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In a closed-loop supply chain, the reuse of spare parts from returned systems is a recovery process referred to as spare parts harvesting. The unpredictability of the parts supply capacity from returned systems is a challenge in healthcare industry as product returns depend on several factors and regulatory and legal requirements must be respected. The focus of this paper is to provide a forecasting method of harvested parts supply capacity in healthcare industry that combines statistical methods with field information and business knowledge to provide an informed forecast. We propose a dynamic forecasting process that gets updated monthly employing TSB-Croston, 12-month moving average, ARIMA, ARIMA with seasonality, and a new business knowledge based model. A prediction method of the inventory state changes is introduced. The forecasters judgment is transformed into validation rules for an automatic forecast adjustment. This method is evaluated on more than 1400 time series with intermittent behaviour representing General Electric Healthcare spare parts harvesting history. We evaluate the performance of our method compared to each tested model using a modified MAPE, MAE, MSE, and RMSE. By means of the designed method, the forecast performance is improved compared to all tested models. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time prescribed performance control problem for a class of axis-motion servo systems subject to the external disturbances and parameter uncertainties. To eliminate the requirement for velocity measurements in the control process, an auxiliary observer is first designed to reconstruct the velocity signal of the controlled plant. Subsequently, an error transformation procedure incorporating a prescribed performance index is integrated into the control system architecture. To overcome the limitations of periodic control design, a robust event-triggered controller is developed, ensuring that the dynamic error of the closed-loop system exhibits fixed-time convergence. Through rigorous theoretical analysis and proofs, it is demonstrated that the proposed event-triggered strategy guarantees Zeno-behavior-free operation. Finally, simulation results are presented to validate the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

This study focuses on examining the issues of extended dissipative-based composite anti-disturbance reliable tracking control for stochastic Takagi–Sugeno fuzzy systems afflicted by sensor faults and multiple disturbances. Explicitly, the multiple disturbances include the autonomous Wiener process and non random noises with partly known information that are generated by an exogenous plant. To address the difficulty of estimating the non random disturbance while considering the partially known information, the study incorporates a fuzzy stochastic disturbance observer. Furthermore, to solve the output tracking issue of the system under consideration, a modified repetitive tracking control is implemented. Then, combining the methodologies of disturbance observer-based control technique with modified repetitive control, a composite anti-disturbance reliable tracking control strategy is developed for achieving the intended tracking goal despite the multiple disturbances and sensor faults encountered. Additionally, the proposed approach involves extended dissipativity scheme, which includes $ H_\infty $ H∞, passivity, dissipativity, and $ \mathfrak {L}_2 - \mathfrak {L}_\infty $ L2−L∞ performances in a unified framework. In particular, by employing Lyapunov–Krasovskii functional approach, the essential stability criteria for the proposed closed-loop system are articulated in the form of linear matrix inequalities. Additionally, numerical illustrations are presented to exemplify the application and efficiency of the developed control protocol. [ABSTRACT FROM AUTHOR]

This paper addresses the co-design of event-triggered dynamic output-feedback (DOF) control for discrete-time linear parameter-varying (LPV) systems. Two independent event-triggering schemes are introduced to determine when to transmit data from the sensor to the controller and from the controller to the actuator channels. Enhanced conditions employing linear matrix inequalities (LMIs) are derived to establish constructive criteria for simultaneously designing the gain-scheduled DOF controller and the triggering mechanisms, ensuring the asymptotic stability of the closed-loop LPV system's origin. Additionally, a convex optimisation problem is proposed to enlarge the inter-event sampling on the independent channels of sensor-to-controller and controller-to-actuator. Numerical examples are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article investigates the trajectory tracking control problem for wheeled mobile robot (WMR) subject to both velocity and torque constraints. Firstly, a more general model is established by considering the displacement between the centre of mass and the midpoint of the driving wheels of the WMR, based on which, a new bounded kinematic controller is proposed by combining the first-order filtering approach to keep the trajectory tracking error asymptotically stable. Then the dynamic controller is designed and the stability of the overall closed-loop system is proved by the Lyapunov stability theory and Barbalat's lemma. Some conditions are given to ensure that both the velocity constraints and torque constraints are satisfied. Finally, some numerical examples are given to show the effectiveness and advantages of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

To enhance the navigation and control efficiency of mobile robots in the field of health monitoring, a novel path planning and control strategy for mobile robots with uncertain dynamics based on improved artificial potential fields is proposed in this paper. Specifically, we propose an attractive potential field rotation method to overcome the limitation that traditional artificial potential fields tend to fall into local minima. Then, we define a new class of attractive potential fields to address the goals non-reachable with obstacles nearby (GNRON) and collisions caused by excessive attractive force at long distances from the target point. Furthermore, a control law is proposed for the mobile robot with uncertain dynamics, and the stability of the closed-loop system is rigorously proven using the Lyapunov method. Finally, the feasibility and effectiveness of the proposed method are verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

Background: Facial palsy causes severe functional disorders and impairs quality of life. Disturbing challenges for patients with acute facial palsy, but also with those with chronic facial palsy with synkinesis, are the loss of the ability to smile and insufficient eyelid closure. A potential treatment for these conditions could be a closed-loop electro-stimulation system that stimulates the facial muscles on the paretic side as needed to elicit eye closure, eye blink and smile in a manner similar to the healthy side. Methods: This study focuses on the development and evaluation of such a system. An artificial intelligence (AI)-based auricular-triggered algorithm is used to classify the intended facial movements. This classification is based on surface electromyography (EMG) recordings of the extrinsic auricular muscles, specifically the anterior, superior, and posterior auricular muscle on the paretic side. The system then delivers targeted surface electrical stimulation to contract the appropriate facial muscles. Results: The evaluation of the system was conducted with 17 patients with facial synkinesis, who performed various facial movements according to a paradigm video. The system's performance was evaluated through a simulation, using previously captured data as the inputs. The performance was evaluated by means of the median macro F1-score, which was calculated based on the stimulation signal (output of the system) and the actual movements the patients performed. Conclusions: This study showed that such a system, using an AI-based auricular-triggered algorithm, can support with a median macro F1-score of 0.602 for the facial movements on the synkinetic side in patients with unilateral chronic facial palsy with synkinesis. [ABSTRACT FROM AUTHOR]

