Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking proce...Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking process often encounters challenges in learning efficiency and generalization.To address this issue,this paper designs a deep deterministic policy gradient(DDPG)-based reinforcement learning strategy that integrates imitation learning and feedforward exploration in the path following process.In imitation learning,the path tracking control data generated by the model predictive control(MPC)method is used to train an end-to-end steering control model of a deep neural network.Another feedforward exploration behavior is predicted by road curvature and vehicle speed,and adds it and imitation learning to the DDPG reinforcement learning to obtain decision-making experience and action prediction behavior of the path tracking process.In the reinforcement learning process,imitation learning is used to update the pre-training parameters of the actor network,and a feedforward steering technique with random noise is adopted for strategy exploration.In the reward function,a hierarchical progressive reward form and a constrained objective reward function referring to MPC are designed,and the actor-critic network architecture is determined.Finally,the path tracking performance of the designed method is verified by comparing various training results,simulations,and HIL tests.The results show that the designed method can effectively utilize pre-training and feedforward prior experience to obtain optimal path tracking performance of an autonomous vehicle,and has better generalization ability than other methods.This study provides an efficient control scheme for improving the end-to-end control performance of autonomous vehicles.展开更多
In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the l...In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.展开更多
The acquisition,tracking,and pointing(ATP)system is widely used in target tracking,counter-UAV operations,and other related fields.As UAV technology develops,there is a growing demand to enhance the tracking capabilit...The acquisition,tracking,and pointing(ATP)system is widely used in target tracking,counter-UAV operations,and other related fields.As UAV technology develops,there is a growing demand to enhance the tracking capabilities of ATP systems.However,in practical applications,ATP systems face various design constraints and functional limitations,making it infeasible to indefinitely improve hardware performance to meet tracking requirements.As a result,tracking algorithms are required to execute increasingly complex tasks.This study introduces a multi-rate feedforward predictive controller to address issues such as low image feedback frequency and significant delays in ATP systems,which lead to tracking jitter,poor tracking performance,low precision,and target loss.At the same time,the pro-posed approach aims to improve the tracking capabilities of ATP systems for high-speed and highly maneuverable targets under conditions of low sampling feedback rates and high feedback delays.The method suggested is also characterized by its low order,fast response,and robustness to model parameter variations.In this study,an actual ATP system is built for target tracking test,and the proposed algorithm is fully validated in terms of simulation and actual system application verification.Results from both simulations and experiments demonstrate that the method effectively compensates for delays and low sampling rates.For targets with relative angular velocities ranging from 0 to 90°/s and angular accelerations between 0 and 470°/s^(2),the system improved tracking accuracy by 70.0%-89.9%at a sampling frequency of 50 Hz and a delay of 30 m s.Moreover,the compensation algorithm demonstrated consistent performance across actuators with varying characteristics,further confirming its robustness to model insensitivity.In summary,the proposed algorithm considerably enhances the tracking accuracy and capability of ATP systems for high-speed and highly maneuverable targets,reducing the probability of target loss from high speed.This approach offers a practical solution for future multi-target tracking across diverse operational scenarios.展开更多
The precise measurement of the antineutrino spectra produced by isotope fission in reactors is of great significance for studying neutrino oscillations,refining nuclear databases,and addressing the reactor antineutrin...The precise measurement of the antineutrino spectra produced by isotope fission in reactors is of great significance for studying neutrino oscillations,refining nuclear databases,and addressing the reactor antineutrino anomaly.In this paper,we report a method that utilizes a feedforward neural network(FNN)model to decompose the prompt energy spectrum observed in a short-baseline reactor neutrino experiment and extract the antineutrino spectra produced by the fission of major isotopes such as^(235)U,^(238)U,^(239)Pu,and^(241)Pu in the nuclear reactor.We present two training strategies for the model and compare them with the traditional X^(2) minimization method by applying them to the same set of pseudo-data corresponding to a total exposure of(2.9×5×1800)GW_(th)·tons·days.The results show that the FNN model not only converges faster and better during the fitting process but also achieves relative errors of less than 1%in the 2−8 MeV range in the extracted spectra,outperforming the X^(2) minimization method.The feasibility and superiority of this method were validated in the study.展开更多
