This paper proposes a differential-fatness-based active disturbance rejection control(ADRC)for high-speed steering control of tracked tank systems.Firstly,a high-speed steering model is established by considering the ...This paper proposes a differential-fatness-based active disturbance rejection control(ADRC)for high-speed steering control of tracked tank systems.Firstly,a high-speed steering model is established by considering the lateral component of the centrifugal force acting on the tank on the basis of modeling and analyzing the dynamic model of the low-speed steering system.Secondly,we propose a differential-flatness ADRC approach by converting the under-actuated system to a fully driven flat one.Moreover,we prove the differential flatness of the steering system,which facilitates a two-channel ADRC development.Finally,we show that both the states of the flat system and the original under-actuated system can track the reference trajectory.On the external interference condition,the system is observed to re-track the target signal within 2 s.展开更多
When applying fiat belts, correct tracking of the belt through the installation has to be assured. Since flat belts are commonly used for conveying and transmission purposes, tracking systems have been well developed,...When applying fiat belts, correct tracking of the belt through the installation has to be assured. Since flat belts are commonly used for conveying and transmission purposes, tracking systems have been well developed, but the ultimate tracking behaviour of the belt can be greatly enhanced by taking special care in the creation of an adequate tracking mechanism. To obtain long-life operation and full value from the equipment, the correct tracking technique plays an important role. This paper deals with two tracking techniques： The skewed and the angled pulley axis. Numerical simulation results are compared with both measurements and an analytical approach. The advantages of numerical simulation compared to experimental tests are ease, convenience and the absence of any safety risk. Compared to analytical approaches the simulation is used for systems for which simple closed form analytic solutions are not possible.展开更多
Some existing methods for chaos control in engineering fields are analyzed and their drawbacks are pointed out. A tracking method can solve these problems to some extent, but it still depends on the mathematical model...Some existing methods for chaos control in engineering fields are analyzed and their drawbacks are pointed out. A tracking method can solve these problems to some extent, but it still depends on the mathematical model of the system to be controlled. An intelligent method based on fuzzy neural network (FNN) is used to control chaos in engineering fields. The FNN is employed to learn the inherent dynamics from the input and output of chaos, which can be used in the inverse system method, so that the method is free of the exact mathematical model of the system to be controlled. This intelligent method is compared with tracking method in the presence of measurement noise and model error. Simulation results show its superiority and feasibility.展开更多
This study focuses on the quantification of the influence of rolling stock failures(RSFs)on railway infrastructure.Taking the wheel flat,a common RSF,as an example,we introduce four quantification indexes to evaluate ...This study focuses on the quantification of the influence of rolling stock failures(RSFs)on railway infrastructure.Taking the wheel flat,a common RSF,as an example,we introduce four quantification indexes to evaluate the influence on the following four deterioration mechanisms:track settlement(TS),track component fatigue(TCF),abrasive wear(AW),and rolling contact fatigue(RCF).Our results indicate that TS,TCF,and AW increase sharply with the increase of the wheel flat length and the vehicle speed,and this increasing trend becomes more acute with the increase of the wheel flat length and the vehicle speed.At low speeds,RCF increases gradually as the wheel flat length increases;at high speeds,it increases sharply at first and then decreases gradually.The influence of the wheel flat on TCF and AW is the most obvious,followed by TS and RCF.These findings can help infrastructure managers(IMs)to better understand infrastructure conditions related to RSFs and can aid them in managing problems with vehicle abnormality in track access charging.展开更多
This paper proposes a scheme of trajectory tracking control for the hovercraft.Since the model of the hovercraft is under-actuated,nonlinear,and strongly coupled,it is a great challenge for the controller design.To so...This paper proposes a scheme of trajectory tracking control for the hovercraft.Since the model of the hovercraft is under-actuated,nonlinear,and strongly coupled,it is a great challenge for the controller design.To solve this problem,the control scheme is divided into two parts.Firstly,we employ differential flatness method to find a set of flat outputs and consider part of the nonlinear terms as uncertainties.Consequently,we convert the under-actuated system into a full-actuated one.Secondly,a reinforcement learning-based active disturbance rejection controller(RL-ADRC)is designed.In this method,an extended state observer(ESO)is designed to estimate the uncertainties of the system,and an actorcritic-based reinforcement learning(RL)algorithm is used to approximate the optimal control strategy.Based on the output of the ESO,the RL-ADRC compensates for the total uncertainties in real-time,and simultaneously,generates the optimal control strategy by RL algorithm.Simulation results show that,compared with the traditional ADRC method,RL-ADRC does not need to manually tune the controller parameters,and the control strategy is more robust.展开更多
