A trajectory tracking controller based on differential flatness is presented for a nonlinear bicycle model. This controller maps the bicycle dynamics into a point mass located at a center of oscillation with an additional degree of freedom of yaw dynamics. A state transformation is performed that reveals structure in the yaw dynamics resembling a Liénard system. A candidate Lyapunov function inspired by this structure is used to assess the stability of the yaw dynamics while tracking straight-line trajectories and steady turns. The basin of attraction of the controller is limited by actuator constraints and the presence of unstable equilibrium points during turns with high lateral acceleration. The controller properties and the stability of yaw dynamics are demonstrated in simulation.

Multirotor aircraft have become a popular platform for indoor flight. To navigate these vehicles indoors through an unknown environment requires the use of a SLAM algorithm, which can be processing intensive. However, their size, weight, and power capacity limit the processing capabilities available onboard. In this paper, we describe an approach to state estimation that helps to alleviate this problem. By using an improved dynamic model we show how to more accurately estimate the aircraft states than can be done with the traditional approach of integrating IMU measurements. The estimation is done with relatively infrequent corrections from accelerometers (40Hz) and even less frequent updates from a vision-based SLAM algorithm (2-5 Hz). This benefit of requiring less frequent updates from processing intensive sources comes without significant increase in the estimator's complexity.

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