This paper studies global stabilization via predictor-based sampled-data output feedback for a class of feedforward nonlinear time-delay systems.Note that the traditional sampled-data observer via zero-order holder ma...This paper studies global stabilization via predictor-based sampled-data output feedback for a class of feedforward nonlinear time-delay systems.Note that the traditional sampled-data observer via zero-order holder may result in the performance degradation of the observer.In this paper,an improved predictor-based observer is designed to compensate for the influence of the unmeasurable states,sampling errors and output delay.In addition,a sampled-data output-feedback controller is also constructed using the gain scaling technique.By the Lyapunov-Krasovskii functional method,the global exponential stability of the resulting closed-loop system can be guaranteed under some sufficient conditions.The simulation results are provided to demonstrate the main results.展开更多
Feedforward control is one of the most effective control techniques to increase the robot’s tracking accuracy.However,most of the dynamic models used in the feedforward controllers are linearly simplified such that t...Feedforward control is one of the most effective control techniques to increase the robot’s tracking accuracy.However,most of the dynamic models used in the feedforward controllers are linearly simplified such that the nonlinear and time-varying characteristics of dynamics in the workspace are ignored.In this paper,an iterative tuning method for feedforward control of parallel manipulators by taking nonlinear dynamics into account is proposed.Based on the robot rigid-body dynamic model,a feedforward controller considering the dynamic nonlinearity is presented.An iterative tuning method is given to iteratively update the feedforward controller by minimizing the root mean square(RMS)of the joint errors at each cycle.The effectiveness and extrapolation capability of the proposed method are validated through the experiments on a 2-DOF parallel manipulator.This research proposes an iterative tuning method for feedforward control of parallel manipulators considering nonlinear dynamics,which has better extrapolation capability in the whole workspace of manipulators.展开更多
Under the condition of large inertia load,the stability of the servo system is more sensitive to the response speed and more likely to produce overshoot oscillations.In order to realize the requirements of high-precis...Under the condition of large inertia load,the stability of the servo system is more sensitive to the response speed and more likely to produce overshoot oscillations.In order to realize the requirements of high-precision and fast-response control of permanent magnet synchronous motor(PMSM)under large inertia load,an improved feedforward control strategy based on position impulse compensation and PD iterative algorithm is proposed to improve the response speed of the PMSM servo system and reduce the overshoot oscillation.This paper analyzes the mathematical models of the speed servo system and position servo system of the PMSM,calculates position overshoot impulse of the PMSM servo system,and improves the traditional feedforward control strategy to reversely compensate when the position is about to overshoot.Moreover,in order to further reduce the position overshoot,the PD iterative control algorithm is superimposed without increasing the complexity of the algorithm.The input signal is continuously corrected through multiple runs to achieve a smoother response control.The effectiveness of the proposed feedforward control strategy is verified by simulation and experiment.展开更多
The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete(RAC)as an eco-friendly alternative to conventional concrete.However,predicting its compressive st...The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete(RAC)as an eco-friendly alternative to conventional concrete.However,predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters.This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks(FFNN),Random Forest(RF),and XGBoost.A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module:Bayesian Regularization,Levenberg-Marquardt,and three conjugate gradient variants—Powell/Beale Restarts,Fletcher-Powell,and Polak-Ribiere.Hyperparameter tuning,dropout regularization,and early stopping were employed to enhance generalization.Comparative analysis revealed that FFNN outperformed RF and XGBoost,achieving an R2 of 0.9669.To ensure interpretability,accumulated local effects(ALE)along with partial dependence plots(PDP)were utilized.This revealed trends consistent with the pre-existent domain knowledge.This allows estimation of strength from the properties of the mix without extensive lab testing,permitting designers to track the performance and sustainability trends in concrete mix designs while promoting responsible construction and demolition waste utilization.展开更多
