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7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011)

作者： Ghalei, Pouya Fatehi, Alireza Arvan, Mohamadreza Advanced Process Automation and Control (APAC) Research Group K.N. Toosi University of Technology Tehran Iran Electrical Engineering Department Malek-Ashtar University of Technology Tehran Iran

ISBN: (纸本)9783037853122

Input-Output data modeling using multi layer perceptron networks (MLP) for a laboratory helicopter is presented in this paper. The behavior of the two degree-of-freedom platform exemplifies a high order unstable, nonlinear system with significant cross-coupling between pitch and yaw directional motions. This paper develops a practical algorithm for identifying nonlinear autoregressive model with exogenous inputs (NARX) and nonlinear output error model (NOE) through closed loop identification. In order to collect input-output identifier pairs, a cascade state feedback (CSF) controller is introduced to stabilize the helicopter and after that the procedure of system identification is proposed. The estimated models can be utilized for nonlinear flight simulation and control and fault detection studies.

关键词： Twin rotor helicopter MLP neural network NARX NOE Cascade State Feedback controller

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A soft sensor development for the estimation of benzene content in catalytic reformate

A soft sensor development for the estimation of benzene cont...

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Proceedings of the International Convention MIPRO

作者： Željka Ujević Andrijić Romano Karlović Boris Žeželj Department of Measurement and Process Control Faculty of Chemical Engineering and Technology Zagreb Croatia Process Automation Rijeka Refinery Kostrena Croatia

As vehicle emission standards become more stringent, there is an increasing need for continual monitoring of benzene content in gasoline. Since the on-line analyzers are often unavailable, and laboratory analyses are infrequently obtained, soft sensors for the estimation of benzene content of light reformate are developed. Soft sensors are developed using system identification methods. Experimental data is acquired from the refinery distributed control system (DCS) and include continuously measured variables and analyzer assays available on-line. In the present work, the development of a Finite Impulse Response (FIR) model, an Output Error (OE) model and an Auto-Regressive Model with Exogenous Inputs (ARX) model are presented. To overcome the problem of selecting the best model parameters by trial and error, genetic algorithm was used. Based on developed soft sensors, it is possible to entirely replace on-line analyzers with soft sensors by embedding the model in a DCS on-site.

关键词： Mathematical model Finite impulse response filter Predictive models Autoregressive processes Data models Estimation Adaptation models

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The effect of control parameters to the quality of small-scale wood pellet combustion

The effect of control parameters to the quality of small-sca...

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TAPPI International Bioenergy and Bioproducts Conference 2012, IBBC 2012

作者： Ruusunen, M. Korpela, T. Björkqvist, T. University of Oulu Control Engineering Laboratory Department of Process and Environmental Engineering P.O. BOX 4300 90014 Oulu Finland Tampere University of Technology Department of Automation Science and Engineering P.O. BOX 692 33101 Tampere Finland

ISBN: (纸本)9781622768424

The target is to clear out control variables and requirements for clean small-scale wood pellet combustion (th). Experimental runs were carried out in the form of design of experiments (DOE) with two commercial 15 kW pellet burners, namely a horizontal gas-burner and a conventional horizontal burner in a 20 kW commercial pellet boiler. Analysed variables were fuel power, draught, air flow, and fuel feed period, and researched variables were CO, O2 and efficiency. The target was to identify and characterise separately the magnitude and direction of the effect of each factor. After process identification and variable optimisation, the results shows strong influence of the studied control parameters on the efficiency and the emissions. The effects and interactions between different process variables were rather similar with both burners. The major effects for CO levels were fuel feed and additionally draught affected in case of wood gas combustion. Additionally, the effects on combustion efficiency is described by draught, air feed and fuel feed period. Furthermore, the fuel feed period affected the excess air level in case of direct combustion principle. It was noticed, however, that the combustion properties and optimal parameter values vary significantly between the two cases.

关键词： Fuels

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Design and Implementation of nonlinear neural network controller based on simulator model in Damavand Tokamak

Design and Implementation of nonlinear neural network contro...

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第24届中国控制与决策会议

作者： H. Tolouei A. Kouhi H. Rasouli C. Rasouli Advanced Process Automation & Control Laboratory (APAC) faculty of Electrical Engineering Control department K.N. Toosi University of TechnologyTehran Iran Plasma Physics and nuclear fusion institute Institute of nuclear science and technology (AEOI)Tehran Iran.

In this study, nonlinear neural network controller will be developed to control plasma radial motion in Damavand Tokamak. It is essential to have a good model in order to design a proper controller for plasma radial motion. To achieve this goal, actuator circuits are simulated and in consequence based on simulator model and simulated actuator circuits nonlinear neural network controller will be designed in Damavand Tokamak. Comparison between neural network controller output and PD controller output shows the efficiency of proposed approach.

