Intelligent process control using neural fuzzy techniques

In this paper, we combine the advantages of fuzzy logic and neural network techniques to develop an intelligent control system for processes having co mplex, unknown and uncertain dynamics. In the proposed scheme, a neural fuz zy controller (NFC), which is constructed by an equivalent four-layer conne ctionist network, is adopted as the process feedback controller. With a der ived learning algorithm, the NFC is able to learn to control a process adap tively by updating the fuzzy rules and the membership functions. To identif y the input-output dynamic behavior of an unknown plant and therefore give a reference signal to the NFC, a shape-tunable neural network with an error back-propagation algorithm is implemented. As a case study, we implemented the proposed algorithm to the direct adaptive control of an open-loop unst able nonlinear CSTR. Some important issues were studied extensively. Simula tion comparison with a conventional static fuzzy controller was also perfor med. Extensive simulation results show that the proposed scheme appears to be a promising approach to the intelligent control of complex and unknown p lants, which is directly operational and does not require any a priori syst em information. (C) 1999 Elsevier Science Ltd. All rights reserved.