リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Development of Advanced Control Methods Applicable to Industrial Processing Systems」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。
リケラボ

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Development of Advanced Control Methods Applicable to Industrial Processing Systems

徐, 松 シュ, ソン Xu, Song 群馬大学

2020.03.24

概要

With the fast development of science and technology, industrial processes such as thermal process, manufacturing process, production process and so on are becoming more and more important, and have higher requirement for the operation performance. Thermal processing system, as one of the most complex processes, has a wide range of applications in the industrial field especially in the food process. For the multi-point (multi-input multi-output) thermal processing systems, temperature control is playing a more and more important role in its application. The proportional-integral- derivative (PID) control technologies have been widely used for most of the industrial processes. However, due to the nonlinearity and large dead time of the temperature control objects, the performance of PID-only control system may not satisfy the expected requirements. Also, the coupling influence and dead time difference in the multi-point temperature system have a significant effect to the transient response of each point.

Two advanced control methods are proposed in this thesis to deal with the two shortcomings mentioned above, respectively. For the coupling influence and dead time difference in the multi-point temperature control system, a pole-zero cancellation method is proposed. While for the nonlinearity and large dead time of the control objects, a reference-model-based artificial neural network (NN) method is proposed.

1) Pole-zero cancellation method for multi-point temperature control system
The proposed method is one kind of the model-based advanced control method. In order to realize the model-based advanced control, the system identification method was performed to obtain the plant model of the control object. The detailed introduction of the system identification method for first order plus time delay (FOPTD) system has been presented. Based on the identified plant model, the multi-input multi-output (MIMO) PI control system was designed as such. Due to the strong coupling effect of the controlled object, the decoupling compensation was added into the MIMO PI control system. The experiments for the MIMO PI control system with and without decoupling compensation were then carried out. Upon these foundations, the pole-zero cancellation method has been proposed for the MIMO temperature control system to ensure proper transient response and to provide more closely controlled temperatures. In the proposed method, the temperature difference and transient response of all points can be controlled by considering the delay time difference and coupling term together with matrix gain compensation, and by investigating the pole-zero cancellation with feedforward reference model to the control loop. The simulations were carried out in the MATLAB/SIMULINK environment, and the experiments were performed based on the DSP controlled system platform. The effectiveness of the proposed pole-zero cancellation method was evaluated by comparing the results to those of a well-tuned conventional PI control system and PI plus decoupling compensation system.

2) Reference-model-based Artificial NN control method for temperature control system
In this method, a reference-model-based artificial neural network (NN) control method has been proposed for the temperature control system. Several types of neural network structure and activation function are investigated, and the multi-layer NN structure is chosen with the ReLU function as its activation function. The control system is driven by using the error signal between system output and reference model output as the teaching signal of the NN controller. The proposed method is a reference-model-based NN system combined with I-PD control structure. The reference model and I-PD parameters are designed based on the FOPTD system. The simulation was carried out in MATLAB/SIMULINK environment to evaluate the control performance of the proposed method by comparing with the conventional feedback error learning NN control system. The effectiveness of the proposed method has been evaluated by focusing on the overshoot and transient response of the controlled system. As a result, the robustness of the proposed reference model-based NN control method for the plant perturbation and disturbance has been successfully verified. In addition, the recurrent type NN structure was then introduced to the control system, and simulations were carried out to compare with the feedforward type NN control system. Finally, the experiments of the proposed control method have been carried out on a DSP-based temperature system platform. The results are quantitatively evaluated by taking the transient response into account.

論文の公開元へ

この論文で使われている画像

関連論文

関連論文をもっと見る

参考文献

[1] Suda, N. PID Control, Asakura Publishing: Tokyo, Japan, 1992.

[2] Oshima, M. Process Control Systems, Corona Publishing: New York, NY, USA, 2003.

[3] Goodwin, G.C, Fraebe, S.F, Salgado, M.E, Control System Design; Prentice Hall: Upper Saddle River, NJ, USA, 2001.

[4] Morari, M, Zafiriou, E. Robust Process Control, Prentice Hall: Upper Saddle River, NJ, USA, 1992.

[5] Maciejowski, J.M. Multivariable Feedback Design, Addison Wesley: Boston, MA, USA, 1989.

[6] Ko, J.S, Huh, J.H, Kim, J.C, Improvement of Temperature Control Performance of ThermoelectricDehumidifier Used Industry 4.0 by the SF-PI Controller, Processes 2019, 7, 98.

