Real-time identification and control of a continuous stirred tank reactor with neural network

A continuous stirred tank reactor (CSTR), modelled by two nonlinear ordinary equations with two manipulable inputs, is online identified and controlled using neural networks. An enhanced learning algorithm is used for training the neural network. The multivariable nonlinear controller is derived from a Lyapunov function, which can ensure that the whole system be globally asymptotically stable at both the stable stationary points and the unstable stationary points. An inverse model of the CSTR, which is online built with neural network, serves as the predictor of the control variables. The temperature and concentration in CSTR can be maintained stable around the setting values in spite of the disturbances of the inlet concentration and inlet temperature of the reactants.<
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