For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a tra...For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.展开更多
This paper presents a method of state estimation for uncertain nonlinear systems described by multiple models approach. The uncertainties, supposed as norm bounded type, are caused by some parameters＇ variations of t...This paper presents a method of state estimation for uncertain nonlinear systems described by multiple models approach. The uncertainties, supposed as norm bounded type, are caused by some parameters＇ variations of the nonlinear system. Linear matri~ inequalities （LMIs） have been established in order to ensure the stability conditions of the multiple observer which lead to determine the estimation gains. A sliding mode gain has been added in order to compensate the uncertainties. Numerical simulations through a state space model of a real process have been realized to show the robustness of the synthesized observer.展开更多
The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are requ...The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are required as these parameters change abruptly in the parameter space. In this paper we consider both the multiple models with switching and tuning methodology as well as multiple models with second level adaptation for this class of systems. We demonstrate that the latter approach is better than the former.展开更多
Of different model-based methods in vision based human tracking,many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution a...Of different model-based methods in vision based human tracking,many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution according to a proper likelihood function.Seldom works perform a framework of interactive multiple models (IMM) to track a human for challenging problems,such as uncertainty of motion styles,imprecise detection of feature points and ambiguity of joint location.This paper presents a two-layer filter framework based on IMM to track human motion.First,a method of model based points location is proposed to detect key feature points automatically and the filter in the first layer is performed to estimate the undetected points.Second,multiple models of motion are learned by the prior motion data with ridge regression and the IMM algorithm is used to estimate the quaternion vectors of joints rotation.Finally,experiments using real images sequences,simulation videos and 3D voxel data demonstrate that this human tracking framework is efficient.展开更多
In this paper we deal with the problem of plants with large parameter variations under different operating modes. A novel intelligent control algorithm based on multiple models is proposed to improve the dynamical res...In this paper we deal with the problem of plants with large parameter variations under different operating modes. A novel intelligent control algorithm based on multiple models is proposed to improve the dynamical response performance. At the same time adaptive model bank is applied to establish models without prior system information. Multiple models and corresponding controllers are automatically established on-line by a conventionally adaptive model and a re-initialized one. A best controller is chosen by the performance function at every instant. The closed-loop system＇s stability and asymptotical convergence of tracking error can be guaranteed. Simulation results have confirmed the validity of the proposed method.展开更多
It is difficult to realize control for some complex nonlinear systems operated in different operating regions. Based on developing local models for different operating regions of the process, a novel algorithm using m...It is difficult to realize control for some complex nonlinear systems operated in different operating regions. Based on developing local models for different operating regions of the process, a novel algorithm using multiple models is proposed. It utilizes dynamic model bank to establish multiple local models, and their membership functions are defined according to respective regions. Then the nonlinear system is approximated to a weighted combination of the local models. The stability of the nonlinear system is proven. Finally, simulations are given to demonstrate the validity of the proposed method.展开更多
Model predictive control is model-based. Therefore, the procedure is inherently not robust to modelling uncertainties. Further, a crucial design parameter is the prediction horizon. Only offline procedures to estimate...Model predictive control is model-based. Therefore, the procedure is inherently not robust to modelling uncertainties. Further, a crucial design parameter is the prediction horizon. Only offline procedures to estimate an upper bound of the optimal value of this parameter are available. These procedures are computationally intensive and model-based. Besides, a single choice of this horizon is perhaps not the best option at all time instants. This is especially true when the control objective is to track desired trajectories. In this paper, we resolve the issue by a time-varying horizon achieved by switching between multiple model-predictive controllers. The stability of the overall system is discussed. In addition, an introduction of multiple models to handle modelling uncertainties makes the overall system robust. The improvement in performance is demonstrated through several examples.展开更多
