The ultrasonic motor (USM) possesses heavy nonlinearities which vary with driving conditions and load-dependent characteristics such as the dead-zone. In this paper, an identification method for the rotary travelling-...The ultrasonic motor (USM) possesses heavy nonlinearities which vary with driving conditions and load-dependent characteristics such as the dead-zone. In this paper, an identification method for the rotary travelling-wave type ultrasonic motor (RTWUSM) with dead-zone is proposed based on a modified Hammerstein model structure. The driving voltage contributing effect on the nonlinearities of the RTWUSM was transformed to the change of dynamic parameters against the driving voltage. The dead-zone of the RTWUSM is identified based upon the above transformation. Experiment results showed good agreement be- tween the output of the proposed model and actual measured output.展开更多
Passive intermodulation(PIM)interference urgently needs to be solved in the satellite communication system,owing to degrading the whole performance.Mainstream research contributions to the cancellation method for PIM ...Passive intermodulation(PIM)interference urgently needs to be solved in the satellite communication system,owing to degrading the whole performance.Mainstream research contributions to the cancellation method for PIM were focused on the analog domain,however,the PIM distortion cannot be eliminated completely with the approaches.Meanwhile,some researchers attempt to tackle the problem through digital signal processing,nevertheless,the proposed methods were not suitable for the practical satellite communication scenario.In this paper,we present a general scheme for the adaptive feedforward PIM cancellation.High-order PIM signals at baseband are estimated by modeling the PIM distortion with Hammerstein model in the digital domain.Based on the reconstructed PIM signal,we adopt the least mean square algorithm to adaptively mitigate the PIM interference for tracking the variation of PIM.The time and frequency synchronization of PIM are based on the correlation of the peak of received signals with the corresponding reconstructed PIM signal.Practical experimental results show that the scheme can effectively cancel the PIM interference,and achieve an interference suppression gain more than 20dB.展开更多
An algorithm based on mixed signals is proposed,to solve the issues of low accuracy of identification algorithm,immeasurable intermediate variables of fractional order Hammerstein model,and how to determine the magnit...An algorithm based on mixed signals is proposed,to solve the issues of low accuracy of identification algorithm,immeasurable intermediate variables of fractional order Hammerstein model,and how to determine the magnitude of fractional order.In this paper,a special mixed input signal is designed to separate the nonlinear and linear parts of the fractional order Hammerstein model so that each part can be identified independently.The nonlinear part is fitted by the neural fuzzy network model,which avoids the limitation of polynomial fitting and broadens the application range of nonlinear models.In addition,the multi-innovation Levenberg-Marquardt(MILM)algorithm and auxiliary recursive least square algorithm are innovatively integrated into the parameter identification algorithm of the fractional order Hammerstein model to obtain more accurate identification results.A simulation example is given to verify the accuracy and effectiveness of the proposed method.展开更多
Considering the fractional-order and nonlinear characteristics of proton exchange membrane fuel cells(PEMFC),a fractional-order subspace identification method based on the ADE-BH optimization algorithm is proposed to ...Considering the fractional-order and nonlinear characteristics of proton exchange membrane fuel cells(PEMFC),a fractional-order subspace identification method based on the ADE-BH optimization algorithm is proposed to establish a fractional-order Hammerstein state-space model of PEMFCs.Herein,a Hammerstein model is constructed by connecting a linear module and a nonlinear module in series to precisely depict the nonlinear property of the PEMFC.During the modeling process,fractional-order theory is combined with subspace identification,and a Poisson filter is adopted to enable multi-order derivability of the data.A variable memory method is introduced to reduce computation time without losing precision.Additionally,to improve the optimization accuracy and avoid obtaining locally optimum solutions,a novel ADEBH algorithm is employed to optimize the unknown parameters in the identification method.In this algorithm,the Euclidean distance serves as the theoretical basis for updating the target vector in the absorption-generation operation of the black hole(BH)algorithm.Finally,simulations demonstrate that the proposed model has small output error and high accuracy,indicating that the model can accurately describe the electrical characteristics of the PEMFC process.展开更多
This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method ...This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.展开更多
