Event Location: NSH 1305

Abstract: As autonomous systems are deployed in increasingly complex and uncertain environments, safe, accurate, and robust feedback control techniques are required to ensure reliable operation. Accurate trajectory tracking is essential to complete a variety of tasks, but this may be difficult if the system’s dynamics change online, e.g., due to environmental effects or hardware degradation. As a result, uncertainty mitigation techniques are also necessary to ensure safety and accuracy.

This problem is well suited to a receding-horizon optimal control formulation via Nonlinear Model Predictive Control (NMPC). NMPC employs a nonlinear model of the plant dynamics to compute non-myopic control policies, thereby improving tracking accuracy relative to reactive approaches. This formulation ensures constraints on the dynamics are satisfied and can compensate for plant model uncertainty via robust and adaptive extensions. However, existing NMPC techniques are computationally expensive, and many operating domains preclude reliable, high-rate communication with a base station. This is particularly difficult for small, agile systems, such as micro aerial vehicles, which have severely limited computation due to size, weight, and power restrictions but require high-rate feedback control to maintain stability. Therefore, the system must be able to operate safely and reliably with typically limited onboard computational resources.

In this thesis, we propose a series of non-myopic, computationally-efficient, feedback control strategies that enable accurate and reliable operation in the presence of unmodeled system dynamics. The key concept underlying these techniques is the reuse of past experiences to reduce online computation and enhance control performance in novel scenarios. The work completed thus far demonstrates high-rate, constrained, adaptive control of agile systems through the use of experience to inform an online-updated estimate of the system dynamics model and the choice of controller for a given scenario. The proposed work aims to enhance robustness to uncertainty, improve computational efficiency, and inform motion planning to facilitate tracking. We also propose two case studies to demonstrate the performance of these techniques.

Committee:Nathan Michael, Chair

Maxim Likhachev

Koushil Sreenath

Nicholas Roy, Massachusetts Institute of Technology