关键词： Tokamak, Actuator circuits, nonlinear neural network controller, Gradient descent algorithm, Momentum algorithm.

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Distributed ANNs in a layered architecture for energy management and maintenance scheduling of renewable energy HPS microgrids

Distributed ANNs in a layered architecture for energy manage...

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International Conference on Advances in Power Conversion and Energy Technologies (APCET)

作者： Y Jaganmohan Reddy Y V Pavan Kumar V Sunil Kumar K Padma Raju Process Solutions-Automation & Control Solutions Honeywell Technology Solutions Lab (Pvt) Ltd Hyderabad India Engineering Test Services-Aerospace Honeywell Technology Solutions Lab (Pvt) Ltd Hyderabad India Department of Electronics and Communication Engineering Jawaharlal Nehru Technological University Kakinada (JNTUK) Kakinada India

With increasing research on the field of alternative energy resources for sustainable development, more renewable energy sources are getting penetrated to the distributed network to form “microgrid”. This leads to more complexity in the distribution network in terms of energy management and realtime control. The objective of this paper is to design a system that forecasts the short (daily), medium (seasonal) and long term (yearly) load demand and the availability of energy resources at the microgrids. For scheduling the storage and transaction of electrical energy between neighboring microgrids, an energy management system (EMS) is designed. The EMS makes use of the forecasted data and real time data all together for managing an array of interconnected microgrids. The seasonal and yearly forecaster for a geographical boundary helps in maintenance scheduling and long term infrastructure development plan respectively. Recently, Artificial Neural Network (ANN) is found as a promising tool for statistical forecasting in real time applications. Hence, this paper makes use of ANN feature to forecast both load and availability of energy resources at microgrids in different scenarios like daily, seasonal, and yearly. The layered ANNs architecture is developed and trained with Levenberg-Marqurardt Back Propagation Algorithm. The entire design is simulated in MATLAB/Simulink. The proposed concept can be used in today's real time energy infrastructure to prevent future energy crises with improved reliability and smooth coordination among microgrids located at different areas.

关键词： Wind forecasting Artificial neural networks Wind speed Maintenance engineering Energy management Solar radiation

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Digital imaging and piezo-dispenser actuator in automatic flocculation control

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Sensors and Transducers 2012年第1期136卷 83-95页

作者： Tomperi, Jani Honkanen, Markus Kallio, Pasi Leiviskä, Kauko Saarenrinne, Pentti Joensuu, Iiris Piironen, Marjatta Control Engineering Laboratory Department of Process and Environmental Engineering University of Oulu P. O Box 4300 FIN-90014 Finland Tampere University of Technology Department of Energy and Process Engineering P. O. Box 589 FIN-33101 Tampere Finland Tampere University of Technology Department of Automation Science and Engineering P. O. Box 692 FIN-33101 Tampere Finland Kemira Oyj Espoo R and D Center P. O. Box 44 FIN-02271 Espoo Finland

This study presents an image-based on-line control system for a coiled pipe flocculator. A digital imaging technique developed previously is utilized to measure the characteristic floc size and a high-pressure piezo-dispenser is introduced for accurate dosing and rapid mixing of the flocculant. The controller is a conventional PI controller. Step change experiments on feed water quality, flow rate and desired floc size have been carried out for controller tuning and testing. The paper shows that the piezo-dispenser provides better flocculation results than a conventional dosing pump, and the flocculation result can be automatically controlled even when the feed water quality rapidly changes. The proposed flocculator is a simple, inexpensive and practical system for long-term laboratory tests to investigate the functionality of flocculants on varying feed *** © 2012 IFSA.

关键词： Water treatment

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Takagi-Sugeno control of the Elevation Channel of A Twin-Rotor System Using Closed-Loop Empirical Data

Takagi-Sugeno Control of the Elevation Channel of A Twin-Rot...

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American control Conference

作者： Hamidreza Azimian Alireza Fatehi Babak N. Araabi Department of Mechanical and Materials Engineering The University of Western Ontario (UWO) London ON Canada Advanced Process Automation and Control (APAC) Research Group Industrial Control Center of Excellence Faculty of Electrical and Computer engineering K. N. Toosi University of Technology Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781457710957

In this paper, design of Takagi-Sugeno controllers for a twin-rotor system from empirical data is addressed. First, identification of linear models using Prediction Error Method in the presence of nonlinear distortions and in feedback systems is revisited, and it is demonstrated that feedback nonlinear systems with an integral action can be locally represented by linear models rather than affine models. Moreover, it is demonstrated that how the consistency and unbiasedness of the identified models can be improved by proper design of the experiments. In order to ensure that the unmodeled contributions in the resulting piecewise linear model are sufficiently small, a criterion is proposed based on the small-gain theorem that can be used for unfalsification of the piecewise linear model. Finally, by taking advantage of the separation principle, design of fuzzy Takag-Sugeno state feedback and observer is formulated in terms of Linear Matrix Inequalities (LMIs). The implementation results on the actual system demonstrate significant improvements with respect to the initial controller and previous work on similar systems.

