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Error model identification of inertial navigation platform based on errors-in-variables model 认领 引用 被引量：6
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作者 Liu Ming Liu Yu Su Baoku 《Journal of Systems Engineering and Electronics》 SCIE EI 2009年第2期388-393,共6页
Because the real input acceleration cannot be obtained during the error model identification of inertial navigation platform,both the input and output data contain noises.In this case,the conventional regression model... Because the real input acceleration cannot be obtained during the error model identification of inertial navigation platform,both the input and output data contain noises.In this case,the conventional regression model and the least squares(LS)method will result in bias.Based on the models of inertial navigation platform error and observation error,the errors-in-variables(EV)model and the total least squares(TLS)method axe proposed to identify the error model of the inertial navigation platform.The estimation precision is improved and the result is better than the conventional regression model based LS method.The simulation results illustrate the effectiveness of the proposed method. 展开更多
关键词 errors-in-variables model total least squares method inertial navigation platform error model identification
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COMBINATION OF DISTRIBUTED KALMAN FILTER AND BP NEURAL NETWORK FOR ESG BIAS MODEL IDENTIFICATION 认领 引用 被引量：6
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作者 张克志 田蔚风 钱峰 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2010年第3期226-231,共6页
By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets ... By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets of multi-measurements of the same ESG in different noise environments are ＂mapped＂ into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias. 展开更多
关键词 model identification distributed Kalman filter（DKF） back propagation neural network（BPNN） electrostatic suspended gyroscope（ESG）
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The Model Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Ant Colony Algorithm 认领 引用 被引量：6
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作者 Xusheng Lei Kexin Guo 《Journal of Bionic Engineering》 SCIE EI CSCD 2012年第4期508-514,共7页
This paper proposes a model identification method to get high performance dynamic model of a small unmanned aerial rotorcraft. With the analysis of flight characteristics, a linear dynamic model is constructed by the ... This paper proposes a model identification method to get high performance dynamic model of a small unmanned aerial rotorcraft. With the analysis of flight characteristics, a linear dynamic model is constructed by the small perturbation theory. Using the micro guidance navigation and control module, the system can record the control signals of servos, the state infor- mation of attitude and velocity information in sequence. After the data preprocessing, an adaptive ant colony algorithm is proposed to get optimal parameters of the dynamic model. With the adaptive adjustment of the pheromone in the selection process, the proposed model identification method can escape from local minima traps and get the optimal solution quickly. Performance analysis and experiments are conducted to validate the effectiveness of the identified dynamic model. Compared with real flight data, the identified model generated by the proposed method has a better performance than the model generated by the adaptive genetic algorithm. Based on the identified dynamic model, the small unmanned aerial rotorcraft can generate suitable control parameters to realize stable hovering, turning, and straight flight. 展开更多
关键词 small unmanned aerial rotorcraft model identification adaptive ant colony
A MODEL IDENTIFICATION METHOD OF VIBRATING STRUCTURES FROM INCOMPLETE MODAL INFORMATION 认领 引用
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作者 郑小平 姚振汉 蘧时胜 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 1995年第10期971-976,共6页
The accurate mathematical models for complicated structures are very difficult to construct.The work presented here provides an identification method for estimating the mass.damping,and stiffness matrices of linear dy... The accurate mathematical models for complicated structures are very difficult to construct.The work presented here provides an identification method for estimating the mass.damping,and stiffness matrices of linear dynamical systems from incomplete experimental data.The mass,stiffness and damping matrices are assumed to be real,symmetric,and positive definite The partial set of experimental complex eigenvalues and corresponding eigenvectors are given.In the proposed method the least squares algorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters.Seeveral illustative examples,are presented to demonstrate the reliability of the proposed method .It is emphasized that the mass,damping and stiffness matrices can be identified simultaneously. 展开更多
关键词 vibrating structures model identification incomplete experiemntal modal data the least squares method iteration technique
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Data-Driven Model Identification and Control of the Inertial Systems 认领 引用 被引量：1
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作者 Irina Cojuhari 《Intelligent Control and Automation》 2023年第1期1-18,共18页
In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the sy... In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation. 展开更多
