A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well...A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these robots.However,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical applications.This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics.Building on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero.An experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction model.This method establishes a mapping relationship between the friction force and motor current.In addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm.Trajectory tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm.This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots.展开更多
The accuracy of nucleation parameter is a critical factor in the simulation of microstructural evolution during dynamic recrystallization(DRX).Based on the flow stress curve under hot deformation conditions,a new appr...The accuracy of nucleation parameter is a critical factor in the simulation of microstructural evolution during dynamic recrystallization(DRX).Based on the flow stress curve under hot deformation conditions,a new approach is proposed to identify the nucleation parameter during DRX.In this approach,a cellular automaton(CA) model is applied to quantitatively simulate the microstructural evolution and flow stress during hot deformation;and adaptive response surface method(ARSM) is applied as optimization model to provide input parameters to CA model and evaluate the outputs of the latter.By taking an oxygen-free high-conductivity(OFHC) copper as an example,the good agreement between the simulation results and the experimental observations demonstrates the availability of the proposed method.展开更多
In this study,a theoretical nonlinear dynamic model was established for a saddle ring based on a dynamic force analysis of the launching process and the structure according to contact-impact theory.The ADAMS software ...In this study,a theoretical nonlinear dynamic model was established for a saddle ring based on a dynamic force analysis of the launching process and the structure according to contact-impact theory.The ADAMS software was used to build a parameterized dynamic model of the saddle ring.A parameter identification method for the ring was proposed based on the particle swarm optimization algorithm.A loading test was designed and performed several times at different elevation angles.The response histories of the saddle ring with different loads were then obtained.The parameters of the saddle ring dynamic model were identified from statistics generated at a 500 elevation angle to verify the feasibility and accuracy of the proposed method.The actual loading history of the ring at a 70°elevation angle was taken as the model input.The response histories of the ring under these working conditions were obtained through a simulation.The simulation results agreed with the actual response.Thus,the effectiveness and applicability of the proposed dynamic model were verified,and it provides an effective method for modeling saddle rings.展开更多
An electrical equivalent circuit model for lithium-ion batteries used for hybrid electric vehicles （HEV） is presented. The model has two RC networks characterizing battery activation and concentration polarization p...An electrical equivalent circuit model for lithium-ion batteries used for hybrid electric vehicles （HEV） is presented. The model has two RC networks characterizing battery activation and concentration polarization process. The parameters of the model are identified using combined experimental and extended Kalman filter （EKF） recursive methods. The open-circuit voltage and ohmic resistance of the battery are directly measured and calculated from experimental measurements, respectively. The rest of the coupled dynamic parameters, i.e. the RC network parameters, are estimated using the EKF method. Experimental and simulation results are presented to demonstrate the efficacy of the proposed circuit model and parameter identification techniques for simulating battery dynamics.展开更多
The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies...The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies. This is based on the solution of an inverse generalized evaluate problem. The stochastic nature of test data is considered and a normal distribution is used for the measurement frequencies. An additional feature is that the engineer's confidence in the measurement frequencies is quantified and incorporated into the identification procedure. A numerical example demonstrates the efficiency of the method.展开更多
The expressions of matrix construction by using the singular value decomposition (SVD) are applied to the physics parameter identification of dynamic model. Then, based upon to the characteristics of a kind of matrix ...The expressions of matrix construction by using the singular value decomposition (SVD) are applied to the physics parameter identification of dynamic model. Then, based upon to the characteristics of a kind of matrix construction method, the orders of the parameter identification model can be reduced. After reducing, the mathematics and physics correspondence relations between the subsystem and the original system are distinct. the condensation errors can be avoided. The numerical example shows the benefit of the presented methodology.展开更多
Based on the concept of optimal control solution to dynamic system parameters identification and the optimal control theory of deterministic system,dyna-mics system parameters identfication problem is brought into cor...Based on the concept of optimal control solution to dynamic system parameters identification and the optimal control theory of deterministic system,dyna-mics system parameters identfication problem is brought into correspondence with optimal control problem. Then the theory and algorithm of optimal control are introduced into the study of dynamic system parameters identification. According to the theory of Hamilton-Jacobi-Bellman (HJB) equations solution, the existence and uniqueness of optimal control solution to dynamic system parameters identification are resolved in this paper. At last, the parameters identification algorithm of determi-nistic dynamic system is presented also based on above mentioned theory and concept.展开更多