Aeropropulsion System Test Facility (ASTF) is required to accurately control the pressure and temperature of the airflow to test the performance of the aero-engine. However, the control accuracy of ASTF is significant...Aeropropulsion System Test Facility (ASTF) is required to accurately control the pressure and temperature of the airflow to test the performance of the aero-engine. However, the control accuracy of ASTF is significantly affected by the flow disturbance caused by aero-engine acceleration and deceleration. This would reduce the credibility of ASTF’s test results for the aero-engine. Therefore, first, this paper proposes a feedforward compensation-based L1 adaptive control method for ASTF to address this problem. The baseline controller is first designed based on ideal uncoupled closed-loop dynamics to achieve dynamic decoupling. Then, L1 adaptive control is adopted to deal with various uncertainties and ensure good control performance. To further enhance the anti-disturbance performance, a feedforward strategy based on disturbance prediction is designed in the L1 adaptive control framework to compensate for the unmatched flow disturbance, which cannot be measured directly. In addition, this strategy takes into account the effects of actuator dynamics. With this method, the feedforward term can be determined from the nominal model parameters despite uncertainties. Finally, to demonstrate the effectiveness of the proposed method, various comparative experiments are performed on a hardware-in-the-loop system of ASTF. The experimental results show that the proposed method possesses excellent tracking performance, anti-disturbance performance and robustness.展开更多
Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dyn...Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.展开更多
The conventional feedforward hybrid active noise control(FFHANC)system combines the advantages of the feedforward narrowband active noise control(FFNANC)system and the feedforward broadband active noise control(FFBANC...The conventional feedforward hybrid active noise control(FFHANC)system combines the advantages of the feedforward narrowband active noise control(FFNANC)system and the feedforward broadband active noise control(FFBANC)system.To enhance its adaptive adjustment capability under frequency mismatch(FM)conditions,this paper introduces a narrowband frequency adaptive estimation module into the conventional FFHANC system.This module integrates an autoregressive(AR)model and a linear cascaded adaptive notch filter(LCANF),enabling accurate reference signal frequency estimation even under significant FM.Furthermore,in order to improve the coherence between narrowband and broadband components in the system’s error signal and its corresponding control filter for the conventional FFHANC system,this paper proposes an algorithm based on autoregressive bandpass filter bank(AR-BPFB)for error separation.Simulation results demonstrate that the proposed FFHANC system maintains robust performance under high FM conditions and effectively suppresses hybrid-band noise.The AR-BPFB algorithm significantly elevates the convergence speed of the FFHANC system.展开更多
This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-...This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller(HCC).The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed,electromagnetic torque,and stator current.Two case studies,one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation,has been validated to show the effectiveness of the proposed control strategy.The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.展开更多
This paper proposes a neural network-based intelligent feedforward gust alleviation framework,which includes a neural network identification model and a neural network controller.A neural network training dataset is f...This paper proposes a neural network-based intelligent feedforward gust alleviation framework,which includes a neural network identification model and a neural network controller.A neural network training dataset is formed by collecting flight data and the gust data encountered during the aircraft flight.A neural network identification model is first trained to accurately predict the aircraft’s output.Then,based on the output of the identification model and the collected flight data,the parameters of the time-delay neural network controller are obtained through a learning process.The simulation results show that the designed intelligent controller has good gust alleviation effects for both continuous turbulence excitation and discrete gust excitation.For example,when the aircraft is 40000 kg and the flight speed is 0.81Ma,the controller achieves a 67.82%reduction in wingtip acceleration and a 35.90%reduction in center of mass acceleration under continuous turbulence excitation.When considering the measurement uncertainties,such as noise existing in the collected data,the trained controller can still achieve an acceptable gust alleviation effect.Finally,considering a flight in which the aircraft mass is constantly changing,the intelligent controller,which continuously learns from new flight data,maintains a good gust alleviation effect throughout the flight.展开更多
