Integrator forwarding is a recursive nonlinear design technique for the stabilization of feed-forward systems. However, this method still has some limitation. An improved design method is proposed to extend the field ...Integrator forwarding is a recursive nonlinear design technique for the stabilization of feed-forward systems. However, this method still has some limitation. An improved design method is proposed to extend the field of application of this technique. This method is used to design a stabilizer for the inertia wheel pendulum system. Moreover, it is shown that the control Lyapunov function which is obtained from this method can also be used to design a globally asymptotically stabilizing controller with optimality.展开更多
Biological robot is a kind of creature controlled by human beings by applying intervention signals through control technology to regulate biological behavior.At present,the research on bio-robot mainly focuses on terr...Biological robot is a kind of creature controlled by human beings by applying intervention signals through control technology to regulate biological behavior.At present,the research on bio-robot mainly focuses on terrestrial mammals and insects,while the research on aquatic animal robot is less.Early studies have shown that the medial longitudinal fasciculus nucleus(NFLM)of carp midbrain was related to tail wagging,but the research has not been applied to the navigation control of the carp robot.The purpose of this study is to realize the quantitative control of the forward and steering behavior of the carp robot by NFLM electrical stimulation.Under the condition of no craniotomy,brain electrode was implanted into the NFLM of the carp midbrain,and the underwater control experiment was carried out by applying different electrical stimulation parameters.Using the ImageJ software and self-programmed,the forward motion speed and steering angle of steering motion of the carp robot before and after being stimulated were calculated.The experimental results showed for the carp robot that was induced the steering motion,the left and right steering motion of 30°to 150°could be achieved by adjusting the stimulation parameters,for the carp robot that was induced the forward motion,the speed of forward motion could be controlled to reach 100 cm/s.The research lays a foundation for the accurate control of the forward and steering motion of the aquatic animal robot.展开更多
This paper presents a simple and robust speed control scheme of Permanent Magnet Synchronous Motor (PMSM). It is to achieve accurate control performance in the presence of load torque and plant parameter variation. A ...This paper presents a simple and robust speed control scheme of Permanent Magnet Synchronous Motor (PMSM). It is to achieve accurate control performance in the presence of load torque and plant parameter variation. A robust disturbance cancellation feed forward controller is used to estimate the torque disturbance. The simple and practical control scheme is easily implemented on a PMSM driver using a TMS320LF2407 DSP. The effectiveness of the proposed robust speed control approach is demonstrated by simulation and experimental results.展开更多
Our previous study shows that the hovering and forward flight of a bumblebee do not have inherent stability （passive stability）. But the bumblebees are observed to fly stably. Stabilization control must have been ap...Our previous study shows that the hovering and forward flight of a bumblebee do not have inherent stability （passive stability）. But the bumblebees are observed to fly stably. Stabilization control must have been applied. In this study, we investigate the longitudinal stabilization control of the bumblebee. The method of computational fluid dynamics is used to compute the control derivatives and the techniques of eigenvalue and eigenvector analysis and modal decomposition are used for solving the equations of motion. Controllability analysis shows that at all flight speeds considered, although inherently unstable, the flight is controllable. By feedbacking the state variables, i.e. vertical and horizontal velocities, pitching rate and pitch angle （which can be measured by the sensory system of the insect）, to produce changes in stroke angle and angle of attack of the wings, the flight can be stabilized, explaining why the bumblebees can fly stably even if they are passively unstable.展开更多
[Objectives] The paper was to study the prevention and control effect of botanical pesticides on Semiaphis heraclei at different temperatures. [Methods] When the daily average temperatures in spring were 15 and 20 ℃,...[Objectives] The paper was to study the prevention and control effect of botanical pesticides on Semiaphis heraclei at different temperatures. [Methods] When the daily average temperatures in spring were 15 and 20 ℃, the control effects of 0.3% azadirachtin EC 500 times dilution and 0.3% matrine AS 1 000 times dilution on S. heraclei were studied. [Results] When the daily average temperature was 15 ℃, 0.3% azadirachtin EC 500 times dilution had relatively good control effect on S. heraclei, with a long duration. The prevention and control of S. heraclei in spring better controlled the population quantity of S. heraclei fundatrix and reduced the degree of harm. [Conclusions] The study provides a basis for the pollution-free control of S. heraclei in the green space of parks.展开更多
Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This stu...Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.展开更多
Frequency droop control is widely used in permanent magnet synchronous generators(PMSGs)based wind turbines(WTs)for grid frequency support.However,under frequency deviations,significant DC-link voltage fluctuations ma...Frequency droop control is widely used in permanent magnet synchronous generators(PMSGs)based wind turbines(WTs)for grid frequency support.However,under frequency deviations,significant DC-link voltage fluctuations may occur during the transient process due to sudden changes in real power of such WTs.To address this issue,a current feedforward control strategy is proposed for PMSG-based WTs to reduce DC-link voltage fluctuations when the WTs are providing frequency support under grid frequency deviations.Meanwhile,the desired frequency support capability of the PMSG-based WTs can be ensured.Simulation results verify the rationality of the analysis and the effectiveness of the proposed control method.展开更多