Based on the contents Of part (Ⅰ) and stochastic optimal control theory, the concept of optimal control solution to parameters identification of stochastic dynamic system is discussed at first. For the completeness o...Based on the contents Of part (Ⅰ) and stochastic optimal control theory, the concept of optimal control solution to parameters identification of stochastic dynamic system is discussed at first. For the completeness of the theory developed in this paper and part (Ⅰ), then the procedure of establishing HamiltonJacobi-Bellman (HJB) equations of parameters identification problem is presented.And then, parameters identification algorithm of stochastic dynamic system is introduced. At last, an application example-local nonlinear parameters identification of dynamic system is presented.展开更多
This paper proposes a zero-moment control torque compensation technique.After compensating the gravity and friction of the robot,it must overcome a small inertial force to move in compliance with the external force.Th...This paper proposes a zero-moment control torque compensation technique.After compensating the gravity and friction of the robot,it must overcome a small inertial force to move in compliance with the external force.The principle of torque balance was used to realise the zero-moment dragging and teaching function of the lightweight collaborative robot.The robot parameter identification based on the least square method was used to accurately identify the robot torque sensitivity and friction parameters.When the robot joint rotates at a low speed,it can approximately satisfy the torque balance equation.The experiment uses the joint position and the current motor value collected during the whole moving process under the low-speed dynamic balance as the excitation signal to realise the parameter identification.After the robot was compensated for gravity and static friction,more precise torque control was realised.The zero-moment dragging and teaching function of the robot was more flexible,and the drag process was smoother.展开更多
The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filte...The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filter and smooth the position sequence and then form a differential signal.However,if the noise and the original signal overlap in the frequency domain,filtering the noise will also filter out the valuable information in the frequency band.This paper proposes an excitation trajectory based on Logistic function,which fully uses the information in the original signal and can accurately solve the angular velocity and angular acceleration without filtering and smoothing the position sequence.The joint angle of the excitation trajectory is mapped to the joint angular velocity and angular acceleration one by one so that the joint angular velocity and joint angular acceleration can be obtained directly according to the position.The genetic algorithm is used to optimize the excitation trajectory parameters to minimize the observation matrix’s condition number and further improve the identification accuracy.By using the strategy of iterative identification,the dynamic parameters identified in each iteration are substituted into the robot controller according to the previous position sequence until the tracking trajectory approaches the desired trajectory,and the actual joint angular velocity and angular acceleration converge to the expected value.The simulation results show that using the step-by-step strategy,the joint angular velocity and joint angular acceleration of the tracking trajectory quickly converge to the expected value,and the identification error of inertia parameters is less than 0.01 in three iterations.With the increase of the number of iterations,the identification error of inertial parameters can be further reduced.展开更多
To identify the parameters of the extended Debye model of XLPE cables,and therefore evaluate the insulation performance of the samples,the sparsity-promoting dynamicmode decomposition(SPDMD)methodwas introduced,aswell...To identify the parameters of the extended Debye model of XLPE cables,and therefore evaluate the insulation performance of the samples,the sparsity-promoting dynamicmode decomposition(SPDMD)methodwas introduced,aswell the basics and processes of its applicationwere explained.The amplitude vector based on polarization current was first calculated.Based on the non-zero elements of the vector,the number of branches and parameters including the coefficients and time constants of each branch of the extended Debye model were derived.Further research on parameter identification of XLPE cables at different aging stages based on the SPDMD method was carried out to verify the practicability of the method.Compared with the traditional differential method,the simulation and experiment indicated that the SPDMD method can effectively avoid problems such as the relaxation peak being unobvious,and possessing more accuracy during the parameter identification.And due to the polarization current being less affected by the measurement noise than the depolarization current,the SPDMD identification results based on the polarization current spectral line proved to be better at reflecting the response characteristics of the dielectric.In addition,the time domain polarization current test results can be converted into the frequency domain,and then used to obtain the dielectric loss factor spectrum of the insulation.The integral of the dielectric loss factor on a frequency domain can effectively evaluate the insulation condition of the XLPE cable.展开更多
