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To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

Although the raison d'etre of the brain is the survival of the body, there are relatively few theoretical studies of closed-loop rhythmic motor control systems. In this paper we provide a unified framework, based on variational analysis, for investigating the dual goals of performance and robustness in powerstroke–recovery systems. To demonstrate our variational method, we augment two previously published closed-loop motor control models by equipping each model with a performance measure based on the rate of progress of the system relative to a spatially extended external substrate—such as a long strip of seaweed for a feeding task, or progress relative to the ground for a locomotor task. The sensitivity measure quantifies the ability of the system to maintain performance in response to external perturbations, such as an applied load. Motivated by a search for optimal design principles for feedback control achieving the complementary requirements of efficiency and robustness, we discuss the performance–sensitivity patterns of the systems featuring different sensory feedback architectures. In a paradigmatic half-center oscillator-motor system, we observe that the excitation–inhibition property of feedback mechanisms determines the sensitivity pattern while the activation–inactivation property determines the performance pattern. Moreover, we show that the nonlinearity of the sigmoid activation of feedback signals allows the existence of optimal combinations of performance and sensitivity. In a detailed hindlimb locomotor system, we find that a force-dependent feedback can simultaneously optimize both performance and robustness, while length-dependent feedback variations result in significant performance-versus-sensitivity tradeoffs. Thus, this work provides an analytical framework for studying feedback control of oscillations in nonlinear dynamical systems, leading to several insights that have the potential to inform the design of control or rehabilitation systems. [ABSTRACT FROM AUTHOR]

Undesired dynamics during a rocket launch can lead to failures such as the fatigue and fracture of a whole spacecraft. Moreover, low-frequency vibration in a spacecraft cannot effectively be controlled through traditional passive isolation. We thus present a novel active–passive hybrid vibration isolation technique based on a piezoelectric hybrid constrained-layer damping treatment to reduce whole-spacecraft vibration and improve the vibration isolation bandwidth. The new technique has the advantages of an active–passive hybrid piezoelectric network and passive constrained-layer damping features. The installation location and structural form of the presented treatment on the satellite–launch vehicle system are proposed by simplifying the link mode of the satellite and rocket structure. On this basis, governing equations of the simplified whole-spacecraft vibration isolation system with the presented treatment are derived by virtue of Hamilton's principle combined with the Rayleigh–Ritz method. The active controller is designed by adopting a velocity feedback control strategy. The vibration suppression effect is illustrated in numerical simulations of the open-loop and closed-loop characteristics. It is notable that good vibration attenuation behaviors are realized in the whole-spacecraft hybrid isolation system. Furthermore, a prototype of the system with the presented treatment is designed and experimental investigations are carried out to verify the accuracy of the theoretical investigation, further illustrating the feasibility and effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]

Laser metal deposition (LMD) with coaxial wire feeding is an additive manufacturing technology in which a metal wire is fed into a laser-induced melt pool. The repeated deposition of weld beads allows three-dimensional geometries to be created that can be used for manufacturing, repair, and modification of metal components. However, the process is highly sensitive to disturbances because the fed wire must always be fully melted, and no self-regulating effects as in powder-based LMD exist. The layer height is particularly important for process stability, as even small deviations accumulate over many layers and, ultimately, lead to the termination of the process. Therefore, monitoring and closed-loop control of the layer height during the deposition process are crucial. Due to process emissions, an interruption of the process is usually necessary for the accurate optical measurement of the layer height, which negatively affects the overall productivity. In order to overcome this drawback, an in-axis optical coherence tomography (OCT) sensor was employed in this work, which enabled real-time measurements of the layer height. It was found that positioning the OCT measurement spot as close as possible to the center of the wire provided the highest signal quality. Based on the real-time height data, a closed-loop layer height control was implemented, applying the wire feed rate as the manipulated variable. The experimental results showed that the proposed system was able to compensate for significant disturbances, ensuring dimensional accuracy and process stability. [ABSTRACT FROM AUTHOR]

Wind farms experience significant power losses due to wake interactions between turbines. Research shows that wake steering can alleviate these losses by redirecting the flow through the farm. However, dynamic closed‐loop implementations of wake steering are rarely presented. We present a model‐free closed‐loop control method using reinforcement learning methodology known as policy gradients in combination with recursive least squares to perform real‐time wake steering in a wind farm. We present dynamic simulations of a four‐turbine wind farm row using HAWC2Farm, implementing the reinforcement learning control method for various inflow conditions and controller configurations. By controlling the three most upstream turbines, mean power gains of 11.6±3.0% and 1.4±0.5% (95% confidence interval) are observed in partial wake and full wake conditions respectively at 7.5% turbulence intensity. The study helps to bridge the gap between theoretical wind farm control and real‐world wind farm systems. [ABSTRACT FROM AUTHOR]

Abstract Wind farms experience significant power losses due to wake interactions between turbines. Research shows that wake steering can alleviate these losses by redirecting the flow through the farm. However, dynamic closed‐loop implementations of wake steering are rarely presented. We present a model‐free closed‐loop control method using reinforcement learning methodology known as policy gradients in combination with recursive least squares to perform real‐time wake steering in a wind farm. We present dynamic simulations of a four‐turbine wind farm row using HAWC2Farm, implementing the reinforcement learning control method for various inflow conditions and controller configurations. By controlling the three most upstream turbines, mean power gains of 11.6±3.0% and 1.4±0.5% (95% confidence interval) are observed in partial wake and full wake conditions respectively at 7.5% turbulence intensity. The study helps to bridge the gap between theoretical wind farm control and real‐world wind farm systems.

