As renewable energy sources and electric vehicles (EVs) gradually integrate into the power system, modern grids have evolved into complex large-scale networked control systems. The increasing complexity of the system internal structure presents greater challenges for load frequency control (LFC). To address these challenges, this paper proposes an enhanced model-free adaptive control (MFAC) approach based on system operating data. Initially, this study constructs a dynamic linearized relationship between system output, input and wind power. Then, leveraging pseudo-partial derivatives (PPD) of the I/O data and the partial derivatives of system output with respect to wind power data, a data-driven MFAC algorithm is designed using observer techniques. Given that additional communication burdens may result from data-driven methods, this paper introduces an event-triggered mechanism based on the system output saturation to reduce communication bandwidth. The theoretical analysis thoroughly outlines the design process of the proposed control algorithm and rigorously proves the system stability. Finally, multiple sets of experiments are conducted to validate the effectiveness of the proposed algorithm. The results demonstrate that the event-triggered MFAC (ETMFAC) algorithm effectively mitigates the impact of load disturbances on the power system, ensuring frequency stability. Sensor fault experiments are also carried out to further evaluate the algorithm’s robustness.

This article deals with fully distributed data-driven bipartite formation control for nonlinear discrete-time multi-input-multi-output multiagent systems (MASs) with unknown dynamics models and quantized information. Initially, a distributed combined measurement error function (DCMEF) is developed for MASs characterized by cooperative and competitive interactions. This function is designed to transform bipartite formation challenges into traditional consensus problems. Subsequently, a distributed compact form dynamic linearization model is established based on the designed DCMEF and input-output data of the MASs, eliminating the need for a strongly connected communication topology. Following this, a logarithmic quantization scheme and a dynamic event-triggered communication mechanism are devised to reduce the communication burden and enhance convergence speed. Finally, a data-driven fully distributed dynamic event-triggered bipartite formation control method is proposed, and its convergence is rigorously proven. Simulation studies and hardware experiments are conducted to validate the effectiveness of the proposed method.

In practical applications, the degradation behavior of lithium-ion batteries exhibits significant differences due to variations in operating conditions. Meanwhile, the scarcity of labeled data poses considerable challenges for capacity prediction in terms of both accuracy and generalization. To address these issues, this article proposes a cross-domain semisupervised capacity prediction framework that integrates multigranularity feature modeling with a confidence controlled pseudolabel selection mechanism. Specifically, the proposed method enhances the model’s ability to capture the granularity of nonlinear degradation trends in battery capacity, thereby improving prediction accuracy and stability. In addition, a pseudolabel learning strategy based on confidence filtering and stagewise regulation is designed to dynamically guide high-quality pseudolabels in the target domain into training, effectively reducing the risk of noisy label propagation. Experiments conducted on eight tasks across two heterogeneous battery datasets demonstrate R$^{2}$improvements of 1.3%–8.7% and Mean Absolute Error (MAE) reductions of 38%–80%, validating the practical potential of the proposed method under complex degradation scenarios.

Speed inconsistency in multi-motor systems may lead to mechanical structure damage, low cooperative precision, and even cause equipment breakdown. Traditional control methods may induce pronounced consensus error fluctuations and prolonged convergence time under complex conditions such as system parameter mismatch, sudden load changes, and inaccurate mechanism models. To address these challenges, we propose a fixed-time cooperative model-free sliding mode control (FCM-SMC) method based on a fractional-order ultra-local model (FOULM). Specifically, the proposed approach integrates fractional-order dynamics into the system model to better capture the non-integer order characteristics of multi-motor systems. Then, to estimate the unknown terms of FOULM, a novel fixed-time disturbance observer (FTDO) is designed. Additionally, grounded in the framework of FOULM, the FCM-SMC protocol is designed to ensure that the consensus error of the multi-motor systems converges within a fixed time. Taking motor #1 as an example, compared with existing methods, the error metrics (ME, MAE, and RMSE) of the proposed method are reduced by up to 64.8%, 70.2%, and 69.7%, respectively.

