With increasing integration of intermittent renewable energy into power systems, effective frequency regulation is more urgent. Virtual power plants (VPPs), aggregating demand-side distributed resources, have great potential for frequency regulation. Nevertheless, deep cyber-physical interactions render VPPs susceptible to diversified cyber attacks, such as denial of service (DoS), false data injection (FDI), and deception attacks, which degrade frequency performance. Notably, existing research on VPPs' frequency regulation largely ignores comprehensive impacts of such attacks. To address this gap, this paper presents a multi-agent system based cooperative power control method for distributed VPPs to ensure frequency regulation under diversified cyber attacks. First, we introduce a novel VPPs cooperative power control strategy to obtain optimal power references for VPPs and mitigate the combined effects of DoS and FDI hybrid attacks. It combines acknowledgment technique, event-triggered mechanism, and trust agents-based state screening and compensation method, enhancing reliability and economic efficiency of VPPs' frequency regulation. Moreover, we propose a unique inverters dynamic power control scheme for accurate reference tracking of each internal unit within VPPs under deception attack. Based on a new event-triggered dynamic power control model with deception attack, it employs a specially designed dynamic event-triggered communication-based control strategy, ensuring robust tracking control and reducing communication burdens. Finally, simulation results demonstrate the validity and superiority of our presented method.

In three-phase PWM rectifiers, abrupt load changes and parameter variations challenge DC-bus voltage regulation and degrade the performance of conventional controllers. To ensure robust regulation under nonlinear and time-varying conditions, this study proposes a type-3 fuzzy logic controller (T3-FLC) for DC-bus voltage regulation. The T3-FLC enhances the conventional type-1 framework by employing a three-dimensional membership structure that captures both vertical and horizontal uncertainties in the fuzzy inference process. This structure improves adaptability and stability in the face of system disturbances. The proposed controller was compared with a conventional proportional-integral (PI) controller and a type-1 fuzzy logic controller (T1-FLC) under different operating conditions: constant reference, reference tracking, load variation, regenerative operation, and grid disturbances. Under reference tracking mode, it settles within approximately 12 ms for the largest reference step, with the overshoot kept below 0.3%, whereas the T1-FLC and PI controllers require noticeably longer settling times and exhibit higher overshoot. In regenerative operation, the T3-FLC maintains tight DC-bus regulation with recovery times of 10–12 ms and an overshoot of about 2.7%, outperforming the benchmark controllers. Power quality analysis further shows that the proposed controller maintains low input-current distortion, with THD approximately 5–13%, and a near-unity power factor across all scenarios. These results confirm the T3-FLC as an effective control strategy for power converters.

Cloud storage is one of the most important technologies used for storing and managing large amounts of digital data in modern computing environments. With the rapid increase in data generation, centralized cloud storage systems are widely adopted by individuals and organizations due to their convenience and accessibility. However, these traditional systems suffer from several major limitations, including data breaches, privacy leakage, single points of failure, unauthorized access, and dependence on third-party service providers. If the central server is compromised, attackers may gain access to or alter sensitive user data. In addition, users often lack full control over how their stored data is managed and protected. To address these challenges, this project proposes a Decentralized Secure Cloud Storage System using Blockchain technology and IPFS (InterPlanetary File System). In the proposed system, user files are first encrypted using the AES encryption algorithm to ensure confidentiality before storage. The encrypted files are then divided into multiple blocks and uploaded to distributed IPFS nodes, where each block receives a unique content-based hash value. These hash values are securely stored on the blockchain using smart contracts, ensuring tamper-proof metadata management. Blockchain provides immutability, transparency, and secure tracking of file references, while IPFS ensures decentralized and highly available distributed storage.