In this paper, we proposed a model for suppressing mosquito populations, evaluating various release strategies for sterilized mosquitoes, including constant continuous release, open-loop control, closed-loop control, and composite control strategies. We established release amount thresholds for each strategy to ensure the ultimate extinction of the wild population. Through numerical simulations, we validated our theoretical results and assessed the efficacy of each strategy. The results indicated that the closed-loop control mechanism significantly enhanced population suppression effectiveness. Higher intensity control notably shortened the control duration, and strategies that incorporated shorter release periods and more frequent population assessments markedly reduced the cumulative release quantity. Furthermore, we found that, compared to closed-loop control, the composite control strategy reduced single release amounts but did not show advantages in other aspects at low control intensities. However, it significantly decreased the cumulative release quantity at high control intensities.

Abstract Background Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions. Methods We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback. Results Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants. Conclusions The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use.

Microelectromechanical System (MEMS) gyroscopes are inertial sensors used to measure angular velocity. Due to their small size and low power consumption, MEMS devices are widely employed in consumer electronics and the automotive industry. MEMS gyroscopes typically use closed-loop control systems, which often use PID controllers with fixed parameters. These classical PID controllers require a trade-off between overshoot and rise time. However, temperature variations can cause changes in the gyroscope's parameters, which in turn affect the PID controller's performance. To address this issue, this paper proposes an adaptive PID controller that adjusts its parameters in response to temperature-induced changes in the gyroscope's characteristics, based on the error value. A closed-loop control system using the adaptive PID was developed in Simulink and compared with a classical PID controller. The results demonstrate that the adaptive PID controller effectively tracked the changes in the gyroscope's parameters, reducing overshoot by 96% while maintaining a similar rise time. During gyroscope startup, the adaptive PID controller achieves faster stabilization with a 0.036 s settling time, outperforming the 0.06 s of the conventional PID controller. [ABSTRACT FROM AUTHOR]

In view of the worse dynamic performance and steady-state accuracy caused by nonlinear friction in turntable servo systems, challenges are posed in precise positioning tasks. However, most of the existing research ignores the effect of friction on system performance. Therefore, it is of great significance to analyze the nonlinear characteristics of the transmission mechanism and study compensation strategies for improving the control quality of non-direct drive turntable servo systems. Therefore, an improved active disturbance rejection control (ADRC) based on state feedback compensation is proposed in this paper to optimize the accuracy of the turntable servo system and improve the robustness of the system under nonlinear friction conditions. Firstly, friction is modeled and analyzed through offline identification, which is the basis for nonlinear friction compensation. Subsequently, the two methods of friction compensation are compared. Since feedforward compensation is prone to under-compensation and over-compensation, it is highly dependent on the parameters, while the traditional ADRC compensation method has poor dynamic performance under gap conditions. Therefore, the advantages of ADRC and state feedback are combined together to reduce the steady-state error and optimize the control performance of the system. Lastly, the effectiveness of the proposed compensation method is verified and compared through simulations and experiments. The method is able to comprehensively compensate the gap and friction nonlinearities, and the experimental steady-state error is reduced from 0.55° to 1/3 (0.19°), which improves the load-side positioning accuracy. Finally, a conclusion can be drawn that the new compensation method can improve the parameter adjustability, speed estimation precision, and system robustness. [ABSTRACT FROM AUTHOR]

To enhance the stabilizing function and boost the output power of the inductive coupling power transfer (ICPT) system, a power stabilization control method based on LCL‐P resonance compensation for a wireless energy transmission system is proposed. “L” represents inductance, “C” represents capacitance, “LCL” refers to the primary‐side compensation structure, and “P” indicates that the secondary side is compensated in parallel . Firstly, this paper synthesizes the modeling principle of the gyrator equivalent model of the resonant circuit and coupled inductor, graphically analyzes the resonant compensation structure, and derives the circuit characteristics of the LCL‐P compensation structure. Then, this paper proposes an improved control strategy for the Maximum Power Point Tracking (MPPT) algorithm to dynamically track the output power and thus obtain the optimal operating point through frequency conversion. Lastly, using MATLAB/Simulink software to build the simulation model of the wireless charging system through parameter design, the impact of the conventional DC/DC power control method is contrasted with the algorithmic control suggested in this paper. The results demonstrate that: the device can realize power transfer of 2.7 KW level, the energy transfer efficiency reaches more than 90%, the inverter realizes soft‐switching operation, and the improved MPPT algorithmic control strategy proposed in this paper is utilized to achieve better closed‐loop control of the system. The excellent characteristics of the LCL‐P compensation structure in high‐power transmission applications, as well as the correctness and feasibility of the control algorithm proposed in this paper, are demonstrated through simulation and practical experiments. This is a significant step towards improving the wide‐range adaptation of the wireless charging system, which is based on the LCL‐P resonance compensation to the changes in the load and coupling. [ABSTRACT FROM AUTHOR]

