This article addresses the problem of prescribed-time fault-tolerant tracking control for a class of nonlinear multiagent systems (MASs) subject to parameter uncertainties and external disturbances. To improve tracking precision, the trajectory reconstruction approach based on cubic spline interpolation is proposed, which effectively reconstructs the discrete reference signals. Then, a class of prescribed-time regulators is meticulously designed to formulate the fault-tolerant tracking controller, ensuring that the outputs of the controlled system converge to the reconstructed trajectory with arbitrary accuracy within the prescribed tracking time. Finally, stability analyses and a simulation example are presented to demonstrate the effectiveness of the proposed prescribed-time fault-tolerant tracking control strategy, validating its theoretical significance and practical applicability in engineering systems.

This article proposes a decentralized resilient adaptive intermittent output feedback fault-tolerant control (FTC) method for interconnected nonlinear systems with sensor deception attacks, multiple actuator faults, and input saturation. First, we constructed a new composite observer using intermittent compromised output signals and fuzzy logic systems (FLSs) to estimate the state variables in the face of deception attacks and unknown external disturbances. On this basis, the reasonable adaptive laws are cleverly devised to handle multiple unpredictable actuator faults. Then, in order to deal with unknown gains caused by deception attacks, a projection operator-based compensation mechanism is proposed. In addition, the “explosion of complexity” in the traditional backstepping design process is solved through command filtering technology. At the same time, an error compensation system and an auxiliary system are designed to reduce the errors caused by command filtering and solve the input saturation problem, respectively. The results indicate that the proposed control scheme can ensure the boundedness of all signals in the closed-loop system, and the tracking errors converge to a bounded compact set. Finally, the effectiveness of the proposed strategy was verified through numerical simulation.

This paper proposes a zonotopic secure estimation framework for discrete-time cyber-physical systems (CPSs) subject to unknown-but-bounded (UBB) disturbances and false data injection (FDI) attacks. A decentralized dynamic event-triggered mechanism (DETM) is developed, allowing each sensor to adapt its triggering threshold using local output deviations, thereby reducing communication while preserving estimation accuracy. Subsequently, a zonotopic interval observer is developed to estimate the system state under DETM. The observer propagates zonotopic error bounds and is designed via LMIs to ensure stability and l1 performance. Furthermore, a zonotope-based attack reconstruction approach is formulated. The attack signal is conservatively enclosed in a residual-based zonotope, and a threshold test is used to isolate attacked channels. Finally, simulation results confirm that the method reduces communication significantly while maintaining reliable estimation, validating its use in resource-constrained CPSs.

This article addresses the problem of distributed adaptive bipartite containment control for nonlinear multiagent systems under deception attacks and actuator faults. By constructing a modified coordinate transformation and designing a Lyapunov function based on predefined accuracy functions, a control scheme is developed that ensures bipartite containment errors converge to a user-defined value, even under deception attacks. To handle unknown time-varying attack signals and actuator faults, a two-step approach is employed in controller design. This guarantees that 1) the outputs of the followers converge to the convex hull formed by the leaders with cooperative relationships, and 2) all closed-loop signals remain bounded. Finally, the effectiveness of the proposed method is demonstrated through a simulation of an RLC circuit system.

This paper investigates the fault detection and isolation problem of a class of leader-follower multi-agent systems subject to unknown-but-bounded disturbances under an event-triggered communication mechanism (ETCM). Firstly, an adaptive ETCM is proposed to avoid the continuous acquisition of outputs from neighboring agents, thereby reducing the communication burden. Secondly, a joint state and fault observer is designed, and H∞ analysis is utilized to improve the robustness of the observer against external disturbances and event-triggered errors. Next, zonotopic reachability analysis is utilized to derive the estimated intervals of the system state and fault signal, based on which a fault detection and isolation method is developed to determine both when the system is faulty and which agent is at fault. Finally, a fighter formation model is employed to verify the effectiveness of the proposed approach.

It is difficult to ensure tracking performance even for general nonlinear systems under the condition that only intermittent output signals are used. This paper studies the problem of adaptive fuzzy consensus tracking for a class of mismatched nonlinear strict-feedback multi-agent systems (MASs) by using intermittent output signals. Firstly, a novel fuzzy state observer using intermittent output states and fuzzy-logic systems is constructed to estimate the unknown state variables of the system. Secondly, dynamic filtering technology is applied to address the issue of virtual controller non-differentiability caused by intermittent output feedback during the backstepping design process. Furthermore, for the situation where the controller is non-differentiable in stability analysis, an alternative solution can be proposed: initially, a distributed continuous control strategy is developed using conventional continuous output signals, and then the output signals in the previous scheme are replaced with event-triggered signals to design a distributed event-triggered control scheme. It is demonstrated that the designed event-triggered control scheme guarantees that all closed-loop system signals remain semi-globally uniformly ultimately bounded (SGUUB), and the distributed consensus tracking errors can converge to a neighborhood of zero. Finally, the effectiveness of the proposed scheme is verified through simulations involving a group of single-link robots.