Parameter identification is a key requirement in the field of automated control of unmanned excavators （UEs）. Furthermore, the UE operates in unstructured, often hazardous environments, and requires a robust paramet...Parameter identification is a key requirement in the field of automated control of unmanned excavators （UEs）. Furthermore, the UE operates in unstructured, often hazardous environments, and requires a robust parameter identification scheme for field applications. This paper presents the results of a research study on parameter identification for UE. Three identification methods, the Newton-Raphson method, the generalized Newton method, and the least squares method are used and compared for prediction accuracy, robustness to noise and computational speed. The techniques are used to identify the link parameters （mass, inertia, and length） and friction coefficients of the full-scale UE. Using experimental data from a full-scale field UE, the values of link parameters and the friction coefficient are identified. Some of the identified parameters are compared with measured physical values. Furthermore, the joint torques and positions computed by the proposed model using the identified parameters are validated against measured data. The comparison shows that both the Newton-Raphson method and the generalized Newton method are better in terms of prediction accuracy. The Newton-Raphson method is computationally efficient and has potential for real time application, but the generalized Newton method is slightly more robust to measurement noise. The experimental data were obtained in collaboration with QinetiQ Ltd.展开更多
Joints are widely used in many kinds of engineering structures, which often leads to the structures to exhibit local nonlinearities, and moreover, they are difficult to model because the complexity of configuration an...Joints are widely used in many kinds of engineering structures, which often leads to the structures to exhibit local nonlinearities, and moreover, they are difficult to model because the complexity of configuration and operating mode. Therefore, parameter identification technique is usually used to model the joints and estimate the model parameters. A novel parameter identification method of nonlinear joints inside a structure is introduced in this paper, by expressing the force transmitted by the joint as a function of its mechanical state and assuming that the other part of the structure is known. In general, the force transmitted by the joints inside a structure and their mechanical state are difficult to measure. To overcome this difficulty, the algorithm of stochastic optimal control is used to identify the force transmitted by the joints and their mechanical state. After that, parameters of the joints can be identified by least squares parameter estimation method. Numerical simulation examples are also given to validate the effectiveness of the proposed method.展开更多
A model to describe the hysteresis damping characteristic of rubber material was presented.It consists of a parallel spring and damper,whose coefficients change with the vibration amplitude and frequency.In order to a...A model to describe the hysteresis damping characteristic of rubber material was presented.It consists of a parallel spring and damper,whose coefficients change with the vibration amplitude and frequency.In order to acquire these relations,force decomposition was carried out according to some sine vibration measurement data of nonlinear forces changing with the deformation of the rubber material.The nonlinear force is decomposed into a spring force and a damper force,which are represented by the amplitude-and frequency-dependent spring and damper coefficients,respectively.Repeating this step for different measurements gives different coefficients corresponding to different amplitudes and frequencies.Then,the application of a parameter identification method provides the requested approximation functions over amplitude and frequency.Using those formulae,as an example,the dynamic characteristic of a hollow shaft system supported by rubber rings was analyzed and the acceleration response curve in the centroid position was calculated.Comparisons with the sine vibration experiments of the real system show a maximal inaccuracy of 8.5%.Application of this model and procedure can simplify the modeling and analysis of mechanical systems including rubber materials.展开更多
A model to describe the hysteresis damper character of rubber material is presented in this paper. It consists of a parallel spring and damper, whose coefficients change with vibration frequencies. In order to acquire...A model to describe the hysteresis damper character of rubber material is presented in this paper. It consists of a parallel spring and damper, whose coefficients change with vibration frequencies. In order to acquire these relations, the force decomposition is carried out according to some sine vibration measurement data about nonlinear forces changing with deformations of the rubber material. The nonlinear force is decomposed into a spring force and a damper force, which are represented by a frcquency-dependent spring and damper coefficient, respectively. Repeating this step for different measurements will give different coefficients corresponding to different frequencies. Then, application of a parameter identification method will provide the requested functions over frequency. Using those formulae, as an example, the dynamic character of a hollow shaft system supported by rubber rings is analyzed and the acceleration response curve in the centroid position is calculated. Comparisons with sine vibration experiments of the real system show a maximal inaccuracy of 8. 8 %. Application of this model and procedure can simplify the modeling and analysis of mechanical systems including rubber materials.展开更多
Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffle...Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the simulation of the investigated process. A clear conclusion is reached highlighting the promising results obtained using this kind of modeling for ANC.展开更多