In this paper, a novel approach for detecting anomalies in the non-linear fully-submerged hydrofoil attitude control system (HACS) is proposed, even in the presence of time-varying disturbances. To address the trade-off between robustness against disturbances and optimality in terms of estimation error, the extended sliding innovation filter (SIF) is employed as a state estimator for the target non-linear HACS. By utilizing a switching gain with a sliding boundary layer, the SIF inherently possesses a degree of robustness to estimation issues that may involve fault conditions or factors of disturbances. A residual framework is subsequently established to achieve state tracking and comparison. The residual evaluation for the fault detection (FD) scheme is then easily conducted using statistical methods such as the modified Z-Score and the peak signal-to-noise ratio (PSNR). Finally, the effectiveness of the developed FD strategy is substantiated through experiments conducted on a hardware-in-loop (HIL) platform. Comparative analysis with state-of-the-art robust UKF algorithms reveals the impressive fault detection proficiency of the proposed strategy. Note to Practitioners—This paper was motivated by the challenges of state estimation and FD for hydrofoil crafts under stochastic ocean wave disturbances. The extended SIF-based approach enhances robustness in the estimation and FD against disturbances by introducing a dynamic layer. Moreover, the adaptive layer indicates system anomalies as significant changes, which can assist engineers in promptly identifying anomalies. Furthermore, the modified statistics used in the FD scheme effectively reduce interference in the results. The developed FD method is easy to implement without linearizing the non-linear target system. The experimental validation conducted on the dSPACE platform utilizing the PCH-1 model illustrates the practicality of the proposed strategy for pertinent practitioners.

This paper addresses the attitude consistency problem in multi-hydrofoil crafts, considering actuator faults, false data injection, stochastic ocean wave disturbances, and topological propagation effects. We propose a game-theoretic fault-tolerant control (FTC) framework integrating a composite controller with a specific performance index. In this framework, both local and neighboring node information is considered. The desired controller, along with non-ideal anomalies and disturbances, is treated as participants, transforming the FTC problem into a multi-player non-cooperative game. Moreover, a single critic neural network (NN) is utilized to alleviate the challenges associated with solving a partial differential equation, thereby facilitating the derivation of the ideal control law. Compared to traditional local information-based control methods, the proposed approach incorporates neighborhood information. It integrates faults, disturbances, and propagation effects within a unified framework for optimal control, enhancing FTC effectiveness. We conduct comparative experiments using a four-craft scenario on the dSPACE platform. The results demonstrate that the proposed method reduces the mean absolute deviation (MAD) of local tracking error by at least 28.00% and the standard deviation (STD) by at least 7.76% compared to the other two methods.

This study aims to tackle the tracking control problem of multiple unmanned surface vessels (USVs). It considers the impact of connectivity-hybrid cyber-attacks in the networked level, and wave-induced disturbances, as well as severe and nonsevere unified modeling rudder angle faults in the physical level. To do this, the study establishes USV models, taking into account actuator fault and cyber-attack modeling. It then presents the augmented estimator-based decentralized fault estimation (FE) and leader-following consensus-based distributed fault-tolerant tracking control (FTTC) protocols. These are incorporated into an integrated structure that ensures the robust asymptotic convergence of estimation errors and excellent tracking performance of multi-USVs. Finally, the study derives criteria for an exponential tracking of composite faulty multi-USVs under cyber-attacks using dual-constraint restriction (attack frequency and excitation rate). Comparative simulations substantiate the advantage of the developed integrated FE and FTTC scheme.

This article aims to address the formation control issue of the unknown nonaffine nonlinear heterogeneous multiagent system (MAS) considering formation tracking accuracy and computational cost. A novel dynamic prescribed boundary-based event-triggered mechanism is proposed first to flexibly adjust the emphasis on these two indicators, and applied to data modeling and controller design simultaneously to reduce their computational cost. On one hand, an observer-based pseudo gradient estimation algorithm is designed under event-triggered framework for model reconfiguration with only input/output data of system rather than mathematical dynamics. On the other hand, an event-triggered constrained control strategy is developed with several modules to cope with complex scenarios. Concretely, a data-driven anti-windup compensator is designed in case of input constraint, and an improved prescribed performance-based fractional order terminal sliding mode controller is explored for enhancement of the formation tracking accuracy and robustness of the controlled MAS with rigorous stability analysis. Both numerical simulation and hard-in-the-loop experiment on distributed energy storage systems are performed to attest the efficacy of the proposed formation control strategy.