This paper presents an innovative scheme to enhance the efficiency of power extraction from wind energy conversion systems (WECSs) under random loads. The study investigates how stochastic load consumption, modeled and predicted using a Markov chain process, impacts WECS efficiency. The suggested approach regulates the rectifier voltage rather than the rotor speed, making it a sensorless and reliable method for small-scale WECSs. Nonlinear WECS dynamics are represented using Takagi–Sugeno (TS) fuzzy modeling. Furthermore, the closed-loop system's stochastic stability and recursive feasibility are guaranteed regardless of random load changes. The performance of the suggested controller is compared with the traditional perturb-and-observe (P&O) algorithm under varying wind speeds and random load variations. Simulation results show that the proposed approach outperforms the traditional P&O algorithm, demonstrating higher tracking efficiency, rapid convergence to the maximum power point (MPP), reduced steady-state oscillations, and lower error indices. Enhancing WECS efficiency under unpredictable load conditions is the primary contribution, with simulation results indicating that the tracking efficiency increases to  99.93 % . [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation. [ABSTRACT FROM AUTHOR]

Metasurfaces, which are ultrathin planar metamaterials arranged in certain global sequences, interact uniquely with the surrounding light field and exhibit unusual effects of light modulation. Many interesting applications have been discovered based on metasurfaces, particularly in invisibility cloaks. However, most invisibility cloaks are limited to working in specific directions. Achieving effectiveness in multiple directions requires the metasurface to be designed with both perception and modulation capabilities. Current multi-directional metasurface cloak systems are implemented with discrete components rather than an integrated sensing component. Here, we propose an intelligent metasurface cloak system that integrates sensing units, resulting in the cloaking effect with the help of a real-time direction sensor and an adaptive feedback control system. A reconfigurable reflective meta-atom based on phase modulation is presented. Sensing units replace parts of the meta-atoms in the designed tunable metasurface, integrating with an FPGA responsible for measuring the direction and frequency of the incident wave, constituting a closed-loop system together with the feedback parts. Experimental results demonstrate that the metasurface cloak system can recognize the different directions of the incoming wave, and can adaptively manipulate the reflected phase of EM waves to conceal objects without any human participation. [ABSTRACT FROM AUTHOR]

An easily extensible interleaved high-voltage gain boost converter is proposed in this paper, which can be applied to electric vehicles, photovoltaic power generation and other fields. The proposed converter is expandable with different voltage multiplication cells according to different gain requirements. The low voltage side of the proposed converter is interleaved with two inductors, and the high voltage side is structured with multiple capacitors in series. The proposed converter has the advantages of low voltage stress for the whole devices, low input current ripple, inductor currents self-averaging, capacitor voltages self-balancing, simple control, and easy extension. First, the structure and principles of the proposed converter are introduced and analyzed. Then the performance of the converter in various aspects is theoretically analyzed and compared with existing converters. Next, the double closed-loop control system is designed. Finally, an experimental prototype with an input of 24 V, an output of 400 V and a rated power of 180 W is built and implemented, and the superior performance of proposed converter is experimentally verified. [ABSTRACT FROM AUTHOR]

This article explores the dissipative control for a class of nonlinear DP-CPS (distributed parameter cyber physical system) within a finite-time interval. By utilizing a Takagi-Sugeno (T-S) fuzzy model to represent the system's nonlinear aspects, the studied system is formulated as a class of fuzzy parabolic partial differential equation (PDE). In order to optimize network resources, both the system state and input signal are subjected to quantization using dynamic quantizers. Subsequently, a dynamic state control strategy is proposed, taking into account potential DoS attack. The finite-time boundedness of the fuzzy parabolic PDE is analyzed, with respect to the influence of quantization, through the construction of an appropriate Lyapunov functional. The article then presents the conditions for finite-time dissipative control design, alongside the adjustment parameters for the dynamic quantizers within the fuzzy closed-loop system. Furthermore, the decoupling of interlinked nonlinear terms in the control design conditions is achieved by using an arbitrary matrix. Finally, an example is provided and the simulation results indicate the effectiveness of the dissipative control method proposed. [ABSTRACT FROM AUTHOR]

This article designs a quantised security controller for partial differential equation (PDE) systems under replay attacks. Initially, the Takagi – Sugeno fuzzy model is utilised to represent the considered nonlinear system and combines pointwise measurement and pointwise control methods to save the whole system's costs. Moreover, to reduce network transmission pressure and simulate the network, hysteretic quantiser and replay attacks are considered. Furthermore, based on the Lyapunov direct method and the inequality technique, sufficient conditions to assure the stability of the closed-loop system are achieved. Ultimately, the effectiveness and superiority of the submitted control strategy are confirmed through numerical examples and comparative analysis. [ABSTRACT FROM AUTHOR]