The dynamic characteristic parameters of Up-time of Flight Counter （U-ToFC） are important for research of structure optimization and reliability. However, the current simulation is performed based on homogenous mate...The dynamic characteristic parameters of Up-time of Flight Counter （U-ToFC） are important for research of structure optimization and reliability. However, the current simulation is performed based on homogenous material and simplified constraint model, the correct and reliability of results are difficult to be guaranteed. The finite element method based on identification of material parameters is proposed for this investigation on dynamic analysis, simulation and vibration experiment of the U-ToFC. The structure of the U-ToFC is complicated. Its＇ outside is made of aluminum alloy and inside contains electronic components such as capacitors, resistors, inductors, and integrated circuits. The accurate material parameters of model are identified difficultly. Hence, the parameters identification tests are performed to obtain the material parameters of this structure. On the basis of the above parameters, the experiment and FEA are conducted to the U-ToFC. In terms of the flight acceptance test level, and two kinds of joints condition between the U-ToFC and fixture are considered. The natural frequencies, vibration shapes and the response of the power spectral density of the U-ToFC are obtained. The results show simulation which is based on parameters identification is similar with vibration experiment in natural frequencies and responses. The errors are less than 10%. The vibration modes of simulation and experiment are consistent. The paper provides a more reliable computing method for the dynamic characteristic analysis of large complicated structure.展开更多
A dynamic frequency-based parameter identification approach is applied for the nonlinear system with periodic responses.Starting from the energy equation,the presented method uses a dynamic frequency to precisely obta...A dynamic frequency-based parameter identification approach is applied for the nonlinear system with periodic responses.Starting from the energy equation,the presented method uses a dynamic frequency to precisely obtain the analytical limit cycle expression of nonlinear system and utilizes it as the mathematic foundation for parameter identification.Distinguished from the time-domain approaches,the strategy of using limit cycle to describe the system response is unaffected by the influence of phase change.The analytical expression is fitted with the value sets from phase coordinates measured in periodic oscillation of the nonlinear systems,and the unknown parameters are identified with the interior-reflective Newton method.Then the performance of this identification methodology is verified by an oscillator with nonlinear stiffness and damping.Besides,numerical simulations under noisy environment also verify the efficiency and robustness of the identification procedure.Finally,we apply this parameter identification method to the modeling of a large-amplitude energy harvester,to improve the accuracy of mechanical modeling.Not surprisingly,good agreement is achieved between the experimental data and identified parameters.It also verifies that the proposed approach is less time-consuming and more accuracy in identification procedure.展开更多
Many industrial processes such as heating furnaces have over damping dynamic characteristics. Based on an innovative impulse response model, a method of identification and control for the over damping plant is introdu...Many industrial processes such as heating furnaces have over damping dynamic characteristics. Based on an innovative impulse response model, a method of identification and control for the over damping plant is introduced in the paper. The number of parameters of the model is much less than conventional impulse response model. The model based on tuning procedure of numerical optimum PID controller parameters is presented. For an actual instance, a large scale airflow circulatory resistance furnace control system with cascades of time delays is developed. In the system, the optimum PID control is used in the inner loop. A nonlinear PI compensation control is applied in the outer loop. The coordinating control among each output is realized by a fuzzy control strategy. A process surveillance organization monitors running situation of system and tunes controller parameters.展开更多
Extracting nonlinear governing equations from noisy data is a central challenge in the analysis of complicated nonlinear behaviors.Despite researchers follow the sparse identification nonlinear dynamics algorithm(SIND...Extracting nonlinear governing equations from noisy data is a central challenge in the analysis of complicated nonlinear behaviors.Despite researchers follow the sparse identification nonlinear dynamics algorithm(SINDy)rule to restore nonlinear equations,there also exist obstacles.One is the excessive dependence on empirical parameters,which increases the difficulty of data pre-processing.Another one is the coexistence of multiple coefficient vectors,which causes the optimal solution to be drowned in multiple solutions.The third one is the composition of basic function,which is exclusively applicable to specific equations.In this article,a local sparse screening identification algorithm(LSSI)is proposed to identify nonlinear systems.First,we present the k-neighbor parameter to replace all empirical parameters in data filtering.Second,we combine the mean error screening method with the SINDy algorithm to select the optimal one from multiple solutions.Third,the time variable t is introduced to expand the scope of the SINDy algorithm.Finally,the LSSI algorithm is applied to recover a classic ODE and a bi-stable energy harvester system.The results show that the new algorithm improves the ability of noise immunity and optimal parameters identification provides a desired foundation for nonlinear analyses.展开更多
基金Supported by Shanghai Municipal Science and Technology Program (Grant No.21511101701)National Key Research and Development Program of China (Grant No.2021YFC0122704)。
文摘A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these robots.However,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical applications.This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics.Building on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero.An experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction model.This method establishes a mapping relationship between the friction force and motor current.In addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm.Trajectory tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm.This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots.