In response to the zooming delay issue during the transition from a wide-area search to high-resolution target identification in high-magnification zoom lenses, we propose a drive technology based on voice coil motors. The linear motion of the motor is directly converted into the linear movement of the zoom lens group, significantly enhancing the zoom speed. Additionally, we introduce a high-precision closed-loop control technology utilizing a magnetic scale to achieve the rapid and precise positioning of the zoom lens group. The magnetic scale detection technology achieves precise positioning by detecting periodic changes in the magnetic field, working in conjunction with tunnel magnetoresistance sensors. Demonstrated with a 40× zoom lens example, this study elaborates on the motion trajectory planning and structural dimension design process of a voice coil motor, culminating in the assembly of a physical prototype. Practical validation experiments show that the full zoom time of the lens utilizing our technology is less than 0.3 s, where the full zoom time refers to the time required for the lens to zoom from the wide-angle end to the telephoto end. In positioning accuracy test experiments, lenses using our technology achieved a positioning deviation of less than 5 μm. [ABSTRACT FROM AUTHOR]

Background: Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions. Methods: We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback. Results: Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants. Conclusions: The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use. [ABSTRACT FROM AUTHOR]

In experiments on a dual-mode scramjet combustor utilizing kerosene fuel, closed-loop control tests are conducted using the resistance-heated pure air supersonic combustion test equipment with the wall pressure or the pressure ratio as controlled parameters. The results revealed faster response to changes in the kerosene flow rate when controlling the wall pressure downstream of the injection point as compared to the near injection point. The closed-loop control scheme achieved objectives with the error smaller than 2% for various parameters, demonstrating the repeatability and the broad applicability. During the hydrogen-single combustion phase, the combustor operated in the supersonic combustion mode, while during the mixed combustion phase of hydrogen and kerosene, the combustor operated in the subsonic combustion mode. The backpressure caused by combustion affects the isolator inlet, and during the kerosene-single combustion phase, the combustor operates in the subsonic combustion mode. [ABSTRACT FROM AUTHOR]

Laser metal deposition (LMD) is a technology for the production of near-net-shape components. It is necessary to control the manufacturing process to obtain good geometrical accuracy and metallurgical properties. In the present study, a closed-loop control method of melt pool temperature for the deposition of small Ti6Al4V blocks in open environment was proposed. Based on the developed melt pool temperature sensor and deposition height sensor, a closed-loop control system and proportional-integral (PI) controller were developed and tested. The results show that with a PI temperature controller, the melt pool temperature tends to the desired value and remains stable. Compared to the deposition block without the controller, a flatter surface and no oxidation phenomenon are obtained with the controller. [ABSTRACT FROM AUTHOR]

Hysteresis is a fundamental characteristic of magnetic materials. The Jiles–Atherton (J-A) hysteresis model, which is known for its few parameters and clear physical interpretations, has been widely employed in simulating hysteresis characteristics. To better analyze and compute hysteresis behavior, this study established a state space representation based on the primitive J-A model. First, based on the five fundamental equations of the J-A model, a state space representation was established through variable substitution and simplification. Furthermore, to address the singularity problem at zero crossings, local linearization was obtained through an approximation method based on the actual physical properties. Based on these, the state space model was implemented using the S-function. To validate the effectiveness of the state space model, the hysteresis loops were obtained through COMSOL finite element software and tested on a permalloy toroidal sample. The particle swarm optimization (PSO) method was used for parameter identification of the state space model, and the identification results show excellent agreement with the simulation and test results. Finally, a closed-loop control system was constructed based on the state space model, and trajectory tracking experiments were conducted. The results verify the feasibility of the state space representation of the J-A model, which holds significant practical implications in the development of magnetically shielded rooms, the suppression of magnetic interference in cold atom clocks, and various other applications. [ABSTRACT FROM AUTHOR]

