This paper proposed a model-free adaptive security control (MFASC) method designed for nonlinear vehicle systems, simultaneously tackling controller saturation, external disturbances, and aperiodic Denial-of-Service (DoS) attacks. To achieve a desired platoon control objective with only input-output data, the dynamics are reconstructed into a differential form with pseudo partial derivative parameters. Under saturation constraints, a de-saturation factor is introduced to ensure the controller’s state remains within permissible limits. Additionally, a disturbance observer is included to estimate external disturbances and mitigate their negative effects. For aperiodic DoS attacks, an attack compensation mechanism is developed. Comprehensive stability analysis demonstrates the ultimate boundedness of the formation error of vehicle systems under aperiodic DoS attacks. The validity of the theoretical results is supported by numerical simulations.

This article investigates the distributed platoon control problem of connected automated vehicles (CAVs) under hybrid cyber attacks, which include false data injection (FDI) and eavesdropping attacks simultaneously. To mitigate the impact of hybrid cyber attacks on vehicle information exchange, an event-triggered dual-layer control strategy with a hidden layer and competitive interconnection structure is proposed. Specifically, to address malicious data injection from FDI attacks, the proposed framework achieves the objective of vehicle platooning through dynamic interaction between the physical and hidden layers only at triggered time instants. Meanwhile, FDI attacks detection and privacy preservation can also be realized through this strategy. Furthermore, a Zeno-free event-triggered mechanism (ETM) is employed to improve the communication efficiency and reduce resource consumption by avoiding unnecessary state transmissions. Sufficient conditions for control parameters are derived to guarantee resilient platoon control objectives under hybrid cyber attacks. Finally, numerical simulations validate the effectiveness of the proposed approach.

The problem of predefined-time (PdT) synchronization of FitzHugh-Nagumo (FHN) neural networks is investigated. The FHN neural networks have shown proficiency in accurately representing the essential characteristics of neurons and effectively simulating the structural dynamics and functioning of the brain. The investigation of synchronization in FHN neural networks will enhance our comprehension of how neurons behave dynamically and promote their valuable utilization in the field of artificial intelligence. In this study, the successful integration of adaptive control and PdT control techniques not only achieves synchronization within a predetermined time but also eliminates the need to preselect controlling gains. The work develops new PdT stability theorems for singular perturbation systems and synchronizing controllers that exclude linear terms. Consequently, a set of sufficient conditions for achieving this synchronization goal is provided. Furthermore, it mathematically demonstrates the implementability of an upper bound for the settling time by parameterizing the predefined time as a controller parameter, thereby eliminating the need for explicit recalculations. Moreover, the control gains can be adjusted adaptively, enhancing the flexibility of control parameter tuning. Finally, several numerical simulations are presented to validate the effectiveness of the designed adaptive PdT control strategies. Note to Practitioners—The study of neural network-based synchronization in nonlinear systems is rapidly emerging as an important tool in many fields, including automation, telecommunications, computer science, mathematics, physics, and biology. Among the neural network architectures, FitzHugh-Nagumo (FHN) neural networks stand out for their ability to closely model the behaviors and dynamics of real neurons. This capability is not just theoretical; it has significant practical applications in enhancing the understanding of brain functions and improving the performance of artificial intelligence systems. In particular, neural networks with different time scales have better self-learning and generalization ability. Our innovative approach is the integration of adaptive control with predefined-time control techniques. This combination not only guarantees synchronization within a specific time frame but also dynamically adjusts these control parameters, enhancing the system’s adaptability to changes and disturbances without requiring prior knowledge of specific control gains. In practical applications, achieving efficient synchronization can significantly enhance AI systems, making them more efficient and reliable. For instance, in autonomous vehicles, better synchronization can improve decision-making processes, while in robotics, it can lead to more advanced and responsive automations.

This article aims to address the distributed robust platooning control of connected automated vehicles (CAVs) with general unknown uncertain dynamics. Despite recent progress in this area, achieving the objective of distributed robust platooning control for CAVs with limited communication resources and uncertain dynamics is an outstanding problem. To solve such a problem, a new Zeno-free event-triggered scheme is successfully established to determine whether the vehicle's state should be sampled and transmitted among the interacting vehicles. An adaptive law for updating the weighting matrix for the neural network approximator is designed, where the relative state variables are utilized only at triggered instants. Moreover, such a neuro-adaptive approach incorporates a low-pass filter structure to effectively mitigate undesirable high-frequency oscillations that may arise with the application of high-gain learning rates. Following this, a new class of distributed event-based neuro-adaptive control protocols is meticulously designed to guarantee the uniform ultimate boundedness of spacing error, relative velocity, and relative acceleration of the whole platoon. Finally, simulation examples with different scenarios are conducted, and it is interesting to find that the proposed protocol has a lower average communication rate than traditional ones without the low-pass filter structure.