Aiming at the difficulties of the health status recognition of yellow feather broilers in large-scale broiler farms and the low recognition rate of current models,a novel method based on machine vision to achieve prec...Aiming at the difficulties of the health status recognition of yellow feather broilers in large-scale broiler farms and the low recognition rate of current models,a novel method based on machine vision to achieve precise tracking of multiple broilers was proposed in this paper.Broilers’behavior in the breeding environment can be tracked to analyze their behaviors and health status further.An improved YOLOv3(You Only Look Once v3)algorithm was used as the detector of the Deep SORT(Simple Online and Realtime Tracking)algorithm to realize the multiple object tracking of yellow feather broilers in the flat breeding chamber,which replaced the backbone of YOLOv3 with MobileNetV2 to improve the inference speed of the detection module.The DRSN(Deep Residual Shrinkage Network)was integrated with MobileNetV2 to enhance the feature extraction capability of the network.Moreover,in view of the slight change in the individual size of the yellow feather broiler,the feature fusion network was also redesigned by combining it with the attention mechanism to enable the adaptive learning of the objects’multi-scale features.Compared with traditional YOLOv3,improved YOLOv3 achieves 93.2%mAP(mean Average Precision)and 29 fps(frames per second),representing high-precision real-time detection performance.Furthermore,while the MOTA(Multiple Object Tracking Accuracy)increases from 51%to 54%,the IDSW(Identity Switch)decreases by 62.2%compared with traditional YOLOv3-based objective detectors.The proposed algorithm can provide a technical reference for analyzing the behavioral perception and health status of broilers in the flat breeding environment.展开更多
基金supported by the National Natural Science Foundation of China(62422305,62373049).
文摘This paper proposes a differential-fatness-based active disturbance rejection control(ADRC)for high-speed steering control of tracked tank systems.Firstly,a high-speed steering model is established by considering the lateral component of the centrifugal force acting on the tank on the basis of modeling and analyzing the dynamic model of the low-speed steering system.Secondly,we propose a differential-flatness ADRC approach by converting the under-actuated system to a fully driven flat one.Moreover,we prove the differential flatness of the steering system,which facilitates a two-channel ADRC development.Finally,we show that both the states of the flat system and the original under-actuated system can track the reference trajectory.On the external interference condition,the system is observed to re-track the target signal within 2 s.
文摘When applying fiat belts, correct tracking of the belt through the installation has to be assured. Since flat belts are commonly used for conveying and transmission purposes, tracking systems have been well developed, but the ultimate tracking behaviour of the belt can be greatly enhanced by taking special care in the creation of an adequate tracking mechanism. To obtain long-life operation and full value from the equipment, the correct tracking technique plays an important role. This paper deals with two tracking techniques： The skewed and the angled pulley axis. Numerical simulation results are compared with both measurements and an analytical approach. The advantages of numerical simulation compared to experimental tests are ease, convenience and the absence of any safety risk. Compared to analytical approaches the simulation is used for systems for which simple closed form analytic solutions are not possible.
文摘Some existing methods for chaos control in engineering fields are analyzed and their drawbacks are pointed out. A tracking method can solve these problems to some extent, but it still depends on the mathematical model of the system to be controlled. An intelligent method based on fuzzy neural network (FNN) is used to control chaos in engineering fields. The FNN is employed to learn the inherent dynamics from the input and output of chaos, which can be used in the inverse system method, so that the method is free of the exact mathematical model of the system to be controlled. This intelligent method is compared with tracking method in the presence of measurement noise and model error. Simulation results show its superiority and feasibility.
