Quasi-translations for fast hybrid nonlinear model predictive control. (April 2020) - Maitland, Anson; McPhee, John
Abstract: We present the quasi-translation (QT) strategy for mixed-integer model predictive control (MPC) problems. This strategy handles the complexity introduced by discrete controls in a simple manner that takes advantage of the sequential nature of MPC optimization problems. Some general criteria is provided guiding their application. The QT strategy can be used to balance controller performance, chatter and turnaround time. We also introduce an approach to handle hybrid models with state-dependent switches for use with a symbolic formulation of the MPC problem that integrates seamlessly with the QT strategy. We investigate the QT strategy for a variety of hybrid nonlinear MPC problems including the classic benchmark Lotka–Volterra fishing problem, a diesel airpath control problem and longitudinal vehicle control. Results show the QT strategy consistently yields a reduction in turnaround times with excellent or minimally degraded performance as compared to competing strategies. Highlights: Balancing chatter, performance and computational complexity of model predictive control using quasi-translations. Bounded complexity of mixed integer model predictive control solutions. A flexible solution strategy for control of externally forced hybrid systems. Control of internally forced hybrid systems with the aid of symbolic computing.