MODELING AND PREDICTIVE CONTROL USING HYBRID INTELLIGENT TECHNIQUES FOR A NONLINEAR MULTIVARIABLE PROCESS
B. Subathra, Thota Karunakaran Radhakrishnan · Instrumentation Science & Technology · 2011
A recurrent neuro fuzzy network (RNFN) model–based multistep ahead predictive control strategy is proposed in this article. The fuzzy logic (FL) and neural networks (NN) are intelligent system approaches, and they complement each other. Hybridization of FL and NN utilizes the concepts of human cognitive capabilities and biological systems, respectively. Dynamic processes necessitate past information about the process input/output variables. In order to store the information, a memory unit is introduced between the fuzzy inference layer and the fuzzification layer. This recurrent structure enhances the prediction capability; hence, this RNFN model can be used to develop the multistep ahead predictive controller. The objective function of model based controller (MPC) minimizes the future control moves. The gradient descent (GD) algorithm is used to the optimize control moves. The proposed RNFN model is used to develop a model predictive controller. The performance of the RNFN-MPC is compared with that of a neuro fuzzy network (NFN)–based MPC for a laboratory scale quadruple tank process.