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This paper addresses the practical prescribed-time control (PPTC) problem for nonlinear strict-feedback systems subject to matched and mismatched disturbances. A composite nonlinear disturbance observer-based PPTC scheme is proposed to ensure the prescribed transient and steady-state performances within a prescribed time. First, a novel error transformation mechanism is developed based on a prescribed-time constraining function incorporating a nonlinear distance measure to the constraint boundary and the tanh⁡(⋅) function. This mechanism transforms the prescribed-time tracking problem into a bounded constraint problem for the error variables. Then, considering the presence of matched and mismatched disturbances, a composite nonlinear disturbance observer (CNDO) is designed based on the transformed error, enabling fast disturbance estimation and improved control performance. Furthermore, to avoid "explosion of complexity", a nonlinear filter is designed based on the transformed filtering error. Building upon the estimates obtained by the CNDOs, a practical prescribed-time tracking controller is designed by employing the backstepping method and the nonlinear filter. This controller not only ensures that the system output satisfies the constraints but also reduces control cost. Finally, theoretical analysis, along with simulations and experimental results conducted on a turntable servo system, verifies that all system state tracking errors and filtering errors converge to prescribed regions within the prescribed time.

Rebar node binding on construction sites is predominantly carried out manually, and existing mobile robots struggle to navigate complex rebar mesh due to desynchronization and kinematic instability caused by the unstructured grid-like holes and uneven nodes. To address this, we propose a dual-motor cross-coupled synchronous control strategy based on impedance control, applied to a wheel-legged rebar binding robot. This innovative strategy is accomplished by integrating synchronization error terms into the impedance model to establish a "virtual mechanical coupling" between bilateral motors, allowing for real-time torque redistribution when the robot encounters uneven environmental resistance. We conduct a dynamic analysis of the robot's lateral movement mechanism, deriving the theoretical output torque, followed by comprehensive validation. Quantitative results demonstrate that under conditions with obstacle interference, the proposed method reduces the peak displacement difference by 29.5% and the displacement variance by 75.9% compared to traditional decoupled impedance control. Notably, in high-friction threaded rebar mesh, the system successfully neutralizes internal force asymmetries, achieving nearly identical cumulative torque profiles for both legs. Furthermore, even at a velocity of 1600 mm/s, the longitudinal error is limited to 2.75 mm, effectively suppressing cumulative deviations stemming from off-center of gravity and mesh deformation. By harmonizing dynamic load distribution, this strategy ensures superior robustness and kinematic consistency for robotic platforms in complex construction environments.

This paper deals with the convergence analysis and evaluation of iterative learning control (ILC) against stochastic disturbances. By minimizing the trace of the covariance matrix of the output tracking error, an optimization-based design method is proposed for stochastic ILC, from which the monotonic convergence of the output tracking error is established in the mean square sense. Moreover, the fastest convergence rate at the order O(1/k) is achieved for both the output tracking and input updating errors in stochastic ILC. Particularly, a notion of stochastic learnability is presented for the input updating process, by which a unified analysis framework to simultaneously address output tracking and input updating problems in stochastic ILC is developed. Simulation tests on a mobile robot are performed to verify the effectiveness of our optimization-based stochastic ILC results.

This paper addresses cooperative transportation of multi-mobile robots (MMRs) under input delays, frictional forces, external disturbances and parameter perturbations by proposing a distributed leader-follower cooperative control strategy based on a Pade-augmented artificial potential field (P-APF) to enhance stability and robustness. Firstly, a dynamic model of the MMRs system (consisting of the MMRs formations and the workpieces) is established, which is further transformed into an approximate delay-free form via the first-order Pade approximation to handle input delays. Subsequently, a fixed-time nonlinear disturbance observer (NDO) is designed to accurately estimate and compensate for the lumped disturbances composed of frictional forces, external disturbances and parameter perturbations, thereby significantly improving the system's resistance to uncertainties. Then, the equivalent delay state variables are established and incorporated into the artificial potential field (APF) function to develop a cooperative controller based on the P-APF method, which ensures accurate formation convergence and maintains inter-robot connectivity. The asymptotic convergence of the nominal error system (residual-free) is proved by LaSalle's invariance theorem and Barbalat's lemma. For the actual closed-loop system, the formation and velocity errors are uniformly ultimately bounded under the bounded residual term. Finally, simulation results demonstrate the effectiveness of the proposed approach in achieving stable cooperative transportation of the MMRs system.

