Background With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emerge...Background With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emergency evacuation scenarios. Correctly and effectively evacuating crowds in virtual emergency scenarios are becoming increasingly urgent. One good solution is to extract pedestrian trajectories from videos of emergency situations using a multi-target tracking algorithm and use them to define evacuation procedures. Methods To implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and Savitzky-Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real time. Results In addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. Conclusions Further comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework.展开更多
In the context of multiple-target tracking and surveillance applications,this paper investigates the challenge of determining the optimal positioning of a single autonomous aerial vehicle or agent equipped with multip...In the context of multiple-target tracking and surveillance applications,this paper investigates the challenge of determining the optimal positioning of a single autonomous aerial vehicle or agent equipped with multiple independently-steerable zooming cameras to effectively monitor a set of targets of interest.Each camera is dedicated to tracking a specific target or cluster of targets.The key innovation of this study,in comparison to existing approaches,lies in incorporating the zooming factor for the onboard cameras into the optimization problem.This enhancement offers greater flexibility during mission execution by allowing the autonomous agent to adjust the focal lengths of the onboard cameras,in exchange for varying real-world distances to the corresponding targets,thereby providing additional degrees of freedom to the optimization problem.The proposed optimization framework aims to strike a balance among various factors,including distance to the targets,verticality of viewpoints,and the required focal length for each camera.The primary focus of this paper is to establish the theoretical groundwork for addressing the non-convex nature of the optimization problem arising from these considerations.To this end,we develop an original convex approximation strategy.The paper also includes simulations of diverse scenarios,featuring varying numbers of onboard tracking cameras and target motion profiles,to validate the effectiveness of the proposed approach.展开更多
A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filte...A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.展开更多
Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communi...Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with others.Eye tracking(ET)has become a useful method to detect ASD.One vital aspect of moral erudition is the aptitude to have common visual attention.The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection.Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection.It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism.The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking.The Chaotic Butterfly Optimization technique is used to identify this specific disturbance.Therefore,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)technique.The presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL models.To accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS.In addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization.Finally,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of ASD.To assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data repository.The experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better performance in the ETASD-CBODL technique over recent approaches.展开更多
Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and proces...Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals.The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes.Design/methodologylapproach-To achieve this objective,the paper simulates actual train operations,incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station.The Monte Carlo simulation method is adopted to solve this problem.This approach transforms a nonlinear model,which includes constraints from probability distribution functions and is difficult to solve directly,into a linear programming model that is easier to handle.The method then linearly weights two objectives to optimize the solution.Findings-Through the application of Monte Carlo simulation,the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model.By continuously adjusting the weighting coefficients of the linear objectives,the method is able to optimize the Pareto solution.Notably,this approach does not require extensive scene data to obtain a satisfactory Pareto solution set.Originality/value-The paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times.The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement.Furthermore,the method's ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.展开更多
The source location based on the hybrid time difference of arrival(TDOA)/frequency difference of arrival(FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA position...The source location based on the hybrid time difference of arrival(TDOA)/frequency difference of arrival(FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA positioning will greatly affect the accuracy of positioning. Using unmanned aerial vehicle(UAV) as base stations, by optimizing the trajectory of the UAV swarm, an optimal positioning configuration is formed to improve the accuracy of the target position and velocity estimation. In this paper, a hybrid TDOA/FDOA positioning model is first established, and the positioning accuracy of the hybrid TDOA/FDOA under different positioning configurations and different measurement errors is simulated by the geometric dilution of precision(GDOP) factor. Second, the Cramer-Rao lower bound(CRLB) matrix of hybrid TDOA/FDOA location under different moving states of the target is derived theoretically, the objective function of the track optimization is obtained, and the track of the UAV swarm is optimized in real time. The simulation results show that the track optimization effectively improves the accuracy of the target position and velocity estimation.展开更多