Battery energy storage systems (BESS) are essential for modern energy management, supporting renewable integration and grid stability. However, fault diagnosis for BESS requires extensive manual network tuning. To overcome this, we introduce a zero-shot neural architecture search approach for BESS fault diagnosis. First, the neural network is broken down into piecewise linear functions, and the Rademacher complexity is calculated for this class of functions. To prevent batch normalization (BN) layers from repeatedly scaling the Rademacher complexity and invalidating network comparisons, the Rademacher complexity is approximated using the variance of the BN layers. Finally, the selected models are then compressed via 8-bit quantization to facilitate deployment on mobile devices. This approach achieves 99.42% accuracy in just 0.51 GPU h, significantly reducing model search time without needing pretrained models. We validate this method on a self-developed BESS platform featuring a battery management system and custom mobile app, accessible online.

This article presents a comprehensive analysis of the limitations associated with single ES in regulating CL voltage, specifically highlighting their restricted adjustment range and suboptimal performance in high-power applications. To address these challenges, a novel parallel configuration of ESs is proposed for the first time, with an in-depth examination of its design principles and critical technical issues. To overcome the energy supply limitations inherent in traditional ES systems, the study integrates PV systems and BESS, replacing the conventional assumption of an ideal dc power source. This integration establishes a PV/BESS model that not only enhances the utilization efficiency of renewable energy but also ensures effective stabilization of the dc bus voltage. Under diverse disturbances, including solar irradiance fluctuations and microgrid voltage variations, the proposed parallel PV/BESS energy storage system achieves seamless coordinated operation via MPC, substantially expanding the CL voltage regulation range. However, disparities in internal parameters and switching states among parallel ESs may induce significant circulating currents, posing a threat to system stability. To mitigate this issue, a multiagent-based MPC approach is introduced. This method incorporates a circulating current suppression term into the cost function alongside the CL voltage regulation objective, ensuring balanced currents distribution across all units while significantly enhancing system stability and coordination. Simulation and experimental results demonstrate the effectiveness of the proposed multiagent MPC strategy, confirming its capability to significantly improve the stability and performance of the parallel PV/BESS ESs system.

The data-driven approach has garnered significant attention in the field of fault detection and diagnosis of blast furnace sensors. However, existing spatial-temporal prediction methods have lacked the modeling of causal relationships in the generation process of spatial-temporal data. This has resulted in the construction of biased spatial-temporal representations under the influence of spurious correlations, leading to decreased performance in downstream tasks and reduced model interpretability. To tackle this challenge, this article proposes a knowledge-enhanced approach for spatial-temporal causal diagnosis of nickel top-blown furnace (TBF) sensors. The approach incorporates domain expertise and causal discovery to establish qualitative causal relationships among the sensors in the form of causal graphs, thereby capturing the spatial relationships between them. In addition, a spatial-temporal causal transformer prediction model is developed to analyze the residuals between predicted and actual values, thereby achieving the objective of sensor fault detection. Experimental results demonstrated the accurate diagnosis of nickel TBF sensor faults using this approach. Analysis of the fault outcomes validated the comprehensibility of the model’s decision-making process in most cases, and its alignment with domain knowledge.

Multiple batteries in uninterruptible power supply (UPS)-microgrid systems based on multiagents composed of multiple electric vehicles (EVs) can encounter state of charge (SoC) consistency problems. To solve this differential expansion and controller saturation problem, an adaptive command filter sliding-mode control strategy based on virtual synchronous generators (VSGs) and considering the power allocation principle is proposed. First, based on directed graph theory, an SoC consistency algorithm and power allocation strategy for multiple EVs were proposed, forming a dc power system with a fixed communication topology. Second, the rotor motion equation of synchronous generator (SG) is introduced into the inverter control algorithm to form the mathematical model of VSG. Third, a low-pass filter (LFP) was introduced in the voltage control process to simulate the excitation attenuation characteristics of the SG. Based on the above, a backstepping control strategy, including a command filter and sliding mode controller is proposed, which improves the operating stability of the system based on the system errors of angle, frequency, and power output. Finally, the UPS-microgrid system based on multiagents is simulated to demonstrate the stability of the system and the effectiveness of the proposed control strategy.