Laser drilling is widely used for fabricating holes in the semiconductor industry due to high throughput and a small heat-affected zone. However, it produces varying depths owing to uncertain external conditions and requires live control at the rate of a few tens of kHz to handle the fast material removal rate. Optical coherent tomography is capable of in situ acquiring a raw interferogram at a high rate (>80 kHz), but the depth extraction is slow due to the involved heavy Fast Fourier Transform (FFT). To address this, an efficient depth-tracking algorithm is proposed to save the FFT. It searches the depth in the raw interferogram locally with a known last depth given the two truths that only one depth exists and the adjacent depths do not change significantly. The proposed algorithm was proven to expedite the measuring rate six times with sub-pixel tracking precision. To further secure the rate against the interrupting of the system, the tracking process is parallelly implemented in a field-programmable gate array. The closed-loop control tests were conducted on probe cards with depth variations introduced by offsetting laser focus. The proposed method maintained a uniform depth, with variations reduced by 80% compared to traditional methods. [ABSTRACT FROM AUTHOR]

Soft robotic manipulators consisting of serially stacked segments combine actuation and structure in an integrated design. This design can be miniaturised while providing suitable actuation for potential applications that may include endoluminal surgery and inspections in confined environments. The control of these robots, however, remains challenging, due to the difficulty in accurately modelling the robots, in coping with their redundancies, and in solving their full inverse kinematics. In this work, we explore a hybrid approach to control serial soft robotic manipulators that combines machine learning (ML) to estimate the inverse kinematics with closed-loop control to compensate for the remaining errors. For the ML part, we compare various approaches, including both kernel-based learning and more general neural networks. We validate the selected ML model experimentally. For the closed-loop control part, we first explore Jacobian formulations using both synthetic models and numerical approximations from experimental data. We then implement integral control actions using both these Jacobians, and evaluate them experimentally. In an experimental validation, we demonstrate that the hybrid control approach achieves setpoint regulation in a robot with six inputs and four outputs. [ABSTRACT FROM AUTHOR]

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: We investigated the potential benefits of automated insulin delivery (AID) among individuals with type 1 diabetes (T1D) in sub-populations of baseline device use determined by continuous glucose monitor (CGM) use status and insulin delivery via multiple daily injections (MDI) or insulin pump. MATERIALS AND METHODS: In a six-month randomized, multicenter trial, 168 individuals were assigned to closed-loop control (CLC, Control-IQ, Tandem Diabetes Care), or sensor-augmented pump (SAP) therapy. The trial included a two- to eight-week run-in phase to train participants on study devices. The participants were stratified into four subgroups: insulin pump and CGM (pump+CGM), pump-only, MDI and CGM (MDI+CGM), and MDI users without CGM (MDI-only) users. We compared glycemic outcomes among four subgroups. RESULTS: At baseline, 61% were pump+CGM users, 18% pump-only users, 10% MDI+CGM users, and 11% MDI-only users. Mean time in range 70-180 mg/dL (TIR) improved from baseline in the four subgroups using CLC: pump+CGM, 62% to 73%; pump-only, 61% to 70%; MDI+CGM, 54% to 68%; and MDI-only, 61% to 69%. The reduction in time below 70 mg/dL from baseline was comparable among the four subgroups. No interaction effect was detected with baseline device use for TIR (P = .67) or time below (P = .77). On the System Usability Questionnaire, scores were high at 26 weeks for all subgroups: pump+CGM: 87.2 ± 12.1, pump-only: 89.4 ± 8.2, MDI+CGM 87.2 ± 9.3, MDI: 78.1 ± 15. CONCLUSIONS: There was a consistent benefit in patients with T1D when using CLC, regardless of baseline insulin delivery modality or CGM use. These data suggest that this CLC system can be considered across a wide range of patients.

Abstract It is essential that people with limb amputation maintain proper prosthetic socket fit to prevent injury. Monitoring and adjusting socket fit, for example by removing the prosthesis to add prosthetic socks, is burdensome and can adversely affect users’ function and quality-of-life. This study presents results from take-home testing of a motor-driven adaptive socket that automatically adjusted socket size during walking. A socket fit metric was calculated from inductive sensor measurements of the distance between the elastomeric liner surrounding the residual limb and the socket’s inner surface. A proportional-integral controller was implemented to adjust socket size. When tested on 12 participants with transtibial amputation, the controller was active a mean of 68% of the walking time. In general, participants who walked more than 20 min/day demonstrated greater activity, less doff time, and fewer manual socket size adjustments for the adaptive socket compared with a locked non-adjustable socket and a motor-driven socket that participants adjusted with a smartphone application. Nine of 12 participants reported that they would use a motor-driven adjustable socket if it were available as it would limit their socket fit issues. The size and weight of the adaptive socket were considered the most important variables to improve.

