Closed-loop Control Research Articles

Design and experiments of a humanoid torso based on biological features.

Among the components of a humanoid robot, a humanoid torso plays a vital role in supporting a humanoid robot to complete the desired motions. In this paper, a new LARMbot torso is developed for obtaining better working performance based on biological features. Through analyzing the anatomy of a human torso and human spine, a parallel cable-driven is proposed to actuate the whole mechanism by using two servo motors and two pulleys. Analysis are conducted to evaluate the properties of the proposed parallel cable-driven mechanism. A closed-loop control system is applied to control the whole LARMbot torso. Experiments in three modes are conducted by using the manufactured prototype for evaluating the characterizations of the proposed design. Results show that the proposed LARMbot can complete the desired motions. For general bending at different directions,the maximum bending angle is 40 degree, the maximum cable tension is 0.68 N, and the maximum required power is 18.3 W. In full rotation motion, the maximum bending angle is 30 degree, the maximum cable tension is 0.75 N, and the maximum power required is 20.5 W. This design is more simple and lightly, its low energy consumption and flexible spatial motion performance meet the needs of the humanoid robot torso's application in complex scenarios and commercial requirements.&#xD.

Closed-loop ventilation and oxygenation with decision support fluid resuscitation to treat major burn injury with smoke induced acute respiratory distress syndrome.

The understanding of the interaction of closed-loop control of ventilation and oxygenation, specifically fraction of inspired oxygen (FiO2) and positive end-expiratory pressure (PEEP), and fluid resuscitation after burn injury and acute lung injury from smoke inhalation is limited. We compared the effectiveness of FiO2, PEEP, and ventilation adjusted automatically using adaptive support ventilation (ASV) and decision support fluid resuscitation based on urine output in a clinically relevant conscious ovine model of lung injury secondary to combined smoke inhalation and major burn injury. Sheep were subjected to burn and smoke inhalation injury under deep anesthesia and analgesia. After injury, sheep were randomly allocated to two groups. 1) Closed-loop group: automated mechanical ventilation (ASV), oxygen FiO2 and PEEP (n = 9); and 2) Control group: mechanically ventilated with standard ASV mode (n = 8). FiO2, PEEP, and the percentage of the minute volume (%MV) were automatically adjusted in group 1, whereas PEEP was held at 5 cmH2O, and FiO2 and %MV were manually adjusted in group 2. Decision support fluid resuscitation was guided based on urine output. Cardiopulmonary hemodynamics were monitored for 48 h. There were no differences in body weight and the severity of smoke injury between the two groups. The Closed-loop group resulted in significantly higher PEEP, %MV, static lung compliance, and survival rate; the driving pressure was significantly lower compared to the Control group. In the Closed-loop group, the net fluid balance at 48 h was significantly greater than in the Control group. Closed-loop ventilation and oxygenation with decision support fluid resuscitation improve lung mechanics and survival in sheep with combined burn and smoke inhalation. There were no negative interactions observed between automated PEEP control and fluid management.

Research on Lift-type Power Catwalk System Based on Dual Closed-loop Error-driven Active Disturbance Rejection Control

Research on Lift-type Power Catwalk System Based on Dual Closed-loop Error-driven Active Disturbance Rejection Control

Advancing Soft Robot Proprioception Through 6D Strain Sensors Embedding.

Soft robots and bioinspired systems have revolutionized robot design by incorporating flexibility and deformable materials inspired by nature's ingenious designs. Similar to many robotic applications, sensing and perception are paramount to enable soft robots to adeptly navigate the unpredictable real world, ensuring safe interactions with both humans and the environment. Despite recent progress, soft robot sensorization still faces significant challenges due to the virtual infinite degrees of freedom of the system and the need for efficient computational models capable of estimating valuable information from sensor data. In this article, we present a new model-based proprioceptive system for slender soft robots based on strain sensing and a strain-based modeling approach called Geometric Variable-Strain (GVS). We develop a flexible 2-Plate 6D strain sensor (Flex-2P6D) capable of measuring the 6 dimensions (6D) strain at specific points of the soft robot with an accuracy higher than 95%. Coupled with the GVS approach, the proposed methodology is able to directly measure the configuration variables and reconstruct complex robot shapes with very high accuracy, even in very challenging conditions. The sensors are embedded inside the soft body, which makes them also suitable for underwater operation and physical interaction with the environment. Something that we also demonstrate experimentally. We believe that our approach has the potential to be applied across a wide variety of applications, including observation and exploration missions, as well as human-robot interaction, where the states of the system are required for implementing precise closed-loop control and estimation methods.