Background The sense of touch plays a crucial role in interactive behavior within virtual spaces,particularly when visual attention is absent.Although haptic feedback has been widely used to compensate for the lack of...Background The sense of touch plays a crucial role in interactive behavior within virtual spaces,particularly when visual attention is absent.Although haptic feedback has been widely used to compensate for the lack of visual cues,the use of tactile information as a predictive feedforward cue to guide hand movements remains unexplored and lacks theoretical understanding.Methods This study introduces a fingertip aero-haptic rendering method to investigate its effectiveness in directing hand movements during eyes-free spatial interactions.The wearable device incorporates a multichannel micro-airflow chamber to deliver adjustable tactile effects on the fingertips.Results The first study verified that tactile directional feedforward cues significantly improve user capabilities in eyes-free target acquisition and that users rely heavily on haptic indications rather than spatial memory to control their hands.A subsequent study examined the impact of enriched tactile feedforward cues on assisting users in determining precise target positions during eyes-free interactions,and assessed the required learning efforts.Conclusions The haptic feedforward effect holds great practical promise in eyeless design for virtual reality.We aim to integrate cognitive models and tactile feedforward cues in the future,and apply richer tactile feedforward information to alleviate users'perceptual deficiencies.展开更多
This paper discusses the problem of global state regulation via output feedback for a class of feedforward nonlinear time-delay systems with unknown measurement sensitivity. Different from previous works, the nonlinea...This paper discusses the problem of global state regulation via output feedback for a class of feedforward nonlinear time-delay systems with unknown measurement sensitivity. Different from previous works, the nonlinear terms are dominated by upper triangular linear unmeasured (delayed) states multiplied by unknown growth rate. The unknown growth rate is composed of an unknown constant, a power function of output, and an input function. Furthermore, due to the measurement uncertainty of the system output, it is more difficult to solve this problem. It is proved that the presented output feedback controller can globally regulate all states of the nonlinear systems using the dynamic gain scaling technique and choosing the appropriate Lyapunov–Krasovskii functionals.展开更多
Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at...Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.展开更多
In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tens...In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward compensation.In addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter information.Then,the fluctuation signal is reconstructed by a third-order auxiliary filter.The estimated signal feedforward compensates for the actual tension fluctuation.Furthermore,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the fluctuation.Finally,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source disturbances.The stability of the method is analyzed by using the Lyapunov theorem.Finally,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations.展开更多
Objective:To analyze the application effect of feedforward control in outpatient blood specimen management.Methods:1,200 patients who had their venous blood collected in outpatient phlebotomy room of our hospital'...Objective:To analyze the application effect of feedforward control in outpatient blood specimen management.Methods:1,200 patients who had their venous blood collected in outpatient phlebotomy room of our hospital's outpatient clinic from January 2021 to April 2021 were selected as study subjects and divided into 600 cases in the control group and 600 cases in the observation group.The two groups of patients were compared in terms of their satisfaction with the staff,the efficiency of the nurses and the quality of nursing care,turnaround time before specimen analysis,the rejection rate of the blood specimens,and the time of result reporting.Results:After the implementation of feedforward control,patients'satisfaction with staff,nurses work efficiency and quality of care,turnaround time before specimen analysis,specimen rejection rate,and result reporting time in the observation group were significantly higher than those in the control group(P<0.05).Conclusion:The application of feedforward control in the management of outpatient blood specimens has significant effect,which effectively improves patients'satisfaction,enhances the efficiency of nurses and the quality of nursing care,shortens the turnaround time of specimens before analysis and the reporting time of results,and reduces the rejection rate of specimens.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.52405104)Jiangxi Provincial Natural Science Foundation(Grant Nos.20242BAB20249 and 20232BAB204041)Science and Technology Project of Department of Transportation of Jiangxi Province(Grant No.2025QN003).
