Hybrid Predictive Controller Based on Fuzzy-Neuro Model

Predictive control is a control strategy that is based on the prediction of the plant output over the extended horizon in the future, which enables the controller to predict future changes of the measurement signal and to base control actions on the prediction. In this paper a hybrid fuzzy-neuro model based predictive control is addressed, proposed and tested. The proposed hybrid fuzzy-neuro convolution model consists of a steady-state fuzzy-neuro model and a gain independent impulse response model. The proposed hybrid fuzzy-neuro convolution model can be considered as a gain-scheduled convolution model. This paper shows advantages of a combination of static nonlinearity and gain-independent dynamic part. The proposed model is tested in a model based predictive control scheme of the concentration control in the chemical reactor, manipulating its flow rate. The paper deals with theoretical and practical methodology, offering approach for intelligent fuzzy-neuro robust control design and its successful application.