The transient behaviors of traditional adaptive control may be very poor in general. A practically feasible approach to improve the transient performances is the adoption of adaptive switc- hing control. For a typical...The transient behaviors of traditional adaptive control may be very poor in general. A practically feasible approach to improve the transient performances is the adoption of adaptive switc- hing control. For a typical class of nonlinear systems disturbed by random noises, mixed multiple models consisting of adaptive model and fixed models were considered to design the switching con- trol law. Under certain assumptions, the nonlinear system with the switching control law was proved rigorously to be stable and optimal A simulation example was provided to compare the performance of the switching control and the traditional adaptive control.展开更多
For a stochastic non-minimum phase multivariable system,a multiple models direct adaptive controller is presented.It is composed of multiple fixed models with two adaptive models.The fixed models are used to cover the...For a stochastic non-minimum phase multivariable system,a multiple models direct adaptive controller is presented.It is composed of multiple fixed models with two adaptive models.The fixed models are used to cover the region where the system parameters jump and improve the transient response,while another two adaptive models are used to guarantee the stability.Utilizing generalized minimum variance design method,it adopts the stochastic system estimation algorithm with optimal controller design method to identify the controller parameter directly.Finally,the global convergence is given.The simulation proves the effectives of the controller proposed.展开更多
When the parameters of the system change abruptly, a new multivariable adaptive feedforward decoupling controller using multiple models is presented to improve the transient response. The system models are composed of...When the parameters of the system change abruptly, a new multivariable adaptive feedforward decoupling controller using multiple models is presented to improve the transient response. The system models are composed of multiple fixed models, one free-running adaptive model and one re-initialized adaptive model. The fixed models are used to provide initial control to the process. The re-initialized adaptive model can be reinitialized as the selected model to improve the adaptation speed. The free-running adaptive controller is added to guarantee the overall system stability. At each instant, the best system model is selected according to the switching index and the corresponding controller is designed. During the controller design, the interaction is viewed as the measurable disturbance and eliminated by the choice of the weighting polynomial matrix. It not only eliminates the steady-state error but also decouples the system dynamically. The gtobel convergence is obtained and several simulation examples are presented to illustrate the effectiveness of the proposed controller.展开更多
An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple ...An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.展开更多
We use the approach of “optimal” switching to design the adaptive control because the design among multiple models is intuitively more practically feasible than the traditional adaptive control in improving the perf...We use the approach of “optimal” switching to design the adaptive control because the design among multiple models is intuitively more practically feasible than the traditional adaptive control in improving the performances. We prove that for a typical class of nonlinear systems disturbed by random noise, the multiple model adaptive switching control based on WLS (Weighted Least Squares) or projected-LS (Least Squares) is stable and convergent.展开更多
This study focuses on predicting the motion states and intentions of HDVs at unsignalized intersections.On the basis of a risk field-driven driving behavior model for uncontrolled intersections,multiple motion hypothe...This study focuses on predicting the motion states and intentions of HDVs at unsignalized intersections.On the basis of a risk field-driven driving behavior model for uncontrolled intersections,multiple motion hypotheses are formulated to characterize the motion planning process of drivers in multivehicle conflict scenarios.Each motion hypothesis is modeled and expressed separately via the extended Kalman filter(EKF)model.These EKF models were combined to construct an interacting multiple model(IMM)framework.This framework estimates which motion hypothesis the driver is more likely to adopt as a strategy.By integrating the predictions of multiple motion hypotheses,more accurate predictions are obtained.Ultimately,it estimates the driver's travel path and acceptable risk level and predicts the spatiotemporal trajectory of HDVs within a future time window.展开更多
The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of ...The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.展开更多
Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the ...Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.展开更多
The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigat...The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigation scheme based on Interacting Multiple Nonlinear Fuzzy Adaptive H_∞ Models(IMM-NFAH_∞) filtering technique for UAV is presented. The proposed IMM-NFAH_∞ strategy switches between two different Nonlinear Fuzzy Adaptive H_∞(NFAH_∞) filters and each NFAH_∞ filter is based on different fuzzy logic inference systems. The newly proposed technique takes into consideration the high order Taylor series terms and adapts the nonlinear H_∞ filter based on different fuzzy inference systems via adaptive filter bounds(di),along with disturbance attenuation parameter c. Simulation analysis validates the performance of the proposed algorithm, and the comparison with nonlinear H_∞(NH_∞) filter and that with different NFAH_∞ filters demonstrate the effectiveness of UAV localization utilizing IMM-NFAH_∞ filter.展开更多