基金Project(2006CB705401) supported by the National Basic Research Program of China
文摘The accuracy of nucleation parameter is a critical factor in the simulation of microstructural evolution during dynamic recrystallization(DRX).Based on the flow stress curve under hot deformation conditions,a new approach is proposed to identify the nucleation parameter during DRX.In this approach,a cellular automaton(CA) model is applied to quantitatively simulate the microstructural evolution and flow stress during hot deformation;and adaptive response surface method(ARSM) is applied as optimization model to provide input parameters to CA model and evaluate the outputs of the latter.By taking an oxygen-free high-conductivity(OFHC) copper as an example,the good agreement between the simulation results and the experimental observations demonstrates the availability of the proposed method.
基金supported by National Natural Science Foundation of China(11472137)the Natural Science Foundation of Jiangsu Province,China(BK20140773)。
文摘In this study,a theoretical nonlinear dynamic model was established for a saddle ring based on a dynamic force analysis of the launching process and the structure according to contact-impact theory.The ADAMS software was used to build a parameterized dynamic model of the saddle ring.A parameter identification method for the ring was proposed based on the particle swarm optimization algorithm.A loading test was designed and performed several times at different elevation angles.The response histories of the saddle ring with different loads were then obtained.The parameters of the saddle ring dynamic model were identified from statistics generated at a 500 elevation angle to verify the feasibility and accuracy of the proposed method.The actual loading history of the ring at a 70°elevation angle was taken as the model input.The response histories of the ring under these working conditions were obtained through a simulation.The simulation results agreed with the actual response.Thus,the effectiveness and applicability of the proposed dynamic model were verified,and it provides an effective method for modeling saddle rings.
文摘An electrical equivalent circuit model for lithium-ion batteries used for hybrid electric vehicles （HEV） is presented. The model has two RC networks characterizing battery activation and concentration polarization process. The parameters of the model are identified using combined experimental and extended Kalman filter （EKF） recursive methods. The open-circuit voltage and ohmic resistance of the battery are directly measured and calculated from experimental measurements, respectively. The rest of the coupled dynamic parameters, i.e. the RC network parameters, are estimated using the EKF method. Experimental and simulation results are presented to demonstrate the efficacy of the proposed circuit model and parameter identification techniques for simulating battery dynamics.
文摘The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies. This is based on the solution of an inverse generalized evaluate problem. The stochastic nature of test data is considered and a normal distribution is used for the measurement frequencies. An additional feature is that the engineer's confidence in the measurement frequencies is quantified and incorporated into the identification procedure. A numerical example demonstrates the efficiency of the method.
文摘The expressions of matrix construction by using the singular value decomposition (SVD) are applied to the physics parameter identification of dynamic model. Then, based upon to the characteristics of a kind of matrix construction method, the orders of the parameter identification model can be reduced. After reducing, the mathematics and physics correspondence relations between the subsystem and the original system are distinct. the condensation errors can be avoided. The numerical example shows the benefit of the presented methodology.
文摘Based on the concept of optimal control solution to dynamic system parameters identification and the optimal control theory of deterministic system,dyna-mics system parameters identfication problem is brought into correspondence with optimal control problem. Then the theory and algorithm of optimal control are introduced into the study of dynamic system parameters identification. According to the theory of Hamilton-Jacobi-Bellman (HJB) equations solution, the existence and uniqueness of optimal control solution to dynamic system parameters identification are resolved in this paper. At last, the parameters identification algorithm of determi-nistic dynamic system is presented also based on above mentioned theory and concept.
