Closed-loop Method Research Articles

Research on Pose Error Modeling and Compensation of Posture Adjustment Mechanism Based on WOA-RBF Neural Network

This paper is aimed to address the issue of decreased accuracy in the ship block docking caused by the structural errors of posture adjustment mechanism. First, inverse kinematic analysis is performed to investigate the sources of static errors in the mechanism. Subsequently, based on the closed-loop vector method, a pose error model for the moving platform is established, which includes eight categories of error terms. The impact of various structural errors on the pose accuracy of the moving platform is then compared and analyzed under both single-limb and multi-limb configurations. Therefore, a compensation method based on the whale optimization algorithm optimized radial basis function neural network is proposed. By transforming pose errors into actuator length errors, it establishes a predictive model between the theoretical pose of the dynamic platform and actuator length errors. After optimizing the network parameters, it yields the actuator length compensation to correct the actual pose of the dynamic platform. Simulation and experimental results validate the effectiveness of this method in enhancing the motion accuracy of the parallel mechanism. The mean pose accuracy of the moving platform is improved by 85.07%, demonstrating a significant compensation effect.

A closed-loop sustainable method for the fabrication of electrospun nanofiber using food waste for filtration of particulate matters (PMs) and volatile organic matter (VOCs) from air

Indoor air pollution significantly impacts human health and the environment. Humans suffer the greatest health burden due to inefficient combustion technologies and polluted fuels. Therefore, air filtration is essential for a healthy and prosperous household. This study aims to develop a sustainable approach for the fabrication of biodegradable electrospun nanofibers (ENs) as air filters from food waste materials and offer a closed-loop circular solution and potential replacement for non-biodegradable HEPA air filters. We have tested banana peel extract (BPE), eggshell membrane, chitosan, and starch blended with polyvinyl alcohol (PVA) for fabricating ENs. Among the nanofibers investigated for air filtration application, BPE/PVA nanofiber shows the best performance in terms of maximum particulate matter (PM) removal efficiency and minimum pressure drop while incorporating > 50 % of the food waste into the electrospinning solution. Adding BPE into a 10 % PVA solution leads to a reduction in the hydrophilic behavior of the BPE/PVA nanofiber as compared to neat 10 % PVA ENs. The BPE/PVA nanofiber shows minimum pressure drop (156–160 Pa) and highest quality factor (Qf) in comparison with other waste-derived nanofibers with > 98 % filtration efficiency of PM2.5. Further, BPE/PVA nanofiber shows the highest adsorption of xylene vapor among other food waste derived nanofibers studied. Based on the above results, it can be concluded that BPE based nanofiber shows the best performance as air filter media among all other food waste-based ENs. The study demonstrates a successful regeneration and reuse of the BPE/PVA filter up to 3 cycles. The leftover material from banana peels after BPE extraction was utilized as a soil ameliorant. It showed 55 % soil water retention (SWR) capacity, making it possible to apply in arid regions or crops with high water requirements.

Closed-loop continuous-time Laguerre-based subspace identification via orthogonal projection approach

This paper presents a closed-loop Laguerre-based subspace identification method for continuous-time systems via orthogonal projection approach. A bank of Laguerre filters operating in the all-pass domain, which is capable of handling the non-equidistant data, is used to get the input–output matrix equation of the systems. We put the emphasis on the parity space opposed to the observable subspace. The instrumental variable method and principal component analysis estimate the system consistently, solving the issue of biased estimate for the closed-loop operation of the system caused by the feedback controller. The simulation results demonstrate the effectiveness of the proposed approach.

Kinematic calibration of a 5-DOF double-driven parallel mechanism with sub-closed loop on limbs

Abstract This paper proposes a kinematic calibration method of a novel 5-degree-of-freedom double-driven parallel mechanism with the sub-closed loop on limbs. At first, considering the introduction of a sub-closed loop significantly increased the complexity and difficulty of kinematic error modeling, an equivalent transformation method is proposed for the limb with a sub-closed loop. Then kinematic error model of the parallel mechanism is established based on the closed-loop vector method and parasitic motion analysis, which is verified by virtual prototype technology. Because the full kinematic error model is generally redundant, error parameter identifiability analysis is carried out by QR decomposition of the identification Jacobian matrix, and the redundant parameters are removed. Additionally, the Sequence Forward Floating Search algorithm is utilized to optimize measurement configurations to reduce the influence of measurement noise. Finally, with a laser tracker as the measuring device, numerical simulations and experiments are implemented to verify the proposed kinematic calibration method. The experiment results show that average position and orientation errors are reduced from 2.778 mm and 1.115° to 0.263 mm and 0.176°, respectively, within the prescribed workspace.

