Real-time Feedback Control Research Articles

A low-cost prosthetic arm with real-time haptic feedback and gesture control for enhanced user autonomy

In daily life, manipulating and feeling objects is essential for autonomy and engagement in various activities. However, individuals relying on prosthetic limbs often encounter barriers such as high costs, ineffective feedback, and limited design adaptability. Unlike conventional prosthetic limbs, which frequently struggle to integrate seamlessly and provide intuitive control, the robotic arm outlined in this study represents a leap in functionality and user experience. At the core of its design lies a specialized glove outfitted with a calibrated array of flex-sensing resistors capable of precisely detecting and interpreting the wearer's movements. These sensors input to the robotic arm, translating the user's gestures into motions that follow human hands. The fingertips are equipped with force-sensing resistors to discern the magnitude of force applied during gripping actions. This real-time sensory feedback is conveyed to the user through a haptic system integrated into the glove. By modulating the intensity and pattern of vibrations, users can gauge their grip strength and manipulate their grasp of the object accordingly. Comprehensive experiments were conducted across various scenarios to assess its performance, agility, strength, and sensory accuracy. Through refinement, the author aims to optimize functionality and reliability, enhancing practicality for users. This project has broader implications for the prosthetics community. By utilizing the scalability and cost-effectiveness of this model, the author envisions tailored solutions that empower individuals with limb differences to live more independently.

LSTM-Inversion-Based Feedforward–Feedback Nanopositioning Control

This work proposes a two-degree of freedom (2DOF) controller for motion tracking of nanopositioning devices, such as piezoelectric actuators (PEAs), with a broad bandwidth and high precision. The proposed 2DOF controller consists of an inversion feedforward controller and a real-time feedback controller. The feedforward controller, a sequence-to-sequence LSTM-based inversion model (invLSTMs2s), is used to compensate for the nonlinearity of the PEA, especially at high frequencies, and is collaboratively integrated with a linear MPC feedback controller, which ensures the PEA position tracking performance at low frequencies. Therefore, the proposed 2DOF controller, namely, invLSTMs2s+MPC, is able to achieve high precision over a broad bandwidth. To validate the proposed controller, the uncertainty of invLSTMs2s is checked such that the integration of an inversion model-based feedforward controller has a positive impact on the trajectory tracking performance compared to feedback control only. Experimental validation on a commercial PEA and comparison with existing approaches demonstrate that high tracking accuracies can be achieved by invLSTMs2s+MPC for various reference trajectories. Moreover, invLSTMs2s+MPC is further demonstrated on a multi-dimensional PEA platform for simultaneous multi-direction positioning control.

Frontiers in construction 3D printing: self-monitoring, multi-robot, drone-assisted processes

AbstractTo overcome productivity issues and revolutionize the stagnating construction industry, a large amount of research efforts has been devoted to robot-assisted construction technology. The advancements in robotics including mechanical system design, tool design, digital system design, and numerical control systems design enabled engineers to create complex geometries that are infeasible for conventional construction methods. In addition, innovative robotic systems that utilize mobile platforms, multiple robots, and unmanned aerial vehicles have demonstrated significant promise in fully automating the construction process. This work will provide a perspective on the state-of-the-art applications of robotics in the revolution of construction, where a comprehensive review of the current development of the relevant software and hardware, 3D concrete printing (3DCP), robot-assisted assembly of discrete prefabricated blocks, real-time quality monitoring and feedback control systems, and typical innovative robot-assisted structural designs are conducted. Finally, the limitations of existing robot-assisted construction technology are identified, which leads to several recommendations for future research toward fully automatic construction.

Design and Research of Intelligent Valve Control

The design and research of intelligent valve control system aims to improve the efficiency and accuracy of fluid control in industrial process automation. By integrating advanced sensing technology, communication protocols and intelligent algorithms, the precise control of valve position, pressure and flow rate is achieved. STM32 controller and a variety of sensors are used in the system to realize real-time monitoring and feedback control of valve status. The control strategy based on PID algorithm enables the valve to maintain stable operating performance under complex working conditions. In addition, the safety and reliability of the system are also considered, and the robustness of the system is improved by redundant design and fault detection mechanism. The experimental results show that the designed intelligent valve control system has significant advantages in improving response speed, control precision and energy efficiency, and has a wide range of industrial application prospects.

