Data-Driven Iterative Learning Cluster Consensus Control for Nonlinear MASs Under Actuator Faults and DoS Attacks

In this article, a data-driven distributed leader–follower iterative learning consensus tracking control approach is proposed for unknown repetitive nonlinear nonaffine discrete-time multi-agent systems. The leader’s command is only communicated to a subset of the following agents and each following agent exchanges information only with its neighbors under a directed graph. A local iterative learning consensus control protocol is designed using only local measurements communicated among neighboring agents without the availability of physical and structural information of each agent by virtue of the dynamic linearization method both on the agent and the ideal distributed learning controller along the iteration axis. The convergent consensus properties of the tracking errors along the iteration axis are rigorously established under the strongly connected iteration-independent and iteration-varying communication topologies. One example is provided to validate the effectiveness of the proposed iterative learning consensus control protocol.

This paper is concerned with an iterative learning fault-tolerant control strategy for discrete-time nonlinear systems where actuator faults arbitrarily occur. First, the stochastic faults occurring in multiplicative and additive manner are considered. Then, statistical behaviors of both faults-corrupted control signals from the actuator to the plant and faults-free ones from the iterative learning controller to the actuator are analyzed. Meanwhile, sufficient conditions of convergence for the proposed strategy are established by resorting to the time-weighted norm technique. Finally, two numerical examples are provided to illustrate the effectiveness and reliability of the proposed results. Both theoretical analysis and simulations indicate that the developed strategy is satisfactory in preserving decent tracking accuracy of the addressed systems subject to actuator faults.

This paper addresses a class of discrete-time linear networked multi-agent systems characterized by time-varying network delays, actuator faults, and aperiodic Denial of Service (DoS) attacks. A distributed fault-tolerant predictive control (DFTPC) method is developed. By incorporating a network predictive control strategy, this method significantly mitigates the adverse effects of network delays and packet losses induced by DoS attacks on system performance. Additionally, an active fault-tolerant control mechanism is employed to counteract the influence of actuator faults. Subsequently, through rigorous stability analysis of the closed-loop system, sufficient and necessary conditions are established to validate the efficacy of the proposed distributed fault-tolerant predictive control scheme. Finally, the effectiveness of the proposed control methodology is demonstrated via numerical simulations. The development of the control method holds significant practical implications for enhancing the adaptability of networked multi-agent systems to harsher external environments.

This study addresses the problem of quantized consensus in multiple quadrotor unmanned aerial vehicles (QUAVs) subject to actuator faults and denial of service (DoS) attacks. Existing dynamic quantization (DQ) strategies typically design scaling factors based on idealized assumptions, often neglecting actuator faults. This oversight can lead to quantizer saturation, potentially causing consensus failure in multi–QUAVs. To address this issue, a novel scaling function incorporating robustness factors is proposed to enhance the resilience of the DQ mechanism. Based on this framework, a robust quantized control strategy is developed to guarantee bounded consensus in multiple linearized QUAVs subject to both actuator faults and DoS attacks. Simulation results on a four QUAV system validate the effectiveness of the proposed approach.

For the discrete linear multi-agent systems with random actuator faults and system disturbances, an iterative learning control strategy with the forgetting factor is proposed. Firstly, the random variation of actuator faults with the number of iterations is considered. A multiplicative stochastic fault model obeying a normal distribution is designed, and the correction mechanism is given. Secondly, an iterative learning consistency control algorithm with a forgetting factor is provided under the consideration of random disturbances in the system. The system stability is analyzed by using the mean square stability theory. A sufficient condition for the consistency in the multi-agent system is given through the relevant mathematical derivation, which makes it possible to realize the state consistency for the discrete multi-agent system under the occurrence of random faults in the actuator. Finally, the feasibility of the algorithm is verified by numerical simulation in a multi-agent system.

The 2D state space system model is established with state delay and actuator faults, and what is more, the iterative learning control (ILC) law required by the batch process model is designed. Using the knowledge of 2D system theory and an iterative learning control law, the 2D state space model can be changed into an equivalent 2D-Roesser closed-loop system model. According to the optimized cost function and Lyapunov stability theory, sufficient conditions for the solvability of model predictive control (MPC) problems in the form of linear matrix inequalities (LMIs) are given. A new solution is proposed which depends on the bound of the state delay. At last, the effectiveness of the method is proved through simulation experiments.

Adaptive iterative learning consensus control for second-order multi-agent systems with unknown control gains

In this article, a class of switched nonlinear discrete-time heterogeneous (NDTH) multi-agent systems (MASs) with switching signals in both agent dynamics and communication topologies are considered, and a distributed iterative learning control (ILC) law is proposed for the output consensus of the switched NDTH MAS so that the outputs of all follower agents can well track the leader agent. With the proposed ILC controller, the iterative learning dynamic process of the switched NDTH MAS is firstly formulated as a two-dimensional (2-D) linear discrete Roesser model with specified boundary states at each switching sub-interval. Afterwards, according to the exploited properties on solution of the 2-D linear discrete Roesser model, a sufficient convergence theorem for the presented ILC controller is derived. At last, the validity of the ILC-based consensus controller is illustrated through a simulation experiment.