Existing distributed secondary control for microgrids struggles to balance voltage/frequency restoration speed and proportional active power sharing accuracy under switching topologies, restricting practical application. To address this, a novel distributed fixed-time secondary controller is proposed to achieve voltage/frequency consensus, proportional active power sharing and topology adaptability for distributed generation units. Lyapunov theory proves asymptotic stability under switching topologies, and bi-limit homogeneity theory yields sufficient conditions for fixed-time stability. Hardware-in-the-loop experiments under typical conditions verify its effectiveness and advantages.

To address the challenges of parameter perturbations and load disturbances in dual permanent magnet synchronous motor systems, this paper proposes a control scheme integrating prescribed performance cross-coupling control (PPCCC), adaptive extended state observer (AESO), and novel sliding mode control (NSMC). Key innovations: (1) A hybrid sliding mode reaching law is designed, which dynamically adjusts the reaching speed through a combination of exponential and power terms. A sigmoid function is incorporated to effectively suppress chattering. (2) PPCCC is introduced to constrain the synchronization error's convergence rate and overshoot through a logarithmic barrier function transformation. (3) AESO with adaptive bandwidth estimates lumped disturbances for real-time compensation. Simulations and experiments verify that the scheme confines the synchronization error within preset bounds, suppresses disturbances effectively, and outperforms existing methods.

This paper investigates the trajectory-tracking problem of an uncertain robotic system subject to coupled output constraints. A decoupling approach is introduced to convert the coupled constraints into independent time-varying constraints on the transformed output coordinates. Based on this decoupling, a partial controller is developed using a time-varying barrier Lyapunov function (TVBLF) to enforce constraint satisfaction. In addition, fuzzy logic systems (FLSs) are employed to approximate the uncertain dynamics caused by unknown system parameters. To reduce unnecessary state transmissions and controller updates, a state-based event-triggered mechanism (ETM) is incorporated without significantly degrading tracking performance. A Lyapunov-based stability analysis is further provided to establish the feasibility of the proposed method. Finally, simulation results validate the effectiveness of the developed control scheme.

This paper proposes a predefined-time data-driven distributed neural sliding mode formation tracking controller for autonomous underwater vehicles (AUVs) with unknown dynamics. Although existing data-driven methods provide model-free solutions, their black box learning limits interpretability and lacks a precise specification of convergence time. Therefore, we develop a novel predefined-time-stable data-driven modeling framework that reconstructs nonlinear dynamics online from input and output data, reducing the dependence on accurate model parameters, and rigorously show that the upper bound of convergence time is prescribable via Lyapunov analysis. Furthermore, the reconstructed dynamics are embedded into the sliding-mode controller, forming a closed-loop framework with a prescribed convergence time upper bound. Bio-inspired neural shunting dynamics alleviate high frequency chattering. Lyapunov analysis substantiates practical predefined-time stability of the closed-loop system, and simulations verify the effectiveness and superiority of the proposed method.

This paper investigates the prescribed-time resilient containment control (PTRCC) problem for leader-following multi-agent systems (MASs) subject to denial of service (DoS) attacks and disturbances. To alleviate communication burdens, a novel resilient dynamic event-triggered mechanism (RDETM) is developed for the controllers, which adaptively adjusts its threshold based on the attack status to reduce redundant transmissions during DoS attack intervals while excluding Zeno behavior. Simultaneously, disturbance observers are integrated into the control scheme to actively compensate for external disturbances. Subsequently, a prescribed-time resilient containment controller is developed for each follower. Theoretical analysis confirms that the proposed control strategy achieves the containment control objective within a user-defined prescribed time. Finally, simulation-based validation is provided to demonstrate the effectiveness of the proposed approach.

