Hai Wang

Efficient global localization of mobile robots in symmetrical indoor environments remains a formidable challenge, given the inherent complexities arising from uniform structures and a dearth of distinctive features. This review paper conducts an in-depth investigation into the nuances of global localization strategies, focusing on symmetrical environments, such as extended corridors, symmetrical rooms, tunnels, and industrial warehouses. The study comprehensively reviews and categorizes key techniques employed in this context, encompassing probabilistic-based approaches, learning-based approaches, Simultaneous Localization and Mapping (SLAM)-based approaches, and optimization-based approaches. The primary goal is to provide a contemporary and thorough literature review, offering insights into existing global localization solutions, followed by extant methods tailored for symmetrical indoor spaces. Also, the paper addresses practical challenges associated with implementing various global localization techniques, contributing to a holistic understanding of their real-world applicability. Comparative experimental results demonstrate that hybrid approaches achieve superior localization accuracy in symmetrical environments compared to any single method alone. These experiments, conducted in indoor settings with different symmetry levels, highlight the hybrid approach’s robustness and precision in resolving symmetry-induced ambiguities. This work signifies a significant step forward in mobile robot global localization, which addresses symmetrical environments’ complexities by leveraging the strengths of hybrid methodologies.

Recently, the output regulation approach has been successfully applied to the speed tracking and disturbance rejection of a permanent magnet synchronous motor (PMSM). Although excellent speed tracking performance can be achieved under parameter uncertainties and unknown time‐varying load torque disturbance, the frequency of the load torque disturbance should be known. To remove such a limitation, this paper further studies an adaptive output regulation problem (ORP) of the PMSM speed servo system under time‐varying load torque disturbance with unknown frequency. An internal model‐based adaptive speed control method is proposed, which can achieve high‐precision speed tracking of PMSM under parameter uncertainties and time‐varying load torque disturbance with unknown frequency. Moreover, the convergence condition of the estimated parameter vector is given. Both the simulation and experimental results validate its effectiveness.

This article explores a nonlinear adaptive estimator-based prescribed performance control method to address the position and attitude trajectory tracking challenges of perturbed quadcopter systems. Integrating adaptive techniques and filtering operations, the method develops nonlinear perturbation estimators to ensure the asymptotic estimation of perturbations. Moreover, a prescribed performance control strategy is devised using estimation information to contain position and attitude tracking errors within predefined bounds under the influence of persistent time-varying perturbations. Using Lyapunov stability theory, the finite-time bounded and asymptotic stability of tracking errors of quadcopter position and attitude subsystems are established with prescribed performance. Following that, simulations are performed to verify the efficiency of the proposed control method.

Reliable transfer of control policies from simulation to real-world robotic systems remains a central challenge in robotics, particularly for car-like mobile robots. Digital Twin (DT) technology provides a robust framework for high-fidelity replication of physical platforms and bi-directional synchronization between virtual and real environments. In this study, a DT-based testbed is developed to train and evaluate an imitation learning (IL) control framework in which a neural network policy learns to replicate the behavior of a hybrid Model Predictive Control (MPC)–Backstepping expert controller. The DT framework ensures consistent benchmarking between simulated and physical execution, supporting a structured and safe process for policy validation and deployment. Experimental analysis demonstrates that the learned policy effectively reproduces expert behavior, achieving bounded trajectory-tracking errors and stable performance across simulation and real-world tests. The results confirm that DT-enabled IL provides a viable pathway for Sim2Real transfer, accelerating controller development and deployment in autonomous mobile robotics.

Highlights What are the main findings? Remote sensing methodologies for crop performance monitoring were systematically reviewed across the themes of productivity, phenology, and environmental stress. Advances and challenges were identified within individual themes and in their integration toward holistic monitoring. What are the implications of the main findings? Emerging integration approaches offer pathways beyond monitoring toward decision-support systems for broadacre agriculture. Future directions of advancing resilience-focused applications of remote sensing are proposed.Highlights What are the main findings? Remote sensing methodologies for crop performance monitoring were systematically reviewed across the themes of productivity, phenology, and environmental stress. Advances and challenges were identified within individual themes and in their integration toward holistic monitoring. What are the implications of the main findings? Emerging integration approaches offer pathways beyond monitoring toward decision-support systems for broadacre agriculture. Future directions of advancing resilience-focused applications of remote sensing are proposed.Abstract Large-scale rainfed cropping systems (broadacre agriculture) face intensifying climate and resource stresses that undermine yield stability and farm livelihoods. Remote sensing (RS) offers critical tools for improving resilience by monitoring crop performance-productivity, phenology, and environmental stress-across large areas and timeframes. This review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring and to identify key challenges and integration opportunities. Peer-reviewed studies across diverse crops and regions were systematically examined to evaluate the strengths, limitations, and emerging trends across the three RS application themes. The review finds that (1) RS enables spatially explicit yield estimation from regional to paddock scales, with vegetation indices (VIs) and phenology-adjusted metrics closely correlated with yield. (2) Time-series analyses of RS data effectively capture phenological transitions critical for forecasting, supported by advances in curve fitting, sensor fusion, and machine learning. (3) Thermal and multispectral indices support the early detection of abiotic (drought, heat, salinity) and biotic (pests, disease) stresses, though specificity remains limited. Across themes, methodological silos and sensor integration barriers hinder holistic application. Emerging approaches, such as multi-sensor/scale fusion, RS-crop model data assimilation, and operational and big data integration, provide promising pathways toward resilience-focused decision support. Future research should define quantifiable resilience metrics, cross-theme predictive integration, and accessible tools to guide climate adaptation.

