Robust Embedded Control using Randomized Switching Algorithms

Multiple model adaptive control (MMAC) is an adaptive control method designed for plant parameter uncertainty given both linear and non-linear plant models. For a system subject to varying operating conditions, the number of controllers necessary to guarantee stable control under nominal-plant uncertainty, or under multiple operating conditions, are both unknown. We propose an automated convex hull (CH)-based controller synthesis approach that can guarantee stability of a system subject to varying operating conditions. We empirically validate this result for a quadcopter, which is subject to faults in rotors and sensors as well as to adverse wind conditions.