Purpose-The purpose of this paper is to probe the recursive identification of piecewise affine Hammerstein models directly by using input-output data.To explain the identification process of a parametric piecewise aff...Purpose-The purpose of this paper is to probe the recursive identification of piecewise affine Hammerstein models directly by using input-output data.To explain the identification process of a parametric piecewise affine nonlinear function,the authors prove that the inverse function corresponding to the given piecewise affine nonlinear function is also an equivalent piecewise affine form.Based on this equivalent property,during the detailed identification process with respect to piecewise affine function and linear dynamical system,three recursive least squares methods are proposed to identify those unknown parameters under the probabilistic description or bounded property of noise.Design/methodology/approach-First,the basic recursive least squares method is used to identify those unknown parameters under the probabilistic description of noise.Second,multi-innovation recursive least squares method is proposed to improve the efficiency lacked in basic recursive least squares method.Third,to relax the strict probabilistic description on noise,the authors provide a projection algorithm with a dead zone in the presence of bounded noise and analyze its two properties.Findings-Based on complex mathematical derivation,the inverse function of a given piecewise affine nonlinear function is also an equivalent piecewise affine form.As the least squares method is suited under one condition that the considered noise may be a zero mean random signal,a projection algorithm with a dead zone in the presence of bounded noise can enhance the robustness in the parameter update equation.Originality/value-To the best knowledge of the authors,this is the first attempt at identifying piecewise affine Hammerstein models,which combine a piecewise affine function and a linear dynamical system.In the presence of bounded noise,the modified recursive least squares methods are efficient in identifying two kinds of unknown parameters,so that the common set membership method can be replaced by the proposed methods.展开更多
The modeling of distillation column process is a very challenging problem because of the complex dynamic behavior.This paper investigates a Nonlinear Autoregressive Moving Average with eXogenous input(NARMAX)model,and...The modeling of distillation column process is a very challenging problem because of the complex dynamic behavior.This paper investigates a Nonlinear Autoregressive Moving Average with eXogenous input(NARMAX)model,and a Hammerstein model to approximate the evolution of the overhead temperature in a separation system.The model development and validation are studied through experiments carried out on a distillation plant of laboratory scale.Three model order selection criteria such as Aikeke’s Information Criterion(AIC),Root Mean Square Error(RMSE)and Nash–Sutcliffe Efficiency(NSE)are used to evaluate the prediction performance of the process behavior.The results illustrate that both models produce acceptable predictions but the NARMAX model outperforms the Hammerstein model.展开更多
In acs paper,the generalized predictive control(GPC)law for Hammerstein model with control horizon NU=1 is presented and the algebraic equation satisfied by the GPC law is derived.Also,the simulation study shows tha t...In acs paper,the generalized predictive control(GPC)law for Hammerstein model with control horizon NU=1 is presented and the algebraic equation satisfied by the GPC law is derived.Also,the simulation study shows tha tthe GPC based on Hammerstein system is such and algorithm which can be controlled by numerical computer with rather strong Robustness but without strict demand for the model.展开更多
Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approa...Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approach, the maximum and minimum of partial derivative for input and output nonlinearities are solved in the neighbourhood of the equilibrium. And several parameter-dependent Lyapunov functions, each one corresponding to a different vertex of polytopic descriptions models, are introduced to analyze the stability of Hammerstein-Wiener systems, but only one Lyapunov function is utilized to analyze system stability like the traditional method. Consequently, the conservation of the traditional quadratic stability is removed, and the terminal regions are enlarged. Simulation and field trial results show that the proposed algorithm is valid. It has higher control precision and shorter blowing time than the traditional approach.展开更多
An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the ...An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the min-max optimization problem.The polytopic invariant set is adopted to replace the traditional ellipsoid invariant set.And the parameter-correlation nonlinear control law is designed to replace the traditional linear control law.Consequently,the terminal region is enlarged and the control effect is improved.Simulation and experiment are used to verify the validity of the wind tunnel flow field control algorithm.展开更多
伴随着新能源产业的飞速发展,锂离子动力电池作为一种高效的储能方式,已成为电动汽车的重要组成部分。在电池管理系统的功能中,电池的高精度建模至关重要。在实际应用中,电池不是一个线性系统,其输入和输出由于外部扰动等原因表现出非...伴随着新能源产业的飞速发展,锂离子动力电池作为一种高效的储能方式,已成为电动汽车的重要组成部分。在电池管理系统的功能中,电池的高精度建模至关重要。在实际应用中,电池不是一个线性系统,其输入和输出由于外部扰动等原因表现出非线性特征,从而直接影响参数识别效果,进而影响模型精度。鉴于此,本文对锂离子动力电池进行了Hammerstein-ARMAX(Autoregressive MovingAverage with Extra Input)模型构建,并对模型参数的估计方法进行研究,旨在提高模型的准确性。实验结果表明了该方法的有效性。展开更多
基金Project supported by the National Natural Science Foundation of China (No. 60572055)the Natural Science Foundation of Guangxi Province (No. 0339068), China
文摘The ultrasonic motor (USM) possesses heavy nonlinearities which vary with driving conditions and load-dependent characteristics such as the dead-zone. In this paper, an identification method for the rotary travelling-wave type ultrasonic motor (RTWUSM) with dead-zone is proposed based on a modified Hammerstein model structure. The driving voltage contributing effect on the nonlinearities of the RTWUSM was transformed to the change of dynamic parameters against the driving voltage. The dead-zone of the RTWUSM is identified based upon the above transformation. Experiment results showed good agreement be- tween the output of the proposed model and actual measured output.
