A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical...A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.展开更多
An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiabilit...An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix, in combination with component energy index （CEI） which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio （SNR）and gives a reliable separation of spurious and physical estimates.展开更多
The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model fo...The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model for a two-link space manipulator in the procedure of capturing an unknown object, and a recursive tracking approach based on the recursive predictor-based subspace identification(RPBSID) algorithm is proposed to identify the manipulator payload mass parameter. Structural rigid motion and elastic vibration are separated, and the dynamics model of the space manipulator is linearized at an arbitrary working point(i.e., a certain manipulator configuration).The state-space model is determined by using the RPBSID algorithm and matrix transformation. In addition, utilizing the identified system state-space model, the manipulator payload mass parameter is estimated by extracting the corresponding block matrix. In numerical simulations, the presented parameter identification method is implemented and compared with the classical algebraic algorithm and the recursive least squares method for different payload masses and manipulator configurations. Numerical results illustrate that the system state-space model and payload mass parameter of the two-link flexible space manipulator are effectively identified by the recursive subspace tracking method.展开更多
Precise states estimation for the lithium-ion battery is one of the fundamental tasks in the battery management system(BMS),where building an accurate battery model is the first step in model-based estimation algorith...Precise states estimation for the lithium-ion battery is one of the fundamental tasks in the battery management system(BMS),where building an accurate battery model is the first step in model-based estimation algorithms.To date,although the comparative studies on different battery models have been performed intensively,little attention is paid to the comparison among different online parameters identification methods regarding model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost.In this paper,based on the Thevenin model,the three most widely used online parameters identification methods,including extended Kalman filter(EKF),particle swarm optimization(PSO),and recursive least square(RLS),are evaluated comprehensively under static and dynamic tests.It is worth noting that,although the built model’s terminal voltage may well follow a measured curve,these identified model parameters may significantly out of reasonable range,which means that the error between measured and predicted terminal voltage cannot be seen as a gist to determine which model is the most accurate.To evaluate model accuracy more rigorously,battery state-of-charge(SOC)is further estimated based on identified model parameters under static and dynamic tests.The SOC prediction results show that EKF and RLS algorithms are more suitable to be used for online model parameters identification under static and dynamic tests,respectively.Moreover,the random offset is added into originally measured data to verify the robustness ability of different methods,whose results indicate EKF and RLS have more satisfactory ability against imprecisely sampled data under static and dynamic tests,respectively.Considering model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost simultaneously,EKF is recommended to be adopted to establish battery model in real application among these three most widely used methods.展开更多
Considering the multivariable and fractional-order characteristics of proton exchange membrane fuel cells(PEMFCs),a fractional-order subspace identification method(FOSIM)is proposed in this paper to establish a fracti...Considering the multivariable and fractional-order characteristics of proton exchange membrane fuel cells(PEMFCs),a fractional-order subspace identification method(FOSIM)is proposed in this paper to establish a fractionalorder state space(FOSS)model,which can be expressed as a multivariable configuration with two inputs,hydrogenflow rate and stack current,and two outputs,cell voltage and power.Based on this model,a novel constrained optimal control law named the Hildreth model predictive control(H-MPC)strategy is created,which employs a Hildreth quadratic programming algorithm to adjust the output power of fuel cells through adaptively regulating hydrogen flow and stack current.dSPACE semi-physical simulation results demonstrate that,compared with proportional-integral-derivative and quadratic programming MPC(QP-MPC),the proposed H-MPC exhibits better tracking ability and strong robustness against variations of PEMFC power.展开更多
文摘A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.
基金This project is supported by National Natural Science Foundation of China (No.10302019).
文摘An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix, in combination with component energy index （CEI） which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio （SNR）and gives a reliable separation of spurious and physical estimates.
基金funded by the National Natural Science Foundation of China (Nos. 11572069 and 51775541)the China Postdoctoral Science Foundation (No. 2016M601354)
文摘The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model for a two-link space manipulator in the procedure of capturing an unknown object, and a recursive tracking approach based on the recursive predictor-based subspace identification(RPBSID) algorithm is proposed to identify the manipulator payload mass parameter. Structural rigid motion and elastic vibration are separated, and the dynamics model of the space manipulator is linearized at an arbitrary working point(i.e., a certain manipulator configuration).The state-space model is determined by using the RPBSID algorithm and matrix transformation. In addition, utilizing the identified system state-space model, the manipulator payload mass parameter is estimated by extracting the corresponding block matrix. In numerical simulations, the presented parameter identification method is implemented and compared with the classical algebraic algorithm and the recursive least squares method for different payload masses and manipulator configurations. Numerical results illustrate that the system state-space model and payload mass parameter of the two-link flexible space manipulator are effectively identified by the recursive subspace tracking method.
基金supported by the State Grid Company Science and Technology Project(Grant No.5230HQ19000J).
文摘Precise states estimation for the lithium-ion battery is one of the fundamental tasks in the battery management system(BMS),where building an accurate battery model is the first step in model-based estimation algorithms.To date,although the comparative studies on different battery models have been performed intensively,little attention is paid to the comparison among different online parameters identification methods regarding model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost.In this paper,based on the Thevenin model,the three most widely used online parameters identification methods,including extended Kalman filter(EKF),particle swarm optimization(PSO),and recursive least square(RLS),are evaluated comprehensively under static and dynamic tests.It is worth noting that,although the built model’s terminal voltage may well follow a measured curve,these identified model parameters may significantly out of reasonable range,which means that the error between measured and predicted terminal voltage cannot be seen as a gist to determine which model is the most accurate.To evaluate model accuracy more rigorously,battery state-of-charge(SOC)is further estimated based on identified model parameters under static and dynamic tests.The SOC prediction results show that EKF and RLS algorithms are more suitable to be used for online model parameters identification under static and dynamic tests,respectively.Moreover,the random offset is added into originally measured data to verify the robustness ability of different methods,whose results indicate EKF and RLS have more satisfactory ability against imprecisely sampled data under static and dynamic tests,respectively.Considering model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost simultaneously,EKF is recommended to be adopted to establish battery model in real application among these three most widely used methods.
基金This work was supported in part by National Natural Science Foundation of China grant No.61374153 and grant No.52377209in part by“Postgraduate Research&Practice Innovation Program of Jiangsu Province”(grant No.SJCX23_0132).
文摘Considering the multivariable and fractional-order characteristics of proton exchange membrane fuel cells(PEMFCs),a fractional-order subspace identification method(FOSIM)is proposed in this paper to establish a fractionalorder state space(FOSS)model,which can be expressed as a multivariable configuration with two inputs,hydrogenflow rate and stack current,and two outputs,cell voltage and power.Based on this model,a novel constrained optimal control law named the Hildreth model predictive control(H-MPC)strategy is created,which employs a Hildreth quadratic programming algorithm to adjust the output power of fuel cells through adaptively regulating hydrogen flow and stack current.dSPACE semi-physical simulation results demonstrate that,compared with proportional-integral-derivative and quadratic programming MPC(QP-MPC),the proposed H-MPC exhibits better tracking ability and strong robustness against variations of PEMFC power.