This paper focuses on fixed-time event-triggered consensus tracking of high-order MAS over digraph and under DOS attack. The considered DOS attack is divided into connectivity-maintained attack and connectivity-broken attack. To detect connectivity-broken attack under event-triggered environment, a new cyberattack detection algorithm is presented. Next, using cyberattack detection results, a fixed-time event-triggered distributed observer is developed to estimate the leader’s states in the presence of DOS attack, which conducts event-triggered update in the normal condition and under connectivity-maintained attack and stops updating in the presence of connectivity-broken attack. Then, an adaptive event-triggered prescribed-time dynamic surface consensus tracking control protocol is proposed to eliminate mismatched disturbances caused by estimation error and achieve prescribed-time convergence of the tracking error. Stability analysis proves fixed-time convergence of the developed consensus tracking strategy and non-zeno analysis shows the developed distributed observer and consensus tracking control strategy exclude zeno behavior. This paper contributes to proposing a fixed-time leader-follower consensus control strategy for MAS under DOS attack via event-triggered approach, which eliminates the effect of DOS attack, achieves consensus tracking within a fixed time and reduces resources consumption. Finally, simulation results show the effectiveness and superiority of the developed consensus tracking approach.

This paper investigates the consensus control problem for unknown nonlinear multi-agent systems (MASs) with both sensor and actuator faults. Different from the existing data-driven control methods for unknown nonlinear MASs without faults, a new data-driven adaptive fault-tolerant control (DDC-FTC) method is proposed. Firstly, the data mapping of agents with sensor and actuator faults is established by the locally dynamic linearisation technique. Then, by employing an adaptive fault compensation method, a fault-tolerant controller is designed for unknown nonlinear MASs. Especially, through the stability analysis, it is proven that the consensus tracking error under the designed controller is bounded, where the error bound is related with the parameters in the proposed compensation mechanism, which can be effectively decreased by tuning the parameters. Finally, simulations are given to verify the correctness of the proposed DDA-FTC approach.

An iterative learning robust fault‐tolerant control algorithm is proposed for a class of uncertain discrete systems with repeated action with nonlinear and actuator faults. First, by defining an actuator fault coefficient matrix, we convert the iterative learning control system into an equivalent unknown nonlinear repetitive process model. Then, based on the mixed Lyapunov function approach, we describe the stability of the nonlinear repetitive mechanism on time and trial indices and have appropriate conditions for the repeated control system’s stability in terms of linear matrix inequality theory. Through LMI techniques, we have obtained satisfactory results and controller stability, and robustness against fault tolerance is also discussed in detail. Finally, the simulation results of the output tracking control of the two exemplary models verify the effectiveness of the proposed algorithm.

This paper considers the consensus control problem of multi-agent systems (MAS) with second-order hyperbolic distributed parameter models. Based on the framework of network topologies, a PI-type iterative learning control protocol is proposed by using the nearest neighbor knowledge. Using Gronwall inequality, a sufficient condition for the convergence of the consensus errors with respect to the iteration index is obtained. Finally, the validity of the proposed method is verified by two numerical examples.

By applying iterative learning control approach, the consensus is studied for multi‐agent systems (MASs) with one‐sided Lipschitz (OSL) nonlinearity. Firstly, the P‐type and D‐type learning schemes with initial state learning are introduced for such MASs. Then, utilizing the OSL and the quadratically inner‐bounded constraints, the convergence conditions of the consensus algorithms are presented and analyzed under a directed communication graph. We show that both algorithms, on a fixed finite‐time interval, can achieve perfect consensus tracking. Finally, the correctness of the obtained results is illustrated with simulation examples.

Iterative learning control for continuous-time multi-agent differential inclusion systems with full learnability

A path tracking and obstacle avoidance system along with iterative control learning for an autonomous mobile robot is investigated. The location of static obstacles is known, and a path planning algorithm is implemented while considering the robot’s kinematic limitations (such as size, weight, and steering angles). The first contribution of this work is the implementation of an artificial potential field algorithm for trajectory generation while avoiding static obstacles. A multi-constrained Proportional-Integral-Derivative controller is used for trajectory tracking. The second contribution is the implementation of an iterative learning control for minimum time iterative control tasks. The system’s distance coordinates, and trajectory is stored for each lap and used to develop recursive feasibility and time efficient performance cost for each iteration. Simulation results show the effectiveness of the proposed control logic.