Background. Post-exercise passive heat exposure, including sauna bathing, infrared sauna, and hot-water immersion, has been proposed as a strategy to influence recovery, performance, and adaptation in sport, but the evidence remains heterogeneous.Purpose. To assess the effects of post-exercise passive heat exposure on recovery-, performance-, and adaptation-related outcomes in athletes and trained individuals.Methods. PubMed and Scopus were searched on 18 March 2026 and supplemented by citation tracking and manual reference screening. Eligible studies examined passive whole-body heat exposure after exercise, training, or competition. Linked reports were grouped. Findings were synthesized narratively, and a retrospective risk-of-bias assessment was performed.Results. Thirteen study programs were included. The most favorable pattern was observed in repeated endurance-oriented applications, particularly when passive heat appeared to support heat adaptation. Acute effects were mixed and outcome-specific, whereas evidence in team-sport, strength-, power-, and hypertrophy-related settings was limited and mostly neutral.Conclusion. Post-exercise passive heat exposure appears to have selective rather than general effects in athletes. Current evidence does not support broad claims of consistent benefit across athletic settings.

This research presents a hybrid geometric computed torque control method for an aerial manipulation system composed of a quadrotor UAV and a 2-DOF planar manipulator. The fully coupled system’s dynamic model is derived following the Euler–Lagrange (E-L) formulation. The proposed control architecture leverages the geometric controller provided by the RotorS simulator as a high-level quadrotor trajectory tracking module. Tracking reference commands are generated using the geometric SE(3) position controller, which computes desired translational and angular accelerations from position/velocity and attitude/angular rate errors, respectively, serving as input to the low-level computed torque controller that explicitly accounts for the coupled 8-DoF aerial manipulator system dynamics. The desired generalized acceleration vector q¨des combines quadrotor translational and rotational acceleration commands with a PD-based joint acceleration command for the attached manipulator. The computed torque controller produces generalized forces for the coupled system, which are subsequently separated into quadrotor forces and moments and manipulator joint torques. The resulting quadrotor forces and moments are mapped to rotor speeds using the standard RotorS control allocation matrix, while the manipulator joints are controlled at the torque level via ROS built-in effort controllers. Extensive simulated experiments demonstrate the effectiveness of the coupled hybrid approach compared to decoupled control strategies, showing significant improvements in tracking accuracy and dynamic response.

This study aimed to conduct a scoping review of studies on non-agentic large language models (LLMs), LLM-based agents, and multi-agent systems reported in emergency medicine, and to identify current research trends and major gaps by analyzing their clinical application scope, system structures, evaluation approaches, and input data characteristics. The Web of Science, Scopus, PubMed, and CINAHL databases were searched for literature published from March 8, 2021, to March 7, 2026. Among English full-text articles, studies addressing the application, evaluation, or benchmarking of non-agentic LLMs, LLM-based agents, or multi-agent systems in emergency medicine or the emergency department (ED) were included. Through reference tracking, 35 studies were analyzed. Of the 35 included studies, 26 were application studies, 6 were framework studies, and 3 were benchmark studies. The studies were concentrated on a limited set of tasks, including triage, diagnostic and treatment decision support, and documentation. In terms of system type, non-agentic LLMs were the most common (n=25), followed by LLM-based agents (n=7) and multi-agent systems (n=3). Inputs were predominantly text-based, and evaluation mainly relied on expert comparison, retrospective record review, vignette-based comparison, and task-specific performance metrics. In contrast, workflow-level, prospective, and safety and trustworthiness-oriented evaluation were limited. LLMs in emergency medicine have shown potential for task-level decision support and documentation. However, current literature remains focused on non-agentic LLM-based task support, while studies reflecting the dynamic workflow of real EDs remain limited. Future research should expand toward workflow-aware design, operational evaluation, multimodal data integration, multi-agent-based role coordination, and safety and trustworthiness validation.

To address the challenges of vibration suppression, reference tracking, constraint satisfaction, and disturbance rejection in robot servo systems with flexible joints, this paper presents a novel model predictive control (MPC) framework that integrates output regulation theory and disturbance observer (DO) techniques. First, multi-source disturbances in permanent magnet synchronous motor (PMSM)-driven robot servo systems-including torsional torque, multi-frequency torque ripple, and parameter uncertainties-are uniformly modeled via output regulation theory as an exosystem. Then, the multi-source disturbances are estimated using a reduced-order DO. Based on these estimations and output regulation theory, a transformed dynamic model is developed that inherently embeds the optimal reference trajectory, eliminating the need for separate trajectory optimization. Furthermore, the tracking problem of the original disturbed system is reformulated as a stabilization problem for the transformed system, which is then addressed by the proposed output regulation MPC (ORMPC) scheme that explicitly incorporates state and control input constraints. Finally, experimental results on a PMSM-driven robot servo system coupled via a spring demonstrate the effectiveness of the proposed MPC framework.