文摘Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking process often encounters challenges in learning efficiency and generalization.To address this issue,this paper designs a deep deterministic policy gradient(DDPG)-based reinforcement learning strategy that integrates imitation learning and feedforward exploration in the path following process.In imitation learning,the path tracking control data generated by the model predictive control(MPC)method is used to train an end-to-end steering control model of a deep neural network.Another feedforward exploration behavior is predicted by road curvature and vehicle speed,and adds it and imitation learning to the DDPG reinforcement learning to obtain decision-making experience and action prediction behavior of the path tracking process.In the reinforcement learning process,imitation learning is used to update the pre-training parameters of the actor network,and a feedforward steering technique with random noise is adopted for strategy exploration.In the reward function,a hierarchical progressive reward form and a constrained objective reward function referring to MPC are designed,and the actor-critic network architecture is determined.Finally,the path tracking performance of the designed method is verified by comparing various training results,simulations,and HIL tests.The results show that the designed method can effectively utilize pre-training and feedforward prior experience to obtain optimal path tracking performance of an autonomous vehicle,and has better generalization ability than other methods.This study provides an efficient control scheme for improving the end-to-end control performance of autonomous vehicles.
文摘In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.
基金supported by the National Natural Science Foun-dation of China(Grant No.52275099).
文摘The acquisition,tracking,and pointing(ATP)system is widely used in target tracking,counter-UAV operations,and other related fields.As UAV technology develops,there is a growing demand to enhance the tracking capabilities of ATP systems.However,in practical applications,ATP systems face various design constraints and functional limitations,making it infeasible to indefinitely improve hardware performance to meet tracking requirements.As a result,tracking algorithms are required to execute increasingly complex tasks.This study introduces a multi-rate feedforward predictive controller to address issues such as low image feedback frequency and significant delays in ATP systems,which lead to tracking jitter,poor tracking performance,low precision,and target loss.At the same time,the pro-posed approach aims to improve the tracking capabilities of ATP systems for high-speed and highly maneuverable targets under conditions of low sampling feedback rates and high feedback delays.The method suggested is also characterized by its low order,fast response,and robustness to model parameter variations.In this study,an actual ATP system is built for target tracking test,and the proposed algorithm is fully validated in terms of simulation and actual system application verification.Results from both simulations and experiments demonstrate that the method effectively compensates for delays and low sampling rates.For targets with relative angular velocities ranging from 0 to 90°/s and angular accelerations between 0 and 470°/s^(2),the system improved tracking accuracy by 70.0%-89.9%at a sampling frequency of 50 Hz and a delay of 30 m s.Moreover,the compensation algorithm demonstrated consistent performance across actuators with varying characteristics,further confirming its robustness to model insensitivity.In summary,the proposed algorithm considerably enhances the tracking accuracy and capability of ATP systems for high-speed and highly maneuverable targets,reducing the probability of target loss from high speed.This approach offers a practical solution for future multi-target tracking across diverse operational scenarios.
基金supported by the China Postdoctoral Science Foundation(No.2024M753715)Fundamental Research Funds for the Central Universities,Sun Yat-sen University(Nos.24qnpy125 and 22lglj11)Guangdong Basic and Applied Basic Research Foundation(No.2023B1515120030).
文摘The precise measurement of the antineutrino spectra produced by isotope fission in reactors is of great significance for studying neutrino oscillations,refining nuclear databases,and addressing the reactor antineutrino anomaly.In this paper,we report a method that utilizes a feedforward neural network(FNN)model to decompose the prompt energy spectrum observed in a short-baseline reactor neutrino experiment and extract the antineutrino spectra produced by the fission of major isotopes such as^(235)U,^(238)U,^(239)Pu,and^(241)Pu in the nuclear reactor.We present two training strategies for the model and compare them with the traditional X^(2) minimization method by applying them to the same set of pseudo-data corresponding to a total exposure of(2.9×5×1800)GW_(th)·tons·days.The results show that the FNN model not only converges faster and better during the fitting process but also achieves relative errors of less than 1%in the 2−8 MeV range in the extracted spectra,outperforming the X^(2) minimization method.The feasibility and superiority of this method were validated in the study.
