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International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Qiang Zhou Jie Zhang Yongmao Xu Institute of Process Control Department of Automation Tsinghua University Beijing Beijing China Centre for process Analytics and Control Technology School of Chemical Engineering & Advanced Materials University of Newcastle Newcastle-upon-Tyne UK

ISBN: (纸本)0780386531

This paper presents two soft-sensing models for predicting the product yields profile and the cracking degree of an ethylene pyrolysis furnace. The model based on single neural network with only one hidden layer trained by Levenberg-Marquardt algorithm with regularisation was first developed. It was found that the single neural network lack generalisation capability in that they can give undesirable performance when applied to unseen data. To improve the generalisation capability of the soft-sensing model, multi-model soft-sensors based on bootstrap aggregated neural networks with sequential training are used. In the sequential training of bootstrap aggregated networks, the first network is trained to minimise its prediction error whereas the rest of the networks are trained not only to minimise their prediction errors but also minimise the correlation among the trained networks. The overall output is obtained by combining all the individual networks. Application results show that the multi-model soft-sensors possess good generalisation capability in that they give good performance when applied to unseen data.

关键词： Furnaces Neural networks process control Predictive models Petrochemicals Chemical industry automation Postal services Chemical analysis Automatic control

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Temperature prediction control based on least squares support vector machines

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control Theory and Technology 2004年第4期2卷 365-370页

作者： Bin LIU, Hongye SU, Weihua HUANG, Jian CHU (National Laboratory of Industrial control Technology, Institute of Advanced process control, Zhejiang University, Yuquan Campus,Hangzhou Zhejiang 310027,China department of automation, College of Information Science and engineering, Wuhan University of Saence and Technology, Wuhan Hubei 430081, China) National Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University Yuquan Campus Hangzhou Zhejiang China Department of Automation College of Information Science and Engineering Wuhan University of Science and Technology Wuhan Hubei China

A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled *** obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time *** results of the experiment verify the effectiveness and merit of the algorithm.

关键词： Predictive control Least squares support vector machines RBF kernel function Generalized prediction control

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Performance assessment of model predictive control in industrial distillation column

Performance assessment of model predictive control in indust...

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World Congress on Intelligent control and automation (WCICA)

作者： Maying Yang Xiaoming Jin Fei Chen Automation Department Information Engineering College Zhejiang University of Technology Hangzhou China National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control University of Zhejiang Hangzhou China

ISBN: (纸本)0780382730

A synthetical method of multivariable control system performance assessment is proposed in this paper, which uses multivariable minimum variance control (MVC) benchmark to determine the stochastic performance, and normalized multivariate impulse response (NMIR) curve as an alternative measure of performance to test the dynamic performance, and with the help of auto-correlation function (ACF) and cross-correlation function (CCF) to analyse if there are oscillations exist. The method is applied to assess the performance of multivariable predictive control system of industrial distillation column.

关键词： Predictive models Predictive control Industrial control Distillation equipment Electrical equipment industry control systems System performance Benchmark testing Stochastic systems Impulse testing

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Nonlinear process identification and predictive control by multiparameter models

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IFAC Proceedings Volumes 2004年第13期37卷 1319-1324页

作者： U. Schmitz R. Haber R. Bars Department of Plant and Chemical Process Engineering Laboratory of Process Control University of Applied Science Cologne D-50679 Koln Betzdorfer Str. 2. Germany fax: +49-22J-8275-2836 Department of Automation and Applied Injormatics Budapest University of Technology and Economics H-1111 Budapest Goldmann Gy. tér 3. Hungary fax: +36-1-463-2871

According to the multi-model approach a nonlinear dynamical process is approximated in different working points by local valid linear models. The global valid model output is calculated as the weighted sum of the sub-model outputs. The parameters of the Gaussian weighting function can be chosen by optimization. The computation time can be reduced if instead of the model outputs the parameters (for example static gain and time constant) of the local valid models are merged. The global valid nonlinear model can be used e.g., for model based predictive control. The new, multi-parameter method is illustrated by a heat exchanger example.

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

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Long-range optimal model and multi-step-ahead prediction identification for predictive control 13

Long-range optimal model and multi-step-ahead prediction ide...

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13th IFAC Symposium on System Identification, SYSID 2003

作者： Haber, R. Schmitz, U. Bars, R. Department of Plant and Process Engineering Laboratory of Process Control University of Applied Science Cologne Betzdorfer Str. 2 KölnD-50679 Germany Department of Automation and Applied Informatics Control Research Group Hungarian Academy of Sciences Budapest University of Technology and Economics Hungary

Long-range optimal prediction algorithms use the predicted output for several steps ahead. The prediction based on traditionally estimated model parameters does not result in an optimal prediction if the measurements are noisy or/and model structure differs from real process structure. In this paper two different identification schemes are presented and compared: long-range predictive single-model identification and simultaneous multi-step-ahead prediction identification. It is shown that the first method is easier to realize but the second one leads to more accurate results. Both methods are derived for a first-order model in details. Simulation runs and a level control example illustrate the algorithms presented. © 2003 International Federation of Automatic control.

关键词： Parameter estimation

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Nonlinear process identification and predictive control by the weighted sum of multi-model outputs 2

Nonlinear process identification and predictive control by t...