文摘Based on the contents Of part (Ⅰ) and stochastic optimal control theory, the concept of optimal control solution to parameters identification of stochastic dynamic system is discussed at first. For the completeness of the theory developed in this paper and part (Ⅰ), then the procedure of establishing HamiltonJacobi-Bellman (HJB) equations of parameters identification problem is presented.And then, parameters identification algorithm of stochastic dynamic system is introduced. At last, an application example-local nonlinear parameters identification of dynamic system is presented.
基金supported by the National Natural Science Foundation of China(52005316,61903269,52005317)the Major Research and Development Program of Jiangsu Province(BE2020082-3).
文摘This paper proposes a zero-moment control torque compensation technique.After compensating the gravity and friction of the robot,it must overcome a small inertial force to move in compliance with the external force.The principle of torque balance was used to realise the zero-moment dragging and teaching function of the lightweight collaborative robot.The robot parameter identification based on the least square method was used to accurately identify the robot torque sensitivity and friction parameters.When the robot joint rotates at a low speed,it can approximately satisfy the torque balance equation.The experiment uses the joint position and the current motor value collected during the whole moving process under the low-speed dynamic balance as the excitation signal to realise the parameter identification.After the robot was compensated for gravity and static friction,more precise torque control was realised.The zero-moment dragging and teaching function of the robot was more flexible,and the drag process was smoother.
基金supported by Aeronautical Science Foundation of China(No.201916052001)China National Key R&D Program(No.2018YFB1309203)Foundation of the Graduate Innovation Center,Nanjing University of Aeronautics and Astronautics(No.xcxjh20210501)。
文摘The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filter and smooth the position sequence and then form a differential signal.However,if the noise and the original signal overlap in the frequency domain,filtering the noise will also filter out the valuable information in the frequency band.This paper proposes an excitation trajectory based on Logistic function,which fully uses the information in the original signal and can accurately solve the angular velocity and angular acceleration without filtering and smoothing the position sequence.The joint angle of the excitation trajectory is mapped to the joint angular velocity and angular acceleration one by one so that the joint angular velocity and joint angular acceleration can be obtained directly according to the position.The genetic algorithm is used to optimize the excitation trajectory parameters to minimize the observation matrix’s condition number and further improve the identification accuracy.By using the strategy of iterative identification,the dynamic parameters identified in each iteration are substituted into the robot controller according to the previous position sequence until the tracking trajectory approaches the desired trajectory,and the actual joint angular velocity and angular acceleration converge to the expected value.The simulation results show that using the step-by-step strategy,the joint angular velocity and joint angular acceleration of the tracking trajectory quickly converge to the expected value,and the identification error of inertia parameters is less than 0.01 in three iterations.With the increase of the number of iterations,the identification error of inertial parameters can be further reduced.
基金supported by the Science and Technology Project of Guizhou Power Grid Co.,Ltd. (No.GZKJXM20210405).
文摘To identify the parameters of the extended Debye model of XLPE cables,and therefore evaluate the insulation performance of the samples,the sparsity-promoting dynamicmode decomposition(SPDMD)methodwas introduced,aswell the basics and processes of its applicationwere explained.The amplitude vector based on polarization current was first calculated.Based on the non-zero elements of the vector,the number of branches and parameters including the coefficients and time constants of each branch of the extended Debye model were derived.Further research on parameter identification of XLPE cables at different aging stages based on the SPDMD method was carried out to verify the practicability of the method.Compared with the traditional differential method,the simulation and experiment indicated that the SPDMD method can effectively avoid problems such as the relaxation peak being unobvious,and possessing more accuracy during the parameter identification.And due to the polarization current being less affected by the measurement noise than the depolarization current,the SPDMD identification results based on the polarization current spectral line proved to be better at reflecting the response characteristics of the dielectric.In addition,the time domain polarization current test results can be converted into the frequency domain,and then used to obtain the dielectric loss factor spectrum of the insulation.The integral of the dielectric loss factor on a frequency domain can effectively evaluate the insulation condition of the XLPE cable.
基金This work was supported by the EPSRC(No.GR/R50738/01).
