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This paper proposes an observer-based hierarchical distributed model predictive control (MPC) strategy for ensuring speed consistency in multi-linear motor traction systems. First, a communication topology is considered to ensure information exchange. Secondly, the control architecture of each agent is divided into upper layers and lower layers. The upper layer utilizes a distributed MPC method to track the leader's speed. The lower layer uses a decentralized MPC method to track the command signals sent by its upper layer controller. In addition, to eliminate the negative impact of disturbance, a nonlinear disturbance observer is designed. We then prove the asymptotic stability of the entire system by properly designing the Lyapunov equation. Finally, the feasibility of the proposed strategy is verified based on several simulations. • A hierarchical distributed model predictive control for multi-linear motor traction systems is introduced. • The nonlinear disturbance observers are adopted to improve the system's robustness. • A strict proof is constructed for the Lyapunov criterion of asymptotic stability. [ABSTRACT FROM AUTHOR]

This paper investigates an anti-disturbance sliding mode control strategy for a rigid satellite system with external disturbance under the prescribed performance constraints. An interval observer is firstly introduced to generate the interval estimation of the attitude angular velocity. Then a finite time identical disturbance reconstruction strategy is developed by using the interval estimation. Based on the novel performance function and error transformation constraints, the attitude tracking error is converted into a new error system that guarantees the desired transient and steady-state responses for the tracking error. Then, by introducing the reconstructed disturbance, a finite time anti-disturbance controller is constructed with the backstepping method. The stability of the strategy is guaranteed by the Lyapunov stability method. Finally, simulation results demonstrate the effectiveness of the proposed approach. • Proposed an interval observer for the rigid satellite attitude system. • Proposed an interval-estimation-based identical disturbance reconstruction method. • Proposed a finite time backstepping prescribed performance controller for the system. [ABSTRACT FROM AUTHOR]

The fault detection and estimation problems for the physical layer network in the cyber-physical systems with unknown external disturbances are investigated in this study. Both bias fault and loss of efficiency scenarios are considered for the actuators. Based on the adaptive threshold method and sliding mode observer approach, a distributed fault detection observer (DFDO) is constructed for each physical layer node to detect the occurrence of actuator faults. Then a relative global estimation error system is defined for the distributed fault estimation observer (DFEO). Compared with the existing results, the proposed DFEO can provide the estimation for not only the actuator bias faults but also the actuators' efficiency factors under the impact of exogenous disturbance with two gain dynamic update processes. Finally, the feasibility and effectiveness of the given DFDO and the DFEO are examined by Lyapunov stability method and the simulation results. • Proposed a distributed robust fault detection observer for CPSs. • Proposed a distributed robust fault estimation observer for actuator faults of CPSs in this paper. • Both bias fault and loss of effectiveness fault are taken into consideration. [ABSTRACT FROM AUTHOR]

Highlights • Proposed a command-filtered backstepping sliding mode controller design method. • Robust nonlinear control for grid-connected inverter with uncertain parameters. • Projection-based adaptive estimation. Abstract In order to solve the problems of power fluctuation in the photovoltaic (PV) grid-connected system and the nonlinearity in the model of inverters, a projection-based adaptive backstepping sliding mode controller with command-filter is designed in the system to adjust the DC-link voltage and the AC side current in the PV grid-connected system. Firstly, the mathematical model of the inverter in PV system is established, then backstepping control method is applied to control it, and the command filter is added to the controller to eliminate the differential expansion of the backstepping controller. Furthermore, the adaptive law based on Lyapunov stability theory is designed to estimate the uncertain parameters in the grid-connected inverter. A projection algorithm is introduced into the adaptive controller due to the demand of guaranteeing the bounded estimated value. Additionally, a sliding mode controller is increased to improve its robustness in this system. Considering the influence of irradiation and temperature changes, a battery energy storage system (BESS) is applied on the DC side to suppress the fluctuation of the output power of the PV system. Finally, the simulation results demonstrate that the presented strategy can control precisely the grid-connected inverter. [ABSTRACT FROM AUTHOR]