Deep closed-loop geothermal systems are a potential technology to provide heating and power generation. Although these systems are not new, they have recently regained interest because the design avoids reservoir stimulation (and potential fluid injection induced seismicity) and thermal short-circuiting of the working fluid. However, the poromechanical response to long-term heat depletion of closed-loop wells at large depths is largely unexplored. This paper investigates the response of rock around closed-loop geothermal system over 30-years of heat depletion through numerical simulation. The numerical solution is based on the theory of thermo-poroelastoplasticity and is solved through the Fenicsx finite element computing platform. The formulation takes effective poroelastoplastic properties of the bulk rock mass to indirectly account for strength and permeability of a rock mass with pre-existing fractures. Results for a normal faulting scenario show that the reservoir response to heat depletion causes vertical and horizontal stress redistribution far around the wellbores. Moreover, the rock mass may respond with partially undrained behavior in deep locations with a sparse fracture network. Shear failure may occur within a fraction -up to 30%- of the geothermal reservoir volume (limited by the thermal diffusion length) mostly driven by increase of effective stress anisotropy between vertical and horizontal stresses. The potential of induced microseismicity in deep closed-loop geothermal systems depends on the shear reactivated reservoir volume and effective rock stiffness. Energy dissipation depends case by case and can extend over the entire operation period, likely a consequence of the elastic perfectly-plastic model. Heat-drainage induced microseismicity is most likely expected to occur for closed-loop wells in geothermal reservoirs under normal faulting regime where small changes of horizontal effective stress may reactivate pre-existing fractures. Furthermore, heat depletion may increase stress intensities around the wellbore and facilitate tensile fracturing. Highlights: Heat depletion around wells changes effective stresses Effective rock stiffness has a first-order impact on mechanical response to cooling Shear reactivated reservoir volume due to heat depletion can be up to 30% in our simulation case Likelihood of thermally induced tensile fracturing of the wellbore increases with operation time [ABSTRACT FROM AUTHOR]

In this paper, the spacecraft formation control problem is investigated under the undirected tree communication topology subjected to connectivity preserving and collision avoidance constraint. To implement the connectivity preservation and collision prevention of initially connected spacecrafts without employing any potential functions, a novel appointed-time performance function is developed to impose restrictions on the relative orbit distance of the adjacent spacecrafts. Moreover, this function can also characterise the upper boundary of the convergence time and steady-state error explicitly. Then a reinforcement-learning-based relative orbit-attitude control scheme is proposed to propel the formation error to the vicinity of the origin. An actor-critic neural network architecture is utilised to online compensate the system uncertainties, in which the critic neural network is introduced to improve the approximate performance of the actor neural network for the unknown uncertainties by evaluating the consensus performance of the formation system. In addition, an auxiliary system is devised to cope with the input saturation constraint. By using the Lyapunov stability theory, the stability and convergence of the closed-loop system are analysed. Finally numerical simulations are conducted to illustrate the feasibility and effectiveness of the proposed formation control scheme. [ABSTRACT FROM AUTHOR]

This paper mainly investigates the problem of finite-time H ∞ control for a class of uncertain Markovian jump nonlinear systems (MJNSs) with time-varying delay and generally uncertain transition rates. By constructing the appropriate Lyapunov–Krasovskii functional and free weighting matrices, a novel criterion on finite-time boundedness for the MJNSs with H ∞ performance is derived. We use a special way to deal with the bilinear terms, the mode-dependent state feedback controller is designed to ensure the H ∞ finite-time boundedness of the closed-loop system in the forms of strict linear matrix inequalities. Finally, numerical and practical examples are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the paper, a dynamic surface-based adaptive fuzzy fixed-time fault-tolerant control scheme is developed for nonstrict feedback nonlinear systems with non-affine faults. Firstly, the computational complexity is reduced by adopting dynamic surface control technique, and unknown nonlinear functions are approximated with the help of fuzzy logic systems. Secondly, non-affine faults involving system states and controller output are taken into account and treated by transforming it into nonlinear in the unknown parameters. Then, under the framework of fixed-time stability, a novel adaptive fuzzy fault-tolerant control strategy is designed so that the closed-loop system is semi-globally practically fixed-time stable. Finally, a numerical simulation and a model simulation are given to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In previous studies, structure vibration control mainly focused on integer-order systems or commensurate fractional-order (FO) dynamics systems. In this paper, we propose an adaptive radial basis function (RBF) neural network feedback (ARBF-FK) controller for FO building structure vibration systems with viscoelastic (VE) dampers, uncertain structure parameters and unknown seismic waves. Firstly, use a FO multi-order state space description for FO building structural vibration systems. Then, the design of the ideal feedback controller is based on the stability theory of FO multi-order systems. Moreover, to reduce the cost and facilitate the practical implementation of the control, unknown earthquake seismic waves is approximated by a RBF neural network, the ARBF-FK controller is proposed. In addition, to guarantee the stability of the closed-loop control system and avoid falling into local optimum, network weights are adapted by the FO Lyapunov stability theory instead of gradient descent algorithms. Finally, the convergence rate of the system is analyzed and perform various tests on the ARBF-FK controller. The simulation results demonstrate that the ARBF-FK controller has superior performance and is very robust to control FO building structure vibration systems with uncertain structure parameters and unknown external earthquake excitation. • A FO multi-order state space description method of FO building vibration systems is constructed. • An online adaptive ARBF-FK robust control strategy for FO building vibration systems is proposed. • Adaptive weights of neural networks are derived using the FO Lyapunov theory. [ABSTRACT FROM AUTHOR]

