Parameter Estimation and Control of Multirotors

A controller based on integral concurrent learning (ICL) has been developed for controlling a multirotor unmanned aerial vehicle with unknown mass and moment of inertia, and it guarantees that the tracking errors and the estimation errors of the parameters asymptotically converge to zero. The condition for convergence of the estimated parameters in the developed ICL controller can be verified online or offline by utilizing the data recorded during normal operation over a period, and satisfying the condition does not require persistence of excitation. Since the dynamics of a multirotor is globally defined, the developed ICL controller ensures almost global tracking and parameter estimation. The developed control architecture can be generalized to control any multirotor of unknown mass and moment of inertia with guaranteed system stability. A stability analysis is conducted to ensure that both the tracking errors and the estimation errors of the parameters asymptotically converge to zero. The performance and efficacy of the ICL controller have been verified in experiments.