In this paper, a multivariable direct adaptive controller using multiple models without minimum phase assumption is presented to improve the transient response when the parameters of the system jump abruptly. The cont...In this paper, a multivariable direct adaptive controller using multiple models without minimum phase assumption is presented to improve the transient response when the parameters of the system jump abruptly. The controller is composed of multiple fixed controller models, a free-running adaptive con- troller model and a re-initialized adaptive controller model. The fixed controller models are derived from the corresponding fixed system models directly. The adaptive controller models adopt the direct adaptive algorithm to reduce the design calculation. At every instant, the optimal controller is chosen out according to the switching index. The interaction of the system is viewed as the measured distur- bance which is eliminated by the choice of the weighing polynomial matrix. The global convergence is obtained. Finally, several simulation examples in a wind tunnel experiment are given to show both effectiveness and practicality of the proposed method. The significance of the proposed method is that it is applicable to a non-minimum phase system, adopting direct adaptive algorithm to overcome the singularity problem during the matrix calculation and realizing decoupling control for a multivariable system.展开更多
Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calcu...Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression （MLR） to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long term monitoring is needed to gather more data that can be used for the development of estimation models.展开更多
Backgrounds:Evaluating the growth performance of pigs in real-time is laborious and expensive,thus mathematical models based on easily accessible variables are developed.Multiple regression(MR)is the most widely used ...Backgrounds:Evaluating the growth performance of pigs in real-time is laborious and expensive,thus mathematical models based on easily accessible variables are developed.Multiple regression(MR)is the most widely used tool to build prediction models in swine nutrition,while the artificial neural networks(ANN)model is reported to be more accurate than MR model in prediction performance.Therefore,the potential of ANN models in predicting the growth performance of pigs was evaluated and compared with MR models in this study.Results:Body weight(BW),net energy(NE)intake,standardized ileal digestible lysine(SID Lys)intake,and their quadratic terms were selected as input variables to predict ADG and F/G among 10 candidate variables.In the training phase,MR models showed high accuracy in both ADG and F/G prediction(R^(2)_(ADG)=0.929,R^(2)_(F/G)=0.886)while ANN models with 4,6 neurons and radial basis activation function yielded the best performance in ADG and F/G prediction(R^(2)_(ADG)=0.964,R^(2)_(F/G)=0.932).In the testing phase,these ANN models showed better accuracy in ADG prediction(CCC:0.976 vs.0.861,R^(2):0.951 vs.0.584),and F/G prediction(CCC:0.952 vs.0.900,R^(2):0.905 vs.0.821)compared with the MR models.Meanwhile,the“over-fitting”occurred in MR models but not in ANN models.On validation data from the animal trial,ANN models exhibited superiority over MR models in both ADG and F/G prediction(P<0.01).Moreover,the growth stages have a significant effect on the prediction accuracy of the models.Conclusion:Body weight,NE intake and SID Lys intake can be used as input variables to predict the growth performance of growing-finishing pigs,with trained ANN models are more flexible and accurate than MR models.Therefore,it is promising to use ANN models in related swine nutrition studies in the future.展开更多
The existing articulated arm coordinate measuring machines（AACMM） with one measurement model are easy to cause low measurement accuracy because the whole sampling space is much bigger than the result in the unstable...The existing articulated arm coordinate measuring machines（AACMM） with one measurement model are easy to cause low measurement accuracy because the whole sampling space is much bigger than the result in the unstable calibration parameters. To compensate for the deficiency of one measurement model, the multiple measurement models are built by the Denavit-Hartenberg＇s notation, the homemade standard rod components are used as a calibration tool and the Levenberg-Marquardt calibration algorithm is applied to solve the structural parameters in the measurement models. During the tests of multiple measurement models, the sample areas are selected in two situations. It is found that the measurement errors＇ sigma value（0.083 4 ram） dealt with one measurement model is nearly two times larger than that of the multiple measurement models（0.043 1 ram） in the same sample area. While in the different sample area, the measurement errors＇ sigma value（0.054 0 ram） dealt with the multiple measurement models is about 40% of one measurement model（0.137 3 mm）. The preliminary results suggest that the measurement accuracy of AACMM dealt with multiple measurement models is superior to the accuracy of the existing machine with one measurement model. This paper proposes the multiple measurement models to improve the measurement accuracy of AACMM without increasing any hardware cost.展开更多
基金This paper is supported by the National Foundamental Research Program of China (No. 2002CB312201), the State Key Program of NationalNatural Science of China (No. 60534010), the Funds for Creative Research Groups of China (No. 60521003), and Program for Changjiang Scholarsand Innovative Research Team in University (No. IRT0421).