The minimal robust positively invariant (mRPI) set plays an important role in robust model predictive control (RMPC). However, the exact computation of the mRPI set is generally intractable. This paper proposes a novel ℋ ∞ -type RMPC approach for linear systems with additive disturbances. This approach enables users to compute an ellipsoidal RPI set that approximates the minimal one in online implementations. Considering that a bilinear matrix inequality is involved in the problem formulation, an alternating optimization method is proposed in this work to ensure that the optimization problem to be solved online is always convex. Furthermore, a novel dual-mode control strategy is introduced that consists of two modes, i.e., with, and without alternating optimizations. The "classic mode" achieves fast convergence of the control algorithm, while the "alternating mode" can precisely approximate the mRPI set. None of the proposed approaches requires any off-line design, which makes practical applications more convenient. Simulations are conducted to demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

This article investigates event-triggered model-free adaptive sliding-mode heading control issues for unmanned surface vehicles with dual-channel denial of service (DoS) attacks and bounded disturbances. Initially, we establish a redefined output. Meanwhile, we establish a virtual linear data model based on the redefined output and pseudo-partial-derivative technology. In addition, to reduce the effects of the external disturbance, we introduce the sliding mode control method to enhance the system's robustness against disturbances. Then, an event-triggered communication scheme is developed to reduce the waste of communication resources. Two compensation schemes are formulated to eliminate the effects of dual-channel DoS attacks. So, the main contributions of the presented method are the enhancement of accuracy, reduction of computational burden, and increased robustness, all of which are demonstrated in the simulation section. [ABSTRACT FROM AUTHOR]

• Novel Uncertainty-Guided Adversarial Network: The paper proposes an Uncertainty-guided Adversarial Augmented Domain (UAAD) network designed for single-domain generalization in sensor fault diagnosis. The method introduces uncertainty estimation in the domain adversarial network, an uncertainty domain adversarial network (UDANet). UDANet will guides the construction of the augmented domain through uncertainty estimation, enhancing fault diagnosis performance under unknown conditions. • Special generation module and Out-of-domain auxiliary domain module: The UAAD network has a generation module constrained by the Wasserstein distance and an out-of-domain auxiliary module using multiscale random convolution. It expands adversarial samples by projecting source samples onto drifting domains, creating pseudo-samples and forming a "fictional domain" for learning. • Domain Adversarial Training with Uncertainty Estimation: The method incorporates domain adversarial training balanced by uncertainty estimation. This iterative optimization process ensures the augmented domain effectively simulates unseen domains, improving the model's ability to generalize and maintain robust diagnostic performance. • Experimental validation under unknown working conditions that include known and unknown fault modes: Experiments using real industrial sensor data from a nickel flash furnace validate the proposed method's superiority. The UAAD network demonstrates significantly higher accuracy in classifying known and unknown fault modes under various working conditions compared to other state-of-the-art methods, achieving an average H-scores of 90%. • Single domain generalization more closely with real-world industrial processes: To relaxing the requirement for multiple source domains and focusing on single domain generalization, the UAAD method offers practical value in industrial applications where data from multiple conditions are challenging to obtain. This approach allows for effective fault diagnosis with limited training data, enhancing its applicability in real-world industrial processes. Domain generalization methods can address cross-domain diagnostic issues in industrial sensors. However, in practical industrial environments, gathering sensor data across difference working conditions is particularly challenging, and new sensor fault modes may arise under unknown working conditions. This paper proposes an uncertainty-guided adversarial augmentation domain network for fault diagnosis using a single domain. Initially, a generation module and an out-of-domain auxiliary module are designed as constraints and relaxations for building the augmented domain. Subsequently, domain adversarial training balances these constraints and relaxations, encouraging the generation module to expand sample distributions beyond the source domain. The augmented domain is built with uncertainty estimation to measure its effectiveness. Finally, the diagnostic task module is trained in this augmented domain, and learns the decision boundaries of sample classes. It identifies and classifies both known and unknown fault modes under unknown working conditions. Experiments with real industrial sensor data validate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

