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Decentralized Robust PI controller Design For An Industrial Utility Boiler - An IMC Method

IFAC Proceedings Volumes 2008年第2期41卷 14235-14240页

作者： Horacio Jose Marquez Batool Labibi Tongwen Chen Advanced Process Automation and Control (APAC) Research Group K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Department University of Alberta Edmonton Canada

This paper presents a scheme for designing a robust decentralized PI controller for an industrial utility boiler system. First, a new method for designing robust decentralized PI controllers for uncertain LTI MIMO systems is presented. Sufficient conditions for closed-loop stability and diagonal dominance of a multivariable system are given. For each isolated subsystem a first order approximation is obtained. Then, achieving robust stability and closedloop diagonal dominance is formulated as local robust performance problems. It is shown by selecting time constants of the closed-loop isolated subsystems appropriately, these local robust performance problems are solved and the interactions between closed-loop stabilized subsystems are attenuated. The internal model control (IMC) method is used to design local PI controllers. The suggested design strategy is applicable to unstable systems as well. Thereafter, the nonlinear model of an industrial utility boiler is linearized about its operating points and the nonlinearity is modeled as uncertainty for a nominal LTI MIMO system. Using the new proposed method, a decentralized PI controller for the uncertain LTI nominal model is designed. The designed controller is applied to the real system. The simulation results show the effectiveness of the proposed methodology.

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Robust Predictive PI controller Based on First-Order Dead Time Model

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IFAC Proceedings Volumes 2008年第2期41卷 5808-5813页

作者： F. Arousi U. Schmitz R. Bars R. Haber Dept. of Automation and Applied Informatics Budapest University of Technology and Economics MTA-BME Control Research group Dept. of Advanced Process Control Shell Rheinland Refinery Köln/Wesseling Germany Inst. of Chemical Process Engineering and Plant Design University of Applied Science Cologne Germany

Predictive control algorithms compute the manipulated variable minimizing a cost function considering expected future errors. PI control algorithms can be equipped with predictive properties. Simple predictive control algorithms are derived using approximation of an aperiodic process by a first-order model with dead time. Applying a noise model the robustness properties of the algorithm are enhanced considering plant-model mismatch. The noise filter is considered as a design parameter. Simulation examples demonstrate the behavior of the predictive PI algorithm and the robustifying effect of the noise filter.

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Lipschitz Numbers: A Medium for Delay Estimation

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IFAC Proceedings Volumes 2008年第2期41卷 7468-7473页

作者： Iman Makaremi Alireza Fatehi Babak Nadjar Araabi Advanced Process Automation & Control (APAC) Research Group Faculty of Electrical Eng. K.N. Toosi University of Technology Tehran Iran Control & Intelligent Processing Center of Excellence School of Electrical & Computer Eng. University of Tehran Tehran Iran

The paper deals with problem of estimating input channel delay in nonlinear system with a model-free approach. The proposed method is based on Lipschitz theory. It is an extension to the Lipschitz method which was proposed for determining the order of a model. Our algorithm consists of two parts which in the first one estimation is made on the proper number of dynamics on the input and in the second part the pure delay of the input is obtained. The method is applied for estimation of the delay of two different models and the estimation was as accurate as possible.

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Real-Time Level Plant control Using Improved BELBIC

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IFAC Proceedings Volumes 2008年第2期41卷 4631-4635页

作者： M. Masoudinejad R. Khorsandi A. Fatehi C. Lucas S. Fakhimi Derakhshan M.R. Jamali Advanced Process Automation & Control (APAC) Research Group Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Center of Excellence for Control and Intelligent Processing Electrical and Computer engineering Department University of Tehran Iran

Brain emotional learning based intelligent controller (BELBIC) is based on computational model of limbic system in the mammalian brain. In recent years, this model was applied in many linear and nonlinear control applications. Previous studies show that this controller has fast response, simple implementation and robustness with respect to disturbances. It is also possible to define emotional signal based on control application objectives. But in the previous studies, internal instability of this controller was not considered and control task were done in limited time period. In this article mathematical description of BELBIC is investigated and improved to avoid internal instability. Simulation and implementation of improved model was done on level plant. The obtained results showed that instability of model has been solved in the new model without loss of performance by using Integral Anti Windup (IAW).

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Intelligent control Based on Reinforcement Learning for Batch Thermal Sterilization of Canned Foods

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IFAC Proceedings Volumes 2008年第2期41卷 3568-3573页

作者： S. Syafiie Carlos Vilas Miriam R. Garcia Fernando Tadeo Antonio A. Alonso Ernesto Martinez Department of Systems Engineering and Automatic Control University of Valladolid 47011 Valladolid Spain { Process Engineering Group IIM-CSIC Vigo Spain National Research Council of Argentina Avellaneda 3657 3000-Santa Fe Argentina

A control technique based on Reinforcement Learning is proposed for controlling thermal sterilization of canned food. Without using an a-priori mathematical model of the process, the proposed Model-Free Learning controller (MFLC) aims to follow a temperature profile during two stages of the process: first heating by manipulating the saturated steam valve and then cooling by opening the water valve) by learning. From the defined state-action space, the MFLC agent learns the environment interacting with the process batch to batch and then using a tabular state-action mapping. The results show the advantages of the proposed technique for this kind of processes.