A Cable-Driven Continuum Robot (CDCR) that consists of a set of identical Cable-Driven Continuum Joint Modules (CDCJMs) is proposed in this paper. The CDCJMs merely produce 2-DOF bending motions by controlling driving cable lengths. In each CDCJM, a pattern-based flexible backbone is employed as a passive compliant joint to generate 2-DOF bending deflections, which can be characterized by two joint variables, i.e., the bending direction angle and the bending angle. However, as the bending deflection is determined by not only the lengths of the driving cables but also the gravity and payload, it will be inaccurate to compute the two joint variables with its kinematic model. In this work, two stretchable capacitive sensors are employed to measure the bending shape of the flexible backbone so as to accurately determine the two joint variables. Compared with FBG-based and vision-based shape-sensing methods, the proposed method with stretchable capacitive sensors has the advantages of high sensitivity to the bending deflection of the backbone, ease of implementation, and cost effectiveness. The initial location of a stretchable sensor is generally defined by its two endpoint positions on the surface of the backbone without bending. A generic shape-sensing model, i.e., the relationship between the sensor reading and the two joint variables, is formulated based on the 2-DOF bending deflection of the backbone. To further improve the accuracy of the shape-sensing model, a calibration method is proposed to compensate for the location errors of stretchable sensors. Based on the calibrated shape-sensing model, a sliding-mode-based closed-loop control method is implemented for the CDCR. In order to verify the effectiveness of the proposed closed-loop control method, the trajectory tracking accuracy experiments of the CDCR are conducted based on a circle trajectory, in which the radius of the circle is 55 mm . The average tracking errors of the CDCR measured by the Qualisys motion capture system under the open-loop and the closed-loop control are 49.23 and 8.40 mm , respectively, which is reduced by 82.94%. [ABSTRACT FROM AUTHOR]

Traditional ventilation methods consume excessive energy but still fail to meet requirements in underground tunnel group construction. Thus, a closed-loop intelligent control system for ventilation-on-demand (VOD) was developed. To address dynamic changes in ventilation load and reduce energy consumption, firstly, the developed system calculates the real-time ventilation load and establishes a ventilation-network-based control mode to represent the ventilation system structure. The deep deterministic policy gradient (DDPG) method was then employed for the closed-loop control ensuring the required air volume in each branch of tunnel groups while minimizing energy consumption. After that, the developed closed-loop intelligent ventilation control system encompasses comprehensive perception, real analysis, real-time control, and continuous optimization. This system treats decision-making, control, and feedback as subsystems that reflect the adaptability between ventilation efficiency, construction progress, and power consumption. Finally, the end-edge-cloud-based software of the system was developed to enable remote control and display on large screens, personal computers (PCs), and mobile applications (Apps) to ensure precise and timely operation. The system was employed in tunnel group under construction at the Xulong Hydropower Station in Southwestern China, and the obtained results validate its advanced closed-loop control based on reinforcement learning (RL) and confirm its feasibility in engineering practice. [ABSTRACT FROM AUTHOR]

The ability to optically interact with cells on both an individual and collective level has applications from wound healing to cancer treatment. Building systems that can facilitate both localised light illumination and visualisation of cells can, however, be challenging and costly. This work takes the Dynamic Optical MicroEnvironment (DOME), an existing platform for the closed-loop optical control of microscale agents, and adapts the design to support live-cell imaging. Through modifications made to the imaging and projection systems within the DOME, a significantly higher resolution, alternative imaging channels and the ability to customise light wavelengths are achieved (Bio-DOME). This is accompanied by an interactive calibration procedure that is robust to changes in the hardware configuration and provides fluorescence imaging (Fluoro-DOME). These alterations to the fundamental design allow for long-term use of the DOME in an environment of higher temperature and humidity. Thus, long-term imaging of living cells in a wound, with closed-loop control of real-time frontier illumination via projected light patterns, is facilitated. [ABSTRACT FROM AUTHOR]

Individuals with spinal cord injury (SCI) usually develop neurogenic detrusor overactivity (NDO), resulting in bladder urgency and incontinence, and reduced quality of life. Electrical stimulation of the genital nerves (GNS) can inhibit uncontrolled bladder contractions in individuals with SCI. An automated closed-loop bladder neuromodulation system currently does not exist but could improve this approach. We have developed a custom algorithm to identify bladder contractions and trigger stimulation from bladder pressure data without need for abdominal pressure measurement. The goal of this pilot study was to test the feasibility of automated closed-loop GNS using our custom algorithm to identify and inhibit reflex bladder contractions in real time. Experiments were conducted in a single session in a urodynamics laboratory in four individuals with SCI and NDO. Each participant completed standard cystometrograms without and with GNS. Our custom algorithm monitored bladder vesical pressure and controlled when GNS was turned on and off. The custom algorithm detected bladder contractions in real time, successfully inhibiting a total of 56 contractions across all four subjects. There were eight false positives, six of those occurring in one subject. It took approximately 4.0 ± 2.6 s for the algorithm to detect the onset of a bladder contraction and trigger stimulation. The algorithm maintained stimulation for approximately 3.5 ± 1.7 s, which was enough to inhibit activity and relieve feelings of urgency. Automated closed-loop stimulation was well-tolerated and subjects reported that algorithm decisions generally matched with their perceptions of bladder activity. The custom algorithm automatically, successfully identified bladder contractions to trigger stimulation to inhibit bladder contractions acutely. Closed-loop neuromodulation using our custom algorithm is feasible, but further testing is needed refine this approach for use in a home environment. [ABSTRACT FROM AUTHOR]

