Controllers optimization for a fluid mixing system using metamodeling approach



Offline optimization of controller parameters for complex non-linear processes can be time consuming, even with high performance computers. This project demonstrates how Metamodeling techniques can be utilized to tune the controller parameters for a non-linear process quickly. The process used in this study is the mixing process which is a multivariable and intrinsically non-linear plant. The Radial Basis Function Neural Network metamodel used was able to give a good approximation to the optimum controller parameters in this case.

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