This paper investigates the asynchronous filtering problem for single-machine infinite bus (SMIB) power systems subject to stochastic transmission line faults. The system is modeled as a discrete-time Markov jump system (MJS) to capture the random switching behavior induced by transmission line faults. To address the asynchrony between the system modes and the filter operation, a hidden Markov model (HMM) is adopted. The filtering problem is reformulated as a regulation problem by introducing a quadratic performance index based on output estimation errors, offering a filtering-based alternative to control strategies. To solve the associated coupled algebraic Riccati equations (CAREs), an inverse reinforcement learning (IRL)-based algorithm is developed, which enables model-free filtering without requiring prior knowledge of the system dynamics or transition probabilities. The convergence of the proposed algorithm is rigorously analyzed, and a numerical example based on an SMIB power system with stochastic faults is provided to validate its effectiveness.

Monitoring of steer-by-wire (SBW) system under intermittent faults is challenging since it is difficult to obtain the priori knowledge of monotonous degradation feature of intermittent fault due to its stochastic nature. To deal with this issue, this paper develops a prognosis method for the SBW system under unknown degradation features based on the concept of competitive degradation process (CDP). With the aid of fault diagnosis module, the possible faulty components can be determined, where the fault isolation estimators and the expanded analytical redundancy relations are used to improve the isolabilities of sensor faults. For each faulty component, three degradation features are defined and the monotonicity of each feature is unknown beforehand. To judge the monotonicity of the feature, the competition index is developed in the CDP and the feature is applicable (i.e. monotonous) if the predefined criterion can be satisfied using the established dataset of the feature. Once the applicable features are determined, the corresponding degradation models are built for remaining useful life prediction. Experiment results are presented to illustrate the performance of the proposed method.

This paper introduces a data-driven approach for computing reduced-order models of leader-less discrete-time multi-agent systems while preserving stability and synchronisation properties. Unlike existing methods, which typically rely on the high-order model to be reduced, the proposed approach does not require a prior model of the system but instead leverages data directly collected from the system itself. The contributions are threefold. First, sufficient conditions on the individual agent for stability and synchronisation of a discrete-time multi-agent system utilizing a model-based algebraic Riccati inequalities (ARI) are provided. However, without a known system model, direct verification of the ARI is infeasible to perform. Therefore, as the second contribution, scenarios where the system model is unknown are addressed by establishing necessary and sufficient conditions for stability and synchronisation in terms of data-driven linear matrix inequality (LMI). This allows the verification of both properties solely through the collected data. Finally, the computation reduced-order models based on the extremal solutions of the ARI or LMI is provided, resulting in stabilised and synchronised systems with lower dimensionality. The effectiveness and good performance of the proposed approach is demonstrated by a simulation study involving a network of aircraft models.

In precision agriculture, accurate and timely identification of plant disease severity is essential for optimizing crop yield and health. However, current methods often face challenges such as high computational cost and reduced accuracy in resource-constrained environments, limiting their practical use on farms. To address these limitations, we propose RSD-YOLO, an improved YOLOv7-tiny model that integrates a Regularized Xception-based Network (ReXNet), a Slim-Neck module, and a Decoupled Head—together forming the RSD design. We construct a dataset of 1,010 oat leaf images, categorized into five severity levels and annotated by experts. RSD-YOLO achieves 91.6% precision, 90.8% recall, and 88.5% mAP@0.5, significantly outperforming YOLOv7-tiny by up to 10%, while maintaining a computational cost of only 11.2 GFLOPs. Recent studies have applied lightweight models such as EfficientSAM and SwiftFormer for crop health monitoring on drones and edge devices. However, these models often struggle to balance accuracy and efficiency. In contrast, RSD-YOLO achieves higher performance with lower computational cost, making it well-suited for real-time deployment in agricultural environments.

The single-phase two-level rectifier (SP-2LR) is a critical component in high-speed train traction systems, significantly influencing the performance and stability of the trains. This paper addresses the fault diagnosis of SP-2LR in high-speed train traction systems by proposing a comprehensive fault diagnosis strategy primarily focused on open-circuit (OC) faults and sensor faults. Initially, by augmenting the SP-2LR system and applying matrix transformations, the strategy successfully decouples OC faults within the augmented system. Subsequently, a reduced-order sliding mode observer (SMO) is designed to achieve precise estimation of system states and sensor faults. Furthermore, this paper presents a method for detecting OC faults and sensor faults based on the residual of the grid-side current and its norm threshold. Leveraging the estimated sensor fault values enables an effective differentiation between sensor faults and OC faults, and ultimately, the strategy accomplishes specific localization of faulty power switches and accurate identification of grid-side current sensor (GS-CS) faults and DC-side voltage sensor (DC-VS) faults, respectively. Experimental results demonstrate that this diagnostic method can simultaneously diagnose OC faults, GS-CS faults, and DC-VS faults with low algorithmic complexity and without requiring extensive data, additional hardware, or extra operations. The detection time for OC faults is less than 0.05 of a fundamental period, demonstrating fast detection speed. Moreover, the method also exhibits excellent robustness under unfavorable conditions, such as grid-side voltage fluctuations, load resistance variations, and grid-side filter resistance changes.