Optimized Fuzzy-Based Wavelet Neural Network Controller for a Non-Linear Process Control System

Abstract

One of the common non-linear process control problems is the continuous stirred tank reactor problem and the reactor is applied widely in chemical process industries. It is of high importance to develop a suitable controller scheme for the concentration and temperature control of the considered non-linear continuous stirred tank reactor (CSTR) model. In this paper, a novel wavelet neural network (WNN) controller model is developed to carry out the control action and to meet the performance requirements of the system model. The developed wavelet neural network model is tuned for its weights employing the proposed deterministic grey wolf optimizer algorithm and its input is fed from the Mamdani fuzzy model. The optimal number of rules to be formulated for the fuzzy rule base model is computed using the deterministic grey wolf optimizer (DGWO) which is the variant of the classic grey wolf optimizer (GWO). WNN controller devise, based on the inputs from the fuzzy model and weights from DGWO, is designed to perform control action on the non-linear CSTR model. The concentration and temperature control is carried out by the proposed controller and the responses are obtained. Also, the respective time response specifications are evaluated for the developed WNN controller. Simulation experiments prove the effectiveness of the proposed controller for the considered non-linear CSTR model in comparison with that of the existing controllers from the literature.

KEYWORDS:

• Continuous stirred tank reactor
• Deterministic grey wolf optimizer
• Mamdani fuzzy model
• Non-linear system
• Wavelet neural network

Additional information

Notes on contributors

S.N. Deepa

S N Deepa received her BE (EEE) in 1999 from Government College of Technology, Coimbatore, India and ME (Control Systems) from PSG College of Technology in 2004. She obtained her PhD degree (Electrical Engineering) in 2008 from PSG College of Technology under Anna University, Tamil Nadu, India. Her research areas include linear and non-linear control system design and analysis, modelling and simulation, soft computing and adaptive control systems.

N. Yogambal Jayalakshmi

N Yogambal Jayalaksmi received her BE in electrical and electronics engineering from Sri Shakthi Institute of Engineering and Technology, Coimbatore, India in 2014, and ME in control and instrumentation engineering from Anna University Regional Campus, Coimbatore in 2016. In 2020 she has completed her PhD in electrical engineering, Anna University, Chennai, Her research area includes control system, soft computing techniques, and renewable energy sources. Email: [email protected]