Study on penetration depth in laser welding: A process information database-based control strategy and OCT measuring verification

Penetration depth acts as a crucial indicator reflecting laser welding quality, thus the control of its stability and the perception of its fluctuation state are increasingly garnering attention. This paper proposes a process information database-based control strategy for penetration depth, and the control validity is verified through penetration depth detection utilizing optical coherence tomography (OCT). The process information database stores diverse expected penetration depth knowledge formed by a substantial quantity of varying welding speeds with fixed other process parameters under undisturbed welding conditions. In the database, the stable average values inside the standard penetration depth information and the corresponding heat input (HI) values are connected and mapped via an artificial neural network (ANN). In response to abnormal variations in the penetration depth curve caused by interferences during welding, according to the HI gap predicted by the trained ANN from the penetration depth gap arising from the curve deviation, the control unit can calculate the new welding speed required to feed the penetration depth curve back to within the steady fluctuation range. Based on OCT, the keyhole depth signal is acquired, and a deep belief network is built to predict the penetration depth curve via the correlation between the reconstructed keyhole depth obtained by ensemble empirical mode decomposition and the penetration depth. This detection method demonstrates that the penetration depth curve can be controlled accurately. Finally, a closed-loop real-time feedback control system for penetration depth is established.

Adaptive feedback control for intelligent phase noise suppression in a figure-9 fiber laser.

Phase noise characteristics of ultrafast fiber lasers are critical to practical applications, such as high-resolution photonics sampling. Herein, we investigated the impact of pump power and linear phase shift difference of counter-propagating light in the nonlinear amplifying loop mirror on phase noise suppression in a figure-9 fiber laser. Based on these results, we proposed a method for intelligent suppression of phase noise through real-time feedback control. By adaptively controlling the linear phase shift difference and pump power, the phase noise can be effectively suppressed in the high offset frequency region even in variable environments. In particular, a reduction of ∼21.40% of integrated timing jitter in the offset frequency region from 10 kHz to 1 MHz was achieved. Our approach was proved to be effective and automatic to obtain ultrafast lasers with low phase noise and may also facilitate the related applications.

LPV interpolation modeling and modal-based pole placement control for ball screw drive with dynamic variations

This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime.

Real-time processing system of a two-color CO2 laser interferometer for density feedback control in JT-60SA.

A real-time processing system for the two-color CO2 laser interferometer on the JT-60SA has been developed for density feedback control. The system has a novel feature that can detect fringe jumps due to off-normal events, such as loss detection due to displacement of the beam axis and changes in the laser wavelengths. Because a phase change due to the JT-60SA plasma is smaller than π/2, corresponding to the line-integral electron density NL of ∼6×1019 m-2 in a short interval of 500ns, the threshold of the fringe jump detection is decided to be π/2. Hence, off-normal events can be detected from a fringe jump, leading to the abort of the real-time feedback control. In the density feedback control of the JT-60SA plasma, the system is employed as a density monitor, with NL being successfully controlled at 16.8% ± 6.6% lower than the reference.

Sensing area-variable electrode for wide-range droplet size detection

Microfluidic droplets of various sizes are applied in a broad spectrum of biological and chemical processes and require high uniformity for reliable outcomes. Although various droplet detection methods have been employed to achieve monodisperse droplets, the capacitive method stands out for its ease of use and rapid response, enabling real-time feedback control. However, this method has limitations in terms of significantly altering the droplet size, resulting in increased continuous phase consumption and the need for microchannel and sensing component replacements. In response, we present the Sensing Area-Variable Electrode (SAVE), which is designed to produce monodisperse droplets across a broad size range without excessive continuous phase consumption and without requiring the frequent replacement of sensing components. The experimental results demonstrate that using the SAVE electrode enables the generation of monodisperse 0.2, 0.4, and 1.0 nL droplets by simply replacing the disposable microchannel while reusing the sensing components. Moreover, the adaptable number of droplets achieved by microchannel replacement allows the SAVE electrode to be effectively utilized in droplet digital polymerase chain reaction (ddPCR) assays, providing a balance between sensitivity and throughput. Consequently, the SAVE electrode addresses the limitations of the capacitive detection method and offers a versatile solution for various droplet-based biological and chemical processes, including ddPCR assays.

