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1 1998

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Modified direct inverse control of CSTR process with disturbances.

Ho K.C., Ponnapalli P. V. S., Thomson M.

Systems Science

1998

Vol. 24, nr 4

37-52

This paper presents the development of a neural network-based direct inverse controller (DIC) for a Continuous Stirred Tank Reactor (CSTR) process. An inverse model of the CSTR process is developed based on a Multiple-type Feedforward Artificial Neural Network (MFANN). Multi-type FANN incorporates one linear node in the hidden layer while the other hidden nodes are typical sigmoidal functions. A Pruning Selection Scheme (PSS), based on the local comparison of the sensitivity values of weight connections, is used to optimize this MFANN structure. This inverse model is then used in a modified version of a direct inverse controller for a CSTR system. The simulation results indicate that the specially pruned MFANN architecture provides better performance in terms of modelling the non-linear process and in controlling it. The modified version of the DIC provides robust control action compared to the original DIC with and without disturbance.