This paper studies a safe model predictive control (MPC)-based disturbance rejection control for a broad range of uncertain nonlinear systems subject to complex state safety constraints. The system under study is composed of a nominal model and an uncertain term that encapsulates modeling uncertainty, control mismatch, and external disturbances. In order to estimate the system state and total uncertainty, an extended state observer (ESO) is first designed. Utilizing the output of the ESO, the control compensates for the total uncertainty in real time and concurrently implements a control barrier function (CBF)-based MPC for the compensated system. The proposed control framework guarantees both safety and disturbance rejection. Compared to the baseline algorithm CBF-MPC, the proposed method significantly enhances system stability with a smaller root mean square (RMS) error of the system state from the equilibrium point. Rigorous theoretical analysis and simulation experiments are provided to validate the effectiveness of the proposed scheme. • A safe control scheme for uncertain nonlinear systems subject to complex state safety constraints. • Extended state observer (ESO) for estimating the system state and disturbance. • Control barrier function (CBF)-based model predictive control (MPC) for guaranteeing the safety constraints. • Sufficient conditions are established for maintaining stability, feasibility and safety of the closed-loop system. • The scheme manifested high performance in the application of adaptive cruise control. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology that combines fractional-order control theory with robust control under a parametric uncertainty approach to enhance the performance of linear time-invariant uncertain systems with integer or fractional order, referred to as Fractional-Order Robust Control (FORC). In contrast to traditional approaches, the proposed methodology introduces a novel formulation of inequalities-based design, thus expanding the potential for discovering improved solutions through linear programming optimization. As a result, fractional order controllers are designed to guarantee desired transient and steady-state performance in a closed-loop system. To enable the digital implementation of the designed controller, an impulse response invariant discretization of fractional-order differentiators (IRID-FOD) is employed to approximate the fractional-order controllers to an integer-order transfer function. Additionally, Hankel's reduction order method is applied, thus making it suitable for hardware deployment. Experimental tests carried out in a thermal system and the assessment results, based on time-domain responses and robustness analysis supported by performance indices and set value analysis in a thermal system test-bed, demonstrate the improved and robust performance of the proposed FORC methodology compared to classical robust control under parametric uncertainty. • New method for fractional controllers using Interval poles, improving performance. • Sets region to sweep frequency based on settling time and stability region of α. • Reduces conservativeness, allowing stability changes for flexible performance. • Ensures target with chosen fractional-order controller based on desired actions. [ABSTRACT FROM AUTHOR]

Introduction: Hybrid closed loop systems are now commercially available for people with type 1 diabetes and are increasingly being adopted into clinical practice. Real-world data reflect both the glycemic and quality of life benefits reported in trials. Areas covered: In this review, we summarize the key clinical efficacy and safety evidence for hybrid closed-loop systems, and the lived experience of users with type 1 diabetes across different age groups and during pregnancy. We comment on recent and emerging advancements addressing performance limitations and user experience, as well as the use of closed-loop systems in other types of diabetes. Expert opinion: Emerging technological developments in closed-loop systems focus on improving performance and increasing automation to further optimize glycemic outcomes and improve quality of life for users. Workforce developments are now urgently required to ensure widespread equitable access to this life-changing technology. Future applications of closed-loop technology are expected to expand into other types of diabetes including type 2 diabetes. [ABSTRACT FROM AUTHOR]

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Food production and pharmaceutical synthesis are posited as essential biotechnologies for facilitating human exploration beyond Earth. These technologies not only offer critical green space and food agency to astronauts but also promise to minimize mass and volume requirements through scalable, modular agriculture within closed-loop systems, offering an advantage over traditional bring-along strategies. Despite these benefits, the prevalent model for evaluating such systems exhibits significant limitations. It lacks comprehensive inventory and mass balance analyses for crop cultivation and life support, and fails to consider the complexities introduced by cultivating multiple crop varieties, which is crucial for enhancing food diversity and nutritional value. Here we expand space agriculture modeling to account for nitrogen dependence across an array of crops and demonstrate our model with experimental fitting of parameters. By adding nitrogen limitations, an extended model can account for potential interruptions in feedstock supply. Furthermore, sensitivity analysis was used to distill key consequential parameters that may be the focus of future experimental efforts. [ABSTRACT FROM AUTHOR]

Deep learning-based models for predicting blood glucose levels in diabetic patients can facilitate proactive measures to prevent critical events and are essential for closed-loop control therapy systems. However, selecting appropriate models from the literature may not always yield conclusive results, as the choice could be influenced by biases or misleading evaluations stemming from different methodologies, datasets, and preprocessing techniques. This study aims to compare and comprehensively analyze the performance of various deep learning models across diverse datasets to assess their applicability and generalizability across a broader spectrum of scenarios. Commonly used deep learning models for blood glucose level forecasting, such as feed-forward neural network, convolutional neural network, long short-term memory network (LSTM), temporal convolutional neural network, and self-attention network (SAN), are considered in this study. To evaluate the generalization capabilities of each model, four datasets of varying sizes, encompassing samples from different age groups and conditions, are utilized. Performance metrics include Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Coefficient of Determination (CoD) for analytical asssessment, Clarke Error Grid (CEG) for clinical assessments, Kolmogorov-Smirnov (KS) test for statistical analysis, and generalization ability evaluations to obtain both coarse and granular insights. The experimental findings indicate that the LSTM model demonstrates superior performance with the lowest root mean square error and highest generalization capability among all other models, closely followed by SAN. The ability of LSTM and SAN to capture long-term dependencies in blood glucose data and their correlations with various influencing factors and events contribute to their enhanced performance. Despite the lower predictive performance, the FFN was able to capture patterns and trends in the data, suggesting its applicability in forecasting future direction. Moreover, this study helps in identifying the optimal model based on specific objectives, whether prioritizing generalization or accuracy. [ABSTRACT FROM AUTHOR]

