In this article, the double-layered iterative learning control (DLILC) approach is adopted to investigate the tracking control problem of repetitive nonlinear multiple-input-multiple-output (MIMO) systems under false data injection (FDI) attacks. Based on historical data, two control loops in the scheme are devised to improve tracking accuracy. More specifically, an outer loop adaptive set-point tuning mechanism is developed, which is independent of the inner-loop controller. Such a mechanism dynamically optimizes learning gains by leveraging historical data and significantly reduces reliance on preset system parameters. In the inner loop, a proportional-derivative controller is employed to form the feedback circuit. Furthermore, the double dynamic linearization technique is adopted to transform complex nonlinearities, coupling effects, and unknown uncertainties into a set of linearly estimable parameters. To address FDI attacks, an output observer-based real-time compensator is constructed, which is capable of promptly mitigating the impact of such attacks on system outputs. Simulation results demonstrate that the proposed scheme ensures high-precision tracking, substantially reduces computational burden, and exhibits superior resilience against attacks. The approach thus provides a new pathway toward secure and efficient iterative learning control of nonlinear systems.

Vehicle platoon control is one of the promising technologies to improve the performance of transportation systems. However, communication connections among vehicles in cyber physical vehicle systems (CPVS) are often intermittent due to environmental factors and limitations of physical equipment. Moreover, the system models of the vehicles are generally unknown in practice, making it challenging to implement platoon control for CPVS. To address these challenges, we investigate the challenge platoon control problem for CPVS with intermittent communication, where the system models of both the leader vehicle and the follower vehicles are unknown. A data-driven-based learning algorithm is first designed to obtain the unknown leader model. Then, based on the learned leader model, a finite-time distributed observer is proposed to estimate the leader vehicle state in finite time under intermittent communication. Furthermore, with the unknown system models, a data-driven-based controller gain learning algorithm is proposed to learn the controller gain. Based on the learned controller gain, adaptive decentralized tracking controllers are designed to perform platoon control for CPVS. Finally, the effectiveness of our result is examined by a simulation example.

The problem of predefined-time cooperative formation control for multiple unmanned surface vehicles with sensor faults and universal global performance constraints is investigated in this paper. Initially, a generic performance constraint strategy is proposed by integrating an improved global performance function, which eliminates the subpar reconfigurability present in the specific performance function-based global constraint schemes. By embedding the saturation compensation function, a feedback mechanism between the saturation constraint and performance constraint is established, instead of cutting off the analysis separately, by giving the constraint boundaries the flexibility to expand and contract. Subsequently, the inherently unmodeled dynamics and uncertainties of the controlled vehicle are reconstructed online by interval type-2 fuzzy logic systems. By integrating the predefined-time differentiator into the recursive design framework, an adaptive fuzzy predefined-time formation protocol is developed to provide a streamlined solution for adjusting the settling time and facilitates engineering implementation. The stability analysis rigorously proves that all the variables are practical predefined-time bounded. The illustrative results verify the feasibility and functionality of the developed formation control strategy.

This article investigates the distributed adaptive fuzzy finite-time fault-tolerant consensus tracking control for a class of unknown nonlinear high-order multiagent systems (MASs) with actuator faults and high powers (ratio of positive odd rational numbers). The fault models include both loss of effectiveness and bias fault. Compared with existing similar results, the MASs considered here are more general and complex, which include the special case when the powers are equal to 1. Besides, the functions in this article are completely unknown and do not need to satisfy any growth conditions. In the backstepping framework, an adaptive fuzzy fault-tolerant consensus tracking controller is designed via adding one power integrator technique and directed graph theory so that the controlled systems are semiglobal practical finite-time stability (SGPFTS). Finally, numerical simulation results further verify the effectiveness of the developed control scheme.

This work studies the issue of global h-synchronization about high-order delayed inertial neural networks via a second-order response system (SORS) approach. Note that the h-synchronization is a flexible definition which can generalize different special synchronization types by choosing different regulation function$\hbar $. By constructing a regulation function-dependent Lyapunov–Krasovskii functional (RFD–LKF), a novel delay-dependent global h-synchronization criterion is obtained. Furthermore, an adaptive control algorithm is designed to estimate control gains online, which is useful to guarantee global h-synchronization performance as well as to decrease the control cost. And finally, the superiority of the method is verified via three numerical examples.

This article studies a cooperative output-feedback secure control problem for distributed cyber-physical systems over an unreliable communication interaction, which is to achieve coordination tracking in the presence of intermittent denial-of-service (DoS) attacks. Under the switching communication network environment, first, a distributed secure control method for each subsystem is proposed via neighborhood information, which includes the local state estimator and cooperative resilient controller. Second, based on the topology-dependent Lyapunov function approach, the design conditions of secure control protocol are derived such that cooperative tracking errors are uniformly ultimately bounded. Interestingly, by exploiting the topology-allocation-dependent average dwell-time (TADADT) technique, the stability analysis of closed-loop error dynamics is presented, and the proposed coordination design conditions can relax time constraints on interaction topology switching. Finally, two numerical examples are presented to demonstrate the effectiveness of the theoretical results.

In this paper, for linear leader-follower networks with multiple heterogeneous actuator faults, including partial loss of effectiveness fault and actuator bias fault, a cooperative fault-tolerant control (CFTC) approach is developed. Assume that the interaction network topology among all nodes is a switching directed graph. To address the difficulty of designing the distributed compensation control laws under the time-varying asymmetrical network structure, a novel distributed-reference-observer-based fault-tolerant tracking control approach is established, under which the global tracking errors are proved to be asymptotically convergent in the presence of actuator failures. First, by constructing a group of distributed reference observers based on neighborhood state information, all followers can estimate the leader's state trajectories directly. Second, a decentralized adaptive fault-tolerant tracking controller via local estimation is designed to achieve the global synchronization. Furthermore, the reliable coordination problem under switching directed topology with intermittent communications is solved by utilizing the presented CFTC approach. Finally, the effectiveness of the proposed coordination control protocol is illustrated by its applications to a networked aircraft system.