关键词 Data-Driven Model Identification Controller Tuning Undamped Transient Response Closed-Loop System Identification PID Controller
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A MODEL IDENTIFICATION METHOD OF VIBRATING STRUCTURES FROM INCOMPLETE MODAL INFORMATION 认领 引用
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作者 郑小平 姚振汉 蘧时胜 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 1995年第5期971-976,共6页
The accurate mathematical models for complicated structures are verydifficult to construct.The work presented here provides an identification method for estimating the mass, damping , and stiffness matrices of linear ... The accurate mathematical models for complicated structures are verydifficult to construct.The work presented here provides an identification method for estimating the mass, damping , and stiffness matrices of linear dynamical systems from incompleteexperimental data. The mass, stiffness, and damping matrices are assumed to be real,symmetric, and positive definite. The partial set of experimental complex eigenvalues and corresponding eigenvectors are given. In the proposed method the least squaresalgorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters. several illustrative examples, are presented to demonstrate the reliability of the proposed method .It is emphasized thatthe mass, damping and stiffness martices can be identified simultaneously. 展开更多
关键词 vibrating structures model identification incompleteexperiemntal modal data the least squares method iteration technique
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Research on the Construction of Audit Risk Identification Model Based on Big Data Technology 认领 引用
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作者 Feiya Duan 《Proceedings of Business and Economic Studies》 2026年第7期66-71,共6页
With the improvement of enterprise business complexity and the surge in data scale,traditional audit risk identification methods are facing bottlenecks such as insufficient full-data processing and delayed risk respon... With the improvement of enterprise business complexity and the surge in data scale,traditional audit risk identification methods are facing bottlenecks such as insufficient full-data processing and delayed risk response.This paper focuses on the construction of an audit risk identification model based on big data technology,combining the advantages of big data in full-volume analysis,real-time processing and intelligent mining,and clarifies the core logic and technical support for model construction.Firstly,it sorts out the core elements of audit risk identification and the adaptability of big data technology,then constructs a model framework from the data layer,technology layer and application layer,and focuses on expounding the design points of multi-source data integration mechanism,intelligent risk feature mining and dynamic risk assessment system.The research shows that the model can break through the limitations of traditional experience-driven methods,realize accurate identification,dynamic tracking and forward-looking early warning of audit risks through data-driven approach,and effectively improve the efficiency and accuracy of audit risk identification.The research of this paper provides a feasible model reference for the digital transformation in the audit field,and is of great significance for promoting the development of audit risk identification towards intelligence and systematization. 展开更多
关键词 Big Data Technology Audit Risk Identification Model Data Integration Feature Mining
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Learning symbolic models of dynamical systems through Kolmogorov–Arnold Networks(KANs)in centralized and distributed settings 认领 引用
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作者 Alessandro Giuseppi Danilo Menegatti Antonio Pietrabissa 《Journal of Automation and Intelligence》 EI CSCD 2026年第2期155-165,共11页
Identifying an interpretable and tractable model is a crucial step for the analysis and control of dynamical systems.In this work,we employ the recently introduced Kolmogorov–Arnold Networks(KANs),a novel neural netw... Identifying an interpretable and tractable model is a crucial step for the analysis and control of dynamical systems.In this work,we employ the recently introduced Kolmogorov–Arnold Networks(KANs),a novel neural network architecture tailored for symbolic regression and interpretability,to learn symbolic models directly from data without any a priori knowledge of the observed dynamics.We then extend our result to the distributed case through federated learning introducing the FedKANs algorithm.FedKANs allows agents observing similar,but non-identical,systems to cooperate to learn more efficiently a symbolic model without the need to exchange any process data.To our knowledge,this represents the first distributed deep learning framework for symbolic regression in dynamical systems.Numerical simulations validate the proposed solutions in various settings,involving linear,nonlinear,discrete-time and continuous-time dynamics. 展开更多
关键词 Model identification Kolmogorov–Arnold Networks(KANs) Data-driven systems Symbolic regression
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Improved algebraic method for linear continuous-time model identification 认领 引用 被引量：1
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作者 Wuhua Hu Jianfeng Mao 《Journal of Control and Decision》 EI 2014年第2期180-189,共10页
