عنوان مقاله
کاربرد شبکه های عصبی مصنوعی فیدفروارد برای بهبود کنترل فرایند الگوریتم های کنترلی
فهرست مطالب
مقدمه
متدولوژی کنترل
حالتهای مطالعه شده
نتایج و بحث ها
نتیجه گیری
بخشی از مقاله
سیستم درجه اول
شبکه عصبی مصنوعی شامل یک لایه ورودی با 4 تا گره، یک لایه پنهان (4 تا گره) با توابع فعال سازی سیگموید و یک لایه خروجی (گره 1) با تابع فعال سازی خطی می باشد. مدل شبیه سازی از سیستم درجه اول با مدل شبکه های عصبی مصنوعی فیدفروارد شامل شده در ساختار کنترل جفت شده است که اجازه محاسبه پارامترهایkt در معادله 1 با معیار ITAE را می دهد. در این حالت، مقدار بهینه kt=4 پیدا شده است.
کلمات کلیدی:
Computers and Chemical Engineering 24 (2000) 853-858 Computers & Chemical Engineering www.elsevier.com/locate/compchemeng Application of feedforward artificial neural networks to improve process control of PID-based control algorithms Fernando G. Martins *, Manuel A.N. Coelho LEP/E, Departamento de Engenharia Quimica, FEUP, Porto, Portugal Abstract The present work reports a new control methodology based on proportional, integral, and derivative (PID) control algorithms conjugated with feedforward artificial neural networks (FAANs). The FANNs were used as predicted models of the controlled variable. This information is transferred to PID controllers, through the readjustment of the pre-established setpoint. The proposed methodology was tested generally for a first order system using a PI controller, a second order system using a PI control, a second order system in series with a first order system using a cascade control structure. The problem of the reaction temperature control in a batch-jacketed reactor with a cascade control structure was also analysed as a particular case. The simulation results shows that better control performances are achieved when the control methodology presented in this work is used as a complementary tool of the PID-based control algorithms. © 2000 Elsevier Science Ltd. All rights reserved. Keywords: Process control; Feedforward artificial neural networks; Proportional, integral, and derivative based control 1. Introduction Since the proportional, integral, and derivative (PID) controller finds widespread use in the process industries, great resistances have been occurred to incorporate other control methodologies in practical situations. The main reasons are the simplicity, robustness and successful applications provided by PID-based control structures (Lee, Park, Lee & Brosilow, 1998). However, better performances of the control systems should be achieved to cope new market requirements in product quality, safety procedures and process complexity. In last few years, artificial neural networks and particularly feed forward artificial neural networks (FANNs) have been extensively studied in academia as process models, and are increasingly being used in industry (Ungar, Hartman, Keeler & Martin, 1996). Artificial neural networks are suggested to generate process models due to their ability to capture nonlinear dynamics. These models are data-driven and have the advantages of computational efficiency and ease of construction (Tsen, Jang & Wong, 1996). * Corresponding author. E-mail address: fgm@fe.up.pt (F.G. Martins). 0098-1354/00/$ - see front matter © 2000 Elsevier Science Ltd. All rights PII: S0098-1354(00)00339-2 This paper demonstrates one way of how FANNs could be used to accomplish the performance of control structures based on PID controllers. The FANNs are used to create generic models to predict future values of the controlled variable. This information is then incorporated in conventional control system structure through the change of pre-established values of setpoint. The main objective is to enhance the control performance of the control system without change drastically the structure of the control system. 2. Control methodology. The control methodology proposed in this work is based on the conventional control conception (PIDbased control structures) conjugated with the development of a model, which is capable of predicting the controlled variable as a function of process measurements and the pre-established setpoint. In this sense, the foreknowledge of the controlled variable value for time t+At at time t allows to evaluate a second setpoint (pseudo-setpoint) for the principal controller, which can be predicted by the following equation: Sp, - kt(y t + at - Sp~ .... ld p q~old "~