Real-time control of radiofrequency ablation using three-dimensional ultrasound echo decorrelation imaging in normal and diseasedex vivohuman liver.

Ultrasound echo decorrelation imaging can successfully monitor and control thermal ablation of animal liver and tumor tissue ex vivo and in vivo. However, normal and diseased human liver has substantially different physical properties that affect echo decorrelation. Here, effects of human liver tissue condition on ablation guidance by three-dimensional echo decorrelation imaging are elucidated in experiments testing closed-loop control of radiofrequency ablation (RFA) in normal and diseased human liver tissue ex vivo. 

Approach: Samples of normal, steatotic, and cirrhotic human liver tissue underwent radiofrequency ablation (RFA), targeting a 20 mm-diameter spherical ablation zone. For each tissue condition, RFA was controlled by echo decorrelation in N > 14 trials, automatically ceasing if average cumulative decorrelation within the targeted ablation zone surpassed a predetermined threshold (successfully controlled trials), or otherwise completing a standard ablation cycle of the RFA generator (unsuccessfully controlled). For comparison, N = 14 RFA trials for each tissue condition followed the RFA generator's standard algorithm without echo decorrelation feedback (uncontrolled). Receiver operating characteristic (ROC) and precision-recall curve analyses compared 3D echo decorrelation maps to segmented ablation zones. To assess effects of closed-loop control and liver condition on treatment reliability, ablation volumes, rates, and Dice coefficients for measured vs. targeted ablation zones were statistically compared among control conditions and liver types. 

Results: ROC curves showed effective prediction of local ablation by echo decorrelation across all liver types and control conditions (0.876 ≤ AUROC ≤ 0.953). Successful control was significantly more frequent, ablated volumes were generally larger, and optimal echo decorrelation thresholds were smaller for normal compared to diseased liver. 

Significance: This study validates three-dimensional echo decorrelation imaging for monitoring and control of RFA in healthy and diseased human liver while elucidating the dependence of radiofrequency ablation and echo decorrelation outcomes on liver condition and resulting implications for clinical applications.

Design and study of moving-iron torque motor for two-dimensional proportional servo valve

This paper proposes a novel moving-iron torque motor designed for two-dimensional (2D) valves. The torque motor features a strap spring in place of the traditional return spring, effectively reducing the overall number of springs. The unique design allows the torque motor to return to its circumferential neutral position through the return torque generated by the strap spring. In order to analyze the torque-displacement characteristics of the torque motor, an analytical model is established, and a simulation model is constructed based on the equivalent magnetic circuit method, which takes into account the magnetic leakage at the control coil and the working air gap. In order to select a strap spring that matches the stiffness of the magnetic spring of the torque motor, the structural parameters of the spring are optimized using the response surface method with multi-objective optimization, and finally three springs with different stiffnesses are manufactured according to the optimization results. The experimental results show that the maximum step-up time of the torque motor is 20.6 ms, the maximum step-down time is 21.6 ms, the maximum hysteresis loop is 12.5%, and the maximum linearity is 20%. In order to improve the −3dB cut-off frequency of the torque motor, this paper adopts the current closed-loop control method, and increases the maximum cut-off frequency not exceeding 25 Hz in the open-loop mode to 110 Hz.The hysteresis loop and linearity of the torque motor decrease with the increase of the spring stiffness. The amplitude frequency −3dB cutoff frequency increases with increasing spring stiffness.

Real-time detection of powder bed defects in laser powder bed fusion using deep learning on 3D point clouds

ABSTRACT Powder bed defects are critical factors affecting the print quality and stability in Laser Powder Bed Fusion (LPBF). However, traditional 2D image-based powder bed defect monitoring methods are limited by sensitivity to lighting conditions and insufficient data capture. This study proposes a real-time defect monitoring system based on 3D point cloud data and deep learning approach. The system uses binocular vision to capture point cloud data in real time, enabling high-precision defect segmentation with advanced deep learning models. However, direct deep learning on point clouds can result in the loss of small defect features during downsampling. To address this, an indirect point cloud deep learning method based on 2D projection is introduced, which improves segmentation accuracy for small defects while reducing inference time. By deploying the trained model, this study establishes a closed-loop control system for powder bed defect detection and conducts real-world printing tests, demonstrating effective defect remediation capabilities. Although larger-scale industrial testing is still required, this research illustrates the significant potential of 3D point cloud-based deep learning in enhancing defect detection and quality control in additive manufacturing.

