Control of the Penicillin Production with Adaptive IMC Using Fuzzy Neural Networks

Abstract This paper introduces the use adaptation in IMC strategy for the control of a simulated penicillin plant. The plant model and control modules are built using FasBack neuro-fuzzy system, featuring fast stable learning guided by matching and error minimisation and good identification performance. Control results show good general performance both in the nominal case and in the presence of noise. FasBack on-line adaptation capabilities are used to develop an adaptive IMC, which shows to improve performance in realistic cases of time varying parameters. Furthermore, real data coming from pilot plants are used to train fuzzy neural networks with satisfactory identification results, and obtained modules are used within IMC with similar results to those build from simulated data.

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