ABSTRACT We propose an adaptive zero‐order‐hold‐based event‐triggered control strategy for a chain of integrators under measurement noise. Unlike existing approaches, the proposed method explicitly accounts for unknown measurement noise containing both DC and AC components. To tackle this challenge, we develop a novel zero‐order‐hold (ZOH) based event‐triggered reference tracking control (ETC) integrating a dynamic scaling factor and a two‐stage control strategy. Compared with existing ETC methods, the proposed ZOH‐based ETC achieves comparable tracking performance while significantly reducing the number of control input updates. Moreover, due to the ZOH based implementation, the proposed scheme eliminates the need for explicit Zeno‐behavior analysis, which is commonly required in conventional event‐triggered designs. The dynamic scaling factor is designed to attenuate the AC component of measurement noise, while the two‐stage control strategy effectively compensates for the DC component of measurement noise. The closed‐loop system is rigorously analyzed using Lyapunov and Razumikhin theories, showing that the ultimate bounds of both system and observer states can be made arbitrarily small. In consequence, our proposed control method exhibits the sufficiently small tracking error. The effectiveness and superiority of the proposed approach are demonstrated through an illustrative example with comprehensive performance comparisons.

In error-based tracking control systems and reference signal tracking systems, the dynamic performance of slow-sampled loops is often inferior to that of fast-sampled loops. This paper focuses on the challenges encountered in such systems when performing error-based tracking tasks and reviews the methods proposed in the literature to address these issues. In particular, it examines how slow-sampled image sequences can be effectively treated as fast-sampled signals for closed-loop tracking, and how efficient and robust control strategies can be designed using slow-sampling sensor data. This paper provides an overview of the framework for input multi-rate digital tracking control systems and presents a comprehensive review, highlighting two research directions aimed at improving the performance of slow-sampled loops and addressing inherent challenges. Finally, it critically evaluates the applicability of these research directions and discusses their limitations as well as opportunities for future investigation.

ABSTRACT This work develops a novel off‐policy safe reinforcement learning (RL) approach for optimal tracking of continuous‐time nonlinear systems, affine in control input. The main contribution consists of the synthesis of an optimal tracker under safety guarantees. A novel formulation is developed enabling optimal tracking of references while satisfying state‐based safety constraints. The tracking error and the state dynamics are considered to form an augmented system, facilitating this dual objective with the primary goal being to guarantee the safety without compromising the system performance. To this end, the safety is achieved during the exploration phase, by dynamically adjusting control inputs that are solutions of quadratic programming (QP) problem that incorporates zeroing control barrier function (ZCBF) conditions. Additionally, the safety during exploitation (operational phase) of the learned policy is strengthened by integrating a reciprocal control barrier function (RCBF) into the cost function, leading to an effective trade‐off between safety and system performance. Neural networks are employed to approximate the optimal control law, and novel mathematically rigorous proofs are developed to guarantee the safety, the stability, and the convergence towards optimality. Finally, the effectiveness of the approach is assessed using a simulation example.

Wheeled bipedal robots are promising for industrial mobility because they combine tight turning, agile balancing, and efficient rolling. Their inherently unstable and underactuated dynamics make reliable reference tracking challenging, particularly in the presence of sustained external disturbances and modeling errors. This paper presents a systematic modeling and control study using a three-degrees-of-freedom sagittal plane representation derived from the original six-degrees-of-freedom dynamics. Two linear tracking controllers are designed and compared: a full state feedback tracking controller and a linear quadratic servo controller with integral action. Practical performance is validated through real-time hardware in the loop simulation, where the controller runs on embedded hardware and the plant is executed on a real-time target including discrete time-sampling effects and analog input output communication noise associated with signal transmission. The results show that both controllers achieve stabilization, while the comparative HILS results reveal a trade-off rather than a uniformly superior controller. The full state feedback controller often yields lower finite-horizon position tracking errors, whereas the linear quadratic servo controller provides tighter body-pitch regulation and the more reliable removal of steady-state offset under sustained constant disturbances. These results demonstrate the feasibility of optimal servo control on cost-effective embedded platforms and indicate that controller selection should depend on the desired balance, considering tracking accuracy, disturbance rejection, convergence behavior, and actuator usage.