A New Closed-Loop Control Paradigm Based on Process Moments

The paper presents a new control concept based on the process moment instead of the process states or the process output signal. The control scheme is based on separate control of reference tracking and disturbance rejection. The tracking control is achieved by additionally feeding the input of the process model by the scaled output signal of the process model. The advantage of such feedback is that the final state of the process output can be analytically calculated and used for control instead of the actual process output value. The disturbance rejection, including model imperfections, is controlled by feeding back the filtered difference between the process output and the model output to the process input. The performance of tracking and disturbance rejection is simply controlled by two user-defined gains. Several examples have shown that the new control method provides very good and stable tracking and disturbance rejection performance.

Water Treatment Technologies: Development of a Test Bench for Optimizing Flocculation-Thickening Processes in Laboratory Applications

This study introduces an automated test bench designed to optimize flocculation-thickening processes in the wastewater treatment industry. Addressing current challenges in operational efficiency and cost reduction, the test bench employs Model-Based Systems Engineering (MBSE) principles, leveraging SysML modeling within the CESAM framework. By integrating advanced technologies, including PLC programming and a closed-loop control system, this bench provides precise and efficient testing under varying operational conditions. Economic implications are explored, demonstrating the cost-effectiveness of optimized flocculation processes, which reduce chemical use and operational expenditures while enhancing water clarity and sludge management. The system’s 3D modeling enables detailed simulations, aiding in both research and pedagogical applications. This platform highlights the potential of MBSE in creating scalable, robust solutions that contribute to sustainable water management.

Design and experimental study of tillage depth control system for electric rotary tiller based on LADRC

This paper proposes an adaptive real-time tillage depth control system for electric rotary tillers, based on Linear Active Disturbance Rejection Control (LADRC), to improve tillage depth accuracy in tea garden intercropping with soybeans. The tillage depth control system comprises a body posture sensor, a control unit, and a hybrid stepper motor, integrating sensor data to drive the motor and achieve precise depth control. Real-time displacement sensor signals are compared with target values, enabling closed-loop control of the rotary tiller. Field experiments conducted at 0.5 km/h and 0.8 km/h, with preset tillage depths of 80 mm and 100 mm, demonstrated the system’s effectiveness. The average standard deviation of tillage depth for the LADRC system was 3.2 mm, compared to 10.5 mm for fuzzy proportional-integral-derivative (PID) control. The LADRC control reduced the rate of change in tillage depth by 68.9% compared to fuzzy PID. This system effectively mitigates potential deviations during operation, ensuring stable and reliable tillage depth control.

A double closed loop digital hydraulic cylinder position system based on switching active disturbance rejection control

A switching active disturbance rejection control (SADRC) strategy was proposed to solve the composite disturbance challenge arising from gap, LuGre friction, hydraulic spring force, and external load disturbance in the double closed-loop digital hydraulic cylinder position control system. Firstly, leveraging the established mathematical model of the double closed-loop digital hydraulic cylinder, the high-order state equation was derived. Subsequently, the high-order double closed-loop digital hydraulic cylinder control system was transformed into a second-order integral series control system using ADRC strategy. Given the pronounced nonlinear characteristics of double closed-loop digital hydraulic cylinders, combined with the characteristics of switching control, a SADRC strategy was proposed, and the stability of the closed-loop system was proved based on Lyapunov theory and switching rules. Finally, the effectiveness of the proposed control method was verified through simulation and experiment. Results indicate that the system's response speed employing the SADRC strategy outperforms the PID control strategy by 32.56%, with a 2.0% reduction in error. The average error in the response speed of the digital hydraulic cylinder and the MATLAB simulation value of the tracking error are 9.0% and 1.50% respectively. Notably, the simulation and experimental results exhibit a consistent overall trend, affirming the feasibility and effectiveness of the control strategy.

Neural-Network-Based Incremental Backstepping Sliding Mode Control for Flying-Wing Aircraft

The nonlinear trajectory tracking control problem is studied for a flying-wing aircraft. Starting from a nonlinear dynamics model of the flying-wing aircraft, the trajectory tracking control is decomposed into multiple loops of position control, flight path control, and attitude control. An incremental backstepping sliding mode control is proposed to implement attitude control, while an incremental nonlinear dynamic inversion and a nonlinear dynamic inversion design are used to deal with the nonlinear system model for the flight path and position control, respectively. In addition, a radial basis function neural-network-based extended state disturbance observer is proposed to deal with model uncertainties, gust disturbances, and unknown faults of the aircraft. The closed-loop control system is proved to be stable using Lyapunov theory. The performance of the proposed disturbance-observer-based incremental backstepping sliding mode control is demonstrated in simulation through a set of three-dimensional tracking scenarios. Compared with both backstepping control and backstepping sliding mode control, tracking performance measured by settling time, tracking error, and overshoot are improved by the proposed design when realistic trajectory tracking missions are executed.