This article addresses the issues of the bipartite time-varying formation (BTVF) control for multiagent systems (MASs) on signed digraphs. All the designs are performed under the assumptions that the leader and followers suffer from external disturbances, the control input signal of the leader is unreachable to any follower, and the state variables of the followers are unmeasurable. To begin with, an unknown input observer (UIO) is designed for each follower using a traditional interval observer to obtain the state estimates. To realize the BTVF tracking, a distributed consensus error dynamic system is constructed. Furthermore, a distributed unknown input reconstruction method is developed to estimate the multiple disturbances in the consensus error system. Then, an event-triggered BTVF control protocol is proposed which allows two antagonistic time-varying formations to be formed, while excluding Zeno behavior. A simulation of a group of wheeled robots is used to demonstrate the performance of the proposed methods.

Self-triggered pseudo terminal ingredients are proposed in this study for the classic dual-mode robust model predictive control (RMPC) of discrete-time linear systems. By resorting to the off-line design of pseudo terminal ingredients, viz. pseudo terminal set and pseudo terminal cost, the optimization problem to be solved online is transformed into several subproblems with short prediction horizons. Furthermore, the controller design is based on the nominal system state, which enables a self-triggered mechanism during the implementation. The proposed approach is able to steer the system state into a predetermined terminal constraint set via intermittent samplings. The resultant computational and communicational burden for the online part is significantly reduced, which brings great convenience for band-limited practical systems. Simulations demonstrate the effectiveness of the proposed approach.

Intending to address the issue of sensor fault-tolerant control, a model-free adaptive fault-tolerant constrained control strategy is proposed with virtual sensor technology. The novel introduction of observer-based model-free adaptive control to fault-tolerant control brings benefits to the nonlinear systems with unknown dynamics in the presence of sensor faults. In the design procedures, the dynamic linearization technique is utilized first to establish the equivalent linear model of nonlinear systems. Then, the virtual sensor technology is introduced by designing the novel state-observer-based estimation algorithm to fulfill the data-driven system dynamic and sensor fault modeling. Besides, the broad learning technique is employed with offline training to provide output information for observer-based data-driven modeling under the fault case. Based on the data-driven model, the fault-tolerant constrained control scheme is explored with the anti-windup compensator against input constraints caused by actuator saturation, and the stability analysis is supplied. Finally, the simulations are carried out for both affine and non-affine nonlinear systems to manifest the effectiveness and superiority of the proposed control strategy.

Diagnosing the fault as early as possible is significant to guarantee the safety and reliability of the high-speed train. Incipient fault always makes the monitored signals deviate from their normal values, which may lead to serious consequences gradually. Due to the obscure early stage symptoms, incipient faults are difficult to detect. This article develops a stacked generalization (stacking)-based incipient fault diagnosis scheme for the traction system of high-speed trains. To extract the fault feature from the faulty data signals, which are similar to the normal ones, the extreme gradient boosting (XGBoost), random forest (RF), extra trees (ET), and light gradient boosting machine (LightGBM) are chosen as the base estimators in the first layer of the stacking. Then, the logistic regression (LR) is taken as the meta estimator in the second layer to integrate the results from the base estimators for fault classification. Thanks to the generalization ability of stacking, the incipient fault diagnosis performance of the proposed stacking-based method is better than that of the single model (XGBoost, RF, ET, and LightGBM), although they can be used to detect the incipient faults, separately. Moreover, to find out the optimal hyperparameters of the base estimators, a swarm intelligent optimization algorithm, pigeon-inspired optimization (PIO), is employed. The proposed method is tested on a semiphysical platform of the CRH2 traction system in CRRC Zhuzhou Locomotive Company Ltd. The results show that the fault diagnosis rate of the proposed scheme is over 96%.