基金Project supported by the Assets4Rail Project Funded by the Shift2Rail Joint Undertaking under the EU’s H2020 Program(No.826250)the China Scholarship Council(No.201707000113)。
文摘This study focuses on the quantification of the influence of rolling stock failures(RSFs)on railway infrastructure.Taking the wheel flat,a common RSF,as an example,we introduce four quantification indexes to evaluate the influence on the following four deterioration mechanisms:track settlement(TS),track component fatigue(TCF),abrasive wear(AW),and rolling contact fatigue(RCF).Our results indicate that TS,TCF,and AW increase sharply with the increase of the wheel flat length and the vehicle speed,and this increasing trend becomes more acute with the increase of the wheel flat length and the vehicle speed.At low speeds,RCF increases gradually as the wheel flat length increases;at high speeds,it increases sharply at first and then decreases gradually.The influence of the wheel flat on TCF and AW is the most obvious,followed by TS and RCF.These findings can help infrastructure managers(IMs)to better understand infrastructure conditions related to RSFs and can aid them in managing problems with vehicle abnormality in track access charging.
基金This paper was supported by the National Natural Science Foundation of China under Grant No.61720106010.
文摘This paper proposes a scheme of trajectory tracking control for the hovercraft.Since the model of the hovercraft is under-actuated,nonlinear,and strongly coupled,it is a great challenge for the controller design.To solve this problem,the control scheme is divided into two parts.Firstly,we employ differential flatness method to find a set of flat outputs and consider part of the nonlinear terms as uncertainties.Consequently,we convert the under-actuated system into a full-actuated one.Secondly,a reinforcement learning-based active disturbance rejection controller(RL-ADRC)is designed.In this method,an extended state observer(ESO)is designed to estimate the uncertainties of the system,and an actorcritic-based reinforcement learning(RL)algorithm is used to approximate the optimal control strategy.Based on the output of the ESO,the RL-ADRC compensates for the total uncertainties in real-time,and simultaneously,generates the optimal control strategy by RL algorithm.Simulation results show that,compared with the traditional ADRC method,RL-ADRC does not need to manually tune the controller parameters,and the control strategy is more robust.
基金funded by Jiangsu Agriculture Science and Technology Innovation Fund(Grant No.CX(21)3058)Xuzhou Key Research and Development Project(Modern Agriculture)(Grant No.KC21135)International Science and Technology Cooperation Program of Jiangsu Province(Grant No.BZ2023013).
文摘Aiming at the difficulties of the health status recognition of yellow feather broilers in large-scale broiler farms and the low recognition rate of current models,a novel method based on machine vision to achieve precise tracking of multiple broilers was proposed in this paper.Broilers’behavior in the breeding environment can be tracked to analyze their behaviors and health status further.An improved YOLOv3(You Only Look Once v3)algorithm was used as the detector of the Deep SORT(Simple Online and Realtime Tracking)algorithm to realize the multiple object tracking of yellow feather broilers in the flat breeding chamber,which replaced the backbone of YOLOv3 with MobileNetV2 to improve the inference speed of the detection module.The DRSN(Deep Residual Shrinkage Network)was integrated with MobileNetV2 to enhance the feature extraction capability of the network.Moreover,in view of the slight change in the individual size of the yellow feather broiler,the feature fusion network was also redesigned by combining it with the attention mechanism to enable the adaptive learning of the objects’multi-scale features.Compared with traditional YOLOv3,improved YOLOv3 achieves 93.2%mAP(mean Average Precision)and 29 fps(frames per second),representing high-precision real-time detection performance.Furthermore,while the MOTA(Multiple Object Tracking Accuracy)increases from 51%to 54%,the IDSW(Identity Switch)decreases by 62.2%compared with traditional YOLOv3-based objective detectors.The proposed algorithm can provide a technical reference for analyzing the behavioral perception and health status of broilers in the flat breeding environment.