文摘Parameter identification is a key requirement in the field of automated control of unmanned excavators （UEs）. Furthermore, the UE operates in unstructured, often hazardous environments, and requires a robust parameter identification scheme for field applications. This paper presents the results of a research study on parameter identification for UE. Three identification methods, the Newton-Raphson method, the generalized Newton method, and the least squares method are used and compared for prediction accuracy, robustness to noise and computational speed. The techniques are used to identify the link parameters （mass, inertia, and length） and friction coefficients of the full-scale UE. Using experimental data from a full-scale field UE, the values of link parameters and the friction coefficient are identified. Some of the identified parameters are compared with measured physical values. Furthermore, the joint torques and positions computed by the proposed model using the identified parameters are validated against measured data. The comparison shows that both the Newton-Raphson method and the generalized Newton method are better in terms of prediction accuracy. The Newton-Raphson method is computationally efficient and has potential for real time application, but the generalized Newton method is slightly more robust to measurement noise. The experimental data were obtained in collaboration with QinetiQ Ltd.
文摘Joints are widely used in many kinds of engineering structures, which often leads to the structures to exhibit local nonlinearities, and moreover, they are difficult to model because the complexity of configuration and operating mode. Therefore, parameter identification technique is usually used to model the joints and estimate the model parameters. A novel parameter identification method of nonlinear joints inside a structure is introduced in this paper, by expressing the force transmitted by the joint as a function of its mechanical state and assuming that the other part of the structure is known. In general, the force transmitted by the joints inside a structure and their mechanical state are difficult to measure. To overcome this difficulty, the algorithm of stochastic optimal control is used to identify the force transmitted by the joints and their mechanical state. After that, parameters of the joints can be identified by least squares parameter estimation method. Numerical simulation examples are also given to validate the effectiveness of the proposed method.
基金Project(50675042) supported by the National Natural Science Foundation of China
文摘A model to describe the hysteresis damping characteristic of rubber material was presented.It consists of a parallel spring and damper,whose coefficients change with the vibration amplitude and frequency.In order to acquire these relations,force decomposition was carried out according to some sine vibration measurement data of nonlinear forces changing with the deformation of the rubber material.The nonlinear force is decomposed into a spring force and a damper force,which are represented by the amplitude-and frequency-dependent spring and damper coefficients,respectively.Repeating this step for different measurements gives different coefficients corresponding to different amplitudes and frequencies.Then,the application of a parameter identification method provides the requested approximation functions over amplitude and frequency.Using those formulae,as an example,the dynamic characteristic of a hollow shaft system supported by rubber rings was analyzed and the acceleration response curve in the centroid position was calculated.Comparisons with the sine vibration experiments of the real system show a maximal inaccuracy of 8.5%.Application of this model and procedure can simplify the modeling and analysis of mechanical systems including rubber materials.
文摘A model to describe the hysteresis damper character of rubber material is presented in this paper. It consists of a parallel spring and damper, whose coefficients change with vibration frequencies. In order to acquire these relations, the force decomposition is carried out according to some sine vibration measurement data about nonlinear forces changing with deformations of the rubber material. The nonlinear force is decomposed into a spring force and a damper force, which are represented by a frcquency-dependent spring and damper coefficient, respectively. Repeating this step for different measurements will give different coefficients corresponding to different frequencies. Then, application of a parameter identification method will provide the requested functions over frequency. Using those formulae, as an example, the dynamic character of a hollow shaft system supported by rubber rings is analyzed and the acceleration response curve in the centroid position is calculated. Comparisons with sine vibration experiments of the real system show a maximal inaccuracy of 8. 8 %. Application of this model and procedure can simplify the modeling and analysis of mechanical systems including rubber materials.
基金CAPES and CNPq(Brazilian federal research agencies)for their financial support.
文摘Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the simulation of the investigated process. A clear conclusion is reached highlighting the promising results obtained using this kind of modeling for ANC.