A model helicopter is more difficult to control than its full scale counterpart. This is due to its greater sensitivity to control inputs and disturbances as well as higher bandwidth of dynamics. This work is focused ...A model helicopter is more difficult to control than its full scale counterpart. This is due to its greater sensitivity to control inputs and disturbances as well as higher bandwidth of dynamics. This work is focused on designing practical tracking controller for a small scale helicopter following predefined trajectories. A tracking controller based on optimal control theory is synthesized as a part of the development of an autonomous helicopter. Some issues with regards to control constraints are addressed. The weighting between state tracking performance and control power expenditure is analyzed. Overall performance of the control design is evaluated based on its time domain histories of trajectories as well as control inputs.展开更多
Based on adaptive dynamic programming(ADP),the fixed-point tracking control problem is solved by a value iteration(VI) algorithm. First, a class of discrete-time(DT)nonlinear system with disturbance is considered. Sec...Based on adaptive dynamic programming(ADP),the fixed-point tracking control problem is solved by a value iteration(VI) algorithm. First, a class of discrete-time(DT)nonlinear system with disturbance is considered. Second, the convergence of a VI algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value,and the control input and disturbance input also converges to the optimal values. Third, a novel analysis pertaining to the range of the discount factor is presented, where the cost function serves as a Lyapunov function. Finally, neural networks(NNs) are employed to approximate the cost function, the control law, and the disturbance law. Simulation examples are given to illustrate the effective performance of the proposed method.展开更多
A robotic fish, BASEMACK1, is designed and fabricated by mimicking the shape of a live mackerel. Three DC servo-motors are serially linked together and actuated to mimic the mackerel's Carangiform motion. Hydrodynami...A robotic fish, BASEMACK1, is designed and fabricated by mimicking the shape of a live mackerel. Three DC servo-motors are serially linked together and actuated to mimic the mackerel's Carangiform motion. Hydrodynamic characteristics of a fish-mimetic test model are experimentally identified and utilized in order to numerically simulate fish swimming. The discrete set of kinematic and dynamic parameters are obtained by considering required horizontal and lateral forces and minimum energy consumption. Using the optimized parameter set, optimal control of the robot is studied.展开更多
This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is int...This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback system.However,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied directly.To address this problem,an augmented system and an augmented performance index function are proposed firstly.Thus,the general nonlinear system is transformed into an affine nonlinear system.The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically.It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function.Moreover,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function online.The stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals.Finally,the effectiveness of the developed optimal parallel control method is verified in two cases.展开更多
In order to improve the shift quality, a linear quadratic optimal tracking control algorithm for automatic transmission shift process is proposed. The dynamic equations of the shift process are derived using a Lagrang...In order to improve the shift quality, a linear quadratic optimal tracking control algorithm for automatic transmission shift process is proposed. The dynamic equations of the shift process are derived using a Lagrange method. And a powertrain model is built in the Matlab/Simulink and veri- fied by the measurements. Considering the shift jerk and friction loss during the shift process, the tracking trajectories of the turbine speed and output shaft speed are defined. Furthermore, the linear quadratic optimal tracking control performance index is proposed. Based on the Pontryagin' s mini- mum principle, the optimal control law of the shift process is presented. Finally, the simulation study of the 1 - 2 upshift process under different load conditions is carried out with the powertrain model. The simulation results demonstrate that the shift jerk and friction loss can be significantly re- duced by applying the proposed optimal tracking control method.展开更多
This paper considers the optimal trajectory tracking control problem for near-surface autonomous underwater vehicles(AUVs) in the presence of wave disturbances. An approximate optimal tracking control(AOTC) approach i...This paper considers the optimal trajectory tracking control problem for near-surface autonomous underwater vehicles(AUVs) in the presence of wave disturbances. An approximate optimal tracking control(AOTC) approach is proposed. Firstly, a six-degrees-of-freedom(six-DOF) AUV model with its body-fixed coordinate system is decoupled and simplified and then a nonlinear control model of AUVs in the vertical plane is given. Also, an exosystem model of wave disturbances is constructed based on Hirom approximation formula. Secondly, the time-parameterized desired trajectory which is tracked by the AUV's system is represented by the exosystem. Then, the coupled two-point boundary value(TPBV) problem of optimal tracking control for AUVs is derived from the theory of quadratic optimal control. By using a recently developed successive approximation approach to construct sequences, the coupled TPBV problem is transformed into a problem of solving two decoupled linear differential sequences of state vectors and adjoint vectors. By iteratively solving the two equation sequences, the AOTC law is obtained, which consists of a nonlinear optimal feedback item, an expected output tracking item, a feedforward disturbances rejection item, and a nonlinear compensatory term. Furthermore, a wave disturbances observer model is designed in order to solve the physically realizable problem. Simulation is carried out by using the Remote Environmental Unit(REMUS) AUV model to demonstrate the effectiveness of the proposed algorithm.展开更多