基金supported by the Autonomous Innovation Team Foundation for“20 Items of the New University”of Jinan City(202228087)the National Natural Science Foundation of China(62073190).
文摘This paper studies global stabilization via predictor-based sampled-data output feedback for a class of feedforward nonlinear time-delay systems.Note that the traditional sampled-data observer via zero-order holder may result in the performance degradation of the observer.In this paper,an improved predictor-based observer is designed to compensate for the influence of the unmeasurable states,sampling errors and output delay.In addition,a sampled-data output-feedback controller is also constructed using the gain scaling technique.By the Lyapunov-Krasovskii functional method,the global exponential stability of the resulting closed-loop system can be guaranteed under some sufficient conditions.The simulation results are provided to demonstrate the main results.
基金Supported by National Natural Science Foundation of China(Grant No.52375502)EU H2020 MSCA R&I Programme(Grant No.101022696).
文摘Feedforward control is one of the most effective control techniques to increase the robot’s tracking accuracy.However,most of the dynamic models used in the feedforward controllers are linearly simplified such that the nonlinear and time-varying characteristics of dynamics in the workspace are ignored.In this paper,an iterative tuning method for feedforward control of parallel manipulators by taking nonlinear dynamics into account is proposed.Based on the robot rigid-body dynamic model,a feedforward controller considering the dynamic nonlinearity is presented.An iterative tuning method is given to iteratively update the feedforward controller by minimizing the root mean square(RMS)of the joint errors at each cycle.The effectiveness and extrapolation capability of the proposed method are validated through the experiments on a 2-DOF parallel manipulator.This research proposes an iterative tuning method for feedforward control of parallel manipulators considering nonlinear dynamics,which has better extrapolation capability in the whole workspace of manipulators.
基金supported in part by the National Natural Science Foundation of China under Project No.52207043。
文摘Under the condition of large inertia load,the stability of the servo system is more sensitive to the response speed and more likely to produce overshoot oscillations.In order to realize the requirements of high-precision and fast-response control of permanent magnet synchronous motor(PMSM)under large inertia load,an improved feedforward control strategy based on position impulse compensation and PD iterative algorithm is proposed to improve the response speed of the PMSM servo system and reduce the overshoot oscillation.This paper analyzes the mathematical models of the speed servo system and position servo system of the PMSM,calculates position overshoot impulse of the PMSM servo system,and improves the traditional feedforward control strategy to reversely compensate when the position is about to overshoot.Moreover,in order to further reduce the position overshoot,the PD iterative control algorithm is superimposed without increasing the complexity of the algorithm.The input signal is continuously corrected through multiple runs to achieve a smoother response control.The effectiveness of the proposed feedforward control strategy is verified by simulation and experiment.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503)。
文摘The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete(RAC)as an eco-friendly alternative to conventional concrete.However,predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters.This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks(FFNN),Random Forest(RF),and XGBoost.A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module:Bayesian Regularization,Levenberg-Marquardt,and three conjugate gradient variants—Powell/Beale Restarts,Fletcher-Powell,and Polak-Ribiere.Hyperparameter tuning,dropout regularization,and early stopping were employed to enhance generalization.Comparative analysis revealed that FFNN outperformed RF and XGBoost,achieving an R2 of 0.9669.To ensure interpretability,accumulated local effects(ALE)along with partial dependence plots(PDP)were utilized.This revealed trends consistent with the pre-existent domain knowledge.This allows estimation of strength from the properties of the mix without extensive lab testing,permitting designers to track the performance and sustainability trends in concrete mix designs while promoting responsible construction and demolition waste utilization.
