This anticle gives a design method of the economical nonlinear controller. The controller is composed of an expert intelligent coordination controller, a fuzzy prediction controller, a fuzzy feedforward controller, a ...This anticle gives a design method of the economical nonlinear controller. The controller is composed of an expert intelligent coordination controller, a fuzzy prediction controller, a fuzzy feedforward controller, a nonlinear controller and so on. The consistence of a distributed control system based on this controller is also shown briefly.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
We present the new predictor-corrector methods for systems of nonlinear differential equations, based on the method of exponential time differencing. We compare the present schemes with the explicit multistep exponent...We present the new predictor-corrector methods for systems of nonlinear differential equations, based on the method of exponential time differencing. We compare the present schemes with the explicit multistep exponential time differencing and Adams–Bashforth–Moulton method. The numerical results show that the schemes are more accurate and more efficient than Adams predictor-corrector method. The exponential time differencing method has been developed and perfected by the present studies.展开更多
As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet...As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was can:led out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model. The research indicates that it is feasible to use the MRA wavelet network in the short-time prediction of ship motion.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
A continuous-time nonlinear model predictive controller(NMPC) was designed for a boiler-turbine unit.The controller was designed by optimizing a receding-horizon performance index,with the nonlinear system approximate...A continuous-time nonlinear model predictive controller(NMPC) was designed for a boiler-turbine unit.The controller was designed by optimizing a receding-horizon performance index,with the nonlinear system approximated by its Taylor series expansion with a certain order,the magnitude saturation constraints on the inputs satisfied by increasing the predictive time,and the rate saturation conditions on the actuators satisfied by tuning the time constant of the reference trajectories in a reference governor.Simulation results showed that the controller can drive the drum pressure and output power of the nonlinear boiler-turbine unit to follow their respective reference trajectories throughout a varying operation range and keep the water level deviation within tolerances.Comparison of the NMPC scheme with the generic model control(GMC) scheme indicated that the responses are slower and there are more oscillations in the responses of the water level,fuel flow input and feed water flow input in the GMC scheme when the boiler-turbine unit is operating over a wide range.展开更多
This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be co...This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.展开更多
In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the tradit...In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.展开更多
It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones. However, the literature on combining forecasts has almost exclusively foc...It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones. However, the literature on combining forecasts has almost exclusively focused on linear combining forecasts. In this paper, a new nonlinear combination forecasting method based on fuzzy inference system is present to overcome the difficulties and drawbacks in linear combination modeling of non-stationary time series. Furthermore, the optimization algorithm based on a hierarchical structure of learning automata is used to identify the parameters of the fuzzy system. Experiment results related to numerical examples demonstrate that the new technique has excellent identification performances and forecasting accuracy superior to other existing linear combining forecasts.展开更多
In this paper, the data-based control problem is investigated for a class of networked nonlinear systems with measurement noise as well as packet dropouts in the feedback and forward channels. The measurement noise an...In this paper, the data-based control problem is investigated for a class of networked nonlinear systems with measurement noise as well as packet dropouts in the feedback and forward channels. The measurement noise and the number of consecutive packet dropouts in both channels are assumed to be random but bounded. A data-based networked predictive control method is proposed, in which a sequence of control increment predictions are calculated in the controller based on the measured output error, and based on the control increment predictions received by the actuator, a proper control action is obtained and applied to the plant according to the real-time number of consecutive packet dropouts at each sampling instant. Then the stability analysis is performed for the networked closedloop system. Finally, the effectiveness of the proposed method is illustrated by a numerical example.展开更多
In nonlinear error growth dynamics,the initial error cannot be accurately determined,and the forecast error,which is also uncertain,can be considered to be a random variable.Entropy in information theory is a natural ...In nonlinear error growth dynamics,the initial error cannot be accurately determined,and the forecast error,which is also uncertain,can be considered to be a random variable.Entropy in information theory is a natural measure of the uncertainty of a random variable associated with a probability distribution.This paper effectively combines statistical information theory and nonlinear error growth dynamics,and introduces some fundamental concepts of entropy in information theory for nonlinear error growth dynamics.Entropy based on nonlinear error can be divided into time entropy and space entropy,which are used to estimate the predictabilities of the whole dynamical system and each of its variables.This is not only applicable for investigating the dependence between any two variables of a multivariable system,but also for measuring the influence of each variable on the predictability of the whole system.Taking the Lorenz system as an example,the entropy of nonlinear error is applied to estimate predictability.The time and space entropies are used to investigate the spatial distribution of predictability of the whole Lorenz system.The results show that when moving around two chaotic attractors or near the edge of system space,a Lorenz system with lower sensitivity to the initial field behaves with higher predictability and a longer predictability limit.The example analysis of predictability of the Lorenz system demonstrates that the predictability estimated by the entropy of nonlinear error is feasible and effective,especially for estimation of predictability of the whole system.This provides a theoretical foundation for further work in estimating real atmospheric multivariable joint predictability.展开更多
