Olesen, Daniel6; Huusom, Jakob Kjøbsted2; Jørgensen, John Bagterp7
1 Department of Applied Mathematics and Computer Science, Technical University of Denmark2 Department of Chemical and Biochemical Engineering, Technical University of Denmark3 Computer Aided Process Engineering Center, Department of Chemical and Biochemical Engineering, Technical University of Denmark4 Center for Energy Resources Engineering, Center, Technical University of Denmark5 Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark6 National Space Institute, Technical University of Denmark7 Copenhagen Center for Health Technology, Center, Technical University of Denmark
We present an optimization based tuning procedure with certain robustness properties for an offset free Model Predictive Controller (MPC). The MPC is designed for multivariate processes that can be represented by an ARX model. The stochastic model of the ARX model identified from input-output data is modified with an ARMA model designed as part of the MPC-design procedure to ensure offset-free control. The MPC is designed and implemented based on a state space model in innovation form. Expressions for the closed-loop dynamics of the unconstrained system is used to derive the sensitivity function of this system. The closed-loop expressions are also used to numerically evaluate absolute integral performance measures. Due to the closed-loop expressions these evaluations can be done relative quickly. Consequently, the tuning may be performed by numerical minimization of the integrated absolute error subject to a constraint on the maximum of the sensitivity function. The latter constraint provides a robustness measure that is essential for the procedure. The method is demonstrated for two simulated examples: A Wood-Berry distillation column example and a cement mill example.
Proceedings of the American Control Conference, 2013, p. 1721-1726