基金supported by the“Shuimu Tsinghua Scholar”Project,China(No.2024SM223)the National Science and Technology Major Project,China(No.Y2022-V-0002-0028).
文摘Aeropropulsion System Test Facility (ASTF) is required to accurately control the pressure and temperature of the airflow to test the performance of the aero-engine. However, the control accuracy of ASTF is significantly affected by the flow disturbance caused by aero-engine acceleration and deceleration. This would reduce the credibility of ASTF’s test results for the aero-engine. Therefore, first, this paper proposes a feedforward compensation-based L1 adaptive control method for ASTF to address this problem. The baseline controller is first designed based on ideal uncoupled closed-loop dynamics to achieve dynamic decoupling. Then, L1 adaptive control is adopted to deal with various uncertainties and ensure good control performance. To further enhance the anti-disturbance performance, a feedforward strategy based on disturbance prediction is designed in the L1 adaptive control framework to compensate for the unmatched flow disturbance, which cannot be measured directly. In addition, this strategy takes into account the effects of actuator dynamics. With this method, the feedforward term can be determined from the nominal model parameters despite uncertainties. Finally, to demonstrate the effectiveness of the proposed method, various comparative experiments are performed on a hardware-in-the-loop system of ASTF. The experimental results show that the proposed method possesses excellent tracking performance, anti-disturbance performance and robustness.
基金supported by the National Natural Science Foundation of China(Grant Nos.52231014 and 52271361)the Natural Science Foundation of Guangdong Province of China(Grant No.2023A1515010684).
文摘Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.
基金supported in part by the Postgraduate Research&Practice Innovation Program of Nanjing University of Aeronautics and Astronautics(No.xcxjh20240326).
文摘The conventional feedforward hybrid active noise control(FFHANC)system combines the advantages of the feedforward narrowband active noise control(FFNANC)system and the feedforward broadband active noise control(FFBANC)system.To enhance its adaptive adjustment capability under frequency mismatch(FM)conditions,this paper introduces a narrowband frequency adaptive estimation module into the conventional FFHANC system.This module integrates an autoregressive(AR)model and a linear cascaded adaptive notch filter(LCANF),enabling accurate reference signal frequency estimation even under significant FM.Furthermore,in order to improve the coherence between narrowband and broadband components in the system’s error signal and its corresponding control filter for the conventional FFHANC system,this paper proposes an algorithm based on autoregressive bandpass filter bank(AR-BPFB)for error separation.Simulation results demonstrate that the proposed FFHANC system maintains robust performance under high FM conditions and effectively suppresses hybrid-band noise.The AR-BPFB algorithm significantly elevates the convergence speed of the FFHANC system.
基金supported by Prince Sultan University,Riyadh,Saudi Arabia,under research grant SEED-2022-CE-95。
文摘This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller(HCC).The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed,electromagnetic torque,and stator current.Two case studies,one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation,has been validated to show the effectiveness of the proposed control strategy.The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.
文摘This paper proposes a neural network-based intelligent feedforward gust alleviation framework,which includes a neural network identification model and a neural network controller.A neural network training dataset is formed by collecting flight data and the gust data encountered during the aircraft flight.A neural network identification model is first trained to accurately predict the aircraft’s output.Then,based on the output of the identification model and the collected flight data,the parameters of the time-delay neural network controller are obtained through a learning process.The simulation results show that the designed intelligent controller has good gust alleviation effects for both continuous turbulence excitation and discrete gust excitation.For example,when the aircraft is 40000 kg and the flight speed is 0.81Ma,the controller achieves a 67.82%reduction in wingtip acceleration and a 35.90%reduction in center of mass acceleration under continuous turbulence excitation.When considering the measurement uncertainties,such as noise existing in the collected data,the trained controller can still achieve an acceptable gust alleviation effect.Finally,considering a flight in which the aircraft mass is constantly changing,the intelligent controller,which continuously learns from new flight data,maintains a good gust alleviation effect throughout the flight.
