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作者： Li, Zairan He, Ting Cao, Luying Wu, Tunhua McCauley, Pamela Balas, Valentina E. Shi, Fuqian Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University Tianjin China Wenzhou Vocational and Technical College Wenzhou China College of Information and Engineering Wenzhou Medical University Wenzhou325035 China Department of Industrial Engineering and Management Systems University of Central Florida OrlandoFL United States Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Romania

An increasing number of applications require the integration of data from various disciplines, which leads to problems with the fusion of multi-source information. In this paper, a special information structure formalized in terms of three indices (the central presentation, population or scale, and density function) is proposed. Single and mixed Gaussian models are used for single source information and their fusion results, and a parameter estimation method is also introduced. Furthermore, fuzzy similarity computing is developed for solving the fuzzy implications under a Mamdani model and a Gaussian-shaped density function. Finally, an improved rule-based Gaussian-shaped fuzzy control inference system is proposed in combination with a nonlinear conjugate gradient and a Takagi-Sugeno (T-S) model, which demonstrated the effectiveness of the proposed method as compared to other fuzzy inference systems. © 2015 - IOS Press and the authors. All rights reserved.

关键词： Fuzzy control

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INTERNAL MODEL-BASED controlLER FOR A SOLAR PLANT

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IFAC Proceedings Volumes 2002年第1期35卷 49-54页

作者： István Farkas István Vajk Department of Physics and Process Control Szent István University Páter K. u. 1. H-2103 Gödöllö Hungary Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest H-1521 Hungary

In this study a model-based control designed for the operation of a solar power plant is discussed. The simplified physically based model of the plant was developed on the basis of the energy balances including the solar insulation as an input, the heat transferred by the flow of the oil as a working media and the overall heat loss from the plant. The outlet temperature of the collector field is the reference/set-point value while the volumetric flow rate of the oil is the manipulating variable to the controller. The solar radiation, the ambient temperature and the inlet oil temperature to the collector field are assumed as the disturbances. The model developed and identified is an internal part of the controller. The initial control experimental results are also presented.

关键词： Solar power plant physically-based model identification internal model control (IMC)

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ROBUST STABILITY CHECKING OF A CLASS OF LINEAR INTERVAL FRACTIONAL ORDER SYSTEM USING LYAPUNOV INEQUALITY

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IFAC Proceedings Volumes 2006年第11期39卷 89-94页

作者： Hyosung Ahn YangQuan Chen Igor Podlubny Center for Self-Organizing and Intelligent Systems (CSOIS) Department of Electrical and Computer Engineering Utah State University Logan UT 84322-4160 USA Department of Applied Informatics and Process Control Technical University of Kosice B. Nemcovej 3 04200 Kosice Slovak Republic

This paper provides a new analytical robust stability checking method of fractional-order linear time invariant interval uncertain system. This paper continues the authors’ previous work (Chen et al., 2005a) where matrix perturbation theory was used. For the new robust stability checking, Lyapunov inequality is utilized for finding the maximum eigenvalue of a Hermitian matrix. Through numerical examples, the usefulness and the effectiveness of the newly proposed method are verified.

关键词： Fractional order linear dynamic systems robust stability interval linear time invariant systems interval matrix

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Nonlinear process Identification and Predictive control by the Weighted Sum of Multi-Model Outputs

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IFAC Proceedings Volumes 2003年第18期36卷 129-134页

作者： U. Schmitz R. Haber R. Bars Department of Plant and Process Engineering Laboratory of Process Control University of Applied Science Cologne D-50679 Köln Betzdorfer Str. 2 Germany fax: +49-221-8275-2836 Department of Automation and Applied Informatics Budapest University of Technology and Economics H-1111 Budapest Goldmann Gy. tér 3. Hungary fax : +36-1-463-287l

Most industrial processes are nonlinear. In such a case only a nonlinear model valid for the whole working area can ensure a good controller design. The nonlinear process is approximated by a multi-model consisting of the intelligent combination of some linear sub-models. As a very practical way the following identification strategy was used: independent model parameter estimation in the different working points and the calculation of the global valid model output as the weighted sum of the sub-models. As a weighting function the Gaussian function is used. The parameters of the Gaussian function were chosen either without or with optimization of the identification cost function. The global valid nonlinear model was used for model based predictive control. A heat exchanger example illustrates the method.

