Implementation aspects of model predictive control for embedded systems

Abstract

We discuss implementation related aspects of model predictive control schemes on embedded platforms. Exemplarily, 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.