Recently a novel algebraic method was proposed for linear continuous-time model identification,which has attracted extensive attention in the literature.This work reveals its connection to classic identification metho... Recently a novel algebraic method was proposed for linear continuous-time model identification,which has attracted extensive attention in the literature.This work reveals its connection to classic identification methods,discusses a limitation and presents a useful modification of the method.The discussions are supported by analysis and numerical experiments. 展开更多
关键词 linear continuous-time model model identification operational calculus least squares instrumental variables derivative estimation
Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics 认领 引用 被引量：1
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作者 Pengfei Cao Xionglin Luo Xiaohong Song 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2018年第1期137-143,共7页
Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and sof... Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms. 展开更多
关键词 soft sensor Modeling Characteristics separation System identification Double auxiliary models
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Identification method for helicopter flight dynamics modeling with rotor degrees of freedom 认领 引用 被引量：4
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作者 Wu Wei 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2014年第6期1363-1372,共10页
A comprehensive method based on system identification theory for helicopter flight dynamics modeling with rotor degrees of freedom is developed. A fully parameterized rotor flapping equation for identification purpose... A comprehensive method based on system identification theory for helicopter flight dynamics modeling with rotor degrees of freedom is developed. A fully parameterized rotor flapping equation for identification purpose is derived without using any theoretical model, so the confidence of the identified model is increased, and then the 6 degrees of freedom rigid body model is extended to 9 degrees of freedom high-order model. Bode sensitivity function is derived to increase the accuracy of frequency spectra calculation which influences the accuracy of model parameter identification. Then a frequency domain identification algorithm is established. Acceleration technique is developed furthermore to increase calculation efficiency, and the total identification time is reduced by more than 50% using this technique. A comprehensive two-step method is established for helicopter high-order flight dynamics model identification which increases the numerical stability of model identification compared with single step algorithm. Application of the developed method to identify the flight dynamics model of BO 105 helicopter based on flight test data is implemented. A comparative study between the high-order model and rigid body model is performed at last. The results show that the developed method can be used for helicopter high-order flight dynamics model identification with high accuracy as well as efficiency, and the advantage of identified high-order model is very obvious compared with low-order model. 展开更多
关键词 Flight dynamics Frequency domain Helicopters Identification Modeling Rotors
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Generalized Nonlinear Irreducible Auto-Correlation and Its Applications in Nonlinear Prediction Models Identification 认领 引用
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作者 侯越先 何丕廉 《Transactions of Tianjin University》 EI CAS 2005年第1期35-39,共5页
There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this ... There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this paper introduces a universal method to achieve nonlinear models identification. Two key quantities, which are called nonlinear irreducible auto-correlation (NIAC) and generalized nonlinear irreducible auto-correlation (GNIAC), are defined and discussed. NIAC and GNIAC correspond with intrinstic irreducible auto-(dependency) (IAD) and generalized irreducible auto-(dependency) (GIAD) of time series respectively. By investigating the evolving trend of NIAC and GNIAC, the optimal auto-regressive order of nonlinear auto-regressive models could be determined naturally. Subsequently, an efficient algorithm computing NIAC and GNIAC is discussed. Experiments on simulating data sets and typical nonlinear prediction models indicate remarkable correlation between optimal auto-regressive order and the highest order that NIAC-GNIAC have a remarkable non-zero value, therefore demonstrate the validity of the proposal in this paper. 展开更多
关键词 prediction models identification information entropy Tsallis entropy neural networks nonlinear irreducible autocorrelation generalized nonlinear irreducible autocorrelation
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Kinematic Calibration of a 5-DoF Parallel Machining Robot with a Novel Adaptive and Weighted Identification Method Based on Generalized Cross Validation 认领 引用
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作者 Lefeng Gu Fugui Xie 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第2期262-278,共17页