[7] Song, B.Q, Mills, J.K, Liu, Y.H, Fan, C. Z, Nonlinear Dynamic Modeling and Control of a Small-Scale Helicopter, Int. J. Control Automat. Syst. 2010, 8, 534–543.

[8] Fujimori, A, Ohara, S, Order Reduction of Plant and Controller in Closed Loop Identification based on Joint Input-Output Approach, Int. J. Control Automat. Syst. 2017, 15, 1217–1226.

[9] Yao, Y, Yang, K, Huang, M, Wang, L, A state-space model for dynamic response of indoor air temperature and humidity, Build. Environ. 2013, 64, 26–37.

[10] Yao, Y, Yang, K, Huang, M, Wang, L, A technique for identification of linear systems, IEEE transactions on Automatic Control. 1965, AC-10, 461–464.

[11] Fliess. M, Marquez. R, Mounier. H, An extension of predictive control, PID regulators and Smith predictors to some linear delay systems. Int. J. Control. 2002, 75, 728–743.

[12] Bai. J, Wang. S, Zhang. X, Development of an adaptive Smith predictor-based self-tuning PI controller for an HVAC system in an experiment room, Energy Build. 2008, 40, 2244–2252.

[13] Xu. M, Li. S, Practical generalized predictive control with decentralized identification approach to HVAC systems. Energy Convers, Manage. 2007, 48, 292–299.

[14] Zhang. J, Experimental study on a novel fuzzy control method for static pressure reset based on the maximum damper position feedback, Energy Build. 2015, 108, 215–222.

[15] Afram. A, Janabi-Sharifi, Theory and applications of HVAC control systems - Areview of model predictive control (MPC). Build, Environ. 2014, 72, 343–355.

[16] Gao, Z, Nguang, S.K, Kong, D.X. Data-driven approaches for complex industrial systems, IEEE Trans. Ind. Inform. 2013, 9, 2210–2212.

[17] Gao, Z, Nguang, S.K, Kong, D.X, Advances in Modelling, Monitoring, and Control for Complex Industrial Systems, Complexity 2019, doi:10.1155/2019/2975083.

[18] Ryu. S. H, Moon. H. J, Development of an occupancy prediction model using indoor environmental data based on machine learning techniques, Build. Environ. 2016, 107, 1–9.

[19] Megri. A.C, Naqa. I. EI, Prediction of the thermal comfort indices using improved support vector machine classifiers and nonlinear kernel functions, Indoor Built Environ. 2016, 25, 6–16.

[20] Badia. M, McCarthy. R, Data estimation methods for predicting temperatures of fruit in refrigerated containers, Biosystems Engineering. 2016, 151, 251–272.

[21] Mercier. S, Marcos. B, Identification of the best temperature measurement position inside a food pallet for the prediction of its temperature distribution, International Journal of Refrigeration. 2017, 76, 147–159.

[22] Badia. M, Qian. J. P, Fan. B. L, Artificial neural networks and thermal image for temperature prediction in apples, Food and Bioprocess Technology. 2012, 9, 1089–1099.

[23] Nunes. M. C. N, Nicometo. M, Emond. J. P, Badia-Melis. R, Uysal. Improvement in fresh fruit and vegetable logistics quality, Berry logistics field studies. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2014, 372, 20130307.

[24] Raab. V, Petersen. B, Kreyenschmidt. J, Temperature monitoring in meat supply chains, British Food Journal. 2011, 113, 1267–1289.

[25] Katic. K, Li. R. L, Verharrt. J, Zeiler. W, Neural network based predictive control of personalized heating systems, Energy Build. 2018, 174, 199–213.

[26] Li. X. M, Zhao. T. Y, Zhang. J. L, Chen. T. T, Predication control for indoor temperature time-delay using Elman neural network in variable air volume system, Energy Build. 2017, 154, 545–552.

[27] Mercier. S, Uysal. I, Neural network models for predicting perishable food temperatures along the supply chain, Biosystems Engineering 2018, 171, 91–100.

[28] Thenozhi. S, Yu. W, Stability analysis of active vibration control of building structures using PD/PID control, Engineering Structures 2014, 81, 208–218.

[29] Guerrero. J, Torres. J, Creuze. V, Chemori. A, Campos. E, Saturation based nonlinear PID control for underwater vehicles: Design, stability analysis and experiments. Mechatronics 2019, 61, 96–105.

[30] Ziegler, J.G, Nichols, N.B, Optimum settings for automatic controllers, Trans. ASME 1942, 64, 759– 768.

参考文献をもっと見る

全国の大学の
卒論・修論・学位論文
を
一発検索！

この論文の関連論文を見る