External low energy electromagnetic fields affect heart dynamics: surrogate for system synchronization, chaos control and cancer patient's health.

All cells in the human body, including cancer cells, possess specific electrical properties crucial for their functions. These properties are notably different between normal and cancerous cells. Cancer cells are characterized by autonomous oscillations and damped electromagnetic field (EMF) activation. Cancer reduces physiological variability, implying a systemic disconnection that desynchronizes bodily systems and their inherent random processes. The dynamics of heart rate, in this context, could reflect global physiological network instability in the sense of entrainment. Using a medical device that employs an active closed-loop system, such as administering specifically modulated EMF frequencies at targeted intervals and at low energies, we can evaluate the periodic oscillations of the heart. This procedure serves as a closed-loop control mechanism leading to a temporary alteration in plasma membrane ionic flow and the heart's periodic oscillation dynamics. The understanding of this phenomenon is supported by computer simulations of a mathematical model, which are validated by experimental data. Heart dynamics can be quantified using difference logistic equations, and it correlates with improved overall survival rates in cancer patients.

Fractional-order Sliding Mode Control of Manipulator Combined with Disturbance and State Observer

A fractional-order sliding mode control (FSMC) method for a manipulator based on disturbance and state observers is proposed. First, a state estimator is designed that can estimate the velocity and acceleration, and only joint position feedback and the mathematical model of the manipulator are needed. The state estimator converges in finite time. Then, the disturbance observer is designed. By designing the nominal system model of the manipulator, a disturbance observation error is introduced into the closed-loop control so that the performance of the manipulator can track the nominal system. Finally, a sliding mode controller (SMC) based on the fractional differential operator theory is also designed. The value of the sliding mode variable in the derivation of the controller is composed of the fractional derivative of the trajectory tracking error of the manipulator, whereas the fractional differentiation operation uses integration in its realization, and the integration is a low-pass filter, thus, high-frequency noise is suppressed. In the experimental section, the method designed is compared with the conventional sliding mode, which further reveals the rapidity and control accuracy of FSMC.

E2E Network Slice Assurance for B5G/6G: Realizing Data Collection and Management, MLOps, and Closed-Loop Control

E2E Network Slice Assurance for B5G/6G: Realizing Data Collection and Management, MLOps, and Closed-Loop Control

In-situ controller autotuning by Bayesian optimization for closed-loop feedback control of laser powder bed fusion process

In-situ controller autotuning by Bayesian optimization for closed-loop feedback control of laser powder bed fusion process

Data model-based sensor fault diagnosis algorithm for closed-loop control systems

Data model-based sensor fault diagnosis algorithm for closed-loop control systems

The Active Gate Driver Based on Hardware Closed-Loop Control for Crosstalk Suppression of SiC MOSFETs With Kelvin-Source Connection

The Active Gate Driver Based on Hardware Closed-Loop Control for Crosstalk Suppression of SiC MOSFETs With Kelvin-Source Connection

A brain-computer interface system for lower-limb exoskeletons based on motor imagery and stacked ensemble approach.

Existing lower limb exoskeletons (LLEs) have demonstrated a lack of sufficient patient involvement during rehabilitation training. To address this issue and better incorporate the patient's motion intentions, this paper proposes an online brain-computer interface (BCI) system for LLE based motor imagery and stacked ensemble. The establishment of this online BCI system enables a comprehensive closed-loop control process, which includes the collection and decoding of brain signals, robotic control, and real-time feedback mechanisms. Additionally, an online experimental protocol that integrates visual and proprioceptive feedback is developed. To enhance decoding precision, we proposed a novel classification algorithm based on the stacking technique, termed weighted random forests-support vector machines (WRF-SVM). In this algorithm, WRFs function as the base learning models, while SVMs act as the meta-learning layer. To assess the efficacy of the BCI system and the classification algorithm, eight subjects were recruited for testing. The outcomes of both online and offline experiments exhibit high classification accuracy, confirming the viability and utility of the BCI system. We are confident that our approach holds significant promise for practical applications in the field of LLE technology.

Model-based prototype design, construction and operation of closed-loop control system of solid waste treatment unit

Model-based prototype design, construction and operation of closed-loop control system of solid waste treatment unit