This paper studies the continuous-time behavior of sampled-data control systems. In these systems, a discrete-time control law is applied to a continuous-time system model using sampling and zero-order hold. Traditionally, sampled-data systems are modeled in discrete-time, and thus, the intersample dynamics cannot be examined. This paper shows that the classical methods (such as the d2c function of MATLAB) for obtaining continuous-time models can be inaccurate between the sampling instants, even when Shannon's sampling theorem is satisfied. In this paper, a new method is also presented which can be used to characterize the continuous-time dynamics of sampled-data control systems. The new continuous-time system model including the intersample behavior is derived analytically from the discrete-time model, and the results are verified by simulations. [ABSTRACT FROM AUTHOR]

The enhancement of self-driving vehicles has the potential to disrupt traditional transportation systems, Utilizing progress in secure and intelligent mobility. However, control of movement in self-driving vehicles is still difficult to carry out driving duties in a constantly changing road environment. The regulation of bearing angle is an essential component in selfdriving vehicles navigation systems, facilitating the secure and efficient operation of vehicles across a range of environments, including urban streets, highways, and off-road terrain. It employs algorithms and sensor fusion to perceive surroundings, compute trajectories, and execute precise steering commands. The bearing angle represents the angle between the vehicle's current and desired directions. By consistently monitoring this angle and implementing appropriate steering inputs, the selfdriving vehicle can accurately stay on track and proactively adapt to obstacles or adhere to a designated route. In this context, we explore the advancements in bearing angle control methods for self-driving vehicles. By conducting simulations of a simplified block diagram for a self-guiding vehicle's bearing angle control techniques, the efficacy of the steering system of self-driving cars has been briefly examined. We provide various methods of control, which are considered approaches for controlling the angle of bearings through lag lead compensation and PID auto-tuned controllers. The results show that the autotuned PID controller outperforms all other controllers in terms of transient and steady-state responses. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we present the development and the validation of a novel index of nociception/anti-nociception (N/AN) based on skin impedance measurement in time and frequency domain with our prototype AnspecPro device. The primary objective of the study was to compare the Anspec-PRO device with two other commercial devices (Medasense, Medstorm). This comparison was designed to be conducted under the same conditions for the three devices. This was carried out during total intravenous anesthesia (TIVA) by investigating its outcomes related to noxious stimulus. In a carefully designed clinical protocol during general anesthesia from induction until emergence, we extract data for estimating individualized causal dynamic models between drug infusion and their monitored effect variables. Specifically, these are Propofol hypnotic drug to Bispectral index of hypnosis level and Remifentanil opioid drug to each of the three aforementioned devices. When compared, statistical analysis of the regions before and during the standardized stimulus shows consistent difference between regions for all devices and for all indices. These results suggest that the proposed methodology for data extraction and processing for AnspecPro delivers the same information as the two commercial devices. [ABSTRACT FROM AUTHOR]

A closed-loop control framework is developed for the co-flow jet (CFJ) airfoil by combining the numerical flow field environment of a CFJ0012 airfoil with a deep reinforcement learning (DRL) module called tensorforce integrated in Python. The DRL agent, which is trained through interacting with the numerical flow field environment, is capable of acquiring a policy that instructs the mass flow rate of the CFJ to make the stalled airfoil at an angle of attack (AoA) of 18 degrees reach a specific high lift coefficient set to 2.0, thereby effectively suppressing flow separation on the upper surface of the airfoil. The subsequent test shows that the policy can be implemented to find a precise jet momentum coefficient of 0.049 to make the lift coefficient of the CFJ0012 airfoil reach 2.01 with a negligible error of 0.5%. Moreover, to evaluate the generalization ability of the policy trained at an AoA of 18 degrees, two additional tests are conducted at AoAs of 16 and 20 degrees. The results show that, although using the policy gained under another AoA cannot help the lift coefficient of the airfoil reach a set target of 2 accurately, the errors are acceptable with less than 5.5%, which means the policy trained under an AoA of 18 degrees can also be applied to other AoAs to some extent. This work is helpful for the practical application of CFJ technology, as the closed-loop control framework ensures good aerodynamic performance of the CFJ airfoil, even in complex and changeable flight conditions. [ABSTRACT FROM AUTHOR]