基金supported by National Natural Science Foundation of China (Grant No. 51105025)Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, China (Grant No. BUAA-VR-12KF-10)
文摘The dynamic characteristic parameters of Up-time of Flight Counter （U-ToFC） are important for research of structure optimization and reliability. However, the current simulation is performed based on homogenous material and simplified constraint model, the correct and reliability of results are difficult to be guaranteed. The finite element method based on identification of material parameters is proposed for this investigation on dynamic analysis, simulation and vibration experiment of the U-ToFC. The structure of the U-ToFC is complicated. Its＇ outside is made of aluminum alloy and inside contains electronic components such as capacitors, resistors, inductors, and integrated circuits. The accurate material parameters of model are identified difficultly. Hence, the parameters identification tests are performed to obtain the material parameters of this structure. On the basis of the above parameters, the experiment and FEA are conducted to the U-ToFC. In terms of the flight acceptance test level, and two kinds of joints condition between the U-ToFC and fixture are considered. The natural frequencies, vibration shapes and the response of the power spectral density of the U-ToFC are obtained. The results show simulation which is based on parameters identification is similar with vibration experiment in natural frequencies and responses. The errors are less than 10%. The vibration modes of simulation and experiment are consistent. The paper provides a more reliable computing method for the dynamic characteristic analysis of large complicated structure.
基金supported by the National Natural Science Foundation of China(Grants 11772218 and 11872044)China-UK NSFC-RS Joint Project(Grants 11911530177 in China and IE 181496 in UK)+1 种基金Tianjin Research Program of Application Foundation and Advanced Technology(Grant 17JCYBJC18900)the National Key Research and Development Program of China(Grant 2018YFB0106200).
文摘A dynamic frequency-based parameter identification approach is applied for the nonlinear system with periodic responses.Starting from the energy equation,the presented method uses a dynamic frequency to precisely obtain the analytical limit cycle expression of nonlinear system and utilizes it as the mathematic foundation for parameter identification.Distinguished from the time-domain approaches,the strategy of using limit cycle to describe the system response is unaffected by the influence of phase change.The analytical expression is fitted with the value sets from phase coordinates measured in periodic oscillation of the nonlinear systems,and the unknown parameters are identified with the interior-reflective Newton method.Then the performance of this identification methodology is verified by an oscillator with nonlinear stiffness and damping.Besides,numerical simulations under noisy environment also verify the efficiency and robustness of the identification procedure.Finally,we apply this parameter identification method to the modeling of a large-amplitude energy harvester,to improve the accuracy of mechanical modeling.Not surprisingly,good agreement is achieved between the experimental data and identified parameters.It also verifies that the proposed approach is less time-consuming and more accuracy in identification procedure.
文摘Many industrial processes such as heating furnaces have over damping dynamic characteristics. Based on an innovative impulse response model, a method of identification and control for the over damping plant is introduced in the paper. The number of parameters of the model is much less than conventional impulse response model. The model based on tuning procedure of numerical optimum PID controller parameters is presented. For an actual instance, a large scale airflow circulatory resistance furnace control system with cascades of time delays is developed. In the system, the optimum PID control is used in the inner loop. A nonlinear PI compensation control is applied in the outer loop. The coordinating control among each output is realized by a fuzzy control strategy. A process surveillance organization monitors running situation of system and tunes controller parameters.
基金The work was supported by the National Science Foundation of China(grant nos.11772218 and 11872044)China-UK NSFC-RS Joint Project(grant nos.11911530177 in China and IE181496 in the UK)Tianjin Research Program of Application Foundation and Advanced Technology(grant no.17JCYBJC18900).
文摘Extracting nonlinear governing equations from noisy data is a central challenge in the analysis of complicated nonlinear behaviors.Despite researchers follow the sparse identification nonlinear dynamics algorithm(SINDy)rule to restore nonlinear equations,there also exist obstacles.One is the excessive dependence on empirical parameters,which increases the difficulty of data pre-processing.Another one is the coexistence of multiple coefficient vectors,which causes the optimal solution to be drowned in multiple solutions.The third one is the composition of basic function,which is exclusively applicable to specific equations.In this article,a local sparse screening identification algorithm(LSSI)is proposed to identify nonlinear systems.First,we present the k-neighbor parameter to replace all empirical parameters in data filtering.Second,we combine the mean error screening method with the SINDy algorithm to select the optimal one from multiple solutions.Third,the time variable t is introduced to expand the scope of the SINDy algorithm.Finally,the LSSI algorithm is applied to recover a classic ODE and a bi-stable energy harvester system.The results show that the new algorithm improves the ability of noise immunity and optimal parameters identification provides a desired foundation for nonlinear analyses.