For the problem of fuzzy model predictive control (FMPC), this paper tackles two fundamental challenges: handling the nonconvexity inherent in the optimisation problem and designing reliable algorithms to enlarge the feasible region. To this end, nonlinear systems are first exactly represented by Takagi-Sugeno (T-S) fuzzy models. By exploiting the representational properties of fuzzy models and applying a convex envelope approach to the membership functions (MFs), the original nonlinear constraints are reformulated into linear ones. Subsequently, building on a dual-mode FMPC framework, two algorithms – iterative FMPC (IFMPC) and hierarchical FMPC (HFMPC) – are proposed. IFMPC decouples fuzzy subsystems by reformulating the optimisation as a quadratic programme, eliminating dependencies on linear mappings between premise variables and MFs. HFMPC employs a hierarchical structure: its upper layer coordinates submodels to approximate the original problem, while the lower layer solves submodel optimisations sequentially. Crucially, HFMPC incorporates free fuzzy control variables into online optimisation, enhancing feasible regions and robustness against parametric disturbances. Both algorithms are rigorously validated through numerical examples encompassing stabilisation, reference tracking, and partial tracking scenarios.

Multifunctional nanocomposites are developing to be productive in the co-delivery of proteins, genes and drugs due to their unique structures and properties, which holds great promise for intervening in biological processes at the cellular and molecular levels. Therefore, this thesis is based sson the application issues of drug and gene co-delivery systems, and from the application requirements, multifunctional nanocomposites CPDs/AuNCs based on carbonized polymer dots and gold nanoclusters were designed and synthesized. The specific properties of the nanomaterials were also investigated through the structural characterization, optical stability, cytotoxicity, and drug loading and releasing. The formation of the CPDs after reacting with the AuNCs The vibration of CPDs/AuNCs nanocomposites disappeared at 3250 cm−1. This transformation could potentially influence how these nanocomposites interact with cell membranes and intracellular components, altering the biomechanical forces at play during cellular uptake and trafficking. The fluorescence intensity of CPDs/AuNCs varied between [81.72,87.74] when the NaCl concentration was elevated from 0 nM to 90 mM. The emission peak of DOX at 420 nm excitation wavelength was located at around 673 nM, whereas the emission peaks of CPDs/AuNCs were located at 647 nm and 693 nm, respectively. The drug release was elevated by about 1.51-fold when the pH was decreased from 7.2 to 5.4. The multifunctional nanocomposites designed by combining CPDs with AuNCs can achieve the co-delivery of drugs and genes, and their strong optical stability also provides a new reference for real-time cell tracking. This research underscores the significance of biomechanics in optimizing cellular interactions with nanomaterials, paving the way for advancements in targeted therapies. By applying biomechanical principles, these nanocomposites can enhance drug delivery efficacy, ultimately improving therapeutic outcomes and supporting innovative approaches in personalized medicine.

This paper proposes a novel prediction-free two-stage coordinated dispatch framework for the real-time dispatch of grid-connected microgrid with generalized energy storages (GES). The proposed framework explicitly addresses grid awareness, non-anticipativity constraints, and the time-coupling characteristics of GES, providing microgrid operators with a near-optimal, reliable, and adaptable dispatch tool. In the offline stage, we generate the hindsight state-of-charge (SoC) trajectories of GES by solving the multi-period economic dispatch with historical scenarios. Subsequently, leveraging this historical information (SoC trajectories, net loads, and electricity prices), we synthesize and dynamically update online references for both SoC and opportunity cost through kernel regression. We propose an adaptive Lagrange multiplier-based online convex optimization algorithm, which innovatively incorporates reference tracking for global vision and expert-tracking for step-size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of both dynamic regret and time-varying hard constraint violation. Numerical studies using ground-truth data from the Australian Energy Market Operator demonstrate that the proposed method outperforms state-of-the-art methods, reducing operational costs by 5.0–6.2% and voltage violations by 0.8–9.1%. These improvements mainly result from mitigating myopia by reference tracking and the adaptive capability provided by dynamically updated references and adaptive Lagrange multipliers. Sensitivity analysis demonstrates the robustness, computational efficiency, and scalability of the proposed method.