A stepwise parameter identification strategy for probe-based robot on-machine measurement system calibration based on sensitivity analysis

The probe-based measurement presents an economical and efficient on-machine measurement (OMM) method. However, the low position accuracy of robot presents significant challenges for probe-based robot OMM. To improve the measurement accuracy of robot OMM system, this paper calibrates the geometric parameters of robot OMM system through a closed-loop calibration method with sphere constraint. First, the geometric error model for robot OMM system is established. Given the difficulty in model parameter identification, this paper defines a new index, the relative distance sensitivity index (RDSI), to reveal the contribution of model parameter to relative distance accuracy. Subsequently, sensitivity analysis is conducted on model parameters based on RDSI and model parameters are grouped according to the analysis results. In parameter identification, parameters are identified step by step according to the grouping results. Experiment results show that after calibration, the max distance measurement error of robot OMM system is reduced from 0.54 mm to 0.12 mm.

Type synthesis of a new parallel mechanism with complete separation of constraints and drives

Abstract Currently, there is an increasing demand for parallel mechanisms with fewer inputs and more outputs in aerospace, antennas, and recreational facilities. Therefore, this paper proposes a new parallel mechanism (PM) with a separation of constraints and drives, facilitating centralized drive management. Based on screw theory, the type synthesis of new parallel mechanisms with 3T (T representing translation), 3R (R representing revolute), 2T1R, and 2R1T is proposed. Three-degree-of-freedom (3-DOF) parallel mechanisms with fewer inputs and multiple outputs are proposed. Taking the 3RPS-PPPS parallel mechanism as an example, the closed-loop vector method is employed to solve for the lengths of actuated branches. A complete Jacobi matrix is constructed. The motion/force transmission performance of the 3RRS/PPPS parallel mechanism is better than the 3RRS parallel mechanism. Centralized management of drive branches greatly reduces the number of drives, making maintenance and repair easier. It has many application scenarios in antennas, recreational facilities, and other occasions.

Comparative analysis of the impedances of key radio engineering devices with PWM based on a linear model and a closed loop method

The problem of comparative analysis of the impedances of switching mode devices with negative feedback by two methods is considered. The use of a linear injection model is justified, on the basis of which analytical expressions of impedances in Laplace form are obtained. The results of numerical modeling and spectral analysis of processes in a closed dynamic system with PWM are presented, confirming the correctness of the analytical model in the frequency range not exceeding half the clock frequency.

A double-blind trial of decoded neurofeedback intervention for specific phobias.

A new closed-loop functional magnetic resonance imaging method called multivoxel neuroreinforcement has the potential to alleviate the subjective aversiveness of exposure-based interventions by directly inducing phobic representations in the brain, outside of conscious awareness. The current study seeks to test this method as an intervention for specific phobia. In a randomized, double-blind, controlled single-university trial, individuals diagnosed with at least two (one target, one control) animal subtype-specific phobias were randomly assigned (1:1:1) to receive one, three, or five sessions of multivoxel neuroreinforcement in which they were rewarded for implicit activation of a target animal representation. Amygdala response to phobic stimuli was assessed by study staff blind to target and control animal assignments. Pretreatment to posttreatment differences were analyzed with a two-way repeated-measures anova. A total of 23 participants (69.6% female) were randomized to receive one (n = 8), three (n = 7), or five (n = 7) sessions of multivoxel neuroreinforcement. Eighteen (n = 6 each group) participants were analyzed for our primary outcome. After neuroreinforcement, we observed an interaction indicating a significant decrease in amygdala response for the target phobia but not the control phobia. No adverse events or dropouts were reported as a result of the intervention. Results suggest that multivoxel neuroreinforcement can specifically reduce threat signatures in specific phobia. Consequently, this intervention may complement conventional psychotherapy approaches with a nondistressing experience for patients seeking treatment. This trial sets the stage for a larger randomized clinical trial to replicate these results and examine the effects on real-life exposure. The now-closed trial was prospectively registered at ClinicalTrials.gov with ID NCT03655262.