An optimal tracking control (OTC) problem for linear time-delay large-scale systems affected by external persistent disturbances is investigated. Based on the internal model principle, a disturbance compensator is c...An optimal tracking control (OTC) problem for linear time-delay large-scale systems affected by external persistent disturbances is investigated. Based on the internal model principle, a disturbance compensator is constructed. The system with persistent disturbances is transformed into an augmented system without persistent disturbances. The original OTC problem of linear time-delay system is transformed into a sequence of linear two- point boundary value (TPBV) problems by introducing a sensitivity parameter and expanding Maclaurin series around it. By solving an OTC law of the augmented system, the OTC law of the original system is obtained. A numerical simulation is provided to illustrate the effectiveness of the proposed method.展开更多
This paper studies the optimal control with zero steady-state error problem for nonlinear large-scale systems affected by external persistent disturbances.The nonlinear large-scale system is transformed into N nonline...This paper studies the optimal control with zero steady-state error problem for nonlinear large-scale systems affected by external persistent disturbances.The nonlinear large-scale system is transformed into N nonlinear subsystems with interconnect terms.Based on the internal model principle,a disturbance compensator is constructed such that the ith subsystem with external persistent disturbances is transformed into an augmented subsystem without disturbances.According to the sensitivity approach,the optimal tracking control law for the ith nonlinear subsystem can be obtained.The optimal tracking control law for the nonlinear large-scale systems can be obtained.A numerical simulation shows that the method is effective.展开更多
Coordinated taxiing planning for multiple aircraft on flight deck is of vital importance which can dramatically improve the dispatching efficiency.In this paper,first,the coordinated taxiing path planning problem is t...Coordinated taxiing planning for multiple aircraft on flight deck is of vital importance which can dramatically improve the dispatching efficiency.In this paper,first,the coordinated taxiing path planning problem is transformed into a centralized optimal control problem where collision-free conditions and mechanical limits are considered.Since the formulated optimal control problem is of large state space and highly nonlinear,an efficient hierarchical initialization technique based on the Dubins-curve method is proposed.Then,a model predictive controller is designed to track the obtained reference trajectory in the presence of initial state error and external disturbances.Numerical experiments demonstrate that the proposed“offline planningþonline tracking”framework can achieve efficient and robust coordinated taxiing planning and tracking even in the presence of initial state error and continuous external disturbances.展开更多
The optimal control principle and procedure of a tandem cold rolling system for tracking problem have been proposed in this paper for the first time. The state-space description of the tandem cold rolling system for t...The optimal control principle and procedure of a tandem cold rolling system for tracking problem have been proposed in this paper for the first time. The state-space description of the tandem cold rolling system for the cold-strip mill of Wuhan Iron and Steel Company is: x_(k+1)=Ax_k+(_k,y_k=Cx_k+η_k The optimum performance index of the system for tracking problem is: J_(min)=minEr[(1/2)sum from k=0 to m e'_kOe_k sum from k=0 to m-1 u'_kRu_k] The authors have deduced the optimal control of the system for tracking problem as follows u_k~*=-S_k(Ay_k+_(k+1)-) the feedback gain matrix S_k is given by S_k=(B'P_(k+1)B-R)^(-1)B'P_(k+1) Pk satisfies the matrix difference equation p_k=P_(k+1)-[(S_k^(-1))'-B']^(-1)R(S_k^(-1)-B)^(-1)+Q with terminal condition P_m=Q. On the above-mentioned principle of optimal control,an optimal control procedure of the tandem cold rolling system has been obtained, and a computer simulation has been performed. The results of the simulation are satisfactory.展开更多
In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming (ADP) algorithm. Via the system transformat...In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming (ADP) algorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some convergence conditions. Two examples are given to demonstrate the validity of the proposed optimal tracking control scheme for chaotic systems.展开更多
This paper estimates an off-policy integral reinforcement learning(IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the...This paper estimates an off-policy integral reinforcement learning(IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman(HJB) equation, an off-policy IRL algorithm is proposed.It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method.展开更多
文摘Background With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emergency evacuation scenarios. Correctly and effectively evacuating crowds in virtual emergency scenarios are becoming increasingly urgent. One good solution is to extract pedestrian trajectories from videos of emergency situations using a multi-target tracking algorithm and use them to define evacuation procedures. Methods To implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and Savitzky-Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real time. Results In addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. Conclusions Further comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework.
基金supported by grants PID2022-142946NA-I00 and PID2022-141159OB-I00,funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.Recommended by Associate Editor Xin Luo.