文摘For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.
文摘This paper presents a method of state estimation for uncertain nonlinear systems described by multiple models approach. The uncertainties, supposed as norm bounded type, are caused by some parameters＇ variations of the nonlinear system. Linear matri~ inequalities （LMIs） have been established in order to ensure the stability conditions of the multiple observer which lead to determine the estimation gains. A sliding mode gain has been added in order to compensate the uncertainties. Numerical simulations through a state space model of a real process have been realized to show the robustness of the synthesized observer.
文摘The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are required as these parameters change abruptly in the parameter space. In this paper we consider both the multiple models with switching and tuning methodology as well as multiple models with second level adaptation for this class of systems. We demonstrate that the latter approach is better than the former.
基金the Research Fund for the Young Teacher of Shanghai(No.Z-2009-12)the New Teacher Fund of Shanghai University of Electric Power (No.K-2010-16)
文摘Of different model-based methods in vision based human tracking,many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution according to a proper likelihood function.Seldom works perform a framework of interactive multiple models (IMM) to track a human for challenging problems,such as uncertainty of motion styles,imprecise detection of feature points and ambiguity of joint location.This paper presents a two-layer filter framework based on IMM to track human motion.First,a method of model based points location is proposed to detect key feature points automatically and the filter in the first layer is performed to estimate the undetected points.Second,multiple models of motion are learned by the prior motion data with ridge regression and the IMM algorithm is used to estimate the quaternion vectors of joints rotation.Finally,experiments using real images sequences,simulation videos and 3D voxel data demonstrate that this human tracking framework is efficient.
基金This work was partly supported by National Natural Science Foundation of China (No. 60574006) the Specialized Research Fund for DoctoralProgram of Higher Education of China (No. 20030286013) Provincial Natural Science Foundation of Jiangsu (No. BK2003405) and GraduateInnovative Project of Jiangsu Province (2005).
文摘In this paper we deal with the problem of plants with large parameter variations under different operating modes. A novel intelligent control algorithm based on multiple models is proposed to improve the dynamical response performance. At the same time adaptive model bank is applied to establish models without prior system information. Multiple models and corresponding controllers are automatically established on-line by a conventionally adaptive model and a re-initialized one. A best controller is chosen by the performance function at every instant. The closed-loop system＇s stability and asymptotical convergence of tracking error can be guaranteed. Simulation results have confirmed the validity of the proposed method.
基金This work was supported by the National Natural Science Foundation of China (No. 60574006).
文摘It is difficult to realize control for some complex nonlinear systems operated in different operating regions. Based on developing local models for different operating regions of the process, a novel algorithm using multiple models is proposed. It utilizes dynamic model bank to establish multiple local models, and their membership functions are defined according to respective regions. Then the nonlinear system is approximated to a weighted combination of the local models. The stability of the nonlinear system is proven. Finally, simulations are given to demonstrate the validity of the proposed method.
