Generating Minimal Controller Sets for Mixing MMAC

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 a learning-based controller synthesis approach that can guarantee stability of a system subject to varying operating conditions. We adopt a convex hull (CH)-based multiple-model controller estimation algorithm that only requires N + M + 1 controllers, where N is the dimension of a compact nominal uncertainty parameter set Θ, and M is the number of additive faults. We empirically validate this result for a quadcopter, which is subject to faults in rotors and sensors as well as to adverse wind conditions.