Abstract This paper presents the design of a nonlinear adaptive flight control system for the Cessna Citation X longitudinal dynamics. The aircraft pitch rate is controlled using a combination of recursive least squares-based nonlinear dynamic inversion and an adaptive neural network controller. The recursive least squares algorithm provides online parameter estimates to support the inversion, while the neural network compensates for residual modeling errors through online weight adaptation. To enhance robustness and ensure stability, a fixed-gain proportional integral derivative controller is integrated into the control structure. Unlike conventional gain-scheduled controllers, where PID gains vary with flight condition, the proposed adaptive controller uses a single baseline set of fixed gains. The adaptive component updates the control action online, enabling the same controller configuration to operate effectively across all 64 cruise conditions without any gain scheduling. A systematic tuning methodology is introduced for initialising the recursive least squares, selecting forgetting factors and applying covariance resets to ensure accurate adaptation. The controller is able to track a pitch-rate reference model that satisfies longitudinal flight quality requirements. Robustness is assessed under realistic disturbances, including wind gusts, Dryden turbulence, actuator loss-of-effectiveness and actuator noise. Simulation results demonstrate that the controller achieves precise reference tracking while maintaining Level 1 flight qualities. Stability is formally guaranteed using Lyapunov-based analysis. The findings highlight the ability of the designed hybrid adaptive controller to overcome limitations of linearisation, gain scheduling and estimator sensitivity, forecasting a practical and certifiable method for the integration of intelligent adaptive flight control systems into commercial aircraft.

Model Predictive Control (MPC) is widely adopted across robotic platforms, yet optimal performance hinges on careful tuning of the quadratic cost, where weighting matrices Q and R balance state tracking and control effort. This study introduces a Convolutional Weighting-Enhanced MPC (CMPC) that dynamically adjusts cost weights based on reference deviation by convolving the reference signal with a predefined shaping kernel. The framework applies to standard MPC as well as Nonlinear MPC (NMPC) and Adaptive MPC (AMPC) and related variants. Because the convolution updates Q and R without adding decision variables or a separate optimization layer for weight adaptation, computational overhead is significantly reduced relative to adaptive schemes. Beyond formulation, we rigorously analyze stability and robustness, including CMPC behavior under noise, and we derive sufficient conditions guaranteeing preservation of closed-loop properties. The approach is validated in a quadrotor simulation with two demanding trajectories: one using time-optimal path parameterization and the other employing minimum-snap planning, chosen to stress high-performance motion requirements. We benchmark CMPC against Linear MPC, NMPC, AMPC, and a Linear Quadratic Regulator (LQR). Across scenarios, the convolutional framework consistently yields lower tracking error and reduced energy consumption compared to conventional methods, supporting its suitability for energy-efficient next-generation UAV reference tracking.

The efficient utilization of solar energy largely depends on the capability of a photovoltaic system to operate at its maximum power point under variable irradiance and temperature conditions. In this context, a control strategy that combines a sliding mode control scheme with a Perturb-and-Observe-based maximum power point tracking (MPPT) algorithm with adaptive step size is proposed and applied to a DC–DC boost–buck converter. The proposed approach aims to improve the dynamic stability of the system, ensure robustness against model uncertainties, and enhance conversion efficiency. The MPPT algorithm employs an adaptive perturbation step that reduces steady-state oscillations and accelerates convergence toward the optimal operating point, while the sliding mode controller guarantees accurate tracking of the converter voltage reference under external disturbances. Simulation and experimental results validate the effectiveness of the proposed strategy, achieving an overall efficiency of 99.42% and a startup time of 180 ms in the implemented version. These results confirm improved transient response, reduced steady-state error, and high efficiency compared to competing control strategies reported in the literature.