Remote state estimation plays an important role in connected vehicle platoons, industrial automation systems, and other networked Cyber-Physical Systems (CPSs), where reliable state information is essential for monitoring, feedback control, and decision-making. However, due to the openness and unreliability of wireless communication links, remote estimation systems are vulnerable to eavesdropping and Denial-of-Service (DoS) attacks, which may cause information leakage, packet loss, and estimation performance degradation. This issue becomes more critical in time-varying wireless environments, where channel conditions and attack opportunities evolve dynamically over time, making conventional static or periodic attack models insufficient for characterizing practical security risks. To address this problem, this paper investigates a utility-aware event-triggered reinforcement learning framework for hybrid attack scheduling against remote state estimation over time-varying wireless channels. The attacker can select among eavesdropping, DoS, and silence actions to balance estimation disruption, information acquisition, and attack resource consumption. The hybrid attack scheduling problem is formulated as a partially observable Markov decision process (POMDP), and a utility-aware event-triggered mechanism is designed to activate attack decisions only when the estimated attack utility is sufficiently significant. At the triggered decision instants, a proximal policy optimization (PPO) algorithm is employed to learn an adaptive hybrid attack mode selection policy. The structural properties of the resulting policy are also analyzed theoretically, showing that the optimal belief-space policy has a piecewise constant structure and that the proposed adaptive threshold preserves a monotone triggering property with respect to the attack utility indicator. Simulation results in a connected vehicle platoon scenario demonstrate that, compared with several benchmark methods, the proposed method achieves a better trade-off among remote estimation degradation, attacker-side information acquisition, and energy consumption. These results indicate that the proposed event-triggered reinforcement learning framework can improve the adaptability and resource efficiency of hybrid attack scheduling under time-varying wireless channels. The study also provides useful insights for security vulnerability assessment, resilient estimation design, and defense strategy development for practical remote estimation systems.

This paper explores the hierarchical optimal control strategy for high-order nonlinear systems with unknown dynamics, with a focus on two-player scenarios. The Stackelberg differential game theory provides a leader-follower framework to investigate the optimal control problem for players within a predefined sequential decision order. The optimized controllers for the corresponding subsystems of each player are designed using the command filtered backstepping method. In each subsystem, an adaptive dynamic programming architecture is employed to derive the Stackelberg equilibrium solution for the player. To this end, actor-critic functions are introduced to approximate the cost function and execute the control behavior for all players. An identifier based on fuzzy logic systems is utilized to estimate the unknown nonlinear dynamics arising from modeling inaccuracies. To avoid repeated differentiation of the virtual control law, a command filter is adopted to reduce the computational burden by directly obtaining virtual control without differentiation. Finally, simulation results demonstrate that the proposed optimal control scheme can achieve the desired control objective under the hierarchical performance index.

Networked control systems are widely adopted in modern applications. Nevertheless, learning optimal control policies from data under communication attacks remains challenging. In this paper, we propose a bias-compensated Q-learning algorithm for knowledge-informed optimal tracking control. The method considers discrete-time linear time-invariant systems subject to control-channel denial-of-service attacks. Through a bias compensation mechanism that addresses control-input losses, the proposed method eliminates the need for explicit expectation computation in Q-function updates and enables direct data-driven learning under DoS attacks. Moreover, the proposed method requires no knowledge of system dynamics beyond the known system structure and supports data collection under arbitrary behavior policies. This design preserves high data efficiency, computational simplicity, and robustness within a model-free framework. Numerical simulations on a planar quadrotor position-tracking task show that a Riccati-equivalent optimal policy can still be learned from data under 40% DoS attacks.

For supervisory control and data acquisition (SCADA) based intelligent fault diagnosis (IFD) of wind turbines, the domain-incremental learning (D-IL) paradigm enables the model to continuously adapt to new operational data while mitigating catastrophic forgetting (CF). However, for wind turbines under complex operating conditions, existing studies still have limitations: on the one hand, operating condition drift disrupts feature consistency, degrading the effectiveness of historical knowledge replay; on the other hand, class boundaries in the feature space become increasingly blurred, leading to a gradual deterioration in the ability to separate fault categories when their feature distributions shift. To address these issues, this paper proposes a D-IL paradigm with prototype calibration and dynamic proxy (PCDP). First, during IFD model training, dynamic proxies adjust the compactness of intra-class feature representations and the separability of inter-class representations, ensuring that feature changes under time-varying operating conditions are properly captured. Then, features extracted from graph structured data are aggregated into class level prototypes to represent the current operational status and support subsequent D-IL training. Finally, during the D-IL process, a prototype calibration network corrects the drift of class level prototypes used for prototype replay. The proposed PCDP is validated in two cases (main-shaft bearing faults and blade icing) and outperforms advanced approaches.