基金financially supported by the Joint Fund of NSFC and the General Purpose Technology Research Program under the contract U1636125,NSFC under the contract U1836201
文摘Passive intermodulation(PIM)interference urgently needs to be solved in the satellite communication system,owing to degrading the whole performance.Mainstream research contributions to the cancellation method for PIM were focused on the analog domain,however,the PIM distortion cannot be eliminated completely with the approaches.Meanwhile,some researchers attempt to tackle the problem through digital signal processing,nevertheless,the proposed methods were not suitable for the practical satellite communication scenario.In this paper,we present a general scheme for the adaptive feedforward PIM cancellation.High-order PIM signals at baseband are estimated by modeling the PIM distortion with Hammerstein model in the digital domain.Based on the reconstructed PIM signal,we adopt the least mean square algorithm to adaptively mitigate the PIM interference for tracking the variation of PIM.The time and frequency synchronization of PIM are based on the correlation of the peak of received signals with the corresponding reconstructed PIM signal.Practical experimental results show that the scheme can effectively cancel the PIM interference,and achieve an interference suppression gain more than 20dB.
基金National Natural Science Foundation of China[grant number 61863034].
文摘An algorithm based on mixed signals is proposed,to solve the issues of low accuracy of identification algorithm,immeasurable intermediate variables of fractional order Hammerstein model,and how to determine the magnitude of fractional order.In this paper,a special mixed input signal is designed to separate the nonlinear and linear parts of the fractional order Hammerstein model so that each part can be identified independently.The nonlinear part is fitted by the neural fuzzy network model,which avoids the limitation of polynomial fitting and broadens the application range of nonlinear models.In addition,the multi-innovation Levenberg-Marquardt(MILM)algorithm and auxiliary recursive least square algorithm are innovatively integrated into the parameter identification algorithm of the fractional order Hammerstein model to obtain more accurate identification results.A simulation example is given to verify the accuracy and effectiveness of the proposed method.
基金This project is supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX22_0124)the National Natural Science Foundation of China(NO.61374153).
文摘Considering the fractional-order and nonlinear characteristics of proton exchange membrane fuel cells(PEMFC),a fractional-order subspace identification method based on the ADE-BH optimization algorithm is proposed to establish a fractional-order Hammerstein state-space model of PEMFCs.Herein,a Hammerstein model is constructed by connecting a linear module and a nonlinear module in series to precisely depict the nonlinear property of the PEMFC.During the modeling process,fractional-order theory is combined with subspace identification,and a Poisson filter is adopted to enable multi-order derivability of the data.A variable memory method is introduced to reduce computation time without losing precision.Additionally,to improve the optimization accuracy and avoid obtaining locally optimum solutions,a novel ADEBH algorithm is employed to optimize the unknown parameters in the identification method.In this algorithm,the Euclidean distance serves as the theoretical basis for updating the target vector in the absorption-generation operation of the black hole(BH)algorithm.Finally,simulations demonstrate that the proposed model has small output error and high accuracy,indicating that the model can accurately describe the electrical characteristics of the PEMFC process.
基金Projects(61573052,61273132)supported by the National Natural Science Foundation of China
文摘This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.