文摘In the context of multiple-target tracking and surveillance applications,this paper investigates the challenge of determining the optimal positioning of a single autonomous aerial vehicle or agent equipped with multiple independently-steerable zooming cameras to effectively monitor a set of targets of interest.Each camera is dedicated to tracking a specific target or cluster of targets.The key innovation of this study,in comparison to existing approaches,lies in incorporating the zooming factor for the onboard cameras into the optimization problem.This enhancement offers greater flexibility during mission execution by allowing the autonomous agent to adjust the focal lengths of the onboard cameras,in exchange for varying real-world distances to the corresponding targets,thereby providing additional degrees of freedom to the optimization problem.The proposed optimization framework aims to strike a balance among various factors,including distance to the targets,verticality of viewpoints,and the required focal length for each camera.The primary focus of this paper is to establish the theoretical groundwork for addressing the non-convex nature of the optimization problem arising from these considerations.To this end,we develop an original convex approximation strategy.The paper also includes simulations of diverse scenarios,featuring varying numbers of onboard tracking cameras and target motion profiles,to validate the effectiveness of the proposed approach.
基金supported by National Natural Science Foundation of China (Nos.62265010,62061024)Gansu Province Science and Technology Plan (No.23YFGA0062)Gansu Province Innovation Fund (No.2022A-215)。
文摘A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.
基金funded by the Deanship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work through Project Number:IFP22UQU4281768DSR145.
文摘Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with others.Eye tracking(ET)has become a useful method to detect ASD.One vital aspect of moral erudition is the aptitude to have common visual attention.The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection.Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection.It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism.The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking.The Chaotic Butterfly Optimization technique is used to identify this specific disturbance.Therefore,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)technique.The presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL models.To accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS.In addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization.Finally,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of ASD.To assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data repository.The experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better performance in the ETASD-CBODL technique over recent approaches.
文摘Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals.The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes.Design/methodologylapproach-To achieve this objective,the paper simulates actual train operations,incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station.The Monte Carlo simulation method is adopted to solve this problem.This approach transforms a nonlinear model,which includes constraints from probability distribution functions and is difficult to solve directly,into a linear programming model that is easier to handle.The method then linearly weights two objectives to optimize the solution.Findings-Through the application of Monte Carlo simulation,the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model.By continuously adjusting the weighting coefficients of the linear objectives,the method is able to optimize the Pareto solution.Notably,this approach does not require extensive scene data to obtain a satisfactory Pareto solution set.Originality/value-The paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times.The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement.Furthermore,the method's ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.
基金supported by the National Natural Science Foundation of China (61502522)Equipment Pre-Research Field Fund(JZX7Y20190253036101)+1 种基金Equipment Pre-Research Ministry of Education Joint Fund (6141A02033703)Hubei Provincial Natural Scie nce Foundation (2019CFC897)。
文摘The source location based on the hybrid time difference of arrival(TDOA)/frequency difference of arrival(FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA positioning will greatly affect the accuracy of positioning. Using unmanned aerial vehicle(UAV) as base stations, by optimizing the trajectory of the UAV swarm, an optimal positioning configuration is formed to improve the accuracy of the target position and velocity estimation. In this paper, a hybrid TDOA/FDOA positioning model is first established, and the positioning accuracy of the hybrid TDOA/FDOA under different positioning configurations and different measurement errors is simulated by the geometric dilution of precision(GDOP) factor. Second, the Cramer-Rao lower bound(CRLB) matrix of hybrid TDOA/FDOA location under different moving states of the target is derived theoretically, the objective function of the track optimization is obtained, and the track of the UAV swarm is optimized in real time. The simulation results show that the track optimization effectively improves the accuracy of the target position and velocity estimation.
文摘A model helicopter is more difficult to control than its full scale counterpart. This is due to its greater sensitivity to control inputs and disturbances as well as higher bandwidth of dynamics. This work is focused on designing practical tracking controller for a small scale helicopter following predefined trajectories. A tracking controller based on optimal control theory is synthesized as a part of the development of an autonomous helicopter. Some issues with regards to control constraints are addressed. The weighting between state tracking performance and control power expenditure is analyzed. Overall performance of the control design is evaluated based on its time domain histories of trajectories as well as control inputs.