Transients in load and consequently in stack current have a significant impact on the performance and durability of fuel cells.The delays in auxiliary equipments in fuel cell systems (such as pumps and heaters) and ba...Transients in load and consequently in stack current have a significant impact on the performance and durability of fuel cells.The delays in auxiliary equipments in fuel cell systems (such as pumps and heaters) and back pressures degrade system performance and lead to problems in controlling tuning parameters including temperature,pressure,and flow rate.To overcome this problem,fast and delay-free systems are necessary for predicting control signals.In this paper,we propose a neural network model to control the stack terminal voltage as a proper constant and improve system performance.This is done through an input air pressure control signal.The proposed artificial neural network was constructed based on a back propagation network.A fuel cell nonlinear model,with and without feed forward control,was investigated and compared under random current variations.Simulation results showed that applying neural network feed forward control can successfully improve system performance in tracking output voltage.Also,less energy consumption and simpler control systems are the other advantages of the proposed control algorithm.展开更多
In the field of biomechanics,customizing complex strain fields according to specific requirements poses an important challenge for bioreactor technology,primarily due to the intricate coupling and nonlinear actuation ...In the field of biomechanics,customizing complex strain fields according to specific requirements poses an important challenge for bioreactor technology,primarily due to the intricate coupling and nonlinear actuation of actuator arrays,which complicates the precise control of strain fields.This paper introduces a bioreactor designed with a 9×9 array of independently controllable dielectric elastomer actuators(DEAs),addressing this challenge.We employ image regression-based machine learning for both replicating target strain fields through inverse control and rapidly predicting feasible strain fields generated by the bioreactor in response to control inputs via forward control.To generate training data,a finite element analysis(FEA)simulation model was developed.In the FEA,the device was prestretched,followed by the random assignment of voltages to each pixel,yielding 10,000 distinct output strain field images for the training set.For inverse control,a multilayer perceptron(MLP)is utilized to predict control inputs from images,whereas,for forward control,MLP maps control inputs to low-resolution images,which are then upscaled to high-resolution outputs through a super-resolution generative adversarial network(SRGAN).Demonstrations include inputting biomechanically significant strain fields,where the method successfully replicated the intended fields.Additionally,by using various tumor-stroma interfaces as inputs,the bioreactor demonstrated its ability to customize strain fields accordingly,showcasing its potential as an advanced testbed for tumor biomechanics research.展开更多
文摘Integrator forwarding is a recursive nonlinear design technique for the stabilization of feed-forward systems. However, this method still has some limitation. An improved design method is proposed to extend the field of application of this technique. This method is used to design a stabilizer for the inertia wheel pendulum system. Moreover, it is shown that the control Lyapunov function which is obtained from this method can also be used to design a globally asymptotically stabilizing controller with optimality.
基金This work was financially supported by the Project of National Natural Science Foundation of China(project number:61573305)Projectof Natural Science Foundation of Hebei Provinceof China(project number:F2019203511)National High-Tech Research and Development Plan of China(863 Plan)Project(2013AA***)Fund.
文摘Biological robot is a kind of creature controlled by human beings by applying intervention signals through control technology to regulate biological behavior.At present,the research on bio-robot mainly focuses on terrestrial mammals and insects,while the research on aquatic animal robot is less.Early studies have shown that the medial longitudinal fasciculus nucleus(NFLM)of carp midbrain was related to tail wagging,but the research has not been applied to the navigation control of the carp robot.The purpose of this study is to realize the quantitative control of the forward and steering behavior of the carp robot by NFLM electrical stimulation.Under the condition of no craniotomy,brain electrode was implanted into the NFLM of the carp midbrain,and the underwater control experiment was carried out by applying different electrical stimulation parameters.Using the ImageJ software and self-programmed,the forward motion speed and steering angle of steering motion of the carp robot before and after being stimulated were calculated.The experimental results showed for the carp robot that was induced the steering motion,the left and right steering motion of 30°to 150°could be achieved by adjusting the stimulation parameters,for the carp robot that was induced the forward motion,the speed of forward motion could be controlled to reach 100 cm/s.The research lays a foundation for the accurate control of the forward and steering motion of the aquatic animal robot.
基金This work was supported High-Tech Research and Development Program of China (No. 2001AA423160)
文摘This paper presents a simple and robust speed control scheme of Permanent Magnet Synchronous Motor (PMSM). It is to achieve accurate control performance in the presence of load torque and plant parameter variation. A robust disturbance cancellation feed forward controller is used to estimate the torque disturbance. The simple and practical control scheme is easily implemented on a PMSM driver using a TMS320LF2407 DSP. The effectiveness of the proposed robust speed control approach is demonstrated by simulation and experimental results.