In the paper, the formation control problem in unknown environments for networked multi-unmanned aerial vehicle systems (UAVSs) is resolved under a nonlinear differential game (NL-DDG) framework. The main challenge of this framework is how to obtain feedback Nash strategies, which typically overly rely on global information and cannot ensure the existence of Nash strategies in unknown environments. Toward this goal, we initially design collision avoidance rules to ensure the safety of each UAV. Subsequently, we utilize an inverse optimal control method to construct the NL-DDG that incorporates both formation control and collision avoidance costs, enabling the derivation of analytical forms of Nash strategies relying solely on local information and minimizing performance metrics. In addition, the existence of feedback Nash strategy can be guaranteed with an undirected and connected information topology, which represents the optimality for the UAVSs. Moreover, we analyze the stability of the closed-loop system. Finally, the simulation results validate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) applications are rapidly expanding across multiple domains and are not limited to prototyping purposes. However, achieving flawless parts in medical, aerospace, and automotive applications is critical for the widespread adoption of AM in these industries. Since AM is a complex process consisting of multiple interdependent factors, deep learning (DL) approaches are adopted widely to correlate the AM process physics to the part quality. Typically, in AM processes, computer vision-based DL is performed by extracting the machine's sensor data and layer-wise images through camera-based systems. This paper presents an overview of computer vision-assisted patch-wise defect localization and pixel-wise segmentation methods reported for AM processes to achieve error-free parts. In particular, these deep learning methods localize and segment defects in each layer, such as porosity, melt-pool regions, and spattering, during in situ processes. Further, knowledge of these defects can provide an in-depth understanding of fine-tuning optimal process parameters and part quality through real-time feedback. In addition to DL architectures to identify defects, we report on applications of DL extended to adjust the AM process variables in closed-loop feedback systems. Although several studies have investigated deploying closed-loop systems in AM for defect mitigation, specific challenges exist due to the relationship between inter-dependent process parameters and hardware constraints. We discuss potential opportunities to mitigate these challenges, including advanced segmentation algorithms, vision transformers, data diversity for improved performance, and predictive feedback approaches. [ABSTRACT FROM AUTHOR]

This paper proposes an improved two‐degree‐of‐freedom active disturbance rejection controller for the coupling problem of asynchronous motor vector system. To simplify the analysis process and accommodate observers of different types, a unified expression based on different controllers for the system output is developed. The closed‐loop transfer function generated by the reference input and load disturbance is given. For the coupling problem of motor output speed and immunity, the structure of a higher‐order extended state observer is reconstructed. The extended state observer estimates both the output speed and the total system disturbance, which serve as feedback and feed‐forward compensation quantities. Compared to the PI controller and traditional active disturbance rejection controller, the proposed controller achieves decoupling of output response speed and immunity, simplifies the process of parameter tuning. Finally, simulation and experiment results verify the feasibility and effectiveness of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

This paper investigates a finite-time adaptive fuzzy prescribed performance fault-tolerant control (FTC) issue for the steer-by-wire vehicle (SBWV) systems with intermittent actuator faults. Different from the steer-by-wire (SBW) system studied by the previous literatures, the SBWV system involved in this study consists of a vehicle dynamics model and an SBW system, including unmeasurable states and unknown nonlinear dynamics. Fuzzy logic systems (FLSs) are first used to identify the unknown model dynamics, and a fuzzy state observer is constructed to estimate the unmeasured states. Then, to compensate for the influence of intermittent actuator faults, a novel finite-time output-feedback prescribed performance adaptive FTC scheme is developed by using the adaptive backstepping control methodology and co-designing the last virtual controller. The presented control scheme not only guarantees that all signals of the closed-loop system are bounded in the presence of actuator faults, but also ensures that the tracking error converges to a small neighborhood of the zero within the prescribed performance bounded. The computer simulation and comparison results demonstrate the effectiveness of the proposed fuzzy control algorithm. [ABSTRACT FROM AUTHOR]

Controlling variable wind speed turbine (VWT) system based on a doubly-fed induction generator (DFIG) is a challenging task. It requires a control law that is both adaptable and robust enough to handle the complex dynamics of the closed control loop system. Sliding mode control (SMC) is a robust control technology that has shown good performance when employed as a passive fault-tolerant control for wind energy systems. To improve the closed control loop of VWT based on DFIG with the aim of improving energy efficiency, even in presence of nonlinearities and a certain range of bounded parametric uncertainties, whether electrically or mechanically, an adaptive passive fault tolerant control (AP-FTC) based on a self-tuning fractional integral sliding mode control law (ST-FISMC) developed from a novel hyperbolic fractional surface is proposed in this paper. ST-FISMC introduces a nonlinear hyperbolic function into the sliding manifold for self-tuning adaptation of control law, while fractional integral of the control law smooths discontinuous sign function to reduce chattering. Additionally, this work introduces an adaptive observer, developed and proved based on a chosen Lyapunov function. This observer is designed to estimate variations in electrical parameters and stator flux, ensuring sensorless decoupling in indirect field-oriented control (SI-FOC) of DFIG. Lyapunov theory is also used to prove stability of states vectors in closed control loop with presence of bounded parameters uncertainties or external disturbances. Simulation results show that the proposed approach offers better performance in capturing optimal wind energy, as well as the ability to regulate active/reactive power and high resilience in presence of occurring parameter uncertainties or external disturbances. [ABSTRACT FROM AUTHOR]

This paper discusses the predefined practical finite-time (PPFT) dynamic positioning (DP) control problem for DP vessels subject to internal/external uncertainties. Those heterogeneity uncertainties are handled by a separatetype treatment approach. The finite-time (FT) DP control is fulfilled by a predefined FT function on the basis of a time-based generator (TBG). Under the dynamic surface control together with the TBG design framework, the convergence time and control accuracy of the DP system can be determined by the designer offline. Meanwhile, the virtual derivation and computational burden problems are dissolved by using a first-order filter and virtual parameter learning technique. To reduce mechanical wear, an event-triggering protocol between the control law and the actuator is built to reduce the operating frequency of the actuator. An event-triggered neuroadaptive PPFT control scheme is presented for DP vessels. The stability of the closed-loop DP control systems is validated via the Lyapunov theorem. Approach efficiency is confirmed by numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, we construct first- and second-order implicit–explicit schemes for the closed-loop geothermal system, which includes the heat transfer between the porous media flow with Darcy equation in the geothermal reservoir and the free flow with Navier–Stokes equation in the pipe. The constructed fully discrete schemes are based on the exponential auxiliary variable method in time, which we have proposed in Li et al. ["New SAV-pressure correction methods for the Navier-Stokes equations: Stability and error analysis," Math. Comput. 91, 141–167 (2022)] and the finite element method in space. These schemes are linear and uniquely solvable, decoupling not only the two flow regions but also the temperature field, and only require solving a sequence of linear differential equations with constant coefficients at each time step. In addition, we rigorously prove that the constructed first- and second-order schemes are unconditionally stable without any time step and stability parameter restrictions. Finally, some numerical simulations, including convergence tests, the benchmark problem for thermal convection in a square cavity, and the heat transfer in simplified closed-loop geothermal systems, are demonstrated to present the reliability and efficiency of the constructed schemes. [ABSTRACT FROM AUTHOR]