To address the issue of DC bus voltage fluctuation in an integrated power system aboard ships, this paper designs a prescribed-performance-based adaptive fractional-order sliding mode controller that ensures rapid convergence and maintains voltage stability, thereby enhancing the overall performance of the system. Firstly, a hybrid energy storage system is introduced into ship DC microgrid to meet the load demand in different scenarios. Then, taking into account the response time requirements of the ship's power grid, the prescribed performance function is set up to ensure that the tracking error of the DC bus voltage strictly converges at the specified speed and range. Meanwhile, fractional-order sliding mode surfaces are designed to reduce voltage chattering and achieve better tracking performance in conjunction with prescribed performance control. Considering the uncertainty of parameters in the model, the adaptive law based on the projection operator is used to estimate the unknown parameters in real time. The effectiveness of the proposed method has been verified through simulation and hardware-in-the-loop experimental results. • Hybrid energy storage system with batteries and supercapacitors integrated into ship DC microgrids, using a novel fractional order sliding mode backstepping controller to enhance robustness. • Prescribed performance control limits ship power system responses, reducing reliance on sliding mode surface selection, minimizing bus voltage chattering, and ensuring system responsiveness and steady-state error. • Algorithm uses adaptive law with projection operator to estimate parameters in real-time, mitigating aging or environmental changes' impact on ship microgrid control, enabling diverse applications. [ABSTRACT FROM AUTHOR]

• In the controller design, all the states of each vehicle are with constraints so as to fit the demands in real application. The proposed BLF-PPC control framework is a novel one in that the singularity problem in traditional PPC methods is solved by using an interval notion-based error transformation approach. To offset the disturbance, an interval observer-based identical reconstruction method is provided and the control input is decoupled from the reconstruction process. • During the attack diagnosis, the proposed virtual system functions as the ideal system which is immune from the FDI attack. Based on this, a fixed threshold-based attack detection method is realized. In addition, the detection process is simplified and the estimation for the authentication signal is eliminated. As for the attack estimation module, the estimation process can be done within a finite time and the accurate bound for the FDI attack is not necessary. • Differing from the existing fixed-threshold fault detection methods, a more accurate detection result can be ensured in this paper by importing the time-varying detection threshold. As for the fault estimation observer, the given intermediate variable even bridges the system states and the fault together such that the fault estimation is accomplished by the pure mathematical calculations of the system state and the intermediate variable. • In this paper, not only the FDI attack but also the actuator fault is considered. Due to the differences in the characteristics and the working mechanism, the impacts caused by FDI attack and actuator fault are discussed separately. This leads to the development of the attack and fault diagnosis modules. In addition, the resilient controller, attack and fault diagnosis modules can be designed separately. This paper investigates the full states-constrained resilient control for vehicle platoon systems with false data injection(FDI) attack, and actuator fault. A cyber-physical notion-based vehicle platoon system is firstly constructed. To meet the constraints for states and the transformed errors generated by the prescribed performance control(PPC) scheme, a novel PPC controller is constructed with barrier Lyapunov function(BLF). For eliminating the impact caused by FDI attack, a threshold-based attack detection mechanism including a virtual system, an authentication signal, and an attack detection filter, is developed. The amplitude of the FDI attack is then estimated by a finite time adaptive attack estimation observer. To offset the influence induced by actuator fault, an adaptive threshold-based fault detection observer is designed which reduces the false alarm ratio affected by external disturbance. Additionally, the amplitude of the actuator fault is estimated by an auxiliary variable-based fault estimation observer. By injecting the estimation of the FDI attack and actuator fault into the reference signal and controller, the resilient control is achieved. Finally, simulation results are given to exemplify the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of accurate speed tracking control of electric vehicles powered by permanent magnet synchronous motor (PMSM) under external load torque disturbance. Firstly, the dynamic model of PMSM is given, and the controller is designed based on backstepping control. Secondly, a second-order sliding mode differentiator is used to approximate the derivative of virtual control law and solve the problem of explosion of complexity. Considering the load disturbance of PMSM in the actual operation due to road roughness, a novel disturbance observer is proposed to the estimate the load disturbance. In addition, in order to achieve prescribed tracking error performance, the prescribed-performance control is proposed to guarantee the tracking error within a given prescribed boundary. Finally, the finite-time stability of the system is proved, the simulation and real-time implementation results verify the effectiveness of the designed controller. • The fuzzy logic systems are used to approximate the nonlinear function of the system. • The PPC is proposed to guarantee the tracking error within a prescribed boundary. • The novel DO is introduced to estimate the load disturbance in the actual operation. • The SOSMD is used to approximate the derivative of the virtual control law. [ABSTRACT FROM AUTHOR]