In building-microgrid communities, renewable generation and time-varying load usually cause power fluctuations, which influence the ancillary support to the main grid. Thermostatically controlled loads (TCLs) can be utilized to compensate such power variations due to their aggregated and controllable power consumptions. Meanwhile, one basic requirement for the users' side of TCLs is to realize the fair sharing of power states and comfort states. This article proposes a distributed event-based control strategy, where information of neighboring TCLs is exchanged only when a dynamic event-triggered condition is satisfied, and thus it intelligently determines the necessary transmission frequency to save communication resources. From a cybersecurity perspective, the communication network of TCLs may be subject to hybrid attacks, for example, denial-of-service (DoS) and false data-injection (FDI) attacks. During DoS attack intervals, no information can be communicated even through the event-triggered condition is satisfied. Furthermore, the control inputs may also be tampered by FDI attacks. By utilizing the Lyapunov stability and hybrid control theories, sufficient conditions regarding the attack parameters are derived such that fair sharing of power states and comfort states of all involved TCLs can be achieved exponentially. The exclusion of Zeno behaviors is proved and a corollary for ideal communication situations is also deduced. Finally, simulation examples with various attack parameters are conducted to verify the effectiveness of the main results.

Distributed consensus tracking problem of networked agent systems with directed topologies under denial-of-service (DoS) attacks is investigated. The considered networked agent network consists of a physical layer with fixed physical links and a cyber layer with cyber control units. Both the communication network connecting these two layers and the cyber communication network within the cyber layer may subject to malicious DoS attacks. These two types of attacks have different impacts on the networked system; the former one affects the timely update of control inputs, and the latter influences the connection weights of the cyber communication graph. First, for DoS signals occurring in the communication network which transmits the state and control input information between the two layers, the distributed control protocol based on the event-triggered scheme and locally deployed estimators are designed. Efficient algorithms for selecting event-triggered control parameters and Zeno-free triggered parameters are given to ensure the mean-square consensus. The relationships between the system's parameters and the features of DoS attacks are successfully revealed. Second, corresponding theoretical analysis is derived for consensus tracking of the networked systems when DoS attacks are launched within the cyber layer. Conditions concerning the length of repairing time and indexes of DoS attacks are given by utilizing hybrid control theory. At last, the effectiveness of the obtained results is demonstrated by performing simulations on multirobot systems.

One fundamental yet challenging issue in security control for resilient complex networks is to construct distributed control laws for the networks to perform various cooperative tasks in the presence of failures and attacks, where resilient indicates that the complex networks are exposed to the environment with cyber uncertainties and malicious adversaries. This is particularly important in today's critical infrastructure networks since most of them are vulnerable to attacks in the era of the Internet. Inspired by this observation, this paper focuses on synchronization control for resilient complex networks subject to cyber and physical attacks, where the states of nodes being attacked may change abruptly (i.e., the synchronization error may suffer impulsive disturbances), and some nodes as well as their corresponding connections may not work in some instances. Suppose that a smart control center is equipped in the considered network to detect the attacks in real time. Furthermore, the nodes and communication channels are assumed to be recovered through some repair work after detecting the attacks. On the theoretical side, by using the M-matrix theory, we get a few sufficient criteria to guarantee the achievement of secure synchronization against attacks on both nodes and communication links. On the algorithmic side, security control algorithm and architecture are proposed to select the coupling strength and the feedback gain matrix to realize synchronization. Finally, we perform two simulation examples to validate our theoretical results.

In this paper, an adaptive perimeter control problem is studied for urban traffic networks with multiple regions, time-varying state, and input delays. After defining state variables by partition the accumulation variable of each region, a system model is formulated as nonlinear ordinary differential equations based on the concept of macroscopic fundamental diagram. Both the travel times of vehicles as well as evacuation process of traffic jams are first introduced into the system dynamics, and they are modeled as input and state delays, respectively. The control objective is to stabilize the number of vehicles in each region to desired values. By employing the model reference adaptive control scheme and asymptotical sliding mode technique, two filters and adaptive laws for control parameters are designed by using only the information of the reference model. With properly constructed Lyapunov functions, the stability of tracking error with regard to the reference signals is analyzed. Lastly, a simulation example is given to demonstrate the effectiveness of the proposed methods.

Under the mild assumption that only the sampled-data output information about the master system is available, synchronization of networked master-salve system consisting of a high-order master system and a low-order slave system is investigated in this paper. Specifically, the dynamics of the master system and those of the slave system are allowed to be characterized by heterogeneous nonlinear systems. The communication between these two systems are transmitted by multiple sensors over a communication network, while at each sampling instant, only one sensor is allowed to transmit its current information to the controller's side according to some carefully designed scheduling protocols. To achieve master-salve synchronization, the stochastic scheduling and the Round-Robin scheduling protocols are, respectively, proposed and utilized. By appropriately designing observer and controller for the slave system, some sufficient synchronization criteria regarding to the gain matrices, sampling intervals and communication delays are derived for the closed-loop master-salve system under respectively the stochastic scheduling and the Round-Robin scheduling protocols. Last, two numerical examples are simulated to validate the effectiveness of the theoretical results.

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.

In this paper, the finite-time stability analysis of non-autonomous and autonomous fractional order memristive systems with a pure time delay and commensurate order between 0 and 1 is proposed. First, two appropriate concepts of the finite-time stability for the mentioned system with and without external input are introduced. Then, a sufficient condition for finite-time stability of the underlying system is derived in the frame of some useful inequalities and appropriate properties of the norm. In particular, the results are presented in form of algebraic inequality, which turn out to be more efficient from the computational point of view. Finally, simulation results are given to testify the merits of the derived conditions.