文摘Model predictive control is model-based. Therefore, the procedure is inherently not robust to modelling uncertainties. Further, a crucial design parameter is the prediction horizon. Only offline procedures to estimate an upper bound of the optimal value of this parameter are available. These procedures are computationally intensive and model-based. Besides, a single choice of this horizon is perhaps not the best option at all time instants. This is especially true when the control objective is to track desired trajectories. In this paper, we resolve the issue by a time-varying horizon achieved by switching between multiple model-predictive controllers. The stability of the overall system is discussed. In addition, an introduction of multiple models to handle modelling uncertainties makes the overall system robust. The improvement in performance is demonstrated through several examples.
基金Supported by the National Natural Science Foundation of China (60704002)
文摘The transient behaviors of traditional adaptive control may be very poor in general. A practically feasible approach to improve the transient performances is the adoption of adaptive switc- hing control. For a typical class of nonlinear systems disturbed by random noises, mixed multiple models consisting of adaptive model and fixed models were considered to design the switching con- trol law. Under certain assumptions, the nonlinear system with the switching control law was proved rigorously to be stable and optimal A simulation example was provided to compare the performance of the switching control and the traditional adaptive control.
基金the National Natural Science Foundation of China (Nos.60504010 and 60774015)the National High Technology Research and Development Program (863) of China (No.2008AA04Z129)+1 种基金the Disbursal Budget Program of Shanghai Municipal Education Commission of China (No.2008093) the Innovation Program of Shanghai Municipal Education Commission of China (No.09YZ241)
文摘For a stochastic non-minimum phase multivariable system,a multiple models direct adaptive controller is presented.It is composed of multiple fixed models with two adaptive models.The fixed models are used to cover the region where the system parameters jump and improve the transient response,while another two adaptive models are used to guarantee the stability.Utilizing generalized minimum variance design method,it adopts the stochastic system estimation algorithm with optimal controller design method to identify the controller parameter directly.Finally,the global convergence is given.The simulation proves the effectives of the controller proposed.
文摘When the parameters of the system change abruptly, a new multivariable adaptive feedforward decoupling controller using multiple models is presented to improve the transient response. The system models are composed of multiple fixed models, one free-running adaptive model and one re-initialized adaptive model. The fixed models are used to provide initial control to the process. The re-initialized adaptive model can be reinitialized as the selected model to improve the adaptation speed. The free-running adaptive controller is added to guarantee the overall system stability. At each instant, the best system model is selected according to the switching index and the corresponding controller is designed. During the controller design, the interaction is viewed as the measurable disturbance and eliminated by the choice of the weighting polynomial matrix. It not only eliminates the steady-state error but also decouples the system dynamically. The gtobel convergence is obtained and several simulation examples are presented to illustrate the effectiveness of the proposed controller.
基金the University of Tabriz through a Grant scheme No.808.
文摘An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.
基金This work was supported by the National Natural Science Foundation of China.
文摘We use the approach of “optimal” switching to design the adaptive control because the design among multiple models is intuitively more practically feasible than the traditional adaptive control in improving the performances. We prove that for a typical class of nonlinear systems disturbed by random noise, the multiple model adaptive switching control based on WLS (Weighted Least Squares) or projected-LS (Least Squares) is stable and convergent.
基金supported by the National Natural Science Foundation of China(52131204 and 52102393)the Fundamental Research Funds for the Central Universities.
文摘This study focuses on predicting the motion states and intentions of HDVs at unsignalized intersections.On the basis of a risk field-driven driving behavior model for uncontrolled intersections,multiple motion hypotheses are formulated to characterize the motion planning process of drivers in multivehicle conflict scenarios.Each motion hypothesis is modeled and expressed separately via the extended Kalman filter(EKF)model.These EKF models were combined to construct an interacting multiple model(IMM)framework.This framework estimates which motion hypothesis the driver is more likely to adopt as a strategy.By integrating the predictions of multiple motion hypotheses,more accurate predictions are obtained.Ultimately,it estimates the driver's travel path and acceptable risk level and predicts the spatiotemporal trajectory of HDVs within a future time window.