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This study investigates the synchronization of semi-Markov jump neural networks (SMJNNs) with two-time-scale properties under hybrid attacks. First, considering the SMJNNs, singular perturbation theory is adapted to enable the decoupling of fast-slow dynamics. A dual-event-triggered mechanism is adopted to independently regulate the communication of fast and slow states to optimize resource utilization. Then, a reinforcement-learning-based attack-responsive control is proposed for secure synchronization and control optimization. To guarantee the stochastic stability of the synchronization error system, which satisfies the mixed passivity and H∞ performance index, a Lyapunov-Krasovskii functional with the singular perturbation parameter is developed to determine sufficient conditions. Validation through secure audio transmission and evaluation metrics confirms the accuracy.

In visual servoing micromanipulation, the hysteresis nonlinearity of piezoelectric stages and image transmission delay significantly degrade positioning accuracy. To address these issues, this paper proposes a dual-layer control strategy based on an improved Extended Kalman Filter (B-W-EKF). First, image block matching combined with a Gaussian kernel interpolation algorithm is employed to obtain high-precision displacement measurements from microscopic image sequences, from which the voltage-displacement hysteresis loop is constructed. Then, the EKF is integrated with the Bouc-Wen (B-W) model, incorporating hysteresis nonlinearity into the state observation equations. Based on this model, a dual-layer control architecture that combines upper-layer Model Predictive Control (MPC) with lower-layer Sliding Mode Control (SMC) is designed: the upper-layer MPC performs global optimization, while the lower-layer SMC regulates position and velocity, thereby improving tracking accuracy. Experimental results show that the RMSE values for SMC, MPC, SMPC, iMPC, and the proposed dual-layer MPC-SMC are 0.1292 µm, 0.1366 µm, 0.0635 µm, 0.0827 µm, and 0.0372 µm, respectively, under triangular wave reference input, demonstrating the effectiveness of the control strategy in enhancing tracking precision.

Accurate fault diagnosis is critical to ensuring the reliable operation of equipment. However, traditional deep learning methods typically rely on a predefined set of known fault types, limiting their ability to identify unknown faults. Thus, this paper proposes an open set fault diagnosis method based on Hyper-opinion Dual Branch Evidential Deep Learning with Distance Perception (HDEDL-DP). Firstly, a dual-branch framework is proposed to capture sharp evidence supporting single proposition and vague evidence supporting composite propositions via hyper-opinion modeling. A projection mechanism converts the vague evidence into sharp evidence for model inference. Secondly, mahalanobis distance is used to provide distance perception. Finally, an OOD score integrating uncertainty estimation and distance perception is designed to identify unknown fault classes. Two case studies validate the effectiveness of HDEDL-DP.

In this paper, the finite-time fault-tolerant containment control for multiple unmanned aerial vehicle (UAV) systems is proposed. Unlike most existing studies that mainly address input magnitude saturation while neglecting rate constraints, a first-order dynamic system is incorporated to generate control inputs subject to both magnitude and rate saturation. Furthermore, a novel auxiliary system and a controller-coupled neural network observer are developed to compensate for the insufficient input signal and estimate the unknown dynamics caused by faults, respectively. Finally, by integrating the observer and auxiliary system, a fault-tolerant finite-time containment controller is constructed, and the effectiveness is validated through hardware-in-the-loop simulations.

In gas chromatography (GC) analysis, the gas flow control performance of the Electronic Pressure Control (EPC) system that is responsible for signal analysis and processing, critically determines the reliability and accuracy of analytical results. However, during the gas flow control, unknown hysteresis characteristics, voltage saturation, and state constraints significantly impact control performance. This study addresses the fuzzy adaptive practically finite-time output feedback and signal processing problem for the EPC system in GC, incorporating system state constraints and unknown hysteresis characteristics. First, a modified Prandtl-Ishlinskii model is employed to accurately describe the valve's unknown asymmetric hysteresis. A dynamic model reflecting the actual system, incorporating gas resistance characteristics, is then established. Second, a fuzzy state observer based on a fuzzy logic system (FLS) is designed to estimate unmeasurable system states. Third, considering state constraints and potential computational complexity, a fuzzy adaptive practically finite-time controller is proposed. This controller, built upon the observer, integrates backstepping, dynamic surface control (DSC), and barrier Lyapunov functions (BLF), utilizing filters for smooth processing of virtual signals. System stability is then proven via Lyapunov theory. Finally, experimental verification is performed using both step and dynamic gas flow targets with a newly constructed EPC system. The results demonstrate that the proposed controller achieves precise and stable tracking control of gas flow, even in the presence of unknown hysteresis, state constraints, and voltage saturation.