically, this article proposes a hybrid control law containing multiple controllers, each of which is designed to counter different types of cyber attacks. By using the stochastic analysis approach, two sufficient criteria are provided to guarantee that the time-varying MASs satisfy the finite horizon$H_{\infty }$consensus performance. Then, the controller parameters are obtained by solving the recursive linear matrix inequality. The usefulness of the theoretic results presented is demonstrated via a numerical example that contains a performance comparison of different secure control schemes.

In this article, the event-triggered security consensus problem is studied for time-varying multiagent systems (MASs) against false data-injection attacks (FDIAs) and parameter uncertainties over a given finite horizon. In the process of information transmission, the malicious attacker tries to inject false signals to destroy consensus by compromising the integrity of measurements and control signals. The randomly occurring stealthy FDIAs on sensors and actuators are modeled by the Bernoulli processes. In order to reduce the unnecessary utilization of communication resources, an event-triggered control mechanism with state-dependent threshold is adopted to update the control input signal. The main objective of this article is to design a controller such that, under randomly occurring FDIAs and admissible parameter uncertainties, the MASs achieve consensus. By utilizing stochastic analysis method, two sufficient criteria are derived to ensure that the prescribed H∞ consensus performance can be achieved. Then, the desired controller gains are derived by solving recursive linear matrix inequalities. Simulation results are presented to illustrate the effectiveness and applicability of the proposed control method.

The problem of finite-horizon H∞state estimator design for periodic neural networks over multiple fading channels is studied in this paper. To characterize the measurement signals transmitted through different channels experiencing channel fading, a multiple fading channels model is considered. For investigating the situation of correlated fading channels, a set of correlated random variables is introduced. Specifically, the channel coefficients are described by white noise processes and are assumed to be correlated. Two sufficient criteria are provided, by utilizing a stochastic analysis approach, to guarantee that the estimation error system is stochastically stable and achieves the prescribed H∞performance. Then, the parameters of the estimator are derived by solving recursive linear matrix inequalities. Finally, some simulation results are shown to illustrate the effectiveness of the proposed method.

This paper studies the problem of event-triggered sliding mode control of discrete-time Markov jump systems (MJSs). Two kinds of classical control schemes, which are observer-based control and state-feedback control schemes, are employed to handle the proposed synthesis problem. The event-triggered observer-based sliding mode controller and event-triggered state-feedback sliding mode controller are established by plunge of discrete-time event detectors into the studied control system, respectively. Moreover, the proposed event-triggered sliding mode controllers can guarantee the MJSs to be stochastically stable with H∞performance, and ensure the finite-time reachability of the specified sliding manifold. Simulation results are provided to illustrate the effectiveness of the proposed theoretical results.