基金supported by the National Key Program for Developing Basic Science(Nos.2022YFF0801702 and 2022YFE0106600)the National Natural Science Foundation of China(Nos.42175060 and 42175021)the Jiangsu Province Science Foundation(No.BK20250200302).
文摘The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.
基金the Project of the Key Open Laboratory of Atmospheric Detection,China Meteorological Administration(2023KLAS02M)the Second Batch of Science and Technology Project of China Meteorological Administration("Jiebangguashuai"):the Research and Development of Short-term and Near-term Warning Products for Severe Convective Weather in Beijing-Tianjin-Hebei Region(CMAJBGS202307).
文摘Firstly,based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021,the correlations of meteorological elements and pollutants with O_(3)concentration were explored to determine the forecast factors of forecast models.Secondly,the O_(3)-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression(MLR),backward propagation neural network(BPNN),and auto regressive integrated moving average(ARIMA),and the predicted values were compared with the observed values to test their prediction effects.The results show that overall,the MLR,BPNN and ARIMA models were able to forecast the changing trend of O_(3)-8h concentration in Baoding in 2021,but the BPNN model gave better forecast results than the ARIMA and MLR models,especially for the prediction of the high values of O_(3)-8h concentration,and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September.The mean error(ME),mean absolute error(MAE),and root mean square error(RMSE)of the predicted values and the observed values of daily O_(3)-8h concentration based on the BPNN model were 0.45,19.11 and 24.41μg/m 3,respectively,which were significantly better than those of the MLR and ARIMA models.The prediction effects of the MLR,BPNN and ARIMA models were the best at the pollution level,followed by the excellent level,and it was the worst at the good level.In comparison,the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole,especially for the pollution and excellent levels.The TS scores of the BPNN model were all above 66%,and the PC values were above 86%.The BPNN model can forecast the changing trend of O_(3)concentration more accurately,and has a good practical application value,but at the same time,the predicted high values of O_(3)concentration should be appropriately increased according to error characteristics of the model.
基金supported by a grant from the National Natural Science Foundation of China(No.61375082)
文摘The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle(UAV).Therefore, new integrated SINS/GPS navigation scheme based on Interacting Multiple Nonlinear Fuzzy Adaptive H_∞ Models(IMM-NFAH_∞) filtering technique for UAV is presented. The proposed IMM-NFAH_∞ strategy switches between two different Nonlinear Fuzzy Adaptive H_∞(NFAH_∞) filters and each NFAH_∞ filter is based on different fuzzy logic inference systems. The newly proposed technique takes into consideration the high order Taylor series terms and adapts the nonlinear H_∞ filter based on different fuzzy inference systems via adaptive filter bounds(di),along with disturbance attenuation parameter c. Simulation analysis validates the performance of the proposed algorithm, and the comparison with nonlinear H_∞(NH_∞) filter and that with different NFAH_∞ filters demonstrate the effectiveness of UAV localization utilizing IMM-NFAH_∞ filter.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 60504010, 60864004)the National High-Tech Research and Development Program of China (Grant No. 2008AA04Z129)+1 种基金the Key Project of Chinese Ministry of Education (Grant No. 208071)the Natural Science Foundation of Jiangxi Province (Grant No. 0611006)
文摘In this paper, a multivariable direct adaptive controller using multiple models without minimum phase assumption is presented to improve the transient response when the parameters of the system jump abruptly. The controller is composed of multiple fixed controller models, a free-running adaptive con- troller model and a re-initialized adaptive controller model. The fixed controller models are derived from the corresponding fixed system models directly. The adaptive controller models adopt the direct adaptive algorithm to reduce the design calculation. At every instant, the optimal controller is chosen out according to the switching index. The interaction of the system is viewed as the measured distur- bance which is eliminated by the choice of the weighing polynomial matrix. The global convergence is obtained. Finally, several simulation examples in a wind tunnel experiment are given to show both effectiveness and practicality of the proposed method. The significance of the proposed method is that it is applicable to a non-minimum phase system, adopting direct adaptive algorithm to overcome the singularity problem during the matrix calculation and realizing decoupling control for a multivariable system.