This work considers the adaptive output feedback consensus asymptotic tracking control problem for full-state-constrained nonlinear multiagent systems (MASs) under the denial-of-service (DoS) attacks and the actuator faults. Since MASs are regardered as a network, where each agent is considered as a network node, the DoS attacks that cut network communication links between agents are studied in this paper. Moreover, to compensate the unknown actuator fault signal, the Nussbaum function method is introduced into the controller design process for each agent. Due to the researched nonlinear MASs contain unknown nonlinearities and the system states of each agent are unmeasurable, an estimator based on fuzzy logic systems (FLSs) is constructed for each agent. Further, the secure control strategy based on the barrier Lyapunov functions (BLFs) is proposed for the researched nonlinear MASs, which guarantees the consensus tracking errors asymptotically converge to zero, all closed-loop signals remain bounded and the full-state constraint requirements are not broken. In the illustrative example, the proposed secure control strategy is applied to the MASs composed of multiple forced damped pendulums (FDPs).Note to Practitioners— In the framework of network communication, how to eliminate the impact of cyber attacks on nonlinear MASs that rely on communication links to transmit information is critical. Furthermore, due to practical systems are inevitably subject to multiple physical and environmental constraints, the problem of dealing with system constraints can not be ignored. Therefore, a secure control algorithm based on the BLFs is proposed to eliminate the effects of the DoS attacks on the researched nonlinear MASs and ensure that the full-state constraints are not broken. To realize the output feedback control and approximate the unknown nonlinearities of the studied system, the estimators combining with the intelligent approximation techniques based on FLSs are developed. The Nussbaum function is introduced into controller to solve the actuator faults problems for each agent.

This paper investigates the problem of adaptive output feedback control for uncertain nonlinear systems with input/output quantization subject to intermittent denial-of-service (DoS) attacks. When a DoS attack becomes active, the system is unable to obtain output signal. In order to address this challenge, we compensate the output signal by introducing an attack compensator. At the same time, a novel quantization compensator-based (QC) state observer is designed, which uses only the quantized compensated signal to reconstruct the system states. In addition, the problem of over-parameterisation is avoided by using parameter projection technique to design the parameter estimator. By combining Lyapunov stability theory and a modified average dwell time (ADT) approach, the stability of the closed-loop system is ensured. Furthermore, the proposed controller ensures that all closed-loop signals are bounded, and the control performance of mean-square error can be adjusted by appropriately selecting certain design parameters. Finally, the effectiveness of the proposed control strategy is validated through a single-link robot simulation results.Note to Practitioners—Considering that resources are limited, the DoS attacks mentioned in this paper are aperiodic, which means that attackers engage in frequent attacks within a relatively short time frame. This aligns more closely with real-world scenarios of network attacks. When the attack is active, the output signal is not available for the system. In this paper, a new QC state observer is designed, which in turn enables the estimation of the system state signals. Furthermore, this paper considers both input and output quantization, which reduces duplicate transmission of samples and improves the use of communication resources. This makes the work in this paper more relevant to the practical engineering context.

In this article, we propose a dynamic event-triggered adaptive prescribed-time output feedback tracking control strategy for nonlinear systems with unknown external disturbances under denial-of-service (DoS) attacks. The presence of malicious intermittent DoS attacks makes the output signal and states of the system unavailable, which in turn leads to more difficulty in the design of the controller. To overcome the above difficulty, we construct a novel composite observer based on the attack compensator, whereby the system states can be reconstructed. Moreover, with the help of the time-varying constraint function, the prescribed-time tracking control problem for nonlinear systems is transformed into the constraint problem of tracking error. At the same time, a new dynamic event-triggered adaptive prescribed-time safety fuzzy controller is built and remains applicable in the systems that operate continuously after the predefined time. The adaptive fuzzy safety control method proposed in this article ensures that the tracking error converges to the user-specified region in the predefined time, all signals of the closed-loop system remain bounded under intermittent DoS attacks, and the repeated transmission of samples from the controller to the actuator can be further reduced, generating fewer events and saving communication resources. Finally, the simulation results of the single-link robotic arm demonstrate the rationality and effectiveness of the developed control algorithm.

This article addresses the practical prescribed-time fault-tolerant tracking control (PFTC) problem for a class of nonlinear systems characterized by uncertain control gains and external disturbances. A novel prescribed-time regulator (PTR) is proposed to construct the controller, thereby achieving the tracking control objective with arbitrary accuracy within the prescribed tracking time. The phenomenon of complete actuator faults is also considered, and a fault-tolerant control strategy with immediate actuator switching is designed to mitigate their impact. Furthermore, a self-regulating control gain based on an error-feedback mechanism is introduced to restrain the control input while improving the overall control performance. To overcome the issue of “complexity explosion” arising from the derivation of virtual controllers, a class of nonlinear filters is designed to estimate the values of the virtual controllers. The PFTC strategy proposed in this article ensures the following: 1) all the signals in the closed-loop system remain bounded; 2) the tracking error is practical prescribed-time tracking stable; and 3) after detection of actuator faults, the tracking error reconverges to the desired range within the prescribed recovery time. Lastly, a simulation example is provided to demonstrate the effectiveness of the proposed control strategy.