Inter-individual variability in Pharmacokinetic (PK) and Pharmacodynamic (PD) models significantly affects the accuracy of Target Controlled Infusion and closed-loop control of anesthesia. We hypothesize that the novel Eleveld PK model captures more inter-individual variability relevant to both open-loop and closed-loop control design, resulting in reduced variability in PD models identified using the Eleveld PK model's plasma prediction compared to the Schuttler or Schnider PK model. We used a dataset of propofol infusion rates and Depth of Hypnosis measurements across three demographic groups: elderly, obese, and adult. PD models are identified based on plasma concentration prediction using three PK models (Schuttler, Schnider, and Eleveld). Validation methods are presented to confirm acceptable predictive performance and comparable PK-PD model variability within each demographic group. To test our hypothesis, we compared coefficient variations in step responses for open-loop control and multiplicative uncertainty of PD model sets for closed-loop control. Validated PKPD models using the Schuttler and Schnider PK model showed no significant differences in predictive response and multiplicative uncertainty compared to the Eleveld PK model. The coefficient variations in step responses of PD model sets and the frequency ranges, corresponding to uncertainty below one, were comparable for all three PK models. The comparison of the accumulated coefficient of variation in the step-response and the uncertainty of the PD model sets indicated that the Eleveld PK model does not offer any advantage for the design of open-loop or closed-loop control of anesthesia. [ABSTRACT FROM AUTHOR]

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This study critically investigates analysis and control of non-isolated boost three-port DC to DC converter (TPC) for standalone PV system. The converter is controlled such that regulates flow of power from PV array to load and batteries as storage devices. According to PV power, there are four operating modes of operation of converter. Simple reduced-order dynamic models of the converter in various modes of operation are obtained using state-space averaging and small-signal techniques. In this work, the controllers of the closed-loop scheme are designed and only two controllers are used to achieve output voltage regulation, and to extract maximum power from PV array under different operating conditions. Closed-loop system simulation is studied to verify the operation of converter with the designed controllers in different modes. Transition between modes is presented with solar radiation variation and change of connected loads. Experimental verification of control systems at different modes of operation is investigated. The experimental results show that the controllers can regulate the power flow between TPC three ports and regulate output voltage under PV irradiance variation and load changing. The controller shows good performance measures such as maximum settling time of 0.06 s, maximum steady-state ripples of ±0.8 %, and 99.3 % power efficiency.

Inductive Wireless Power Transfer (IWPT) using magnetic induction has seen remarkable progress over the past few decades. Despite their growing popularity, IWPT systems suffer from output power instability due to variations in system parameters. This paper provides a comprehensive review of the strategies for maintaining stable output power under system parameters that deviate from the original design specifications. First, the root cause of power instability and the state-of-the-art techniques for avoiding the output power decrease are carefully examined. In addition, by comparing these methods, this paper clarifies their respective application scenarios and offers insights into their practical benefits. Furthermore, this paper outlines potential future advancements in improving the robustness and reliability of IWPT systems. As presented, the output power instability is induced by frequency mismatch and coupling coefficient variation. Resonant frequency regulation, operation frequency regulation, and coupling optimization are targeted techniques for the issues. Based on the comparison, respective application scenarios are clarified. Moreover, the trade-offs should be carefully weighed in terms of their impact, complexity, and cost. Future research directions on variable devices, closed-loop control, and new mechanisms for power stability enhancement are highlighted, thereby facilitating the pervasive application of IWPT systems.

In addressing the issue of high common-mode voltage amplitude and output waveform distortion caused by low switching frequency in medium and high-voltage high-power variable frequency speed control systems, the neutral-point clamped three-level inverter is taken as the research object, from the perspective of switching sequence design, a detailed analysis of the switching sequence design method to mitigate the common-mode voltage amplitude, minimize the switching times and enhance output waveform quality. An improved synchronous space vector pulse width modulation strategy is proposed. Secondly, the improved strategy is applied to the closed-loop control of permanent magnet synchronous motor. Aiming at the defects of single modulation mode, the multi-mode modulation strategy based on the improved strategy is proposed, and the smooth switching of modulation mode is realized by limiting the switching phase. The compensation schemes for angular deviation under synchronous modulation and angular delay under digital control are studied to improve the system’s stability. Finally, a simulation and experimental platform is built to verify the correctness and effectiveness of the improved and multi-mode modulation strategies.

A closed-loop control framework is developed for the co-flow jet (CFJ) airfoil by combining the numerical flow field environment of a CFJ0012 airfoil with a deep reinforcement learning (DRL) module called tensorforce integrated in Python. The DRL agent, which is trained through interacting with the numerical flow field environment, is capable of acquiring a policy that instructs the mass flow rate of the CFJ to make the stalled airfoil at an angle of attack (AoA) of 18 degrees reach a specific high lift coefficient set to 2.0, thereby effectively suppressing flow separation on the upper surface of the airfoil. The subsequent test shows that the policy can be implemented to find a precise jet momentum coefficient of 0.049 to make the lift coefficient of the CFJ0012 airfoil reach 2.01 with a negligible error of 0.5%. Moreover, to evaluate the generalization ability of the policy trained at an AoA of 18 degrees, two additional tests are conducted at AoAs of 16 and 20 degrees. The results show that, although using the policy gained under another AoA cannot help the lift coefficient of the airfoil reach a set target of 2 accurately, the errors are acceptable with less than 5.5%, which means the policy trained under an AoA of 18 degrees can also be applied to other AoAs to some extent. This work is helpful for the practical application of CFJ technology, as the closed-loop control framework ensures good aerodynamic performance of the CFJ airfoil, even in complex and changeable flight conditions.