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2nd IFAC Conference on control Systems Design, CSD 2003

作者： Schmitz, U. Haber, R. Bars, R. Department of Plant and Process Engineering Laboratory of Process Control University of Applied Science Cologne Betzdorfer Str. 2 KolnD-50679 Germany Department of Automation and Applied Lnformatics Budapest University of Technology and Economics Goldmann Gyter 3 BudapestH-1111 Hungary

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. © 2003 IFAC.

关键词： Heat exchangers

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Optimal choice of horizons for predictive control by using genetic algorithms 2

Optimal choice of horizons for predictive control by using g...

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2nd IFAC Conference on control Systems Design, CSD 2003

作者： Haber, R. Schmitz, U. Bars, R. Department of Plant and Process Engineering Laboratory of Process Control University of Applied Science Cologne Betzdorfer Str. 2 KolnD-50679 Germany Department of Automation and Applied Informatics Budapest University of Technology and Economics Goldmann Gy. ter 3 BudapestH-1111 Hungary

For predictive control in industry often very long horizons for control error and manipulated signal are used because of the slow processes which take place in the petrochemical industry. In order to reduce the computational effort some commercial predictive control program packages offer the ability to reduce the number of points in both horizons but do not recommend how to select the points which have to be considered in the horizon of the control error and manipulated variable. in this work the authors introduce an optimal choice not only of the horizon lengths itself but also for the strategy of reducing the number of points in the horizons. A genetic optimization algorithm was used both for the search for the optimal length of the horizons and for the best allocation of the points in the horizons. The results of the optimization process where used to deduct a simple rule. © 2003 IFAC.

关键词： Genetic algorithms

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An new evolutionary multi-objective optimization algorithm

An new evolutionary multi-objective optimization algorithm

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2003 Congress on Evolutionary Computation, CEC 2003

作者： Mu, Sheng-Jing Su, Hong-Ye Chu, Jian Wang, Yue-Xuan Nat. Lab. of Ind. Control Technol. Zhejiang Univ. Institute of Advanced Process Control Hangzhou 310027 China National CIMS Engineering Research Center Department of Automation Tsinghua University Beiiing 100084 China

ISBN: (纸本)0780378040

We introduce a new, simple and efficient evolutionary algorithm to multiobjective optimization problem, which based on neighborhood and archived operation (NAGA). The innovations contain two main parts: neighborhood identify procedure to obtain Pareto optimal solutions from the population and neighborhood crowding procedure to maintain the diversity of Pareto optimal solutions previously found. The neighborhood identify procedure is composed of two steps, first to identify the locally nondominated solutions from the population and then to obtain the global nondominated solutions among the locally solutions. The neighborhood crowding is introduced to maintain a widely distributed set of Pareto solutions along the Pareto optimal front, which through implementing a comparison among the neighborhood bounds of new identified Pareto solutions and those of solutions in the archive. The winners, which are not in any ranges of the solutions in the archive, will be copied to the archive. A well-tuned fitness assignment method is structured to guide the population converging to the true Pareto optimal front. This method is pragmatic compromise between the computational simplicity and efficiency. Four nicely balanced test problems are provided to check the performance of the approach. © 2003 IEEE.

关键词： Evolutionary algorithms

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Hybrid IMC dead-beat controller design in delta domain 2

Hybrid IMC dead-beat controller design in delta domain

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2nd IFAC Conference on control Systems Design, CSD 2003

作者： Preit, Z. Bars, R. Vajk, I. Haber, R. Department of Automation and Applied Informatics Budapest University of Technology and Economics MTA-BME Control Research Group Goldmann Gyter 3 BudapestH-1111 Hungary Department of Plant and Process Engineering University of Applied Science Cologne Germany

The paper presents a control system design technique in delta domain for IMC (Internal Model control) structure. Also a generalised form for delta domain Dead-beat control algorithm is given, a hybrid implementation of the controller (Z-domain combined with delta domain) is presented and its architecture is compared with the pure deltadomain implementation. The effect of placing the limitations in the IMC structure is also studied. The hybrid control structure is compared with a similar Dead-beat controller in Z-domain for second and third order plants (benchmarks). An illustrative sensitivity analysis between the hybrid control system and the Z-domain system has been performed. © 2003 IFAC.

关键词： controllers

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Near minimax robust control of flexible structures

Near minimax robust control of flexible structures

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American control Conference (ACC)

作者： M. Muenchhof T. Singh Department of Mechanical and Aerospace Engineering SUNY at Buffalo Buffalo NY USA Institute of Automatic Control Laboratory of Control Systems and Process Automation Darmstadt University of Technology Darmstadt Germany

This paper presents an approach to calculate prefilters that are insensitive to parameter deviations. The cost function chosen for this approach is the magnitude of the time-delay filter transfer function. By approximating the cost function with a polynomial, the effort of calculating minimax robust profiles can be reduced. The resulting control profiles yield a performance similar to the minimax robust controller. This is verified by the numerical results included in this paper.

关键词： Minimax techniques Robust control Flexible structures Filters Uncertainty Frequency Cost function Vibrations control systems Delay effects

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