Spray drying is an energy-intensive process in industrial use, making energy recovery a critical focus for improving overall efficiency. This study investigates the potential of integrating heat-recovery systems, including an innovative air reheater, into a closed-loop spray-drying unit to maximise energy savings. Through detailed pinch analysis, the system achieved a very low approach temperature, averaging 3.48 K, which is significantly lower than that of conventional open-loop systems. The study quantifies the energy-recovery potential by demonstrating that the integration of heat-recovery components can reduce the external heating demand by up to 30%. This not only enhances heat-transfer efficiency but also lowers operational costs and reduces the system's environmental impact. The results suggest that closed-loop systems with air reheaters offer a scalable solution for improving energy efficiency across different industrial applications. The research highlights a new paradigm: focusing on latent energy within the system rather than adjusting individual operational variables. [ABSTRACT FROM AUTHOR]

Purpose: Asteroids have the characteristics of noncooperative, irregular gravity and complex terrain on the surface, which cause difficulties in successful landing for conventional landers. The purpose of this paper is to study the trajectory tracking problem of a multi-node flexible lander with unknown flexible coefficient and space disturbance. Design/methodology/approach: To facilitate the stability analysis, this paper constructs a simplified dynamic model of the multi-node flexible lander. By introducing the nonlinear transformation, a concurrent learning-based adaptive trajectory tracking guidance law is designed to ensure tracking performance, which uses both real-time information and historical data to estimate the parameters without persistent excitation (PE) conditions. A data selection algorithm is developed to enhance the richness of historical data, which can improve the convergence rate of the parameter estimation and the guidance performance. Findings: Finally, Lyapunov stability theory is used to prove that the unknown parameters can converge to their actual value and, meanwhile, the closed-loop system is stable. The effectiveness of the proposed algorithm is further verified through simulations. Originality/value: This paper provides a new design idea for future asteroid landers, and a trajectory tracking controller based on concurrent learning and preset performance is first proposed. [ABSTRACT FROM AUTHOR]

This paper addresses the fuzzy resilient control of DC microgrids with constant power loads. The DC microgrid is subject to abrupt parameter changes which are described by the Markov jump model. Due to the constant power loads, the DC microgrid exhibits nonlinear dynamics which are characterized by a T-S fuzzy model. According to the parallel distributed compensation principle, mode-dependent fuzzy resilient controllers are designed to stabilize the resultant T-S fuzzy Markov jump DC microgrid. The "resilient" means the controller could cope with the uncertainty caused by the inaccurate execution of the control laws. This uncertainty is governed by a Bernoulli distributed random variable and thus may not occur. Then, the mean square exponential stability is analyzed for the closed-loop system by using the mode-dependent Lyapunov function. Since the stability conditions are not convex, a design algorithm is further derived to calculate the fuzzy resilient controller gains. Finally, simulations are provided to test the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper, a gradient dynamics-based control method is proposed to directly tackle the singularity problem in the backstepping control design of the TORA system. This method is founded upon the construction of an energy-like positive function, which includes an auxiliary variable in terms of the intermediate virtual control law. On this basis, a gradient dynamics is created to obtain a new virtual control command, which is capable of making the auxiliary variable gradually approach zero, thereby mitigating the issue of division by zero. The core innovation is the integration of the gradient dynamics into the recursive backstepping design to overcome the singularity problem and stabilize the system at the equilibrium quickly. In addition, it rigorously proves that all the signals in the closed-loop control system are uniformly ultimately bounded, and the tracking errors converge to a small neighborhood around zero through a Lyapunov-based stability analysis. Comparative simulations demonstrate that the proposed approach not only avoids the singularity issue, but also achieves a better transient performance over other methods. [ABSTRACT FROM AUTHOR]

Power systems are naturally prone to numerous uncertainties. Power system functioning is inherently unpredictable, which makes the networks susceptible to instability. Rotor-angle instability is a critical problem that, if not effectively resolved, may result in a series of failures and perhaps cause blackouts (collapse). The issue of state feedback sliding mode control (SMC) for the excitation system is addressed in this work. Control is decentralized by splitting the global system into several subsystems. The effect of the rest of the system on a particular subsystem is considered a disturbance. The next step is to build the state feedback controller with the disturbance attenuation level in mind to guarantee the asymptotic stability of the closed-loop system. The algorithm for SMC design is introduced. It is predicated on choosing the sliding surface correctly using the invariant ellipsoid approach. According to the control architecture, the system motion in the sliding mode is guaranteed to only be minorly affected by mismatched disturbances in power systems. Furthermore, the proposed controllers are expressed in terms of Linear Matrix Inequalities (LMIs) using the Lyapunov theory. Lastly, an IEEE test system is used to illustrate how successful the suggested approach is. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the impact of advanced hybrid closed loop (AHCL) on glycemic control throughout the menstrual cycle (MC) in women with type 1 diabetes. We included 39 pairs of spontaneous MCs from 13 participants, before and after switching from sensor-augmented pump to AHCL. Baseline time below range <70 mg/dL (TBR <70) was significantly higher during the midfollicular phase than during late luteal phase (5.7% ± 5.0% vs. 4.1% ± 3.0%), but similar time in range 70–180 mg/dL (TIR) was observed throughout the MC. After switching to AHCL, a reduction in TBR <70 and an increase in TIR were observed in all phases. Phase-dependent changes in insulin infusion were detected and pre-existing differences in TBR <70 were eradicated (3.5% ± 3.2% vs. 3.0% ± 3.0%). However, TIR became significantly higher during the early follicular phase than during the late luteal phase (79.1% ± 9.3% vs. 74.5% ± 10.0%). In conclusion, AHCL improved glycemic control throughout the MC, but performance differed according to phase. [ABSTRACT FROM AUTHOR]