Kinematic synthesis and mechanism design of a six-bar jumping leg for elastic energy storage and release based on dead points

Small jumping robots widely adopt complex catapult mechanisms. This paper presents a novel jumping strategy using dead point instead of traditional catapult mechanisms, achieving efficient energy storage and release without increasing mechanical complexity. Single degree-of-freedom (DOF) planar six-bar linkages are widely used in bionic mechanism design due to their simple control and strong design flexibility. However, their complex configuration and numerous parameters make it challenging to carry out multi-objective and multi-constraint designs. In this paper, a design method of single DOF six-bar linkages based on dead-point constraints is proposed to design a frog-inspired leg mechanism. By enumerating the basic configuration atlas and using a stepwise closed-loop method, initial value screening is completed to improve the efficiency of objective function optimization. The dead-point constraints are simplified with graphical geometric properties. The resulting mechanism satisfies multiple objectives and constraints, including shape, motion posture and trajectory, demonstrating the feasibility of the method. Simulations and experiments confirmed the excellent jumping performance of the 147.1-g prototype, with a jump height of 8.55 times leg length and an energy-storing capacity of 35.39 J/kg.

Closed-loop state of charge estimation of Li-ion batteries based on deep learning and robust adaptive Kalman filter

The state of charge (SOC) is among the most crucial monitoring states in battery management systems. To accurately and robustly estimate the battery SOC, a closed-loop SOC estimation method based on deep neural networks is proposed in this study. Initially, a hybrid deep learning model, TGMA, is established by combining a temporal convolutional network (TCN) with a gated recurrent unit (GRU) and incorporating a multi-head self-attention mechanism (MHA) to estimate SOC. Subsequently, to further enhance the accuracy of estimating the battery SOC and the ability to resist outliers, the pre-estimated SOC from the deep learning model TGMA is used as the noisy observation in the Kalman filter, and robust factor and noise adaptive filtering are introduced into the Kalman filter, proposing a closed-loop estimation method based on deep learning and robust adaptive Kalman filter (RAKF). Finally, through comparison with other similar methods, the proposed TGMA-RAKF method is validated to have higher accuracy, generalization, and robustness against outliers under different operating conditions and temperatures, with root mean square error, mean absolute error, and maximum absolute error all less than 1 %, 1 %, and 2.5 % respectively.

Multi-scale closed-loop coupled real-time water quantity optimization scheduling of cascade pumping station in water supply canal systems

Closed-loop control is an effective control method to deal with uncertain disturbance in water supply canal system (WSCS). However, previous researches mainly focus on closed-loop control with a single time scale, and it is difficult to solve the uncertainty problem with a long time dimension. In this paper, a multi-scale closed-loop coupled real-time scheduling framework is established and solved by considering both large and small scale closed-loop scheduling models, so as to improve the performance of real-time scheduling in a longer time dimension under uncertain perturbations. The framework is applied to the virtual simulation test of the Xuhong River WSCS of the east route of the South-to-North Water Diversion project under different water distribution disturbances. The results show that the closed-loop control method can update the system status regularly through rolling optimization, and effectively reduce the prediction error of water level and energy consumption. In the two water distribution disturbance scenarios, the proposed method reduces the average relative deviation of water supply plan execution by 3.4 % and 5.9 %, and the unit energy consumption of cascade pumping station operation by 1.7 kWh/104m3 and 1.5 kWh/104m3, respectively, compared with the single-scale closed-loop scheduling method. In addition, the proposed method can effectively deal with the uncertainty of water distribution disturbance in limited computing time, and can meet the requirements of real-time scheduling in a long time dimension under uncertain disturbance.

A double-blind trial of decoded neurofeedback intervention for specific phobias.

A new closed-loop fMRI method called multi-voxel neuro-reinforcement has the potential to alleviate the subjective aversiveness of exposure-based interventions by directly inducing phobic representations in the brain, outside of conscious awareness. The current study seeks to test this method as an intervention for specific phobia. In a randomized, double-blind, controlled single-university trial, individuals diagnosed with at least two (1 target, 1 control) animal subtype specific phobias were randomly assigned (1:1:1) to receive 1, 3, or 5 sessions of multi-voxel neuro-reinforcement in which they were rewarded for implicit activation of a target animal representation. Amygdala response to phobic stimuli was assessed by study staff blind to target and control animal assignments. Pre-treatment to post-treatment differences were analyzed with a 2-way repeated-measures ANOVA. A total of 23 participants (69.6% female) were randomized to receive 1 (n=8), 3 (n=7), or 5 (n=7) sessions of multi-voxel neuro-reinforcement. Eighteen (n=6 each group) participants were analyzed for our primary outcome. After neuro-reinforcement, we observed an interaction indicating a significant decrease in amygdala response for the target phobia but not the control phobia. No adverse events or dropouts were reported as a result of the intervention. Results suggest multi-voxel neuro-reinforcement can specifically reduce threat signatures in specific phobia. Consequently, this intervention may complement conventional psychotherapy approaches with a non-distressing experience for patients seeking treatment. This trial sets the stage for a larger randomized clinical trial to replicate these results and examine the effects on real-life exposure. The now-closed trial was prospectively registered at ClinicalTrials.gov with ID NCT03655262.

