Zometa, PabloPabloZometaKögel, Markus J.Markus J.KögelFaulwasser, TimmTimmFaulwasserFindeisen, RolfRolfFindeisen2024-02-282024-02-282012American Control Conference (ACC 2012)978-1-4577-1095-7978-1-4577-1096-4https://hdl.handle.net/11420/46077We discuss implementation related aspects of model predictive control schemes on embedded platforms. Ex-emplarily, we focus on fast gradient methods and present results from an implementation on a low-cost microcontroller. We show that input quantization in actuators should be exploited in order to determine a suboptimality level of the online optimization that requires a low number of algorithm iterations and might not significantly degrade the performance of the real system. As a case study we consider a Segway-like robot, modeled by a linear time-invariant system with 8 states and 2 inputs subject to box input constraints. The test system runs with a sampling period of 4 ms and uses a horizons up to 20 steps in a hard real-time system with limited CPU time and memory. © 2012 AACC American Automatic Control Council).enembedded systemsfast gradient methodLEGO NXTmodel predictive controlreal-time implementationComputer SciencesElectrical Engineering, Electronic EngineeringConference Paper10.1109/acc.2012.6315076Conference Paper