Thermoelectric-Feedback Nanocomposite Hydrogel for Temperature-Synchronized Monitoring and Regulation in Accurate Photothermal Therapy.

Photothermal therapy (PTT) is a promising approach for tumor ablation and cancer treatment. However, controlling the therapeutic temperature during treatment remains challenging, and imprecise thermal regulation can harm adjacent healthy tissues, reduce therapeutic accuracy, and promote the thermotolerance of cellular phenotypes, potentially leading to tumor invasion and recurrence. Although existing methods provide basic temperature control by adjusting irradiation power and photothermal agent dosing, they lack real-time temperature monitoring and feedback control capabilities, underscoring the urgent need for more integrated and precise PTT systems. In this context, an innovative photothermoelectric (PTE) cobalt-infused chitosan (CS) nanocomposite hydrogel (PTE-Co@CS) is developed for precise temperature-regulated PTT, exhibiting desirable mechanical properties and exceptional biocompatibility. Enhanced by embedded nanoparticles, PTE-Co@CS demonstrates superior photothermal conversion efficiency compared with existing methods, while also featuring thermoelectric responsiveness and increased sensitivity to photostimuli. Its advantageous PTE response characteristics ensure a linear correlation between temperature shifts and resistance changes (e.g., R2 = 0.99919 at 0.5Wcm⁻2), enabling synchronized qualitative and quantitative control of PTT temperature through electrical signal monitoring. This allows for real-time monitoring and regulation during PTT, effectively addressing the issue of uncontrollable temperatures and improving therapeutic efficacy.

Enhanced perturbation measurement range chirped pulse ϕ-OTDR for distributed static measurements using feedback frequency-shift compensation technology

Chirped-pulse phase-sensitive optical time-domain reflectometry (CP-ϕOTDR) is a newly developed distributed optical fiber sensing (DOFS) technology that can measure physical quantities across the length of optical fiber. The measurement of perturbations as strain or temperature changes is performed by comparing the time domain trace of Rayleigh backscattered (RBS) light with a reference trace. However, large strain or temperature fluctuations or laser frequency noises necessitate frequent updates of the reference, and short-term change measurements must be integrated to obtain absolute strain or temperature, which introduces a 1/f noise component due to the combination of noise with each cumulative change. In this study, we propose a novel CP-ϕOTDR system based on frequency-shift compensation (FSC) technology. This system utilizes real-time feedback control of the central frequency of the incident laser to compensate for time domain trace distortion caused by large perturbations. It overcomes the limitations associated with the total chirp bandwidth and eliminates the 1/f noise component by obviating the need for frequent updates of the reference trace. The measuring range for perturbations is only limited to the wavelength range supported by the integrated optical devices, typically on the order of nanometers, corresponding to at least tens of Kelvin in temperature fluctuations or hundreds of micro-strains. To validate the effectiveness, we conducted a temperature measurement experiment involving a 1 K variation, which is about 90 times the maximum measurable perturbation that the original CP-ϕOTDR system can demodulate with the reference unchanged. The results demonstrate that the FSC method significantly expands the perturbation measurement range of the CP-ϕOTDR system.

In-situ process monitoring and adaptive quality enhancement in laser additive manufacturing: A critical review

Laser Additive Manufacturing (LAM) presents unparalleled opportunities for fabricating complex, high-performance structures and components with unique material properties. Despite these advancements, achieving consistent part quality and process repeatability remains challenging. This paper provides a comprehensive review of various state-of-the-art in-situ process monitoring techniques, including optical-based monitoring, acoustic-based sensing, laser line scanning, and operando X-ray monitoring. These techniques are evaluated for their capabilities and limitations in detecting defects within Laser Powder Bed Fusion (LPBF) and Laser Directed Energy Deposition (LDED) processes. Furthermore, the review discusses emerging multisensor monitoring and machine learning (ML)-assisted defect detection methods, benchmarking ML models tailored for in-situ defect detection. The paper also discusses in-situ adaptive defect remediation strategies that advance LAM towards zero-defect autonomous operations, focusing on real-time closed-loop feedback control and defect correction methods. Research gaps such as the need for standardization, improved reliability and sensitivity, and decision-making strategies beyond early stopping are highlighted. Future directions are proposed, with an emphasis on multimodal sensor fusion for multiscale defect prediction and fault diagnosis, ultimately enabling self-adaptation in LAM processes. This paper aims to equip researchers and industry professionals with a holistic understanding of the current capabilities, limitations, and future directions in in-situ process monitoring and adaptive quality enhancement in LAM.

