This paper investigates adaptive iterative learning control (ILC) of robot manipulators with input saturation. Modified functions are adopted to fix the continuity of errors along the iteration axis under a relaxed alignment condition. Auxiliary systems are designed to compensate for input saturation. Virtual and actual controllers are constructed via backstepping technique. Simplified updating laws with a more readily available parameterized matrix are proposed to reduce the computational burden. A unified composite energy function is developed to analyze the tracking errors. Finally, the proposed method is demonstrated by simulation examples.

This article addresses the fault detection and isolation problem for discrete-time Lipschitz nonlinear systems represented by Takagi-Sugeno fuzzy models. A zonotopic set-membership estimation framework is proposed to robustly handle system uncertainties and bounded disturbances. First, an $H_\infty$ observer is designed based on linear matrix inequality conditions to ensure robust performance. The estimation error is bounded within a recursively propagated zonotope, where generator growth is controlled via zonotope order reduction. Second, a fault detection scheme is developed by constructing residual zonotopes and checking whether the residual lies within the associated bounding boxes. Third, to achieve fault isolation, two augmented observers are designed, respectively, addressing actuator fault isolation and sensor fault isolation. For actuator fault isolation, a state-augmented observer is proposed to eliminate the influence of sensor faults in the residual. For sensor fault isolation, a disturbance-augmented observer is constructed to decouple the actuator fault effect. In both cases, zonotopic residual bounds are derived to detect and isolate the fault source. Finally, simulation results on a fuzzy nonlinear system illustrate the effectiveness and robustness of the proposed fault detection and isolation approach.

In this study, the observer-based asynchronous stabilization is addressed for networked system under multichannel attacks, in which the asynchronous phenomenon refers to the mismatch between the controller mode and the actual attack mode. To accurately depict complex attack behaviors, a piecewise homogeneous semi-Markov chain (SMC) model modulated by a superstratum Markov chain is introduced, which can simultaneously describe the randomness of attack mode transitions and the time-varying nature of transition probabilities. Considering that the actual attack modes are inaccessible, an observer-based mode switching delay technique is designed to solve this challenge. Under the framework of a piecewise homogeneous SMC, a sufficient criterion is established to ensure the $\varsigma $ -error mean-square stability under random multichannel denial-of-service attacks by means of a Lyapunov function depending on observed attack modes, piecewise homogeneous variables, and elapsed time. Moreover, matrix decoupling and convexification techniques are employed to reduce the computational complexity. Finally, the effectiveness of the proposed method is demonstrated through two practical simulation cases.

The dynamic behavior of nonlinear systems is often influenced by both time delay and impulsive effects. These systems experience instantaneous state jumps (impulses) at certain moments while also being affected by time delays. In fields such as biology, communications and economics, time delays often depend on impulsive responses. Therefore, conducting stability analysis of such systems provides a scientific basis and technical support for their design and optimization. This paper investigates the stability issue of impulsive systems with impulse-dependent varying delays by applying a monotone-delay-interval-based (MDI-based) looped Lyapunov functional approach. Firstly, two novel integral inequalities are introduced. One is a free-matrix-based integral inequality that exploits two-sided impulsive characteristics, thereby enabling the derived conditions to capture richer information on impulsive intervals. The other is established by integrating a free-matrix formulation with a monotone-delay-interval-based looped functional, which leads to less conservative stability criteria.This combination effectively captures both the two-sided impulsive characteristics and the variation in time delays. Afterward, by leveraging these inequalities and proposed MDI-based looped Lyapunov functional, several stability criteria and two related corollaries are established. Finally, an example of practical application in fishery management and additional numerical examples are provided to verify the applicability and efficiency of the proposed approach.

Memristive neural networks (MNNs) with reaction-diffusion characteristics (RDCs) have broad applications in sentiment analysis and secure communication. As two key aspects of MNNs with RDCs, stabilization and security applications constitute significant research issues. First, a new model for MNNs with RDCs is established. Compared to the existing models of MNNs with RDCs, the proposed model continuously depends on fluxes and exhibits memory properties. Second, a secure adaptive event-triggered control scheme is designed, which demonstrates robust resilience against the influence of Denial-of-Service (DoS) attacks. Third, based on the established MNN model with RDCs, a new secure image communication (SIC) scheme is proposed. This SIC scheme incorporates the CRYSTALS-Kyber post-quantum key encapsulation mechanism (KEM). Compared to the existing SIC schemes, the SIC scheme with CRYSTALS-Kyber can guarantee quantum-resistant key confidentiality and ensure the security of image transmission.

