Offset-free Nonlinear Model Predictive Control by the Example of Maglev Vehicles
P. Schmid, Peter Eberhard
Abstract
Offset-free action of model predictive control (MPC) for nonlinear systems with the number of measured outputs greater than controlled outputs is a nontrivial problem. Motivated by investigating the possible application of MPC for the control system of magnetic levitation (Maglev) vehicles – tracking only the gap while measuring the gap, acceleration, and current – for higher speeds than hitherto travelled, different possible offset-free nonlinear MPC strategies are reviewed and discussed. Moreover, a novel approach based on a two-stage observer is introduced, requiring less effort to design an offset-free MPC. The different strategies are applied and analyzed in detail for a realistic model of the control system of the latest Transrapid vehicle, called TR09. Unlike usual offset-free MPC studies, the controller performance is also studied in a typical dynamic scenario, revealing the conflict and trade-off between the requirements for speed of command response and the robustness of closed-loop stability.