AC/DC hybrid grids are the future development trends of grids, and energy routers (ERs) can play key roles in controlling power flow, realizing the regulation of electric energy and improving power quality in the grids. At present, there have been many studies on the structure and control strategy of ERs, but only a few studies are on the power flow distribution and optimization of AC/DC hybrid grids with ERs. To enhance the efficacy of ERs in system-wide regulation and operational optimization, a multi-objective optimal power flow model is formulated for AC–DC hybrid systems with multiple ERs. In order to make ERs play a better role in overall regulation and operation optimization, the multi-objective optimal power flow model of AC–DC hybrid system with multi-ERS is established based on Newton–Raphson method (NR) and alternating iteration method with power consumption and voltage deviation as optimization objectives. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the model, and a smoother Pareto solution set is obtained compared with multi-objective particle swarm optimization (MOPSO). Then, fuzzy membership method is used to make compromise decision on the Pareto solution set to obtain the optimal compromise solution. Simulation and analysis on an enhanced IEEE 69 bus system verifies the validity of the model. In addition, the results obtained from the compromise solution are compared with the optimization results of MOPF obtained by MOPSO and that of single-objective power flow (SOPF) obtained by Particle Swarm Algorithm (PSO) in the same system. It is proved that the optimization model established can achieve the optimization objectives of reducing voltage deviation and power loss simultaneously to improve the overall performance of the system and optimize the operation state of the system. • AC–DC hybrid grid with multiple energy routers. • A multi-objective optimal power flow model is established. • Taking power loss and voltage deviation as objectives, MOEA/D is used. • Compare the optimization effect with other methods. [ABSTRACT FROM AUTHOR]

Abstract: Rock masses possess typical inhomogeneity that can be attributed to discontinuities, and the representative volume element (RVE) reflects this inhomogeneity. The employment of a three-dimensional fracture network enables the determination of the RVE of rock masses. However, previous studies have not fully considered inhomogeneity. Rock mass parameters and the changing trend of increasing study size in different regions vary. The rock mass parameters are characterized by the spatial effect, and RVE values vary for different regions. This thesis discusses the existence of the spatial effect and RVE variation using the 3-D fracture network of the White Crane Dam. By comprehensively examining the parameters P 32, mean directions, and concentration parameter as research foci, the Chi-square goodness-of-fit test, Kolmogorov–Smirnov goodness-of-fit test, and Watson test are conducted, and the RVE (11m) of the rock mass is calculated. This strategy is more objective and reasonable for mitigating the impact of the spatial effect of the rock mass. [Copyright &y& Elsevier]

Developing high-strength welded joint of heavy-thick titanium alloy plates is in immediate need to unravel the strength-enhanced behaviors for high-pressure and low-temperature performance of deep-sea submersibles. Here, we synergistically employed transmission electron microscope (TEM) and electron back-scattered diffraction (EBSD) technologies to micro-crystallographically unravel the evolution behaviors of weld microstructure, grain orientation and grain boundaries for the welded joint of the heavy-thick Ti-6Al-4V plates with the thickness of up to 60 mm. Resulted, the strength-enhanced welded joint was evidently ascribed to the transformation of lath martensite into basketweave-martensite and the rigid-flexible mixture strategy of the enhanced conical-texture and the weakened basal-texture and cylindrical-texture. Furthermore, the remarkably lowered geometrically necessary dislocations (GNDs) density and the greatly decreased deformation-prone grains number due to the transformation of low-into-high angle grain boundaries indispensably strengthened the joint strength. Therefore, this work has made clear the strength-enhanced behaviors of Ti-6Al-4V welded joints for substantially improving high-strength welded structure of heavy-thick titanium alloy plates, and sufficiently promoting the service safety for deep-sea submersibles. • Transformation of lath martensite into basketweave-martensite enhanced the welded joint strength. • Thermal cycling caused the rigid-flexible mixture of the enhanced conical-texture and the weakened basal-texture. • Transformation of low-into-high angle grain boundaries lowered GNDs density and decreased deformation-prone grains number. [ABSTRACT FROM AUTHOR]