Background: Carbohydrate counting (CC) and meal announcements, before eating, introduce a significant burden for individuals managing type 1 diabetes (T1D). An automated insulin delivery system with automatic bolusing that eliminates the need for CC and premeal bolusing (i.e., a hands-free closed-loop [HFCL] system) was assessed in a feasibility trial of adults with T1D. Methods: The system included the MiniMed™ 780G pump and a smartphone-paired smartwatch with the Klue application (Klue, Inc.) that detects eating and drinking gestures. A smartphone algorithm converted gestures into carb amounts that were transmitted to the pump for automatic bolusing. For 5 days, participants (N = 17, 18–75 years of age) used the system at home with meal announcements based on traditional CC, with the Klue application disabled (Home-stay phase). Thereafter, participants moved to a supervised hotel setting, where the Klue application was enabled for 5 days and meals were not announced (Hotel-stay phase). Participants consumed the same eight test meals (six solid and two liquid) of varying caloric and carb size at the same time and day of the week for both phases, and glycemic metrics were compared. Otherwise, there were no other meal restrictions. Results: The overall time in range (70–180 mg/dL) was 83.4% ± 7.0% and 80.6% ± 6.7% for the Home-stay and Hotel-stay, respectively (P = 0.08). The average time at <70 mg/dL was 3.1% and 3.0% (P = 0.9144), respectively, and the average time at >180 mg/dL was 13.5% and 16.3% (P = 0.1046), respectively. Postprandial glycemia following low-carb test meals was similar between the two phases. The system's ability to accommodate high-carb meals was somewhat limited. There were no episodes of severe hypoglycemia or diabetic ketoacidosis. Conclusion: Preliminary findings show that a HFCL system was safe and maintained overall glycemic control, similar to that observed with traditional CC and manual meal bolusing. By eliminating these daily T1D burdens, a HFCL system may improve quality of life for individuals with T1D. ClinicalTrials.gov number: NCT04964128 [ABSTRACT FROM AUTHOR]

Aim: To assess the long‐term glycaemic outcomes, with additional metrics, in adults with type 1 diabetes (T1D) using the Tandem t:slim X2 with Control‐IQ technology advanced hybrid closed‐loop (AHCL) system. Methods: This was a single‐centre, retrospective study involving 56 T1D patients who transitioned to the Tandem t:slim X2 with Control‐IQ system. The primary and secondary endpoints consisted of variations in time in tight range (TiTR; 70‐140 mg/dL) and the glycaemia risk index (GRI), respectively. Additional standardized continuous glucose monitoring (CGM) metrics, mean sensor glucose, coefficient of variation, the glucose management indicator (GMI), HbA1c and insulin daily dose, were also evaluated. Variables were measured at baseline and at 15 days, 3 months, 6 months and 1 year after Tandem t:slim X2 Control‐IQ initiation. Glucose outcomes are expressed as mean (standard deviation). Results: Use of Tandem t:slim X2 with Control‐IQ over 1 year was associated with an increase in mean TiTR, from 38.11% (17.05%) to 43.10% (13.20%) (P =.059), and with a decline in the GRI, from 41.03 (25.48) to 28.55 (16.27) (P =.008). CGM metrics, including time in range and time above range, showed consistent improvements. Mean sensor glucose, the GMI and HbA1c decreased significantly over time. After an initial increase, insulin daily dose remained stable throughout the 12 months. Conclusions: The results highlight the sustained effectiveness of Tandem t:slim X2 with Control‐IQ in improving glycaemic outcomes over 1 year and support the use of this technology for the management of T1D. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of trajectory tracking for wheeled mobile robots subject to time-varying input delay and bounded external disturbances. First, we establish a dynamic model for a wheeled mobile robot under sliding and skidding conditions, and determine the maximum allowable input delay that maintains system stability using Razumikhin-type stability analysis without prior knowledge of the variation in delay. The proposed adaptive robust controller combined with super-twisting sliding mode control is resilient to disturbances such as input delay, system uncertainty, and parameter variation. The proposed adaptive law enables real-time modification of switching gain based on tracking error without predefined knowledge of uncertainty bounds. Compared with traditional sliding mode control strategies, the super-twisting algorithm can eliminate chattering phenomenon while combined robust methods further reduce modeling uncertainties' influence on system performance. Finally, we select an appropriate Lyapunov function to analyze and prove uniformly ultimately bounded of the closed-loop system. MATLAB simulation comparison results demonstrate that this approach achieves high tracking accuracy, faster response speed, and robustness. [ABSTRACT FROM AUTHOR]