基金the National Natural Science Foundation of China (10732030)
文摘Our previous study shows that the hovering and forward flight of a bumblebee do not have inherent stability （passive stability）. But the bumblebees are observed to fly stably. Stabilization control must have been applied. In this study, we investigate the longitudinal stabilization control of the bumblebee. The method of computational fluid dynamics is used to compute the control derivatives and the techniques of eigenvalue and eigenvector analysis and modal decomposition are used for solving the equations of motion. Controllability analysis shows that at all flight speeds considered, although inherently unstable, the flight is controllable. By feedbacking the state variables, i.e. vertical and horizontal velocities, pitching rate and pitch angle （which can be measured by the sensory system of the insect）, to produce changes in stroke angle and angle of attack of the wings, the flight can be stabilized, explaining why the bumblebees can fly stably even if they are passively unstable.
基金Supported by Construction Science and Technology Research Project of Hebei Province (2019-2072)。
文摘[Objectives] The paper was to study the prevention and control effect of botanical pesticides on Semiaphis heraclei at different temperatures. [Methods] When the daily average temperatures in spring were 15 and 20 ℃, the control effects of 0.3% azadirachtin EC 500 times dilution and 0.3% matrine AS 1 000 times dilution on S. heraclei were studied. [Results] When the daily average temperature was 15 ℃, 0.3% azadirachtin EC 500 times dilution had relatively good control effect on S. heraclei, with a long duration. The prevention and control of S. heraclei in spring better controlled the population quantity of S. heraclei fundatrix and reduced the degree of harm. [Conclusions] The study provides a basis for the pollution-free control of S. heraclei in the green space of parks.
基金supported by the Malaysia Ministry of Higher Education under Fundamental Research Grant Scheme with Project Code:FRGS/1/2024/TK07/USM/02/3.
文摘Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.
基金This work is jointly supported by the National Key R&D Programme of China(No.2017YFB0902000)the National Natural Science Foundation of China(No.U1766206)the Science and Technology Programme of the State Grid Corporation(No.52110418000P).
文摘Frequency droop control is widely used in permanent magnet synchronous generators(PMSGs)based wind turbines(WTs)for grid frequency support.However,under frequency deviations,significant DC-link voltage fluctuations may occur during the transient process due to sudden changes in real power of such WTs.To address this issue,a current feedforward control strategy is proposed for PMSG-based WTs to reduce DC-link voltage fluctuations when the WTs are providing frequency support under grid frequency deviations.Meanwhile,the desired frequency support capability of the PMSG-based WTs can be ensured.Simulation results verify the rationality of the analysis and the effectiveness of the proposed control method.
文摘Transients in load and consequently in stack current have a significant impact on the performance and durability of fuel cells.The delays in auxiliary equipments in fuel cell systems (such as pumps and heaters) and back pressures degrade system performance and lead to problems in controlling tuning parameters including temperature,pressure,and flow rate.To overcome this problem,fast and delay-free systems are necessary for predicting control signals.In this paper,we propose a neural network model to control the stack terminal voltage as a proper constant and improve system performance.This is done through an input air pressure control signal.The proposed artificial neural network was constructed based on a back propagation network.A fuel cell nonlinear model,with and without feed forward control,was investigated and compared under random current variations.Simulation results showed that applying neural network feed forward control can successfully improve system performance in tracking output voltage.Also,less energy consumption and simpler control systems are the other advantages of the proposed control algorithm.
基金supported by the Purdue startup funding to A.C.and by NSF award 2301509.
文摘In the field of biomechanics,customizing complex strain fields according to specific requirements poses an important challenge for bioreactor technology,primarily due to the intricate coupling and nonlinear actuation of actuator arrays,which complicates the precise control of strain fields.This paper introduces a bioreactor designed with a 9×9 array of independently controllable dielectric elastomer actuators(DEAs),addressing this challenge.We employ image regression-based machine learning for both replicating target strain fields through inverse control and rapidly predicting feasible strain fields generated by the bioreactor in response to control inputs via forward control.To generate training data,a finite element analysis(FEA)simulation model was developed.In the FEA,the device was prestretched,followed by the random assignment of voltages to each pixel,yielding 10,000 distinct output strain field images for the training set.For inverse control,a multilayer perceptron(MLP)is utilized to predict control inputs from images,whereas,for forward control,MLP maps control inputs to low-resolution images,which are then upscaled to high-resolution outputs through a super-resolution generative adversarial network(SRGAN).Demonstrations include inputting biomechanically significant strain fields,where the method successfully replicated the intended fields.Additionally,by using various tumor-stroma interfaces as inputs,the bioreactor demonstrated its ability to customize strain fields accordingly,showcasing its potential as an advanced testbed for tumor biomechanics research.