文摘Background The sense of touch plays a crucial role in interactive behavior within virtual spaces,particularly when visual attention is absent.Although haptic feedback has been widely used to compensate for the lack of visual cues,the use of tactile information as a predictive feedforward cue to guide hand movements remains unexplored and lacks theoretical understanding.Methods This study introduces a fingertip aero-haptic rendering method to investigate its effectiveness in directing hand movements during eyes-free spatial interactions.The wearable device incorporates a multichannel micro-airflow chamber to deliver adjustable tactile effects on the fingertips.Results The first study verified that tactile directional feedforward cues significantly improve user capabilities in eyes-free target acquisition and that users rely heavily on haptic indications rather than spatial memory to control their hands.A subsequent study examined the impact of enriched tactile feedforward cues on assisting users in determining precise target positions during eyes-free interactions,and assessed the required learning efforts.Conclusions The haptic feedforward effect holds great practical promise in eyeless design for virtual reality.We aim to integrate cognitive models and tactile feedforward cues in the future,and apply richer tactile feedforward information to alleviate users'perceptual deficiencies.
基金supported by the fund of Beijing Municipal Commission of Education(Nos.22019821001 and KM202210017001)the Natural Science Foundation of Henan Province(No.222300420253).
文摘This paper discusses the problem of global state regulation via output feedback for a class of feedforward nonlinear time-delay systems with unknown measurement sensitivity. Different from previous works, the nonlinear terms are dominated by upper triangular linear unmeasured (delayed) states multiplied by unknown growth rate. The unknown growth rate is composed of an unknown constant, a power function of output, and an input function. Furthermore, due to the measurement uncertainty of the system output, it is more difficult to solve this problem. It is proved that the presented output feedback controller can globally regulate all states of the nonlinear systems using the dynamic gain scaling technique and choosing the appropriate Lyapunov–Krasovskii functionals.
基金the National Natural Science Foundation of China under Grant(42274119)the Liaoning Revitalization Talents Program under Grant(XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
文摘Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.
基金funded by the National Natural Science Foundation of China(Grant Number 52075361)Shanxi Province Science and Technology Major Project(Grant Number 20201102003)+3 种基金Lvliang Science and Technology Guidance Special Key R&D Project(Grant Number 2022XDHZ08)National Natural Science Foundation of China(Grant Number 51905367)Shanxi Natural Science Foundation General Project(Grant Numbers 202103021224271,202203021211201)Shanxi Province Key Research and Development Plan(Grant Number 202102020101013).
文摘In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward compensation.In addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter information.Then,the fluctuation signal is reconstructed by a third-order auxiliary filter.The estimated signal feedforward compensates for the actual tension fluctuation.Furthermore,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the fluctuation.Finally,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source disturbances.The stability of the method is analyzed by using the Lyapunov theorem.Finally,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations.
文摘Objective:To analyze the application effect of feedforward control in outpatient blood specimen management.Methods:1,200 patients who had their venous blood collected in outpatient phlebotomy room of our hospital's outpatient clinic from January 2021 to April 2021 were selected as study subjects and divided into 600 cases in the control group and 600 cases in the observation group.The two groups of patients were compared in terms of their satisfaction with the staff,the efficiency of the nurses and the quality of nursing care,turnaround time before specimen analysis,the rejection rate of the blood specimens,and the time of result reporting.Results:After the implementation of feedforward control,patients'satisfaction with staff,nurses work efficiency and quality of care,turnaround time before specimen analysis,specimen rejection rate,and result reporting time in the observation group were significantly higher than those in the control group(P<0.05).Conclusion:The application of feedforward control in the management of outpatient blood specimens has significant effect,which effectively improves patients'satisfaction,enhances the efficiency of nurses and the quality of nursing care,shortens the turnaround time of specimens before analysis and the reporting time of results,and reduces the rejection rate of specimens.
