In this work, a nonlinear process is modeled in a so called 'grey box' format. This format divides the model into a known linear part and an unknown nonlinear part. The linear part is modeled using simple linear discrete dynamics whereas the nonlinear pan is modeled using a neural network with some weights. An adaptive controller is designed to incorporate the unknown nonlinear dynamics into the overall process dynamics. A smaller number of neural network weights can be used in this format so that the controller can be used adoptively for on-line control. The effectiveness of this approach is demonstrated using simulation and finally is used for real time control of a pressure tank process with excellent results.
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