A Switched Systems Approach for Navigation and Control with Intermittent Feedback

Imaging systems provide unique sensing capabilities for estimation and control. Image feedback typically involves tracking the evolution of image features in time/space to determine the relative motion of an autonomous system or tracked targets. The resulting image dynamics are nonlinear and include uncertainties inherent to the Euclidean-space to image-space mapping. Moreover, such feedback is intermittent due to the inevitable loss of features because of the limited camera field-of-view or feature occlusion. The intermittent nature of image feedback can lead to degradation or instabilities in developed state estimates or controllers. Efforts in this project seek to generalize the typical assumption of continuous feature point observation from a single imaging source for estimation and control problems. To develop estimation and control solutions that use image-based feedback, fundamental problems associated with analyzing the stability of uncertain switched nonlinear systems must be addressed. The focus of Aim 1 is the development of switched systems tools using Lyapunov-based methods to enable arbitrary switching between different image sources (i.e., synthetic persistence where switching is between stable subsystems) using asymptotic adaptive update laws. In Aim 2, a novel data-based integral concurrent learning (ICL) method is proposed to yield exponential learning to facilitate the development of dwell-time.