Nonlinear lumped-parameter force factor Bl(x), stiffness Kms(x) and inductance Le(x) of electrodynamic loudspeakers change frequency responses and generate some nonlinear effects for large stimulus: harmonic and inter...Nonlinear lumped-parameter force factor Bl(x), stiffness Kms(x) and inductance Le(x) of electrodynamic loudspeakers change frequency responses and generate some nonlinear effects for large stimulus: harmonic and intermodulation distortion, DC component in diaphragm displacement, instability of vibration and jumping effects. By modeling the nonlinear system under large-signal conditions, relationship between the nonlinear parameters and large-signal behavior can be revealed and help to provide guidance to diagnose loudspeakers. Agreement between the measured and predicted responses of a real loudspeaker validates the modeling and enables new methods for loudspeaker diagnosis.展开更多
The recently developed short-time linear response algorithm,which predicts the response of a nonlinear chaotic forced-dissipative system to small external perturbation,yields high precision of the response prediction....The recently developed short-time linear response algorithm,which predicts the response of a nonlinear chaotic forced-dissipative system to small external perturbation,yields high precision of the response prediction.However,the computation of the short-time linear response formula with the full rank tangent map can be expensive.Here,a numerical method to potentially overcome the increasing numerical complexity for large scale models with many variables by using the reduced-rank tangent map in the computation is proposed.The conditions for which the short-time linear response approximation with the reduced-rank tangent map is valid are established,and two practical situations are examined,where the response to small external perturbations is predicted for nonlinear chaotic forced-dissipative systems with different dynamical properties.展开更多
With the combination of engine and two electric machines, the power split device allows higher efficiency of the engine. The operation modes of a power split HEV are analyzed, and the system dynamic model is establish...With the combination of engine and two electric machines, the power split device allows higher efficiency of the engine. The operation modes of a power split HEV are analyzed, and the system dynamic model is established for HEV forward simulation and controller design. Considering the fact that the operation modes of the HEV are event-driven and the system dynamics is continuous time-driven for each mode, the structure of the controller is built and described with the hybrid automaton control theory. In this control structure, the mode selection process is depicted by the finite state machine （FSM）. The multi-mode switch controller is designed to realize power distribution. Furthermore, the vehicle mode operations are optimized, and the nonlinear model predictive control （NMPC） strategy is applied by implementing dynamic programming （DP） in the finite pre- diction horizon. Comparative simulation results demonstrate that the hybrid control structure is effective and feasible for HEV energy management design. The NMPC optimal strategy is superior in improving fuel economy.展开更多
文摘This anticle gives a design method of the economical nonlinear controller. The controller is composed of an expert intelligent coordination controller, a fuzzy prediction controller, a fuzzy feedforward controller, a nonlinear controller and so on. The consistence of a distributed control system based on this controller is also shown briefly.
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.
基金The project supported by National Natural Science Foundation of China under Grant No.19902002
文摘We present the new predictor-corrector methods for systems of nonlinear differential equations, based on the method of exponential time differencing. We compare the present schemes with the explicit multistep exponential time differencing and Adams–Bashforth–Moulton method. The numerical results show that the schemes are more accurate and more efficient than Adams predictor-corrector method. The exponential time differencing method has been developed and perfected by the present studies.
基金Supported by the National Defence Science and Industry Committee(41314020201)
文摘As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was can:led out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model. The research indicates that it is feasible to use the MRA wavelet network in the short-time prediction of ship motion.
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the Natural Science Foundation of China (No.50636010)
文摘A continuous-time nonlinear model predictive controller(NMPC) was designed for a boiler-turbine unit.The controller was designed by optimizing a receding-horizon performance index,with the nonlinear system approximated by its Taylor series expansion with a certain order,the magnitude saturation constraints on the inputs satisfied by increasing the predictive time,and the rate saturation conditions on the actuators satisfied by tuning the time constant of the reference trajectories in a reference governor.Simulation results showed that the controller can drive the drum pressure and output power of the nonlinear boiler-turbine unit to follow their respective reference trajectories throughout a varying operation range and keep the water level deviation within tolerances.Comparison of the NMPC scheme with the generic model control(GMC) scheme indicated that the responses are slower and there are more oscillations in the responses of the water level,fuel flow input and feed water flow input in the GMC scheme when the boiler-turbine unit is operating over a wide range.
基金Project (No. 2003 AA517020) supported by the Hi-Tech Researchand Development Program (863) of China
文摘This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.
基金Supported by the National High Technology Research and Development Programme of China （ No. 2007AA01Z401 ） and the National Natural Science Foundation of China （No. 90718003, 60973027）.
文摘In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.