This article investigates the fault-tolerant control (FTC) problem for nonlinear systems subject to actuator faults and bounded disturbances. To address the conservatism of traditional robust control and the transient uncertainties of adaptive schemes, we propose a unified FTC based on the fully actuated system (FAS) framework and interval estimation. By using the FAS theory, the nonlinear system is expressed in FAS form in the physical/mathematical sense, allowing inherent nonlinearities to be completely compensated. Specifically, an interval estimation-based detection mechanism is designed, which constructs a state envelope estimation to separate actuator faults from background disturbances under prescribed bounded-disturbance conditions. Then, a fault interval observer (FIOB) tailored for the high-order FAS model is designed to reconstruct fault information. Furthermore, a unified FTC strategy is synthesized to seamlessly transition between active compensation and static robust modes, utilizing the estimated interval center for nominal cancellation and the interval radius for dynamic robust suppression. Experimental results on a rotary flexible joint platform comparatively demonstrate that the proposed strategy achieves improved control accuracy and reduced control chattering compared to the existing methods. Additional simulation cases of dual-rotor helicopter and three-axis satellite attitude systems further validate the applicability of the proposed method.

This paper is devoted to event-triggered control for nonlinear networked control systems via a data-based representation. A controller is built on this representation using the second-order Taylor series expansion. An improved data-dependent static event-triggered mechanism with relaxing parameter is proposed to reduce communication transmission. A dynamic version is further developed to establish an adjustable inter-event interval. Sufficient conditions are exploited to solve the control gain for both cases in terms of linear matrix inequalities and ensure the local stability of the closed-loop systems. Illustrative examples are simulated to verify the validity of the proposed scheme.

The linear optimal output regulation problem (LOORP) of discrete-time (DT) cyber-physical systems (CPSs) under false data injection attacks (FDIAs) is investigated in this article. First, the LOORP under FDIAs is reduced to a static optimization problem and a dynamic minimax problem, and the corresponding model-based schemes are provided to solve these two problems. Afterward, a hybrid iteration (HI)-based Q-learning scheme is proposed to solve the two issues online. This scheme requires neither exact system dynamics information nor an initially stabilizing control gain, which also achieves a fast iteration speed. Finally, a discretized $LCL$ -coupled inverter-based distributed generation system is presented to demonstrate the performance of the proposed scheme.

In this article, from a pure discrete-time point of view, considering nonuniform sampling periods and uncertain large delays, exponential stability analysis with a given decay rate and controller design are investigated by applying integral quadratic constraints (IQCs) theory for load frequency control (LFC) of a power system with renewable energy sources (RESs). First, a decentralized dynamic model of a nonuniform sampled-data LFC scheme with RESs is structured by considering uncertain delays. Second, when delays may be smaller or larger than the sampling interval, control inputs that are available in one sampling interval are precisely determined. Then, a discrete-time model is derived for the modeled LFC system, which includes message rejection. Based on the uncertain discrete-time model, a feedback interconnection of a nominal linear time-invariant system and a perturbation operator is constructed, where the perturbation contains variable sampling intervals and uncertain delays. Third, scaling operators are introduced to obtain a related scaled feedback interconnection, and a matrix multiplier is constructed, which can specify IQC for the operator. Next, based on the equivalence between the exponential stability of feedback interconnection and the linear stability of a related scaled interconnection, an exponential stability criterion with a decay rate is developed for the resultant LFC system under the framework of IQC theory. Moreover, by using matrix decoupling to handle nonlinear terms in stability conditions, an improved LFC scheme is designed, and the corresponding heuristic algorithm is presented to find controller gains. Finally, based on the one-area power system, three-area power system, and IEEE 39-bus system, case studies are conducted for LFC of power systems to demonstrate the validity and advantages of the presented results.

This paper discusses the finite-time synchronization and energy estimation of T-S fuzzy complex networks. A nonlinear fuzzy controller incorporating a sign function is proposed to realize synchronization performance. Under this controller, a synchronization criterion is obtained via the Jensen inequality. The upper bound of the energy consumption is estimated based on the derived setting time. In the end, an example is provided to explain the effectiveness of the proposed method.