文摘Purpose-The purpose of this paper is to probe the recursive identification of piecewise affine Hammerstein models directly by using input-output data.To explain the identification process of a parametric piecewise affine nonlinear function,the authors prove that the inverse function corresponding to the given piecewise affine nonlinear function is also an equivalent piecewise affine form.Based on this equivalent property,during the detailed identification process with respect to piecewise affine function and linear dynamical system,three recursive least squares methods are proposed to identify those unknown parameters under the probabilistic description or bounded property of noise.Design/methodology/approach-First,the basic recursive least squares method is used to identify those unknown parameters under the probabilistic description of noise.Second,multi-innovation recursive least squares method is proposed to improve the efficiency lacked in basic recursive least squares method.Third,to relax the strict probabilistic description on noise,the authors provide a projection algorithm with a dead zone in the presence of bounded noise and analyze its two properties.Findings-Based on complex mathematical derivation,the inverse function of a given piecewise affine nonlinear function is also an equivalent piecewise affine form.As the least squares method is suited under one condition that the considered noise may be a zero mean random signal,a projection algorithm with a dead zone in the presence of bounded noise can enhance the robustness in the parameter update equation.Originality/value-To the best knowledge of the authors,this is the first attempt at identifying piecewise affine Hammerstein models,which combine a piecewise affine function and a linear dynamical system.In the presence of bounded noise,the modified recursive least squares methods are efficient in identifying two kinds of unknown parameters,so that the common set membership method can be replaced by the proposed methods.
文摘The modeling of distillation column process is a very challenging problem because of the complex dynamic behavior.This paper investigates a Nonlinear Autoregressive Moving Average with eXogenous input(NARMAX)model,and a Hammerstein model to approximate the evolution of the overhead temperature in a separation system.The model development and validation are studied through experiments carried out on a distillation plant of laboratory scale.Three model order selection criteria such as Aikeke’s Information Criterion(AIC),Root Mean Square Error(RMSE)and Nash–Sutcliffe Efficiency(NSE)are used to evaluate the prediction performance of the process behavior.The results illustrate that both models produce acceptable predictions but the NARMAX model outperforms the Hammerstein model.
文摘In acs paper,the generalized predictive control(GPC)law for Hammerstein model with control horizon NU=1 is presented and the algebraic equation satisfied by the GPC law is derived.Also,the simulation study shows tha tthe GPC based on Hammerstein system is such and algorithm which can be controlled by numerical computer with rather strong Robustness but without strict demand for the model.
基金Project(61074074) supported by the National Natural Science Foundation,ChinaProject(KT2012C01J0401) supported by the Group Innovative Fund,China
文摘Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approach, the maximum and minimum of partial derivative for input and output nonlinearities are solved in the neighbourhood of the equilibrium. And several parameter-dependent Lyapunov functions, each one corresponding to a different vertex of polytopic descriptions models, are introduced to analyze the stability of Hammerstein-Wiener systems, but only one Lyapunov function is utilized to analyze system stability like the traditional method. Consequently, the conservation of the traditional quadratic stability is removed, and the terminal regions are enlarged. Simulation and field trial results show that the proposed algorithm is valid. It has higher control precision and shorter blowing time than the traditional approach.
基金Project(61074074)supported by the National Natural Science Foundation,ChinaProject(KT2012C01J0401)supported by the Group Innovation Fund,China
文摘An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the min-max optimization problem.The polytopic invariant set is adopted to replace the traditional ellipsoid invariant set.And the parameter-correlation nonlinear control law is designed to replace the traditional linear control law.Consequently,the terminal region is enlarged and the control effect is improved.Simulation and experiment are used to verify the validity of the wind tunnel flow field control algorithm.
文摘伴随着新能源产业的飞速发展,锂离子动力电池作为一种高效的储能方式,已成为电动汽车的重要组成部分。在电池管理系统的功能中,电池的高精度建模至关重要。在实际应用中,电池不是一个线性系统,其输入和输出由于外部扰动等原因表现出非线性特征,从而直接影响参数识别效果,进而影响模型精度。鉴于此,本文对锂离子动力电池进行了Hammerstein-ARMAX(Autoregressive MovingAverage with Extra Input)模型构建,并对模型参数的估计方法进行研究,旨在提高模型的准确性。实验结果表明了该方法的有效性。