Control-oriented modelling of wind direction variability

Abstract. Wind direction variability significantly affects the performance and lifetime of wind turbines and wind farms. Accurately modelling wind direction variability and understanding the effects of yaw misalignment are critical towards designing better wind turbine yaw and wind farm flow controllers. This review focuses on control-oriented modelling of wind direction variability, which is an approach that aims to capture the dynamics of wind direction variability for improving controller performance over a complete set of farm flow scenarios, performing iterative controller development and/or achieving real-time closed-loop model-based feedback control. The review covers various modelling techniques, including large eddy simulations (LESs), data-driven empirical models, and machine learning models, as well as different approaches to data collection and pre-processing. The review also discusses the different challenges in modelling wind direction variability, such as data quality and availability, model uncertainty, and the trade-off between accuracy and computational cost. The review concludes with a discussion of the critical challenges which need to be overcome in control-oriented modelling of wind direction variability, including the use of both high- and low-fidelity models.

Developing a Gantry Robot with Pre-calculated pure S-curve Motion Profiles for Delicate Egg Handling: Utilizing ESP32, FreeRTOS, and AS5600 Encoders

The poultry industry faces challenges due to egg breakage during transfer. This paper describes a gantry robot specifically designed for delicate egg handling. The robot utilizes pre-calculated pure S-curve motion profiles to achieve smooth and precise movements, minimizing stress on the eggs. This approach leverages the computational efficiency of pre-calculation, making it suitable for low-power microcontrollers like the ESP32. FreeRTOS(Free Real-Time Operating System) ensures real-time task management for profile execution and data collection every 4 milliseconds from the AS5600 encoders. These encoders provide high-resolution angular position feedback, allowing for comparison with the planned S-curve profile after each movement step. This system offers advantages such as reduced egg breakage, improved transfer efficiency, and a simpler design compared to real-time control. However, limitations include limited adaptability to significant environmental changes and disturbances. Future work may investigate incorporating real-time feedback control for enhanced robustness.

Development of real-time density feedback control on MAST-U in L-mode

In this paper we report on the development and demonstration of density feedback control for MAST-U. Sinusoidal perturbations are used to measure the frequency response from a deuterium gas valve (actuator) to line-integrated core electron density measured by the interferometer (sensor). In the frequency range relevant for control design, only two system-identification experiments were needed to regress a first-order dynamic model. This control-oriented model informs the offline design of a proportional integral controller with the established loop-shaping controller design method. After offline verification of the controller implementation, control is demonstrated by experimentally tracking a staircase reference for the line-integrated electron density. This paper demonstrates the efficiency of controller design using system-identification and loop-shaping, providing reliable density control for MAST-U.

Real-time feedback control of β p based on deep reinforcement learning on EAST

Recently, with the advancement of the AI field, reinforcement learning (RL) has increasingly been applied to plasma control on tokamak devices. However, possibly due to the generally high training costs of reinforcement learning based on first-principle physical models and the uncertainty in ensuring simulation results align perfectly with tokamak experiments, feedback control experiments using reinforcement learning specifically for plasma kinetic parameters on tokamaks remain scarce. To address this challenge, this work proposes a novel design scheme including the development of a low computational cost environment. This environment is derived from EAST modulation experiments data through system identification. To tackle issues of noise and actuator limitations encountered in experiments, data preprocessing methods were employed. During training, the agent collected data across multiple plasma scenarios to update its strategy, and the performance of the RL controller was fine-tuned by adjusting the weight of the integral term of the error in the reward function. The effectiveness and robustness of the proposed design were then validated in a simulated environment. Finally, the scheme was successfully implemented on EAST, effectively tracking the β p target with lower hybrid wave (LHW) at 4.6 GHz as the actuator, and providing reference for implementing feedback control based on reinforcement learning in tokamaks.