基金provided by the Korean Ministry of Environment and Eco Star Project
文摘Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression （MLR） to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long term monitoring is needed to gather more data that can be used for the development of estimation models.
基金funded by the National Natural Science Foundation of China(32072764, 31702121)the 2115 Talent Development Program of China Agricultural UniversityNational Key Research and Development Program of China (2019YFD1002605)
文摘Backgrounds:Evaluating the growth performance of pigs in real-time is laborious and expensive,thus mathematical models based on easily accessible variables are developed.Multiple regression(MR)is the most widely used tool to build prediction models in swine nutrition,while the artificial neural networks(ANN)model is reported to be more accurate than MR model in prediction performance.Therefore,the potential of ANN models in predicting the growth performance of pigs was evaluated and compared with MR models in this study.Results:Body weight(BW),net energy(NE)intake,standardized ileal digestible lysine(SID Lys)intake,and their quadratic terms were selected as input variables to predict ADG and F/G among 10 candidate variables.In the training phase,MR models showed high accuracy in both ADG and F/G prediction(R^(2)_(ADG)=0.929,R^(2)_(F/G)=0.886)while ANN models with 4,6 neurons and radial basis activation function yielded the best performance in ADG and F/G prediction(R^(2)_(ADG)=0.964,R^(2)_(F/G)=0.932).In the testing phase,these ANN models showed better accuracy in ADG prediction(CCC:0.976 vs.0.861,R^(2):0.951 vs.0.584),and F/G prediction(CCC:0.952 vs.0.900,R^(2):0.905 vs.0.821)compared with the MR models.Meanwhile,the“over-fitting”occurred in MR models but not in ANN models.On validation data from the animal trial,ANN models exhibited superiority over MR models in both ADG and F/G prediction(P<0.01).Moreover,the growth stages have a significant effect on the prediction accuracy of the models.Conclusion:Body weight,NE intake and SID Lys intake can be used as input variables to predict the growth performance of growing-finishing pigs,with trained ANN models are more flexible and accurate than MR models.Therefore,it is promising to use ANN models in related swine nutrition studies in the future.
基金Supported by National Natural Science Foundation of China(Grant No.51265017)Jiangxi Provincial Science and Technology Planning Project,China(Grant No.GJJ12468)Science and Technology Planning Project of Ji’an City,China(Grant No.20131828)
文摘The existing articulated arm coordinate measuring machines（AACMM） with one measurement model are easy to cause low measurement accuracy because the whole sampling space is much bigger than the result in the unstable calibration parameters. To compensate for the deficiency of one measurement model, the multiple measurement models are built by the Denavit-Hartenberg＇s notation, the homemade standard rod components are used as a calibration tool and the Levenberg-Marquardt calibration algorithm is applied to solve the structural parameters in the measurement models. During the tests of multiple measurement models, the sample areas are selected in two situations. It is found that the measurement errors＇ sigma value（0.083 4 ram） dealt with one measurement model is nearly two times larger than that of the multiple measurement models（0.043 1 ram） in the same sample area. While in the different sample area, the measurement errors＇ sigma value（0.054 0 ram） dealt with the multiple measurement models is about 40% of one measurement model（0.137 3 mm）. The preliminary results suggest that the measurement accuracy of AACMM dealt with multiple measurement models is superior to the accuracy of the existing machine with one measurement model. This paper proposes the multiple measurement models to improve the measurement accuracy of AACMM without increasing any hardware cost.
