Controllers Optimization for a Fluid Mixing System Using Metamodelling Approach

Abstract Offline optimization of controller parameters for complex non-linear processes can be time consuming, even with high performance computers. This paper demonstrates how Metamodelling techniques can be utilized to quickly tune the controller parameters for a nonlinear process. 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 founded a good approximation to the optimum controller parameters in this case. This paper proposes an intuitive methodology to use only a small fraction of the design space to create a Radial Basis Function Neural Network Metamodel that is good enough to optimize the system. Comparisons were made between the controllers optimized using the Metamodelling technique and the original large space design. (Received in May 2008, accepted in October 2008. This paper was with the authors 1 month for 1 revision.)