Universal Deoxidation of Semiconductor Substrates Assisted by Machine Learning and Real-Time Feedback Control.

Substrate oxidation is inevitable when exposed to ambient atmosphere during semiconductor manufacturing, which is detrimental to the fabrication of state-of-the-art devices. Optimizing the deoxidation process in molecular beam epitaxy (MBE) for random substrates poses a multidimensional challenge and is sometimes controversial. Due to variations in substrates and growth processes, the determination of the deoxidation condition heavily relies on the individual's expertise, yielding inconsistent results. This study employs a machine learning model that integrates interpolation and vision transformer (Interpolation-ViT) techniques. The model utilizes reflection high-energy electron diffraction videos as input to predict the status of the substrate, enabling automated deoxidation within a controlled architecture for various substrates. Furthermore, we highlight the potential of models trained on data from specific MBE equipment to achieve high-accuracy deployment on different pieces of equipment. In contrast to traditional methods, our approach holds exceptional value, as it standardizes deoxidation temperatures across diverse equipment and substrates. This significantly advances the standardization of the semiconductor process. The concepts and methods presented are expected to revolutionize semiconductor manufacturing processes in the optoelectronic and microelectronic industries.

Online Detection of Laser Welding Penetration Depth Based on Multi-Sensor Features.

The accurate online detection of laser welding penetration depth has been a critical problem to which the industry has paid the most attention. Aiming at the laser welding process of TC4 titanium alloy, a multi-sensor monitoring system that obtained the keyhole/molten pool images and laser-induced plasma spectrum was built. The influences of laser power on the keyhole/molten pool morphologies and plasma thermo-mechanical characteristics were investigated. The results showed that there were significant correlations among the variations of the keyhole-molten pool, plasma spectrum, and penetration depth. The image features and spectral features were extracted by image processing and dimension-reduction methods, respectively. Moreover, several penetration depth prediction models based on single-sensor features and multi-sensor features were established. The mean square error of the neural network model built by multi-sensor features was 0.0162, which was smaller than that of the model built by single-sensor features. The established high-precision model provided a theoretical basis for real-time feedback control of the penetration depth in the laser welding process.

Decoupled magnetic control of spherical tokamak divertors via vacuum harmonic constraints

Power exhaust is a critical challenge for spherical tokamak reactors, making the design, optimisation and control of advanced divertor configurations crucial. These tasks are greatly simplified if the poloidal magnetic fields in the core and divertor regions can be varied independently. We present a novel method which facilitates decoupling of the core plasma equilibrium from the divertor geometry optimisation and control, using vacuum spherical harmonic (SH) constraints. This has the advantage that it avoids iterative solution of the Grad–Shafranov equation, making it easy to use, rapid and reliable. By comparing a large number of MAST-U equilibrium reconstructions against their approximations using SHs, a small number ( ∼4 ) of harmonics is found to be sufficient to closely reproduce the plasma boundary shape. We show experimentally that poloidal field changes designed to leave harmonics unaffected indeed have no effect on the core plasma shape. When augmented with divertor geometry constraints, this approach gives a powerful tool for creating advanced magnetic configurations, and its simplicity brings improvements in speed and robustness when solving coil position optimisation problems. We discuss the clear benefits to real-time feedback control, feed-forward scenario design and coilset optimisation with a view to future reactors.

Stable Optimal Feedback Control for Landers Based on Machine Learning

Stability certification is critical before controllers are rolled out onto real systems. Despite recent progress in the development of neural network systems for feedback-optimal control, enforcement and assessment of the stability of the trained controllers remains an open problem. In this investigation, a comprehensive framework is developed to achieve certifiably stable fuel-optimal feedback control of pinpoint landers in four different formulations of varying complexity. By preconditioning a deep neural network policy and a deep neural network Lyapunov function, and then applying a constrained parameter optimization approach, we are able to address the shape mismatch problem posed by the standard sum-of-squares Lyapunov function and achieve feedback-optimal control. Phase-space plots of the Lyapunov derivative show the level of certificate enforcement achieved by the developed algorithms, and Monte Carlo simulations are performed to demonstrate the stable, optimal, real-time feedback control provided by the policy.