Soft Computing Techniques for Process Control Applications

Abstract

Technological innovations in soft computing techniques have brought automation capabilities to new levels of applications. Process control is an important application of any industry for controlling the complex system parameters, which can greatly benefit from such advancements. Conventional control theory is based on mathematical models that describe the dynamic behaviour of process control systems. Due to lack in comprehensibility, conventional controllers are often inferior to the intelligent controllers. Soft computing techniques provide an ability to make decisions and learning from the reliable data or expert\textquoteright{}s experience. Moreover, soft computing techniques can cope up with a variety of environmental and stability related uncertainties. This paper explores the different areas of soft computing techniques viz. Fuzzy logic, genetic algorithms and hybridization of two and abridged the results of different process control case studies. It is inferred from the results that the soft computing controllers provide better control on errors than conventional controllers. Further, hybrid fuzzy genetic algorithm controllers have successfully optimized the errors than standalone soft computing and conventional techniques.