关键词： process identification nonlinear models predictive control multi-model heat exchanger

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LEARNING A controlLER FOR A COUPLED DRIVES APPARATUS USING VRFT STRATEGY

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IFAC Proceedings Volumes 2007年第13期40卷 133-138页

作者： Frantiŝek Gazdoŝ Petr Dostál Department of Process Control Faculty of Applied Informatics Tomas Bata University in Zlín Nad stránêmi 4511 760 05 Zlín Czech Republic phone: +420-576-035-199 fax:+420-576-032-719

The paper utilizes the Virtual Reference Feedback Tuning methodology for the iterative way of controller design and fine-tuning. It uses a series of experiments with no restriction on data generation to design an optimal controller of desired structure without the intermediate plant identification step. The approach is shown to be successful for the design and fine-tuning of a controller for a coupled drives system by means of adjusting a desired settling-time of a controlled variable.

关键词： iterative methods controller tuning direct methods virtual reference feedback tuning coupled drives apparatus

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Analysis of modified optimization in multivarible predictive control with regards to control quality

International Journal of Mechatronics and Applied Mechanics

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International Journal of Mechatronics and applied Mechanics 2019年第5期2019卷 7-12页

作者： Kubalcik, Marek Bobal, Vladimir Barot, Tomas Department of Process Control Tomas Bata University in Zlin Faculty of Applied Informatics Nad Stranemi 4511 Zlin760 05 Czech Republic Department of Mathematics with Didactics University of Ostrava Faculty of Education Frani Sramka 3 Ostrava709 00 Czech Republic

Model predictive control (MPC) has been a widely researched strategy in the modern control theory. Various modifications of MPC are focused on improving of two subsystems – the predictor and the optimizer, which cooperate mutually on the receding horizon. In constrained predictive control, the optimization is a quadratic programming problem, which has to be solved numerically. Namely, in constrained multivariable MPC, the constraints and the multi-variability of the process cause excessive increasing of the computational complexity. Reducing of computational time and decreasing of a number of numerical operations are particularly desirable. This is the reason for efforts to modify algorithms of numerical optimization. These modifications can influence control quality. This paper deals with one modification of the Hildreth optimization method and accurate analysis of its impact to quality of control. For analysis of impact to control quality, descriptive statistical methods are frequently used;particularly, methods based on analysis of floating point operations are complemented with their regression analysis for expression of complexity function O. However, the regression analysis of these results belongs only to non-inductive statistical methods depending on particular measurements of a computational complexity. For purposes of increasing responsibility of control quality indicators, set of specific statistical methods aimed on testing hypotheses is recommended in this paper. Testing hypotheses can be applied with strictly defined significance level. Practical implementation of modified MPC algorithm is statistically analyzed in comparison with unmodified MPC using proposed set of particular statistical methods. © 2019, Cefin Publishing House. All rights reserved.

关键词： Quadratic programming

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Robust stabilization of interval plants using Kronecker summation method

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WSEAS Transactions on Systems 2010年第9期9卷 917-926页

作者： Matušů, Radek Prokop, Roman Matejičková, Katarína Bakošová, Monika Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University in Zlín Nad Stráněmi 4511 76005 Zlín Czech Republic Department of Information Engineering and Process Control Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Radlinského 9 81237 Bratislava Slovakia

This paper deals with design of continuous-time robustly stabilizing Proportional-Integral (PI) controllers for interval systems using the combination of Kronecker summation method, sixteen plant theorem and an algebraic approach to controller tuning. The effectiveness and practical applicability of the proposed method is demonstrated in both simulation and real experiments including control of a third order nonlinear electronic plant.

