Experimental Implementation of An Adaptive Fuzzy Logic Controller for Process Control
Jun Lu, Gordon Lee, Warren J. Jasper · Intelligent Automation & Soft Computing · 1995
ABSTRACTComplex industrial processes such as batch chemical reactors, steelmaking, dyeing processes and metal forming offer challenges in control due to the uncertainty and complexity of the processes. Model-based control methods generally require some knowledge of system structure and possibly some bounds on the uncertainty of the system parameters. Such methods as robust control and adaptive control belong to this category. In a case where system identification is not feasible, knowledge-based methods offer an alternative solution to control. One such method, fuzzy logic control (FLC), may be used to simulate the decision making process of an experienced expert. Usually, the control decisions of an expert can be expressed linguistically as a set of heuristic decision rules. The rules are used to build rulebases for FLC; further, algorithms are used to convert the results of the rules to quantitative outputs.This article presents an adaptive multi-input, multi-output (MIMO) fuzzy logic control method whi...