Controlling Biochemical Reactors
We describe in this chapter a general method for adaptive model-based control of non-linear dynamic plants using Neural Networks, Fuzzy Logic and Fractal Theory. The new neuro-fuzzy-fractal method combines Soft Computing (SC) techniques with the concept of the fractal dimension for the domain of Non-Linear Dynamic Plant Control. The new method for adaptive model-based control has been implemented as a computer program to show that our neuro-fuzzy-fractal approach is a good alternative for controlling non-linear dynamic plants. We illustrate in this chapter our new methodology with the case of controlling biochemical reactors in the food industry. For this case, we use mathematical models for the simulation of bacteria growth for several types of food. The goal of constructing these models is to capture the dynamics of bacteria population in food, so as to have a way of controlling this dynamics for industrial purposes. We use the fractal dimension for bacteria identification during the production process. We use neural networks for control and parameter identification, and fuzzy logic for modelling the complete dynamic system.
KeywordsFractal Dimension Fuzzy Logic Adaptive Control Biochemical Reactor Fuzzy Inference System
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