In this article, the power distribution and tracking problems of the distributed energy storage system (ESS) are addressed by designing a cooperative adaptive terminal sliding mode (CATSM) controller based on a multi-agent network topology for each ESS. First, a novel adaptive power allocation algorithm (APAA) is proposed to achieve a consistent state-of-charge (SOC) for each battery in a distributed ESS. Then, considering the capacity degradation of the battery during long-term charging and discharging, we use real-time current and SOC to estimate the current capacity of the battery, which ensures the accuracy of the algorithm. Then, based on the proposed algorithm, the CATSM controllers are designed for each ESS via a multi-agent network topology for the distributed ESS, and the projection operator is used to guarantee the boundedness of adaptive estimation. Finally, the simulation results are provided to validate the feasibility and effectiveness of the proposed control strategy.

In order to deal with the speed cooperative control problem in the multiple linear induction traction systems consists of multiple linear induction motors, a model-free cooperative adaptive sliding-mode-constrained-control strategy is proposed considering the input magnitude and rate constraints which may cause the problem of actuator and integral saturation. First, the equivalent circuit topology of the single motor in the system is investigated. Besides, the system is considered as the multiagent system with fixed communication topology due to the interaction between adjacent motors. Then, the output observer is presented to estimate the output and the estimation algorithm of pseudo-partial derivative parameter and uncertainties is proposed. Based on the above, the proposed control scheme is presented by designing an integral sliding-mode surface containing the systematic error and an anti-windup compensator is added to eliminate the saturation. Finally, the simulations of the proposed control strategy for multiagent systems are carried out to demonstrate the effectiveness and superiority of the proposed control strategy.

With the aim to solve the problems related to the power distribution and current chattering in a distributed energy storage system (DESS), which can be considered as a multiagent system in dc microgrid, a model-free cooperative sliding mode control scheme with a directed-graph-based observer is proposed in this article. First, a state-of-charge (SoC)-based droop control strategy is combined with a directed-graph-based voltage regulation method to balance the SoCs of each energy storage unit (ESU), allocate the power, and maintain the bus voltage stability. Besides, a directed-graph-based observer is designed to estimate the battery current considering the communication between the ESUs, and based on this observer, an estimation algorithm for adaptive coefficients is developed. Then, the multiagent sliding mode control strategy with an antiwindup compensator of input constraint is developed to track the reference current with little chattering in the DESS, and the stability proof is given. Finally, simulations are conducted to validate the effectiveness and superiority of the newly designed control strategy for the DESS.

This paper proposes a novel control scheme for hybrid energy storage system(HESS) in electric vehicles. The HESS above is composed of two parts: fuel cell (FC) and super capacitor(SC). FC, as the main power supply, provides a large proportion of required power. Whereas, SC, as the auxiliary energy storage source, plays a great role when starting and braking an electric vehicle(EV). Both energy sources are connected to the DC bus with DC-DC converters. Two converters share the very DC bus which is connected to the traction motor with a power inverter. To make sure the performances of the control system, a control method named generic model control is applied. Simulation results indicate the robustness and static performance of the proposed control scheme.

The linear vernier permanent-magnet (LVPM) motor incorporates the merits of high efficiency and low cost, which is very suitable for long stroke applications. This paper proposed a new direct thrust force control for dual inverter fed open-end winding LVPM motor to reduce force ripple. First, the topology and structural characteristics of the LVPM motor are briefly presented. Second, the mathematic model of the LVPM motor is derived. Then, a new direct thrust force control based on dual inverter fed open-end winding technique is proposed. The open-end winding drive is obtained by connecting six windings terminal of the LVPM motor to dual inverter respectively, which provides greater numbers of voltage vector and higher classes of output voltage to improve control performance. An improved space vector pulse width modulation based on dual inverter is configured to minimize force and flux ripples, which results constant switching frequency of operation. Finally, the simulation results are given to verify well steady-state and dynamic performances of the proposed control strategy.

In this paper, two adaptive observer-based strategies are proposed for control of nonlinear processes using input/output (I/O) data. In the two strategies, pseudo-partial derivative (PPD) parameter of compact form dynamic linearization and PPD vector of partial form dynamic linearization are all estimated by the adaptive observer, which are used to dynamically linearize a nonlinear system. The two proposed control algorithms are only based on the PPD parameter estimation derived online from the I/O data of the controlled system, and Lyapunov-based stability analysis is used to prove all signals of close-loop control system are bounded. A numerical example, a steam-water heat exchanger example and an experimental test show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations.