Nonlinear model predictive control of a laboratory gas turbine installation

The feasibility of model predictive control (MPC) applied to a laboratory g as turbine installation is investigated. MPC explicitly incorporates (input and output) constraints in its optimizations, which explains the choice fo r this computationally demanding control strategy. Strong nonlinearities, d isplayed by the gas turbine installation cannot always be handled adequatel y by standard linear MPC. Therefore, we resort to nonlinear methods, based on successive linearization and nonlinear prediction as well as the combina tion of these. We implement these methods, using a nonlinear model of the i nstallation and compare them to linear MPC. It is shown that controller per formance can be improved, without increasing controller execution-time exce ssively.