基金supported in part by the National Natural Science Foundation of China(61873300,61722312)in part by the Fundamental Research Funds for the Central Universities(FRF-GF-17-B45)
文摘Based on adaptive dynamic programming(ADP),the fixed-point tracking control problem is solved by a value iteration(VI) algorithm. First, a class of discrete-time(DT)nonlinear system with disturbance is considered. Second, the convergence of a VI algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value,and the control input and disturbance input also converges to the optimal values. Third, a novel analysis pertaining to the range of the discount factor is presented, where the cost function serves as a Lyapunov function. Finally, neural networks(NNs) are employed to approximate the cost function, the control law, and the disturbance law. Simulation examples are given to illustrate the effective performance of the proposed method.
文摘A robotic fish, BASEMACK1, is designed and fabricated by mimicking the shape of a live mackerel. Three DC servo-motors are serially linked together and actuated to mimic the mackerel's Carangiform motion. Hydrodynamic characteristics of a fish-mimetic test model are experimentally identified and utilized in order to numerically simulate fish swimming. The discrete set of kinematic and dynamic parameters are obtained by considering required horizontal and lateral forces and minimum energy consumption. Using the optimized parameter set, optimal control of the robot is studied.
基金supported in part by the National Key Reseanch and Development Program of China(2018AAA0101502,2018YFB1702300)in part by the National Natural Science Foundation of China(61722312,61533019,U1811463,61533017)in part by the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles。
文摘This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback system.However,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied directly.To address this problem,an augmented system and an augmented performance index function are proposed firstly.Thus,the general nonlinear system is transformed into an affine nonlinear system.The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically.It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function.Moreover,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function online.The stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals.Finally,the effectiveness of the developed optimal parallel control method is verified in two cases.
基金Supported by the National Natural Science Foundation of China(51475043)
文摘In order to improve the shift quality, a linear quadratic optimal tracking control algorithm for automatic transmission shift process is proposed. The dynamic equations of the shift process are derived using a Lagrange method. And a powertrain model is built in the Matlab/Simulink and veri- fied by the measurements. Considering the shift jerk and friction loss during the shift process, the tracking trajectories of the turbine speed and output shaft speed are defined. Furthermore, the linear quadratic optimal tracking control performance index is proposed. Based on the Pontryagin' s mini- mum principle, the optimal control law of the shift process is presented. Finally, the simulation study of the 1 - 2 upshift process under different load conditions is carried out with the powertrain model. The simulation results demonstrate that the shift jerk and friction loss can be significantly re- duced by applying the proposed optimal tracking control method.
基金supported in part by the National Natural Science Foundation of China (41276085)the Natural Science Foundation of Shandong Province (ZR2015FM004)
文摘This paper considers the optimal trajectory tracking control problem for near-surface autonomous underwater vehicles(AUVs) in the presence of wave disturbances. An approximate optimal tracking control(AOTC) approach is proposed. Firstly, a six-degrees-of-freedom(six-DOF) AUV model with its body-fixed coordinate system is decoupled and simplified and then a nonlinear control model of AUVs in the vertical plane is given. Also, an exosystem model of wave disturbances is constructed based on Hirom approximation formula. Secondly, the time-parameterized desired trajectory which is tracked by the AUV's system is represented by the exosystem. Then, the coupled two-point boundary value(TPBV) problem of optimal tracking control for AUVs is derived from the theory of quadratic optimal control. By using a recently developed successive approximation approach to construct sequences, the coupled TPBV problem is transformed into a problem of solving two decoupled linear differential sequences of state vectors and adjoint vectors. By iteratively solving the two equation sequences, the AOTC law is obtained, which consists of a nonlinear optimal feedback item, an expected output tracking item, a feedforward disturbances rejection item, and a nonlinear compensatory term. Furthermore, a wave disturbances observer model is designed in order to solve the physically realizable problem. Simulation is carried out by using the Remote Environmental Unit(REMUS) AUV model to demonstrate the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(60574023)the Natural Science Foundation of Shandong Province(Z2005G01).
文摘An optimal tracking control (OTC) problem for linear time-delay large-scale systems affected by external persistent disturbances is investigated. Based on the internal model principle, a disturbance compensator is constructed. The system with persistent disturbances is transformed into an augmented system without persistent disturbances. The original OTC problem of linear time-delay system is transformed into a sequence of linear two- point boundary value (TPBV) problems by introducing a sensitivity parameter and expanding Maclaurin series around it. By solving an OTC law of the augmented system, the OTC law of the original system is obtained. A numerical simulation is provided to illustrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(No.60574023)the Natural Science Foundation of Shandong Province(No.Z2005G01)
文摘This paper studies the optimal control with zero steady-state error problem for nonlinear large-scale systems affected by external persistent disturbances.The nonlinear large-scale system is transformed into N nonlinear subsystems with interconnect terms.Based on the internal model principle,a disturbance compensator is constructed such that the ith subsystem with external persistent disturbances is transformed into an augmented subsystem without disturbances.According to the sensitivity approach,the optimal tracking control law for the ith nonlinear subsystem can be obtained.The optimal tracking control law for the nonlinear large-scale systems can be obtained.A numerical simulation shows that the method is effective.
