A data-driven hierarchical control strategy is proposed to address the leader-following consensus problem for nonlinear networked multi-agent systems (MASs) with unknown dynamics under Byzantine attacks. The proposed hierarchical framework decouples the leader-following consensus control into leader state estimation and trajectory tracking, only using input/output data of systems. Specifically, a discrete-time distributed observer is first proposed to securely estimate the leader state utilizing the mean-subsequence-reduced-falgorithm, which effectively filters out malicious data from Byzantine agents in networked MASs. Then, a data-driven decentralized controller is developed to track the estimated leader state. In addition, we extend this strategy into a dynamic event-triggered data-driven hierarchical control algorithm, which not only securely achieves the leader-following consensus but also conserves network communication resources. Finally, simulation results demonstrate the effectiveness of the proposed methods.

This paper studies the experiential assistance control problem for a class of networked Markov jump systems by using multi-conditions adaptive event-triggered approach. First, a novel multi-conditions adaptive event-triggered scheme is proposed to greatly reduce the data transmission rate and save network resources more effectively. Second, the experience-based assistance strategy is introduced to design a human-machine hybrid resilient controller, which can effectively reduce the impact of denial of service attacks and spoofing attacks. By combining these techniques with mode-dependentH∞performance index, we derive the desired controller solution conditions. In the end, simulation examples of DC-DC buck converter circuit model are presented to test the effectiveness, availability and advantages of the presented control method.

A novel approach is proposed for flexible performance-based control of strict-feedback systems subject to input saturation. The core design consists of three key components. First, the regulation of the performance function (PF) is achieved by reformulating it as an adaptive modification of its exponential index, exploiting its inherent structural properties. Second, a performance indicator function (PIF) is constructed based on output-side information by analyzing the system behavior in the absence of saturation, thereby avoiding the reliance on input-side compensation signals with limited differentiability. Third, a first-order auxiliary system is designed to adaptively adjust the exponential index in real time, driving the PIF to closely track the upper envelope of the actual tracking error. As a result, the proposed self-adjusting PF (SAPF) is able to maintain a dynamic balance between input and output behaviors by relaxing performance boundaries when constraint violations are imminent while actively accelerating PF contraction to enhance transient performance under saturation constraints. Building on this framework, an SAPF-based control algorithm is developed with rigorous closed-loop stability guarantees. Finally, the effectiveness and superiority of the proposed method are demonstrated through quantitative simulation comparisons with two advanced algorithms in a vehicle lane-keeping task.

The situation of interest is where a vehicle is equipped with multiple sensors to measure the distance to the leading vehicle but does not need to obtain data from speed and acceleration sensors. The distance measurements are susceptible to asynchronous sampling and noise, nearly half of which may be manipulated by malicious attackers. In this situation, the event-triggered vehicle-following control problem of nonlinear autonomous vehicles with unknown parameters is studied. First, a secure event-triggered mechanism that can resist manipulation is devised to alleviate the burden of data transmission and processing caused by multiple sensors. Then, a novel adaptive sensor fusion algorithm is developed to estimate the actual distance. Subsequently, an improved adaptive observer is designed based on the event-triggered estimated distance to estimate continuous-time distance, velocity, acceleration, and system parameters. Finally, the following controller is designed using the estimated states and parameters with the help of Levant differentiators. The effectiveness of the proposed control scheme is validated through simulation studies.Note to Practitioners—This work aims to develop a secure following control method for nonlinear automated vehicles with unknown states and parameters, which can effectively handle sparse sensor problems caused by attacks, faults, saturation, etc. To address practical issues such as sampling intervals and limited computing and transmission resources, we propose a discrete sampling–event-triggered transmission–continuous estimation and control framework for continuous-time systems. Despite the presence of measurement interferences and potential corruption, the designed event-triggered mechanism and sensor fusion algorithm can be used to reduce data transmission and estimate the actual output, respectively. The designed adaptive observer can estimate continuous-time system states and parameters whether the output is obtained in a continuous, short-interval discrete or suitable event-triggered manner. This capability facilitates control design and real-time monitoring of vehicle states. Additionally, the presented backstepping control design and analysis method utilizing Levant differentiators can be applied in situations where the controlled system’s states possess at least first-order differentiability.

A fully distributed model-free adaptive sliding mode control (MFASMC) strategy is proposed in this paper for unknown nonlinear multi-agent systems (MASs), in which topology networks are exposed to hybrid attacks consisting of denial-of-service (DoS) and false data injection attacks. Hybrid attacks in topology networks can result in neighbor information dropouts and inaccuracies among agents. First, the MASs with unknown dynamics are translated to equivalent linear data equations by the dynamic linearization technique. Second, the impact of neighbor information dropouts caused by DoS attacks is mitigated by a designed attack compensation mechanism, in which the compensation error is guaranteed to be bounded in the sense of mathematical expectation. Then, a fully distributed MFASMC algorithm, which does not depend on knowledge of the Laplacian matrix, is designed to improve the robustness of MASs with topology networks exposed to hybrid attacks, thus indirectly mitigating the impact of neighbor information inaccuracies. Finally, the consensus error is rigorously proved to be bounded in the sense of mathematical expectation, and the validity of the proposed strategy is confirmed by simulations. Note to Practitioners—This paper aims to develop a fully distributed MFASMC method to address the consensus problem for unknown MASs with hybrid attacks in network topologies. A hybrid attack compensation mechanism is proposed to mitigate the effects of neighbor information dropouts caused by hybrid attacks. By combining the sliding mode control theory with the model-free adaptive control strategy, the system’s robustness is improved to relieve the impact of neighbor information inaccuracies attributed to hybrid attacks. Since the proposed algorithm does not depend on the mathematical model and Laplace matrix of systems, it can be applied to large-scale MASs with unknown models to solve network topology security problems, such as multiple subway trains, wireless communication systems, and microgrid systems. Furthermore, the proposed method is simple in design, widely adaptable, and robust, making it easier to apply to practical systems and more friendly to control engineers.