Based on output triggering, an adaptive prescribed-time tracking control strategy is proposed for a class of uncertain strict-feedback nonlinear systems with unknown control gains in this article. The nondifferentiability of the virtual control signals is identified as the most prominent design difficulty in this research. In order to solve the above difficulty, a new fuzzy state observer is built by using triggered output signal and fuzzy logic systems (FLSs), which in turn generates alternative continuous states. Simultaneously, the estimated signals are utilized to design virtual control signals, making certain that the virtual control signals have a well-defined first derivative. On this basis, by introducing a time-varying constraint function, a new adaptive prescribed-time fuzzy controller is constructed, so that the controller can still be applied to continuously operating systems after the predefined time. Additionally, we introduce the command filtering technique to mitigate repeated differentiation of virtual control signals during the backstepping design process. Combining the constructed logarithmic Lyapunov functions and bounded control technique with backstepping design, it is possible to guarantee that the established adaptive prescirbed-time event-triggered control method satisfies the following: 1) within the predefined time, the tracking error converges to the user-specified region and 2) the full range of signals involved in the closed-loop system is kept bounded. At last, the results of the single-link arm simulation example verify the reasonableness and effectiveness of the established control scheme.

This paper investigates event-triggered based finite-time bipartite consensus tracking control problem for multi-agent systems (MASs) over signed directed graphs. Compared with the existing related results, the main features of the results presented in this paper are as follows: (i) The strict limitation for nonlinear MASs with a structurally balanced digraph is removed by introducing a prioritized strategy. (ii) A state estimator is constructed by combining neural networks (NNs), which reconstructs the immeasurable system states of each agent in nonstrict-feedback form for the first time and approximates the completely unknown nonlinearities that exist in the system. (iii) A new distributed control algorithm, named finite-time distributed control strategy is designed by introducing a novel first-order filter, which ensures that the consensus errors can obtain a fast convergence. (iv) The results are successfully extended to the event-based bipartite tracking control for MASs by using relative threshold strategy. Moreover, the proposed control algorithm is applied to the consensus problem of a group of forced damped pendulums (FDPs). Based on backstepping method and Lyapunov stability theory, the distributed consensus tracking errors can converge to a small neighborhood of the origin at a finite-time under the proposed protocol, and all the signals of the closed-loop system remain bounded. Finally, the simulation results illustrate the validity of the proposed schemes.Note to Practitioners—The studies of the bipartite consensus problems of multi-agent systems (MASs) have gained great attention due to its applications in multi-spacecraft systems, natural networks, and biological communities, and a new event-triggered based finite time bipartite tracking consensus control method for multi-agent systems (MASs) is studied in this paper. In practical applications, the finite time method can obtain the optimal control performance in time. Then, in order to save the communication and computation resources for the nonlinear MASs, this paper extends the proposed control strategy to the event-based bipartite tracking control problem for MASs by using the relative threshold event-triggered rules, which greatly conserves the communication resources between the control signal and the actuator. In addition, the dynamic model and backstepping technology used in this paper are general and practical.

In this article, the resilient adaptive intermittent output feedback control is investigated for a class of uncertain strict-feedback nonlinear systems under unknown sensor deception attack. The underlying problem is rather challenging not only because the system contains mismatched uncertainties, the output information is corrupted by an additional attack signal, and only the intermittent false output is available for control design, but also because the virtual control terms are nondifferentiable due to the utilization of event triggering. To circumvent these obstacles, we construct a new fuzzy state estimator via intermittent false output and fuzzy-logic systems (FLSs), which, in conjunction with a new set of error surfaces employing the false and estimated state information, allows a novel observer-based resilient adaptive intermittent control strategy to be developed. The results of the proposed resilient adaptive output event-triggered control shown that the stabilization errors converge to a vicinity of the origin, and all the signals of the overall system remain bounded. Numerical simulations confirm the efficiency of the proposed methodology.

This article presents a novel adaptive bipartite consensus tracking strategy for multiagent systems (MASs) under sensor deception attacks. The fundamental design philosophy is to develop a hierarchical algorithm based on shortest route technology that recasts the bipartite consensus tracking problem for MASs into the tracking problem for a single agent and eliminates the need for any global information of the Laplacian matrix. As the sensors suffer from malicious deception attacks, the states cannot be measured accurately, we thus construct a novel dynamic estimator to estimate the actual states, which, together with a new coordinate transformation involving the attacked and estimated state variables, allows a distributed security control scheme to be developed, in which the singularity of the adaptive iterative process involved in existing works is completely avoided. Furthermore, the Nussbaum functions are included in the controller to account for the influence of the unknown control gains caused by sensor deception attacks. It is shown that the distributed consensus tracking errors converge to a small neighborhood of the origin, and all the signals in the closed-loop system remain bounded. Simulation on a forced damped pendulums (FDPs) is conducted to demonstrate and verify the effectiveness of the proposed strategy.