Accurate kinematic calibration is the very foundation for robots'application in industry demanding high precision such as machining.Considering the complex error characteristic and severe ill-posed identification ... Accurate kinematic calibration is the very foundation for robots'application in industry demanding high precision such as machining.Considering the complex error characteristic and severe ill-posed identification issues of a 5-DoF parallel machining robot,this paper proposes an adaptive and weighted identification method to achieve high-precision kinematic calibration while maintaining reliable stability.First,a kinematic error propagation mechanism model considering the non-ideal constraints and the screw self-rotation is formulated by incorporating the intricate structure of multiple chains and a unique driven screw arrangement of the robot.To address the challenge of accurately identifying such a sophisticated error model,a novel adaptive and weighted identification method based on generalized cross validation(GCV)is proposed.Specifically,this approach innovatively introduces Gauss-Markov estimation into the GCV algorithm and utilizes prior physical information to construct both a weighted identification model and a weighted cross-validation function,thus eliminating the inaccuracy caused by significant differences in dimensional magnitudes of pose errors and achieving accurate identification with flexible numerical stability.Finally,the kinematic calibration experiment is conducted.The comparative experimental results demonstrate that the presented approach is effective and has enhanced accuracy performance over typical least squares methods,with maximum position and orientation errors reduced from 2.279 mm to 0.028 mm and from 0.206°to 0.017°,respectively. 展开更多
关键词 Parallel machining robot Accurate kinematic calibration Weighted identification model Adaptive identification algorithm
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Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm 认领 引用
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作者 LIU Yang HU Shaochuang 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2024年第4期458-475,共18页
A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductiv... A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors. 展开更多
关键词 semitransparent medium coupled conduction-radiation heat transfer thermophysical properties simultaneous identification multilayer artificial neural networks(ANNs) evolutionary algorithm hybrid identification model
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THE APPLICATION OF ACOUSTIC LOGGING METHOD IN THE MODEL AND PARAMETERS IDENTIFICATION OF ROCK MASS 认领 引用
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作者 于师建 《Journal of Coal Science & Engineering(China)》 1999年第1期38-43,共6页
This paper describes the new method that is introduced into prediction of subsidence using system engineering method with acoustic logging and density logging. According to the result of acoustic logging, the real and... This paper describes the new method that is introduced into prediction of subsidence using system engineering method with acoustic logging and density logging. According to the result of acoustic logging, the real and complex rock beds are divided into a set of different bed groups and the equivalent mechanical model is to be built. Based on the modern control theory,according to the input data (convergence or settlement of the roof) and the output data (surface movement and deformation) of the system, the static parameters of equivalent rock beds can be derived from back calculation using the optimum method. Then the reqression relationship between the dynamic and static parameters can be built. So the prediction of rock and ground movements for other areas in the same district can be done, when using this relationship with the acoustic logging data and density logging data in situ. 展开更多
关键词 model and parameters identification p-w velocity subsidence prediction
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A Linear Domain System Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Genetic Algorithm 认领 引用 被引量：13
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作者 Xusheng Lei Yuhu Du 《Journal of Bionic Engineering》 SCIE EI 2010年第2期142-149,共8页
This paper proposes a new adaptive linear domain system identification method for small unmanned aerial rotorcraft.Byusing the flash memory integrated into the micro guide navigation control module,system records the ... This paper proposes a new adaptive linear domain system identification method for small unmanned aerial rotorcraft.Byusing the flash memory integrated into the micro guide navigation control module,system records the data sequences of flighttests as inputs(control signals for servos)and outputs(aircraft’s attitude and velocity information).After data preprocessing,thesystem constructs the horizontal and vertical dynamic model for the small unmanned aerial rotorcraft using adaptive geneticalgorithm.The identified model is verified by a series of simulations and tests.Comparison between flight data and the one-stepprediction data obtained from the identification model shows that the dynamic model has a good estimation for real unmannedaerial rotorcraft system.Based on the proposed dynamic model,the small unmanned aerial rotorcraft can perform hovering,turning,and straight flight tasks in real flight tests. 展开更多
关键词 small unmanned aerial rotorcraft dynamic space model model identification adaptive genetic algorithm
A New Model Parameter Identification Technique for Magnetorheological Dampers 认领 引用
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作者 Aditya Venkatesan 《Journal of Civil Engineering and Architecture》 2011年第12期1111-1116,共6页