Endoatmospheric powered descent guidance

This paper addresses the problem of guiding a rocket-powered vehicle to land using aerodynamic and thrust controls. The vehicle initially glides towards the landing site, flying at an unprecedentedly high angle-of-attack to decelerate, thereby reducing the propellant needed for a later retro-thrust landing. The high angle-of-attack pure aerodynamic flight leads to highly nonlinear dynamics that have not been addressed by existing powered descent guidance methods. In particular, real-time optimization of the vehicle’s landing trajectory has been very challenging. In this paper, a closed-loop guidance method is proposed that optimizes a restricted 6-degrees-of-freedom (6-DoF) trajectory in real-time. To remedy the difficulty caused by the highly nonlinear dynamics, the general propellant-optimal problem is regularized with two modifications. The first is to augment the angular velocity to the performance index, removing possible singularity arcs that cause numerical difficulties. The second is to parameterize the thrust magnitude with a piecewise-constant form, reducing the number of control variables to be optimized. The regularized problem is then solved with successive convexification to update the restricted 6-DoF landing trajectory in each guidance cycle. Monte Carlo experiments with aerodynamic and thrust dispersions are conducted to examine the proposed guidance method.

Evaluation of accurate measurement methods for radon exhalation rate with advection effect and application in field

In decommissioning of a uranium tailings pond, radon exhalation rates on a beach surface should meet regulatory standards. Accurate measurements of the radon exhalation rate are demanded. However, current studies fail to consider the impact of advection under temperature variations or pressure gradients caused by gas movement on measurements using an accumulation chamber. Two proposed methods were therefore evaluated to accurately measure radon exhalation rates on the loose medium surface under advective conditions. Repeated experiments were conducted on a laboratory experimental platform filled with uranium tailings sand under advective flow rates of 0.03 and 0.3 L/min to validate the stability and reliability. Deviations between measured and true values were 0.1–6.1 % and 6.3–29.2 % for the two methods, respectively. Subsequently, numerical simulation was used to analyze defects of traditional methods and mechanisms of the new methods. In a field study, all methods were compared, and a predictive map of radon exhalation rates was created using interpolated data from 20 random sites using the new method. Results from the proposed methods, compared with traditional ones, were closer to true values under advective conditions, and accurate assessment of beach surface treatment was expected.

A planar bisection strategy for multipoint path synthesis of four-bar linkages

In order to improve the global search ability of the optimization algorithm when solving the path synthesis problem, this paper proposes a synthesis method of a four-bar mechanism based on the bisection strategy of the Lévy-PSO evolutionary algorithm. First, a synthesis equation of the target point is established based on the closed-loop vector method. Use the bisection strategy to cut the hinge coordinate variation range to reduce the probability of premature maturity of the algorithm. The error algorithm of the circular proximity function is used to reduce the search dimension of the optimization algorithm and improve search efficiency. Use the improved particle swarm algorithm based on Lévy to improve global search capabilities. Four multi-point path synthesis problem examples are given to verify the effectiveness and accuracy of the method.

A novel multi-attention reinforcement learning for the scheduling of unmanned shipment vessels (USV) in automated container terminals

To improve the operating efficiency of container terminals, we investigate a closed-loop scheduling method in an autonomous inter-terminal system that employs unmanned shipment vessels (USVs) to transport containers among operational berths (Dedicated to USVs) in seaport terminals. Our USVs scheduling model is developed by considering energy replenishment, time windows, and berth restrictions, aiming to obtain cost-saving USV transportation solutions and conflict-free paths. To solve this optimization model more efficiently, we propose the multi-attention reinforcement learning (MARL) algorithm by integrating an encoder-decoder framework and an unsupervised auxiliary network. The MARL algorithm provides instant problem-solving capabilities and benefits from extensive offline training. Experimental results demonstrate that our method can obtain efficient solutions for our USVs scheduling problem, and our algorithm outperforms other compared algorithms on computing time and solution accuracy.