This paper investigates the optimal tracking control problem for cyber-physical systems (CPSs) under mixed attacks based on the Stackelberg game strategy. First, an improved value function is designed to meet performance criteria by considering the control signal, mixed attack signal, and tracking performance. Then, based on the Stackelberg game theory and the principle of optimality, the optimal control strategy and false data injection attack policy are derived by solving a coupled algebraic Riccati equation (CARE). The proposed control strategy can effectively alleviate the adverse impact of mixed attacks on the control performance of CPSs. Subsequently, sufficient conditions are provided to guarantee the existence of the solution to the CARE. Additionally, an improved Q-learning algorithm is proposed to learn the optimal control scheme through state reconstruction, which avoids the need for access to state vectors and facilitates data-based controller design. Using the Lyapunov stability theory, it is demonstrated that the presented algorithms are convergent and the output of CPSs can track the reference trajectory. Finally, the proposed approach is validated by numerical simulations. Note to Practitioners—In the engineering application scenarios, the sharing feature of network communication may expose the controlled system to malicious attacks (such as DoS attacks and FDI attacks). The majority of the current control methods focus on one-sided analyses. In this paper, the dynamic interaction between attackers and defenders is described using a Stackelberg game model. Within this model, an optimal tracking control algorithm is proposed to mitigate the impact of mixed attacks on CPSs. Moreover, sufficient conditions for tolerable probability of attack are derived, which enables practitioners to determine the conditions under which the attacked system’s stable tracking performance may still be maintained.

An incremental iterative Q-learning algorithm (IIQLA) is proposed to tackle the optimal secure control problem for cyber-physical systems under false data injection attacks. Within a zero-sum game framework, the secure control problem is transformed into solving an iterative algebraic Riccati equation. To derive the optimal secure control policy from the equation, the IIQLA is developed, which does not require prior knowledge of the system dynamics. This algorithm utilizes two auxiliary variables to separate the behavior policy and the target policy, thereby improving the exploration of data. As a result, the devised control policies are more conservative, leading to solutions that are closer to the optimal policy. Moreover, by introducing an adaptive learning rate, the proposed IIQLA can accelerate the convergence speed and decrease the number of required iterations, thus alleviating the computation burden. The convergence of the proposed IIQLA with different learning rates is also analyzed. In addition, the closed-loop system is guaranteed to be asymptotically stable, and the exploration noise does not introduce bias into the optimal policies. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed approach.

This article addresses the adaptive iterative learning formation control (AILFC) problem of multiagent systems (MASs) with unmeasurable state subject to Denial-of-Service (DoS) attacks. To alleviate DoS attacks, a neural network (NN)-based compensation mechanism is proposed to learn communication signals, and a learning-based distributed output observer is designed to estimate the leader output. Moreover, an improved extended state observer (ESO) is designed to deal with unmeasurable states and total disturbance. Then, a time-varying boundary layer method with a normalized function is constructed to address the initial error problem. Furthermore, a multiple observer-based AILFC scheme is developed via the backstepping control technique, and the stability analysis of MASs is given by the Lyapunov theory. Finally, a simulation example is shown to illustrate the effectiveness of the developed AILFC algorithm.

In this paper, the tracking control problem is studied in the model-free adaptive control (MFAC) framework for a class of discrete-time single-input single-output nonlinear systems affected by dynamic sparse attacks and measurement disturbances. The system outputs are measured by multiple sensors, but an attacker can manipulate nearly half of the sensors simultaneously in a time-varying manner. First, considering the communication burden caused by multiple sensors, a voting-based event-triggered mechanism is introduced to minimize data transmission under attacks. The triggering condition is designed according to tracking performance so that the system is updated only at the triggering instants while maintaining satisfactory control performance. Then, to minimize the effects of measurement disturbances and dynamic sparse attacks on the control performance of the MFAC algorithm, two data fusion algorithms are developed to estimate the system output from the transmitted data. Moreover, an event-triggered extended state observer is designed to mitigate the negative impact of nonlinear residual terms caused by estimation errors on the MFAC algorithm, and based on this, a controller that updates only at the triggering instants is designed. Finally, simulation examples confirm the effectiveness of the proposed MFAC algorithm.

This paper investigates the secure consensus tracking problem for continuous-time nonlinear multiagent systems with sensor attacks. By designing a secure data selector, the unattacked output data is extracted from a group of output measurements under sparse sensor attacks. Then, by virtue of the obtained data and neural networks, a state observer is constructed to estimate the unavailable system states, where the convex combination theory is introduced to reduce the difficulty of solving observation gains. To utilize the limited communication resources reasonably, a novel saturated threshold event-triggered control strategy is proposed to reduce control updates, and then each update is encoded into a binary signal (0 or 1) to further reduce the occupation of communication bandwidth. The designed control scheme ensures that all closed-loop signals are semi-globally uniformly ultimately bounded, and its effectiveness is verified via a simulation of attitude control of unmanned aerial vehicles.