Magnetorheological （MR） Dampers offer rapid variation in damping properties, making them ideal in semi-active control of structures. They potentially offer highly reliable operation and can be viewed as fail safe, i... Magnetorheological （MR） Dampers offer rapid variation in damping properties, making them ideal in semi-active control of structures. They potentially offer highly reliable operation and can be viewed as fail safe, in that in the worst case, they become passive dampers. Perfect understanding of the response is necessary when implementing these in operation in conjunction with a control mechanism. There are many models used to predict the behavior of MR dampers. One of these is the Bouc-Wen model. It is extremely popular as it is numerically tractable, very versatile and can exhibit a wide range of hysteretic behavior. It is necessary to first identify the characteristic parameters of the model before response prediction is possible. However, characteristic parameters identification of the Bouc-Wen model needs an experimental base, which has its own limitations. The extraction of these characteristic parameters by trial and error and optimization techniques leaves significant difference between observed and simulated results. This paper deals with a new approach to extract characteristic parameters for the Bouc-Wen model. 展开更多
关键词 Magneto-rheological dampers bouc wen model hysteretic behaviour identification of parameters of the bouc wen model
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A hybrid original approach for prediction of the aerodynamic coefficients of an ATR-42 scaled wing model 认领 引用 被引量：3
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作者 Abdallah Ben Mosbah Ruxandra Mihaela Botez Thien My Dao 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2016年第1期41-52,共12页
A new approach for the prediction of lift,drag,and moment coefficients is presented.This approach is based on the support vector machines(SVMs)methodology and an optimization meta-heuristic algorithm called extended g... A new approach for the prediction of lift,drag,and moment coefficients is presented.This approach is based on the support vector machines(SVMs)methodology and an optimization meta-heuristic algorithm called extended great deluge(EGD).The novelty of this approach is the hybridization between the SVM and the EGD algorithm.The EGD is used to optimize the SVM parameters.The training and validation of this new identification approach is realized using the aerodynamic coefficients of an ATR-42 wing model.The aerodynamic coefficients data are obtained with the XFoil software and experimental tests using the Price-Paidoussis wind tunnel.The predicted results with our approach are compared with those from the XFoil software and experimental results for different flight cases of angles of attack and Mach numbers.The main pur-pose of this methodology is to rapidly Predict aircraft aerodynamic coefficients. 展开更多
关键词 Aerodynamic coefficients Estimation Extended great deluge Metaheuristic Model identification Optimization Support vector machines Wind tunnel tests
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Distortion Identification Technique Based on Hilbert-Huang Transform in Video Stabilization 认领 引用 被引量：2
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作者 刘艳 邹谋炎 王强 《Transactions of Tianjin University》 EI CAS 2011年第1期68-74,共7页
A distortion identification technique is presented based on Hilbert-Huang transform to identify distortion model and distortion frequency of distorted real-world image sequences. The distortion model is identified sim... A distortion identification technique is presented based on Hilbert-Huang transform to identify distortion model and distortion frequency of distorted real-world image sequences. The distortion model is identified simply based on Hilbert marginal spectral analysis after empirical mode decomposing. And distortion frequency is identified by analyzing the occurrence frequency of instantaneous frequency components of every intrinsic mode functions. Rational digital frequency filter with suitable cutoff frequency is designed to remove undesired fluctuations based on identification results. Experimental results show that this technique can identify distortion model and distortion frequency of displacement sequence accurately and efficiently. Based on identification results, distorted image sequence can be stabilized effectively. 展开更多
关键词 image sequence distortion video stabilization distortion model identification distortion frequency identification Hilbert-Huang transform
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Decentralized closed-loop identification and controller design for a kind of cascade processes 认领 引用
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作者 陈庆 Li Shaoyuan Xi Yugeng 《High Technology Letters》 EI CAS 2005年第4期401-405,共5页
A new decentralized closcd-loop identification and predictive controller design method for a kind of cascade processes composed of several sub-processes is studied. This kind of cascade processcs has the characteristi... A new decentralized closcd-loop identification and predictive controller design method for a kind of cascade processes composed of several sub-processes is studied. This kind of cascade processcs has the characteristies of one-way connection. The process is divided into several two-input-two-output （TITO） sub-systems. The parameters of the first-order plus dead-time models for the transfer function matrices can be obtained using least squares method. Hence a distributed model predictive contn,ller is designed based on the coupling models of each sub-process. Simulation results on the temperature control of a reheating furnace are given to show the efficiency of the algorithm. 展开更多
关键词 plant-wide control model identification cascade processes predictive control
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