In the direct laser deposition (DLD) process, it is common to employ constant processing parameters. The utilization of the constant parameters may lead to fluctuations in the dynamic evolution of the molten pool, primarily due to the intricate thermal effects involved, which will significantly impact the processing quality. To address this issue, this study proposed a closed-loop control approach that effectively modifies processing parameters in real time by targeting on the molten pool transient area. The type of the laser used in this study is a flat-top beam. Laser power, powder feeder rate, and scanning speed are the input variables of the study to control molten pool, considering the manufacturing system. The most suitable processing parameter to control the molten pool area was found to be the laser power by a set of orthogonal experiments, with a correlation coefficient of 0.706, and significance level of 0.002. Then, the dynamic response relationship between laser power and the molten pool area was mathematically characterized by a third-order transfer function model to simplify the complex physical model of the DLD process. Subsequently, a PID controller with a filtering coefficient and anti-windup compensation was chosen compared with the other controller. In the validation experiments, it was observed that the closed-loop processing group demonstrated improved stability in maintaining the molten pool transient area, with a notable decrease of 33.7% in variability compared to the open-loop processing group. As a result, the deposited layer of the closed-loop processing group exhibited a much more satisfying surface quality and heat affect zone than the open-loop group. This study established a third-order mathematical model of the dynamic molten pool and realized the optimization of deposition quality by controlling the molten pool transient area with an enhanced PID controller, providing a fundamental basis for improving the consistency of the direct laser deposition processing quality through the implementation of real-time feedback control of molten pool physics. [ABSTRACT FROM AUTHOR]

In this contribution, the development of a 3D-printed soft actuator integrated with shape memory alloys (SMA) wires capable of bending in two directions is presented. This work discusses the design, manufacturing, modeling, simulation, and feedback control of the actuator. The SMA wires are encased in Polytetrafluoroethylene (PTFE) tubes and then integrated into the 3D-printed matrix made of thermoplastic polyurethane (TPU). To measure and control the deformation angle of the soft actuator, a computer vision system was implemented. Based on the experimental results, a mathematical model was developed using the system identification method and simulated to describe the dynamics of the actuator, contributing to the design of a controller. However, achieving precise control of the deformation angle in systems actuated by SMA wires is challenging due to their inherent nonlinearities and hysteretic behavior. A proportional-integral (PI) controller was designed to address this challenge, and its effectiveness was validated through real experiments. [ABSTRACT FROM AUTHOR]

Novel conceptual aircraft designs have been enabled by more electrified aircraft components providing enhanced capability and versatility. Through the advancement of multidisciplinary design optimization, control co-design methods have become a popular approach for system design conceptualization wherein the plant and control actions are designed simultaneously to account for the coupling between vehicle subsystems and power management systems. Many prior efforts have focused on open-loop control co-design that can later be adapted for a more realistic operating case. This work focuses on the development and scalability of closed-loop control co-design that would result in a physically realizable plant and closed-loop control law. The theoretical approach is demonstrated practically through the design of a hybrid electric unmanned air vehicle and two feedback controllers that operate the hybrid power split and propulsion system. The system is designed to complete a dynamic seven phase mission consisting of multiple cruise, dash, engage, dive, and climb segments as quickly as possible. Given the scale of the dynamic design problem, a convergence study is introduced that facilitates accurate and computationally tractable design optimization studies. The study is conducted for independent, sequential, and simultaneous design approaches. The results indicate high-speed motors, high voltage batteries, and responsive control gains result in a fast vehicle with high thrust-to-weight ratio. The simultaneous design solution had the best closed-loop performance, outclassing a baseline system design by over 30%. [ABSTRACT FROM AUTHOR]

Laser remanufacturing is a key technology for remanufacturing, which has been widely used in aerospace, metallurgy, energy and other fields. However, the problem of shape control has been the bottleneck that restricts the scale and industrial application. In the laser remanufacturing process, the molten pool temperature is the critical to determine the shape and performance stability of remanufactured parts. Based on this, the paper proposes a real-time online control method for laser remanufacturing molten pool temperature based on state space model, which solves the problem of difficult to establish control mechanism model and multiple variable constraints. Develop a real-time online control system for molten pool temperature of laser remanufacturing process based on state space model and incremental integral separation PID control, which uses a single sampling point as temperature feedback, solving the problem of lag generated by traditional feedback cycle, realizing real-time online control of molten pool temperature of laser remanufacturing process. Finally, the experimental results show that the developed molten pool temperature control system can effectively eliminate the sudden and gradual temperature change in the deposition process and significantly improve the thermal accumulation phenomenon in the deposition process, thus improving the forming quality and performance stability of remanufactured parts. [ABSTRACT FROM AUTHOR]