Papers by Keyword: Predictive Control

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Authors: Martin Mariška, Ivan Taufer, Imrich Koštial, Petr Doležel, Pavol Palička
Abstract: This paper deals with a proposal of predictive control system for the magnesite thermal treatment in rotary furnace. The mathematical model based on the initial principles and elementary balances method providing a comprehensive view of the rotary furnace work was calibrated based on the measured operating data. This model was used as the data model for the development of the approximation model in the form of an artificial neural network after identifying the critical points of the production process of sintered magnesia production. The paper represents the process of the approximation model development and the principle of the seeking of the optimal values of the specified control variables in order to ensure the required quality throughout the whole period of the rotary furnace operation.
Authors: T.C. Kuo, Y.J. Huang, B.W. Hong, C.H. Chang, H.Y. Tseng
Abstract: In this paper, a novel robust control method is proposed for uncertain time-delayed systems. The objective is to guarantee system stability and robustness against modeling errors. A fuzzy estimator is developed to predict the uncertain plant time-delay. Smith predictor is exploited. An adaptive tuning law is developed to further improve the control system performance. The proposed robust control method is successfully applied to a heat exchanger control system. Simulation results show that the approach is indeed effective. System robustness as well as stability is achieved.
Authors: André Ribeiro Lins de Albuquerque, Cecília Sosa Arias Peixoto, Luiz Teruo Kawamoto Júnior, Georgea Duarte, Jones Erni Schmitz, Cesar Xavier
Abstract: This work has developed a predictive control solution based on specific models for the process of ethanol distillation. The advantages of such control are relative to the prediction of the consequences of the disturbances by the model, thus enabling the control action to be done in a previous manner, resulting in the minimization of the variables fluctuation controlled by the process. This results in, among other advantages, energy economy, in the improvement of the ethanol produced and in the increasing production capacity. Another desirable characteristic in this control mode is its capacity to act in non-linear systems as is the case of the distillation columns. Finally, it must be noted that with the application of an advanced control solution, as proposed in this study, it becomes viable, in a second moment, for the ethanol plants to operate in multiple operational conditions, such as: 1) maximum energy economy (scarcity of raw material, for example) and: 2) maximum production condition (for situations with excess of materials to be distilled). The models developed in this project will consist of purely empirical models. Several tests will be done in the different types of models to measure the precision and robustness. The proposed control strategy demonstrated be able to control selected control loops adequately. Steam savings and reduction of product losses were observed.
Authors: Ming Qiu Li, Yan Xin Yu
Abstract: Reactor is hard to control because it has the characteristics of time-varying, nonlinear and large time delay. There are many uncertainties in its working process. In order to improve the dynamic performance of reactor temperature control, predictive control technology was adopted in this paper. On the basis of considering the system performance requirements and uncertainties, predictive control model was established. Improved dynamic matrix control (DMC) control algorithm with system uncertainty was designed to control reactor temperature. The simulation result shows that the system has no overshoot and its steady-state error is little than 6.2645e-004. The system has stronger robust performance when uncertainty parameter of system unknown uncertainty matrix G changes. Improved predictive DMC control algorithm can make the system have better dynamic performance than PID control and meet the need of the industry productive process.
Authors: Guang Bin Wang, Y.Q. Kong, Ke Wang
Abstract: In the rolling process, serious deviation will cause product quality drop and rolling equipment fault. This reserch propose tail deviation’s predictive control method of the tandem rolling strip based on manifold learning. Based on real deviation data in the rolling production site,tail deviation patterns are divided according to deviation’s value. Using manifold learning method to deviation data in middle rolling stage , tail deviation pattern and scope are obtained. According to regression model between the control variable and deviation, predictive control strategy of the tandem rolling strip may be implemented. Experiment shows this method may control tail deviation in preconcerted permission range.
Authors: Sheng Wang Li, Xin Duan, Zhen Liu
Abstract: Cement decomposing furnace is a typical multi-variable, nonlinear, large delay and strong coupling complex control object, its difficult to establish accurate mathematical model, the conventional control algorithm is difficult to get satisfactory control effect. By applying adaptive BP(back propagation) algorithm in neural network modeling, make the neural network predicts the decomposing furnace outlet temperature, then modify the pulverized coal flow rate value that obtained by the fuzzy controller to control the decomposing furnace outlet temperature. The field application shows that the control software which is designed by the control algorithm in this paper responses quickly, the error between actual temperature and the expected value is small, it has a good reliability, adaptability and robustness.
Authors: Rao Bin
Abstract: Network latency was usually uncertain or random, and the packet loss and temporal disorders could also be attributed to a certain degree of time delay. This paper briefly described the basic principle of predictive control and deduced the predictive control algorithm based on Toeplitz equation, finally combined with the simulation example to verify the validity and superiority of the new algorithm, from the operation speed of the algorithm, the signal tracking capabilities when time delay existed, and performance for overcoming the influence of model mismatch.
Authors: Jun Hui Wu, Jun Kai Wang, Jie Chen, Hui Ping Si, Kai Yan Lin
Abstract: In this paper, a review of the control of fixed-bed reactors for biomass pyrolysis is presented, which is divided into several parts: the mechanism and identification modeling, model reduction methods and system parameter estimation and control methods of fixed-bed reactors etc. Fixed-bed reactors in chemical industry are much similar with those in biomass pyrolysis, so the control methods of fixed-bed reactors in chemical industry can be applied to biomass pyrolysis processes. As a result, some comments are given about control strategies in this field
Authors: Lian Dong Lin, Pei Jia Ren
Abstract: To enhance the current regulation capability for PMSM when transient state, which caused by the existence of numerical delay such as current sampling and Pulse-Width Modulation (PWM) duty-cycle updating, this paper proposed a predictive current control algorithm for permanent magnet synchronous motor which based on deadbeat control. The numerical delays in conventional FOC system are eliminated in theory. Simulation and experiment results show that the PMSM (Permanent Magnet Synchronous Motor) current predictive control scheme improves both the dynamic performance and steady-state precision of the PMSM control system.
Authors: Zhen Ping Ji, Zhan Shi
Abstract: Three-tank water level system has the characteristics of nonlinear, large time delay, time-varying parameters, and the system is broadly representative in the research of non-linear and inertial process control. For the controlled object’s strong-nonlinear characteristic when the liquid level given value changes, a multi-model predictive PID cascade control strategy was presented to improve response performance of the control system. On the basis of analyzing the dynamic characteristics of the object, multiple sub-model of the object was established, and a global approximate model of the complex object was obtained as the predictive model by the weighted sub-model. Owing to the rapid response characteristic of PID control, we built the predictive control PID cascade control system by combining PID algorithm with predictive control. The experiment results show that the control algorithm can achieve good tracking control, and greatly improve dynamic and static performance of the system.
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