Date of Award
Doctor of Philosophy (PhD)
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 uncertainty in the state and dynamics model 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 air vehicles, that 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 and state uncertainty. The key concept underlying these techniques is the reuse of past experiences to reduce online computation and enhance control performance in novel scenarios. These experiences inform an online-updated estimate of the system dynamics model and the choice of controller to optimize performance for a given scenario. We present a set of simulation and experimental studies with a small aerial robot operating in windy environments to assess the performance of the proposed control methodologies. These results demonstrate that leveraging past experiences to inform feedback control yields high-rate, constrained, robust-adaptive control and enables the deployment of predictive control techniques on systems with severe computational constraints.
Desaraju, Vishnu R., "Safe, Efficient, and Robust Predictive Control of Constrained Nonlinear Systems" (2017). Dissertations. 954.