关键词： Reinforcement Learning Intelligent process control Sterilization process Batch process

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Improvement of the crown and shape control model for the hot rolling

SEAISI Quarterly (South East Asia Iron and Steel Institute)

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SEAISI Quarterly (South East Asia Iron and Steel Institute) 2008年第3期37卷 38-43页

作者： Hayashi, Kanji Omura, Akihiro Furumoto, Hideaki Kanemori, Shinya Guobin, Ding Research and Developing Office Mitsubishi-Hitachi Metals Machinery Inc. Hiroshima Japan Electrical and Automation Engineering Group Mitsubishi-Hitachi Metals Machinery Inc. Hiroshima Japan Materials and Manufacturing Technology Laboratory Mitsubishi Heavy Industries Ltd. Hiroshima Japan Control Systems Laboratory Mitsubishi Heavy Industries Ltd. Hiroshima Japan Cold Rolling Mill Stainless Steel Branch Baoshan Iron and Steel Co. Ltd. Shanghai China

It has long been recognized that the crown and shape of hot-rolled strips are Important in terms not only of product quality but also of good yield and operational stability in production. The controlling of the thickness distribution along the width direction, so called "the strip crown", is essential for the requirements. To meet the requirements, the pair cross mills and crown-control model have been developed. Recently, the requirements for closer and closer tolerance on the crown and flatness of hot-rolled strips have been increasing especially for the region at the strip edges along the width direction. And so, the crown-control model has been improved which is taking into account the width spread at the strip edges and based on Rigid-Plastic FEM and Neural-Network method. This advanced model for the controlling the crown and shape is applied to the production mill, and the measured strip crown agreed well with the predicted one.

关键词： Hot rolling

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An Improved Method for Tracking a Single Target in Variable Cluttered Environments

An Improved Method for Tracking a Single Target in Variable ...

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IEEE International Symposium on Signal processing and Information Technology (ISSPIT)

作者： Fatemeh Rahemi Ali Khaki Sedigh Alireza Fatehi Farbod Razzazi Department of Electrical Engineering Islamic Azad University Science and Research Tehran Iran Advanced Process Automation and Control (APAC) Research Group Electrical Engineering Department K.N. Toosi University of Technology Tehran Iran

Tracking moving objects in variable cluttered environments is an active area of research. It is common to use some simplifying assumption in such environments to facilitate the design. In this paper a new method for simulating the completely non-Gaussian cluttered environments is presented. The method is based on using the variable variance of process noise as a description of variability in such environments. The key objective is to find an effective algorithm for tracking a single moving object in variable cluttered environments, with utilization of the presented method. The new methodology is presented in two steps. In the first step we compare the accuracy of estimators in tracking a moving object, and in the second step, the goal is to find the best algorithm for tracking a single moving target in variable cluttered environments.

关键词： Target tracking Particle filters Radar tracking Working environment noise Stochastic systems Design automation Automatic control process control Electric variables control Jacobian matrices

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Identification and Abnormal Condition Detection of a Cement Rotary Kiln

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IFAC Proceedings Volumes 2008年第2期41卷 7233-7238页

作者： Iman Makaremi Alireza Fatehi Babak N. Araabi Morteza Azizi Ahmad Cheloian Advanced Process Automation & Control (APAC) Research Group Faculty of Electrical Eng. K.N. Toosi University of Technology Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical & Computer Eng. University of Tehran Tehran Iran Saveh Cement Company Saveh Iran

In this paper, we use system identification methods for abnormal condition detection of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. A novel approach is used in order to estimate the delays of the input channel of the kiln. By means of that, the identification task gets easier and the results are more accurate. To identify the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Finally, a model for the healthy mode of the kiln is obtained through which it is possible to detect abnormal conditions in the process. We distinguished two common abnormal conditions in kiln and another one which was not characteristically known for cement experts as well.

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Cement rotary kiln control: A supervised adaptive model predictive approach

Cement rotary kiln control: A supervised adaptive model pred...

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IEEE India Conference (INDICON)

作者： Javaneh Ziatabari Alireza Fatehi Mohamad T. H. Beheshti Department of Electrical Engineering University of Trabiat Modares Tehran Iran Advanced Process Automation and Control (APAC) research group Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran

Considering the need of an advanced process control in cement industry, this paper presents an adaptive model predictive algorithm to control a white cement rotary kiln. As any other burning process, the control scenario is to expect the controller to regulate the temperature and the period of baking a fixed quantity of raw material as desired, as well as to have the concentration of the combustion gases under control. To achieve these goals, this work presents a strategy which includes multivariable online identification of the kiln process and a constrained generalized predictive controller. An MLP neural network model derived from real plant data of Saveh cement factory in Iran is used as the kiln process simulator. The control efforts are made taken into account the operating constraints. At last the proposed control strategy is modified so as to gain good disturbance rejection ability.

关键词： Kilns Programmable control Adaptive control Predictive models Temperature control process control Cement industry Prediction algorithms Raw materials Combustion

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A Neuro-Fuzzy controller for Rotary Cement Kilns

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IFAC Proceedings Volumes 2008年第2期41卷 13259-13264页

作者： Maryam Fallahpour Alireza Fatehi Babak N. Araabi Morteza Azizi Control & Intelligent Processing Center of Excellence School of Electrical & Computer Eng. University of Tehran Tehran Iran Advanced Process Automation & Control (APAC) Research Group Electrical Engineering Faculty K. N. Toosi University of Technology Tehran Iran Saveh Cement Co. Saveh Iran

In this paper, we design a neurofuzzy controller to control several variables of a rotary cement kilns. The variables are back-end temperature, pre-heater temperature, oxygen content and CO2 gas content of the kiln. The fuzzy control system, as an advanced control option for the kilns, is intended to minimize the operator interaction in the control process. The proposed fuzzy controller uses a neural network to optimize TSK-type fuzzy controller. Since there is no generally applicable analytical model for cement kilns, we use the real data derived from Saveh cement factory for the plant identification. A model, which is very similar to the real plant, is identified then; and the identified model is used for control design and simulations. Extensive simulation studies justify the effectiveness and applicability of the proposed control scheme in intelligent control of cement plant.

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