High precision identification of dynamic model for accelerometer based on VMD

The dynamic model of accelerometer reflects characteristics of dynamic output changes over time, directly affecting dynamic measurement performance. To solve the problem of low identification accuracy due to pure delay, nonlinearity, and noise, a high-precision closed-loop identification method for dynamic model based on variational mode decomposition (VMD) is proposed. In particular, emphasis is placed on highlighting the issue that equivalent stiffness parameter is more susceptible to noise due to its low parameter sensitivity. Additionally, to address the effects of pure delay and nonlinearity on model identification, a dynamic model of accelerometer containing both is proposed. In response to the impact of noise on model identification, it is proposed to use VMD for noise separation and data reconstruction. Finally, a test platform is established for validation. And experimental results indicate that the model identified using the proposed method shows an increase in model matching degree (MMD) from 90% to 98.8%.

Non-excitation closed-loop identification based on hysteresis bias relay feedback

This paper proposes a closed-loop system identification method based on hysteresis loop bias relay feedback without using excitation. The method utilizes hysteresis loop bias relay instead of a controller to ensure the informativity of data without external excitation and with only noisy feedback. Additionally, this paper proposes a parameter scheme for the hysteresis loop bias relay to guide the selection of parameters more rationally, thus improving the accuracy of the identification model. Compared to recently proposed switching controller methods, the advantage of this method lies in the fact that it requires fewer samples and shortens data collection time. The method's effectiveness is verified through simulation comparisons with typical methods.

Multi-robot multi-station welding flow shop closed-loop rescheduling with deep reinforcement learning and improved artificial bee colony algorithm

Multi-robot multi-station manufacturing system has gained significant popularity in welding workshop for processing large jobs. The implementation of cooperative welding robots effectively enhances the efficiency and flexibility of the system. However, robot failures also bring challenges for rescheduling to determine the rescheduling timing and re-assign appropriate robots for the affected processes so as to minimize the losses in performance and the maximum completion time. Distinct from open-loop rescheduling approach, this paper proposes a closed-loop rescheduling method via deep reinforcement learning and improved artificial bee colony algorithm. This method addresses two crucial questions: when to trigger a rescheduling, and how to provide a rescheduling scheme. For the first issue, current methods make decisions according to indicators derived from expert experience, failing to adapt to the dynamic environment. Therefore, we propose a deep reinforcement learning method to learn the adaptive trigger policy for the trigger agent under real-time running state. For the second issue, we present an improved artificial bee colony algorithm to re-assign robots without changing processing sequence and feed the performance back to trigger agent. Specially, a variable local search method is incorporated to avoid falling into local optimum. Finally, the proposed method is applied to solve the instances with different scales and a practical case. The experimental results of both demonstrate that the proposed closed-loop rescheduling method can greatly reduce the deviation of the total completion time and the maximum completion time and have superior performance over other algorithms.

A closed-loop calibration method for serial robots based on position constraints and local area measurement

This paper presents a cost-effective and efficient closed-loop calibration method to enhance the absolute positioning accuracy of industrial robots. The method utilizes an economical and highly accurate set of simple measurement devices, including a device mounted on the robot's end effector and a spherical constraint device positioned within the robot's workspace. A novel local area measurement method is employed for the spherical constraint device, effectively converting position errors into the errors in the sphere center position of the constraint device. Furthermore, the error model considers both kinematic parameter deviations of the robot itself as well as those of the measurement device, along with non-kinematic errors caused by link self-weight. The closed-loop calibration model relies on position constraints, enabling identification and compensation of all error parameters using Levenberg Marquardt method after substituting measured data. The effectiveness of this proposed method is demonstrated through simulation and experimentation. In simulations, average position error reduces from 8.249 mm to 0.8384 mm, while absolute mean difference in sphere center positions obtained using our measurement equipment decreases from 1.206 mm to 0.1027 mm. Significant reductions in both position error and difference in sphere center position are indicated after calibration. This proves that the absolute mean value of the sphere center position difference can be used as the basis for judging the size of the robot's end position error. Experimental results show that absolute mean difference in sphere center position decreases from 0.4319 mm to 0.02869 mm, leading to substantial improvement in overall positioning accuracy.