Optimization of PID controllers 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.