文摘Coordinated taxiing planning for multiple aircraft on flight deck is of vital importance which can dramatically improve the dispatching efficiency.In this paper,first,the coordinated taxiing path planning problem is transformed into a centralized optimal control problem where collision-free conditions and mechanical limits are considered.Since the formulated optimal control problem is of large state space and highly nonlinear,an efficient hierarchical initialization technique based on the Dubins-curve method is proposed.Then,a model predictive controller is designed to track the obtained reference trajectory in the presence of initial state error and external disturbances.Numerical experiments demonstrate that the proposed“offline planningþonline tracking”framework can achieve efficient and robust coordinated taxiing planning and tracking even in the presence of initial state error and continuous external disturbances.
基金Supported by National Natural Science Foundation of China (61304079, 61125306, 61034002), the Open Research Project from SKLMCCS (20120106), the Fundamental Research Funds for the Central Universities (FRF-TP-13-018A), and the China Postdoctoral Science. Foundation (201_3M_ 5305_27)_ _ _
文摘为有致动器浸透和未知动力学的分离时间的系统的一个班的一个新奇最佳的追踪控制方法在这份报纸被建议。计划基于反复的适应动态编程(自动数据处理) 算法。以便实现控制计划,一个 data-based 标识符首先为未知系统动力学被构造。由介绍 M 网络,稳定的控制的明确的公式被完成。以便消除致动器浸透的效果, nonquadratic 表演功能被介绍,然后一个反复的自动数据处理算法被建立与集中分析完成最佳的追踪控制解决方案。为实现最佳的控制方法,神经网络被用来建立 data-based 标识符,计算性能索引功能,近似最佳的控制政策并且分别地解决稳定的控制。模拟例子被提供验证介绍最佳的追踪的控制计划的有效性。
文摘The optimal control principle and procedure of a tandem cold rolling system for tracking problem have been proposed in this paper for the first time. The state-space description of the tandem cold rolling system for the cold-strip mill of Wuhan Iron and Steel Company is: x_(k+1)=Ax_k+(_k,y_k=Cx_k+η_k The optimum performance index of the system for tracking problem is: J_(min)=minEr[(1/2)sum from k=0 to m e'_kOe_k sum from k=0 to m-1 u'_kRu_k] The authors have deduced the optimal control of the system for tracking problem as follows u_k~*=-S_k(Ay_k+_(k+1)-) the feedback gain matrix S_k is given by S_k=(B'P_(k+1)B-R)^(-1)B'P_(k+1) Pk satisfies the matrix difference equation p_k=P_(k+1)-[(S_k^(-1))'-B']^(-1)R(S_k^(-1)-B)^(-1)+Q with terminal condition P_m=Q. On the above-mentioned principle of optimal control,an optimal control procedure of the tandem cold rolling system has been obtained, and a computer simulation has been performed. The results of the simulation are satisfactory.
基金supported by the Open Research Project from SKLMCCS (Grant No. 20120106)the Fundamental Research Funds for the Central Universities of China (Grant No. FRF-TP-13-018A)+1 种基金the Postdoctoral Science Foundation of China (Grant No. 2013M530527)the National Natural Science Foundation of China (Grant Nos. 61304079, 61125306, and 61034002)
文摘In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming (ADP) algorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some convergence conditions. Two examples are given to demonstrate the validity of the proposed optimal tracking control scheme for chaotic systems.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61304079 and 61374105)the Beijing Natural Science Foundation,China(Grant Nos.4132078 and 4143065)+2 种基金the China Postdoctoral Science Foundation(Grant No.2013M530527)the Fundamental Research Funds for the Central Universities,China(Grant No.FRF-TP-14-119A2)the Open Research Project from State Key Laboratory of Management and Control for Complex Systems,China(Grant No.20150104)
文摘This paper estimates an off-policy integral reinforcement learning(IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman(HJB) equation, an off-policy IRL algorithm is proposed.It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method.