This paper studies the event-triggered control for uncertain switched systems under injection attacks. An adaptive event-triggered control method for neural network–approximated switched systems (NNA-SSs) is proposed. The main works are as follows: First, a neural network is introduced to approximate the uncertain nonlinear item of the systems. Second, the observer-based adaptive event-triggering (OB-AET) strategy is designed to efficiently utilize communication and computing resources. Furthermore, the closed-loop switched systems considering injection attacks are established. By utilizing the Lyapunov function method and average dwell time technique, sufficient conditions for the exponential stability of the closed-loop switched systems are given. Accordingly, the gains of the state feedback controllers and observers are solved. Finally, simulation examples are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The paper studies the design of interval observer–based event-triggered H ∞ controller for switched systems with frequent asynchronism. We first propose an interval observer–based event-triggered mechanism, which is used to transmit the observer states and the activated mode information for controller updating. Then, we design an interval observer–based event-triggered H ∞ controller for the considered systems. By using the controller mode–dependent Lyapunov function method, sufficient conditions for the globally exponential stability of the closed-loop switched systems are obtained under average dwell time switching law. Moreover, we prove that the interevent interval length is a positive constant, which can exclude the Zeno behavior. Compared with the literature, the paper removes the strict requirement of at most one switch between two event-triggered instants, that is, frequent switches within an interevent interval are allowed. Finally, a numerical example is given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies the exponential finite-time formation problems for a group of locomotive agents that maintain order on a circle. In an ideal environment, that is, without external disturbances, a combined protocol with linear continuous-time states and their nonlinear continuous functions is proposed. Each agent can adjust the movement speed itself by changing the exponent of the nonlinear function. The obtained closed-loop system will converge with an exponential speed when the initial states of the agents are far from the target location, and with a finite-time speed nearby. For the case with external disturbances, a combined protocol with linear continuous-time states and their sign functions is presented. Due to the discontinuity of the sign functions, the Filippov solutions are employed. The exponential finite-time circle formation will be achieved even in the existence of external disturbances. Moreover, the above result is extended to the application in circle containment, where the defenders will form a circle formation to surround the swarm of protectees. Finally, several computational simulations illustrate the validity of the proposed protocols. [ABSTRACT FROM AUTHOR]

For systems with higher control accuracy requirements, the requirements for convergence time are also higher, such as requiring the system to maintain stability in a finite time. This paper studies the finite-time control for input-saturated system based on the event-triggered strategy and the parametric Lyapunov equation. A finite-time state feedback controller and a finite-time output feedback controller are designed by introducing a time-varying parameter which can be obtained by solving a differential equation. The proposed methods make the closed-loop system stable in finite time and save the system resources. The designed controller overcomes the problem of slow convergence speed caused by low-gain feedback control. The numerical simulation results demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

During autonomous rendezvous and docking (AR&D) with a tumbling target, multiple unfavorable engineering constraints exist, such as constraints on the control, velocity, or collision avoidance. Meanwhile, many sources of uncertainty exist, including incomplete and inaccurate measurements, control deviation, dynamic uncertainty, and inertial parameter uncertainty of the tumbling target, which significantly complicate the design of the guidance and control algorithm. Considering these challenges, this paper proposes a robust identification, planning, and control unified framework for AR&D with a tumbling target, which incorporates an extended Kalman filter (EKF) with a robust tube-based model predictive control for tracking (TMPT) controller. First, the EKF is designed to estimate the system state and inertial parameters of the target and then predict the future behavior of the tumbling target. Additionally, the prediction information of the target is transmitted to the designed TMPT controller as a reference signal. This TMPT controller unites trajectory planning and control in a single layer that can ensure the stability and recursive feasibility of the algorithm with a short prediction horizon. Moreover, it can guarantee that the closed-loop system is robustly and asymptotically convergent to the reachable set whose center is the optimal reachable periodic trajectory concerning the docking port of the tumbling target. The unified framework represents a robustness-to-optimality control strategy that can accomplish the AR&D mission efficiently while considering multiple engineering constraints and uncertainties. Finally, the efficiency of the proposed control framework is verified through a numerical simulation. [ABSTRACT FROM AUTHOR]

This article considers the following problems for a parametric uncertain (affine) linear time-invariant system. The first problem is: Let a feedback gain matrix be designed such that the states of the closed loop system satisfy the specified transient performance bounds. Then, compute a ball in the uncertain parameter space such that for all parameter perturbations within it, the state response continues to satisfy the same transient performance bounds. The second problem is on the synthesis of a robust static state feedback control, which i) minimises the norm of the gain matrix, ii) maximises the radius of the ball in the uncertain parameter space, and iii) ensures achieving specified transient performance in the closed loop. For this, a sub-optimal linear matrix inequality optimisation is formulated. The developed results are demonstrated with numerical examples. It also highlights how the proposed methodology can be applied to design robust static state feedback control for a polytopic uncertain system. [ABSTRACT FROM AUTHOR]

This paper addresses the vibration reduction and disturbance attenuation problems of a flexible arm, which is modelled as a fourth-order partial differential equation. The main control objectives lie in reducing the influence of the disturbance, steering the flexible arm to the desired angular position and suppressing the vibration, simultaneously. To achieve the objectives, firstly, two different disturbance observers are designed. Secondly, we construct boundary controllers based on the two designed disturbance observers and boundary measurable signals. Then, with the two disturbance observers, the boundary disturbance compensation is achieved, and with the designed boundary controllers, the uniform boundedness of the flexible arm is guaranteed. Moreover, the well-posedness of the closed-loop system under the designed boundary controllers is discussed by means of the operator semigroup theory. Finally, the effectiveness and advantages of the proposed controllers are demonstrated through simulations and physical experiments. [ABSTRACT FROM AUTHOR]

Transient performance is a highly valued factor in real control systems. Having a suitable transient performance not only guarantees the safety of the system but also makes the response more desirable by reducing transient factors like the overshoot and the settling time. This paper is the first attempt to design a novel controller to improve the transient performance of switched singular time-delay systems. The proposed controller comprises a combination of linear and nonlinear parts. First, a linear part is designed to fulfill the stabilisation. Then a nonlinear part is added to it to improve the transient response. The admissibility of the closed-loop system is investigated through some given theorems based on the Lyapunov-Krasovskii approach. The closed-loop system's regularity and impulse freeness are also guaranteed in the given theorems. Computer simulation of some numerical and practical examples will endorse the theoretical achievements afterward. [ABSTRACT FROM AUTHOR]