关键词： Nonlinear systems Closed loop systems Observers Uncertainty Data models Predictive models

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Identification of Hammerstein-Wiener ARMAX Systems Using Extended Kalman Filter

Identification of Hammerstein-Wiener ARMAX Systems Using Ext...

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2011 Chinese control and Decision Conference（CCDC）

作者： M.Mansouri H.Tolouei M.Aliyari Shoorehdeli Intelligent System Laboratory (ISLAB) faculty of Electrical EngineeringControl departmentK.N.Toosi University of Technology Advanced Process Automation & Control Laboratory (APAC) faculty of Electrical EngineeringControl departmentK.N.Toosi University of Technology Advanced Process Automation & Control Laboratory (APAC) faculty of Electrical EngineeringMechatronics departmentK.N.Toosi University of Technology

In this study,Extended Kalman Filter (EKF) algorithm is developed to estimate the parameters of Hammerstein-Wiener (H-W) ARMAX *** basic idea is to estimate the original parameters of the identification model,which are appeared in the form of product terms,***,other algorithms like Extended Forgetting Factor Stochastic Gradient (EFG),Extended Stochastic Gradient (ESG),Forgetting Factor Recursive Least Square (FFRLS) and Kalman Filter (KF),estimate parameters in the product form and they need another algorithms such averaging method (AVE method),singular value decomposition method (SVD method) to separate the ***,the computational complexity of the proposed approach *** show the efficiency of this method the results are compared with EFG and ESG method.

关键词： Hammerstein-Wiener model,Extended Kalman Filter algorithm,Extended Stochastic Gradient algorithm,Extended Forgetting Factor Stochastic Gradient algorithm

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Novel Tuning Strategy for Two-Degree-of-Freedom PI controllers

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IFAC Proceedings Volumes 2011年第1期44卷 6757-6762页

作者： Peyman Bagheri Hossein Nemati Advanced Process Automation & Control (APAC) Research Group K. N. Toosi University of Technology Tehran Iran Department of Electrical Engineering Sistan and Baluchestan University Iran

Abstract In this paper, a new tuning procedure for PI controllers in Two-Degree-of-Freedom (2DOF) structure is proposed. In design procedure, First Order plus Dead Time (FOPDT) model is used. The aim is to have good set point tracking and disturbance rejection and also maximum robustness to model uncertainties. The tuning strategy is based on using analytical rules and some conceptual rules about closed loop poles and also an exhaustive search. Simulation results demonstrate the effectiveness and validity of proposed method in coping with conflicting design objectives for a wide variety of processes including minimum phase and non-minimum phase and also integrating processes.

关键词： 2DOF PI controller FOPDT model Optimization Robustness

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Feature selection and classification of breast MRI lesions based on multi classifier

Feature selection and classification of breast MRI lesions b...

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International Symposium on Artificial Intelligence and Signal processing (AISP)

作者： Farzaneh Keyvanfard Mahdi Aliyari Shoorehdeli Mohammad Teshnehlab Electrical and Computer Engineering Department Khaje Nasir Toosi University of Technology Tehran Iran Electrical and Computer Engineering Department Advanced Process Automation and Control (APAC) Khaje Nasir Toosi University of Technology Tehran Iran

This paper aims to increase the classification specificity by using multi classifier system. First, a novel pixel search approach is applied to find significant region in images. Fuzzy C-means is utilized to determine the clear boundary of tumor. Then, shape and texture features are extracted from region of interest. Genetic algorithm is applied to select the best feature used for classifiers. Several neural networks and support vector machine are considered as classifiers that classify the data into benign and malignant group. To improve the performance of classification, three classifiers that have the best results among all applied methods are combined together that they have been named as multi classifier system. For each lesion, final detection as malignant or benign has been evaluated, when the same results are achieved from two classifiers of multi classifier system. Notice that the Jack-Knife technique is applied in this study, because it is useful for small data base as ours gotten from Milad Hospital in Tehran, Iran.

关键词： Lesions Artificial neural networks Support vector machines Magnetic resonance imaging Feature extraction Breast cancer

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