关键词： Continuous time systems

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Evolutionary optimization methods for predictive on-off control

Evolutionary optimization methods for predictive on-off cont...

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作者： Schmitz, Ulrich Haber, Robert Bars, Ruth Shell Deutschland Oil Rhineland Refinery Department for Technology and Services Ludwigshafener Strasse l D-50389 Wesseling Germany University of Applied Science Cologne Institute of Process Engineering and Plant Design Betzdorfer Str. 2 D-50679 Cologne Germany Budapest Univ. of Technology and Economics Dept. of Automation and Applied Informatics MTA-BME Control Research Group Goldmann Gy.ter 3 H-1111 Budapest Hungary

ISBN: (纸本)9783902661661

On-off control is still widely used in the industry, especially for discontinuous actuators, e.g. compressors, steam turbines or furnace burners. Exact set-point control is not possible by an on-off controller. Lower and upper limits are defined for the control signal which should not be violated. For this predictive gap control quadratic and horizon dependent cost functions are defined. Traditional numerical optimization fails with longer prediction horizons, but a genetic algorithm can be effectively applied even for long horizons.

关键词： Actuators

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Adaptive predictive control of laboratory heat exchanger

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WSEAS Transactions on Systems 2014年第1期13卷 470-481页

作者： Bobál, Vladimír Kubalík, Marek Dostál, Petr Novák, Jakub Centre of Polymer Systems University Institute Tomas Bata University in Zlín T. G. Masaryka 5555 Zlín Czech Republic Department of Process Control Faculty of Applied Informatics Tomas Bata University in Zlín T. G. Masaryka 5555 Zlín Czech Republic

Heat exchange belongs to the class of basic thermal processes which occur in a range of industrial technologies, particularly in the energetic, chemical, polymer and rubber industry. The process of heat exchange is often implemented by through-flow heat exchangers. It is apparent that for an exact theoretical description of dynamics of heat exchange processes it is necessary to use partial differential equations. Heat exchange is namely a process with distributed parameters. It is also necessary to take into account its nonlinear and stochastic character. In spite of these facts, most of thermal equipment is controlled by digital modifications of PID controllers at present. This paper deals with identification of a dynamic behaviour of a through-flow heat exchanger and a design of a self-tuning predictive controller for its control. The designed controller was verified by a real-time control of an experimental laboratory heat exchanger.

关键词： Model predictive control

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Self-tuning control of non-linear servomotor: Standard versus dual approach

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WSEAS Transactions on Systems 2013年第6期12卷 304-313页

作者： Bobál, Vladimír Chalupa, Petr Dostál, Petr Kubalcík, Marek Centre of Polymer Systems University Institute Tomas Bata University in Zlín T. G. Masaryka 5555 760 01 Zlín Czech Republic Department of Process Control Faculty of Applied Informatics Tomas Bata University in Zlín T. G. Masaryka 5555 760 01 Zlín Czech Republic

The majority of processes met in the industrial practice have stochastic characteristics and eventually they embody non-linear behaviour. Traditional controllers with fixed parameters are often unsuitable for such processes because their parameters change. The changes of process parameters are caused by changes in the manufacturing process, in the nature of the input materials, fuel, machinery use (wear) etc. Fixed controllers cannot deal with this. One possible alternative for improving the quality of control for such processes is the use of adaptive control systems. Different approaches were proposed and utilized. One successful approach is represented by self-tuning controller (STC). This approach is also called system with indirect adaptation (with direct identification). The main idea of an STC is based on the combination of a recursive identification procedure and a selected controller synthesis. Presently, most of the STCs are based on the Certainty Equivalence (CE) Principle, which is only suboptimal. One of the possibilities to improve the quality of these adaptive control methods is usage of an Adaptive Dual control (the bicriterial approach). In this paper, the bicriterial approach is verified and compared with some other adaptive control approaches based on the CE Principle by the real-time control of a highly non-linear laboratory model, the DR300 Speed control with Variable Load.

关键词： Real time control

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