This study introduces a $k$ -step look-ahead active concurrent learning-based dual control of exploration and exploitation (KSLCL-DCEE) framework designed to address the challenges of auto-optimization in systems with unknown references and environments, inherently balancing parameter estimation and optimal reference tracking. The KSLCL-DCEE algorithm incorporates two loops that employ future gradients of the cost function to generate the subsequent control command by looking ahead $k$ -steps: the inner loop generates $k$ -step look-ahead gradients (i.e., estimated reference trajectory), while the outer loop utilizes the gradient at the $k$ th step to generate the dual control commands which act on a general linear system. Active concurrent learning with a modified learning rate in the initial period is introduced to relax the reliance on the condition of persistent excitation and achieve faster convergence. A comprehensive stability analysis of KSLCL-DCEE is provided. The effectiveness and performance of KSLCL-DCEE are demonstrated through numerical studies and applications on photovoltaic (PV) arrays.

Nuclear power plants generate clean and cheap energy in comparison with fossil fuel–based power plants. However, nuclear reactors belong to the class of systems that are complex and unstable in nature. Due to their high-order nonlinear dynamics and open-loop instability, the design of control schemes for such a class of energy-generating systems is an important engineering problem. The important features of the control scheme for nuclear reactors are good reference tracking and disturbance rejection capabilities. In addition to these features, it is also desired that the control system should possess a simplified and low-complexity design. Thus, in this paper, we consider the problem of designing a low-complexity model-free control scheme for robust tracking of a load-following pressurized water reactor (PWR). To solve this problem, we consider a funnel control technique; however, the basic funnel control algorithm requires a sufficient smoothness of the reference signal, and therefore, it may not be simply applied for the load-following operation of a PWR. To overcome this drawback, we propose a shifting-funnel function–based funnel control. The proposed funnel algorithm ensures the stability of the PWR in load-following mode by confining the error surface within the prespecified boundary even when a change in power demand is not sufficiently smooth. A detailed mathematical analysis of the closed-loop system is carried out to investigate the stability of the proposed scheme. For validating the performance of the closed-loop system, different simulation scenarios are considered and evaluated. The results of these simulation scenarios show that the proposed control scheme efficiently tracks the power demand while effectively rejecting disturbances.

Lead ion (Pb2+) contamination in agricultural soils poses serious risks to ecosystem stability and human health due to its persistence and bioaccumulative toxicity. Traditional detection techniques are constrained by complex sample preparation, matrix interference, long analysis cycles, and limited in situ applicability. To overcome these limitations, artificial intelligence (AI)-based approaches have been increasingly adopted. Given the heterogeneity of agricultural soils and sensing signals, model selection is inherently scenario-specific: Support vector machine/support vector regression are suitable for complex matrices and multi-ion interference; artificial neural networks are preferred for low-concentration or multi-ion detection; convolutional neural networks are effective for high-dimensional, weak, or multi-modal signals; and least absolute shrinkage and selection operator/Ridge methods enable rapid, low-cost field screening. The performance of AI models depends on key factors—such as training dataset, hyperparameter optimization, and validation metrics. Coordinated optimization of these parameters enables robust, precise, and interpretable Pb2+ quantification. AI applications in soil Pb2+ detection are categorized into three main approaches: (i) single-modality approaches enhance sensitivity and specificity to address Pb2+ alloying and weak signal issues; (ii) multi-modal fusion strategies effectively mitigate interferences from complex soil matrices; and (iii) automated integrated platforms enable fast, field-deployable analyses while minimizing manual intervention. The approaches form a coherent technical chain that progressively addresses key bottlenecks in agricultural soil Pb2+ detection. Nevertheless, research in this area still faces challenges, including field adaptability, chemical speciation decoupling, and interference under variable soil conditions. Future research should focus on synergistic strategies that integrate materials, AI, and field applications. This review provides a targeted reference for the accurate tracking and control of Pb2+ contamination in farmland soils.