The study of dynamics in complex systems has increasingly incorporated higher order interactions, which capture the collective influence among three or more units, extending beyond traditional pairwise connections. Although such interactions are observed in biological neural networks, their precise role in shaping network dynamics and the feasibility of controlling these dynamics remain unclear. This article proposes a controlled diffusion hub neural network model that explicitly includes higher order interactions. To regulate the resulting spatiotemporal dynamics, a cross-node associated delayed feedback control (CNADFC) method is further introduced. Our analysis establishes conditions for local stability, Turing instability, and Hopf bifurcation. We show that while Turing instability cannot arise, spatially periodic patterns emerge under specific parametric conditions. Numerical simulations confirm these theoretical findings and highlight the pronounced effects of self-feedback, control, and first-order interaction on stability and dynamic behaviors; in contrast, higher order interactions exert a comparatively modest influence. Furthermore, simulations illustrate how the CNADFC method can effectively optimize spatiotemporal dynamics. This work advances the understanding of diffusion neural network behavior under complex higher order interaction and provides a reference for the effective control of such networks.

This article addresses the optimal state observation and leader-following consensus for a nonlinear multiagent system (MAS) with input saturation under the Stackelberg game framework. The dynamics and states of followers are unknown, the leader’s dynamics is unknown, and the leader’s state is accessible only to a subset of followers. First, a distributed estimation algorithm is developed for each follower to estimate the leader’s state. Then, a game-based observer is designed to estimate the follower state, where the bidirectional interaction between the observer and follower dynamics is considered. The follower dynamics and observer are modeled as leader and follower players in the Stackelberg game, respectively. Based on the proposed structure, an optimal auxiliary controller for the observer and an optimal consensus controller are developed. Furthermore, a fuzzy reinforcement learning approach approximates the unknown dynamics and derives the optimal state observers and leader-following consensus controllers. All closed-loop signals are guaranteed to be uniformly ultimately bounded based on the Lyapunov method. Finally, simulations are provided to validate the effectiveness of the proposed approach.

This work addresses the problem of secure consensus in heterogeneous multi-agent systems under false data injection attacks. To balance the impact of malicious attacks against system performance, an H∞ consensus control scheme is developed, which treats attacks as worst-case disturbances, attenuates their effects, and guarantees consensus of system trajectories. Moreover, a novel hybrid iteration (HI) algorithm based on reinforcement learning is developed to address the graphical game algebraic Riccati equations without relying on prior information of complete system dynamics. By combining the merits of policy iteration and value iteration, the proposed HI algorithm can be applied to cases without an initial stable control policy while ensuring a fast iteration speed. Finally, the effectiveness and superiority of the proposed control method are validated through numerical examples and comparative cases.

In this paper, an auxiliary system method is proposed to address the predefined-time distributed constrained optimization with an event-triggered mechanism, which is applied to target monitoring in multi-robot systems. For multi-robot target monitoring, coupled equality and inequality constraints are handled simultaneously, and a predefined-time event-triggered algorithm with a time-based generator is proposed. Furthermore, the given algorithm eliminates the Zeno behavior by proving that the time interval length between two adjacent triggering instants is greater than a positive constant. Finally, a numerical example with simulations is provided to substantiate the effectiveness of the obtained results.

This paper discusses the robust $\mathcal {H}\_{\infty }$ control problem for continuous-time nonlinear hidden Markov jump systems under false-data-injection (FDI) attacks by means of a mode- and rule-dependent extended observer-based method. To capture both nonlinearity and parameter uncertainty, the system is modeled via an interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy framework, where the asynchronous switching between plant and controller is described by a hidden Markov model. During the data transmission, the measurement data may be corrupted by FDI attacks generated by an exogenous system subject to external disturbance. To handle this, a mode- and rule-dependent extended observer that simultaneously detects and estimates the plant states and the injected attack is constructed. Building on these estimates, an asynchronous $\mathcal {H}\_{\infty }$ controller is synthesized within the IT2 T-S fuzzy setting. Moreover, some sufficient conditions, guaranteeing the stability of the resulting systems in a stochastic sense and satisfying the $\mathcal {H}\_{\infty }$ performance, are derived by using tools such as the Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed approach are illustrated via the simulation results.