基金Funded by the Excellent Young Teachers of MOE (350) and Chongqing Education Committee Foundation
文摘It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones. However, the literature on combining forecasts has almost exclusively focused on linear combining forecasts. In this paper, a new nonlinear combination forecasting method based on fuzzy inference system is present to overcome the difficulties and drawbacks in linear combination modeling of non-stationary time series. Furthermore, the optimization algorithm based on a hierarchical structure of learning automata is used to identify the parameters of the fuzzy system. Experiment results related to numerical examples demonstrate that the new technique has excellent identification performances and forecasting accuracy superior to other existing linear combining forecasts.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61673023,61203230,61273104,61333003,61210012,and 61490701the Beijing Municipal Natural Science Foundation under Grant No.4152014+3 种基金the Great Wall Scholar Candidate Training Program of North China University of Technology(NCUT)the Excellent Youth Scholar Nurturing Program of NCUTthe Outstanding Young Scientist Award Foundation of Shandong Province of China under Grant No.BS2013DX015the Research Fund for the Taishan Scholar Project of Shandong Province of China
文摘In this paper, the data-based control problem is investigated for a class of networked nonlinear systems with measurement noise as well as packet dropouts in the feedback and forward channels. The measurement noise and the number of consecutive packet dropouts in both channels are assumed to be random but bounded. A data-based networked predictive control method is proposed, in which a sequence of control increment predictions are calculated in the controller based on the measured output error, and based on the control increment predictions received by the actuator, a proper control action is obtained and applied to the plant according to the real-time number of consecutive packet dropouts at each sampling instant. Then the stability analysis is performed for the networked closedloop system. Finally, the effectiveness of the proposed method is illustrated by a numerical example.
基金supported by National Natural Science Foundation of China (Grant No. 40975031)
文摘In nonlinear error growth dynamics,the initial error cannot be accurately determined,and the forecast error,which is also uncertain,can be considered to be a random variable.Entropy in information theory is a natural measure of the uncertainty of a random variable associated with a probability distribution.This paper effectively combines statistical information theory and nonlinear error growth dynamics,and introduces some fundamental concepts of entropy in information theory for nonlinear error growth dynamics.Entropy based on nonlinear error can be divided into time entropy and space entropy,which are used to estimate the predictabilities of the whole dynamical system and each of its variables.This is not only applicable for investigating the dependence between any two variables of a multivariable system,but also for measuring the influence of each variable on the predictability of the whole system.Taking the Lorenz system as an example,the entropy of nonlinear error is applied to estimate predictability.The time and space entropies are used to investigate the spatial distribution of predictability of the whole Lorenz system.The results show that when moving around two chaotic attractors or near the edge of system space,a Lorenz system with lower sensitivity to the initial field behaves with higher predictability and a longer predictability limit.The example analysis of predictability of the Lorenz system demonstrates that the predictability estimated by the entropy of nonlinear error is feasible and effective,especially for estimation of predictability of the whole system.This provides a theoretical foundation for further work in estimating real atmospheric multivariable joint predictability.
基金supported by the National Natural Science Foundation of China(Grant No. 11274172)
文摘Nonlinear lumped-parameter force factor Bl(x), stiffness Kms(x) and inductance Le(x) of electrodynamic loudspeakers change frequency responses and generate some nonlinear effects for large stimulus: harmonic and intermodulation distortion, DC component in diaphragm displacement, instability of vibration and jumping effects. By modeling the nonlinear system under large-signal conditions, relationship between the nonlinear parameters and large-signal behavior can be revealed and help to provide guidance to diagnose loudspeakers. Agreement between the measured and predicted responses of a real loudspeaker validates the modeling and enables new methods for loudspeaker diagnosis.
基金Project supported by the National Science Foundation (No.DMS-0608984)the Office of Naval Research(No.N00014-06-1-0286)
文摘The recently developed short-time linear response algorithm,which predicts the response of a nonlinear chaotic forced-dissipative system to small external perturbation,yields high precision of the response prediction.However,the computation of the short-time linear response formula with the full rank tangent map can be expensive.Here,a numerical method to potentially overcome the increasing numerical complexity for large scale models with many variables by using the reduced-rank tangent map in the computation is proposed.The conditions for which the short-time linear response approximation with the reduced-rank tangent map is valid are established,and two practical situations are examined,where the response to small external perturbations is predicted for nonlinear chaotic forced-dissipative systems with different dynamical properties.
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the National Natural Science Foundation of China(Grant Nos.51475213&51305167)the Scientific Research Innovation Projects of Jiangsu Province(Grant No.KYLX_1022)
文摘With the combination of engine and two electric machines, the power split device allows higher efficiency of the engine. The operation modes of a power split HEV are analyzed, and the system dynamic model is established for HEV forward simulation and controller design. Considering the fact that the operation modes of the HEV are event-driven and the system dynamics is continuous time-driven for each mode, the structure of the controller is built and described with the hybrid automaton control theory. In this control structure, the mode selection process is depicted by the finite state machine （FSM）. The multi-mode switch controller is designed to realize power distribution. Furthermore, the vehicle mode operations are optimized, and the nonlinear model predictive control （NMPC） strategy is applied by implementing dynamic programming （DP） in the finite pre- diction horizon. Comparative simulation results demonstrate that the hybrid control structure is effective and feasible for HEV energy management design. The NMPC optimal strategy is superior in improving fuel economy.
