عنوان مقاله
کنترل پیش بینانه مدلی بر پایه شبکه عصبی برای فرایند پیکلینگ فولاد
فهرست مطالب
مقدمه
شرح فرآیند
مدل سازی شبکه عصبی
کنترل پیش بینانه مدل شبکه عصبی
نتایج شبیه سازی
نتیجه گیری
بخشی از مقاله
مدل سازی شبکه عصبی
شبکه های عصبی مزیت هایی از قبیل فرایند اطلاعاتی توزیع شده و محاسبات موازی و احتمالی ذاتی را دارد. در بیش تر حالت ها، زمانی که اطلاعات غنی و به حد کافی در دسترس است و زمانی که معادلات مدل معلوم نیست آن ها می توانند مدل های نسبتا درستی برای کنترل غیرخطی فراهم کنند. اگرچه انواع مختلف شبکه های عصبی از قبیل درک چندلایه، شبکه شعاعی پایه ای تابع و شبکه عصبی بازگرداننده وجود دارند، همه آن ها شامل مفاهیم پایه ای یکسان هستند: گره، لایه ها و اتصالات.
کلمات کلیدی:
Neural network based model predictive control for a steel pickling process Paisan Kittisupakorn a , Piyanuch Thitiyasook a , M.A. Hussain b,*, Wachira Daosud c aDepartment of Chemical Engineering, Chulalongkorn University, Bangkok, Thailand b Chemical Engineering Department, University of Malaya, 50603 Kuala Lumpur, Malaysia c Chemical Engineering Department, Burapha University, Chonburi 20131, Thailand article info Article history: Received 6 December 2007 Received in revised form 21 August 2008 Accepted 6 September 2008 Keywords: Model predictive control Feedforward neural network Multivariable systems Steel pickling process abstract A multi-layer feedforward neural network model based predictive control scheme is developed for a multivariable nonlinear steel pickling process in this paper. In the acid baths three variables under controlled are the hydrochloric acid concentrations. The baths exhibit the normal features of an industrial system such as nonlinear dynamics and multi-effects among variables. In the modeling, multiple input, singleoutput recurrent neural network subsystem models are developed using input–output data sets obtaining from mathematical model simulation. The Levenberg–Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. The proposed algorithm is tested for control of a steel pickling process in several cases in simulation such as for set point tracking, disturbance, model mismatch and presence of noise. The results for the neural network model predictive control (NNMPC) overall show better performance in the control of the system over the conventional PI controller in all cases. 2008 Elsevier Ltd. All rights reserved. 1. Introduction It has been known that many chemical industrial plants cause environmental problems due to the usage of chemicals in their production lines. One such industry is the steel pickling plant which is a fundamental industry in Thailand and has long existed and served the country’s steel demand. The steel pickling process utilizes concentrated chemicals in the production lines and the wastewater released from the process contains hazardous materials and usually causes major environmental problems. Therefore, production scheduling and control of this pickling process are inevitably needed to minimize the amount of hazardous material contained in the released wastewater and also to maintain the concentration of acid solution in the tanks in order to obtain the maximum reaction rate at the same time. The steel pickling process presents many challenging control problems, including: nonlinear dynamic behavior; multivariable interactions between manipulated and controlled variables and constraints on manipulated and state variables. A number of control approaches and algorithms that are able to handle some of the above process characteristics have been presented in the academic literature in recent years. Bequette [1] gives a review of these various approaches such as the internal model approaches, differential geometric approaches, reference system synthesis techniques, including internal decoupling and generic model control (GMC), model predictive control (MPC) and also various special and ad hoc approaches. Many of these approaches are not able to handle the various process characteristics and requirements met in industrial applications and some of the approaches can only be applied for special classes of models. MPC appears to be one of the general approaches which can handle most of the common process characteristics and industrial requirements in a satisfactory way. It also seems to be the approach which is most suitable for the development of a general and application independent software, which is essential for the development of cost-effective applications. However, the key in the successful use of MPC in solving these process problems is the existence of an accurate model. Chemical processes such as this steel pickling process have been traditionally controlled using linear system analysis even though they are inherent nonlinear process. However to obtain accurate model for the steel pickling process and predicting its interacting and nonlinear behavior is actually highly difficult. Recently, neural networks have been successfully applied in the identification and control of nonlinear processes. Neural networks offer alternative nonlinear models for implementing MPC in industrial systems [2–5]. Different ways of neural models being embedded in MPC systems were reviewed by two recent surveys [6,7]. It is noted that while neural network modeling and control