This article investigates the security issue of malicious adversaries undermining the remote estimator over cyber-physical systems (CPSs). A critical-data-oriented (CDO) attack strategy is proposed to enable attackers to utilize essential data judiciously during data transmission, thereby degrading remote state estimation maximally. In addition, the proposed scheme is compared with commonly studied attack strategies such as Bernoulli-distributed and periodic denial-of-service (DoS) attacks within a unified framework. The evaluation of its impact focuses on two factors: the weighting matrix (WM) and the residual information. Furthermore, the consideration of attack energy distribution (AED) and quality of service (QoS) constraints is merged. The theoretical analysis based on stochastic methods reveals the inherent associations among WM, attack energy, and signal-to-interference-plus-noise ratio (SINR). The attack impact on channel throughput is analyzed and formulated as a convex optimization problem. The extended part explores the selection of optimal WM under special scenarios. Finally, two examples are established to validate the correctness and practicality of the concerned CDO attack strategy.

This article investigates an event-triggered security control strategy for networked autonomous ground vehicles (AGVs) against actuator attacks. First, a novel bandwidth-aware average-event-triggered protocol (BAAETP) is proposed to improve the usage efficiency of limited network resources. There are two main merits of this protocol: the one is that the average of historical data is used to mitigate the over-triggering resulting from random data jitter and the second is that a bandwidth-aware dynamic triggering threshold is constructed to increase the adaptation ability of the protocol to the time-varying bandwidth status. Second, in order to implement state feedback control, a dynamic observer based on a dynamic variable related to the observation error is presented to enhance the state estimation accuracy. Third, a new attack estimator relying on a hyper basis function neural network (HBFNN) is devised to estimate the unknown malicious attacks injected into the actuator. By combining the observed state and estimated attack, an attack-compensation-based security control strategy is developed to mitigate the negative influence caused by actuator attacks and further increase control performance. Then, by using Lyapunov theory and linear matrix inequality techniques, several sufficient criteria to calculate the controller and observer matrices are derived. Finally, the validity of the addressed strategy is verified via some simulation outcomes.

This paper explores the stability assessment and the development of controllers for aperiodic sampling load frequency control scheme for wind power-integrated power systems, accounting for communication delays and packet losses. Firstly, transform the multi-area continuous time model into a discrete time load frequency control model. By incorporating linear operators to manage the uncertainties including communication delays, packet losses and sampling interval within the system, the discrete time load frequency control system is converted into a feedback interconnected system that consists of uncertain operators and linear time-invariant systems. Secondly, the exponential stability condition of the system is given by utilizing the integral quadratic constraint theory and linear matrix inequality technique, which can be used to compute the upper bounds for aperiodic sampled-data intervals and delays. Furthermore, based on the proposed stability conditions, a controller iteration optimization algorithm is designed, which effectively addresses issues related to delays and packet losses. Finally, an example study based on one-area, three-area load frequency control system and IEEE 39-bus system show that the method can obtain larger sampling upper bounds and better control performance than previous methods.

This paper focuses on iterative learning control of permanent magnet synchronous motor servo systems with output constraints under varying iteration lengths. A Barrier Lyapunov Function is adopted to deal with output constraints. An actual controller is constructed via backstepping approach. Segmented parameter updating laws are proposed to estimate parameter uncertainties and disturbances. A virtual error based Barrier Composite Energy Function is provided to demonstrate error convergence along the iteration axis under varying iteration lengths. Finally, the effectiveness of the proposed approach is verified by a second-order permanent magnet synchronous motor servo system.

This article proposes a novel two-stage reinforcement learning (TSRL) scheme to address the linear quadratic tracking (LQT) control problem of discrete-time interconnected systems (DTISs), commonly encountered in Internet of Things (IoT) applications. The proposed approach consists of two main stages: 1) the derivation of an admissible control policy and 2) the computation of the optimized tracking control policy. In the first stage, regulation parameters are introduced, and a scaling technique is employed to construct an artificially stable system. By gradually decreasing the regulation parameter, an admissible control policy is obtained that makes the original system Schur stable. In the second stage, the original system is augmented, and a discounted performance index is introduced, thereby transforming the LQT problem of interconnected systems into the solution of a discrete-time (DT) discounted Lyapunov equation. Based on the admissible control policy obtained in the first stage, an optimized tracking control policy is further derived. The proposed tracking controller design method eliminates the requirement for the complete system dynamics and an initial stable control policy. Finally, a numerical example and a networked DC microgrid system are provided to demonstrate the effectiveness and practicality of the proposed method.