Research Highlights
Integrated planning and control under uncertainties
Motion planning and tracking control for robotic and autonomous systems with nonlinear, underactuated and uncertain dynamics with guaranteed safety remains a challenging problem. In this project, we pioneered nonlinear robust tracking controllers for general nonlinear systems subject to model uncertainties, which provide certificate tubes around the nominal state and input trajectories that are guaranteed to contain actual trajectories in the presence of uncertainties. The tracking controllers can be conveniently incorporated for planning robust and safety-guaranteed trajectories. Our methods are based on (robust) contraction metrics, which leverage convex optimization and differential dynamics to synthesize controllers for general nonlinear systems.
Safe and robust learning-enabled controlÂ
In this project, we explore the integration of machine learning (ML) and control theory for safe and robust learning-enabled control. In particular, we are interested in leveraging adaptive or disturbance observer-based control to actively compensate for the uncertainties that may be induced by environmental change (in a model-free setting) or the inaccuracy of the learned dynamics (in a model-based setting). This uncertainty compensation-based approach enables a fast reaction to sudden dynamics change and less conservative control performance in the presence of a poorly learned dynamics model compared to robust approaches without uncertainty compensation.
Adaptive geometric control of quadrotors
Precise control of quadrotors is very challenging in the presence of model uncertainties and disturbances from various sources, such as aerodynamic drag, unknown payload, and wind/gust. In this project, we proposed an adaptive geometric controller based on L1 adaptive augmentation of a geometric controller, which provides guaranteed tracking performance in the presence of a broad class of uncertainties. We experimentally validated the performance of the controller on a custom-built quadrotor platform.
Robust adaptive control with aerospace applications
Motivated by the need to adjust the desired dynamics in the control of systems with large operating envelopes, we developed adaptive controllers that allow the desired dynamics to be systematically scheduled or switched, e.g., according to the operating conditions. The developed adaptive controllers incorporate LPV and switching systems to characterize the desired dynamics. In particular, for LPV systems subject to unmatched uncertainties, we proposed a novel approach based on peak-to-peak gain minimization and analysis from robust control to mitigate the effect of unmatched uncertainties, while allowing for the controllers to provide transient performance guarantees. Additionally, the adaptive controller with switched desired dynamics was used to facilitate safe real-time learning of aerial vehicle dynamics and was tested on a UAV.
Intelligent agricultural management with reinforcement learning
The world's agricultural system is facing significant challenges to bridge the gap between the amount of food produced today and that needed to feed a population of 9.6 billion by 2050 while reducing environmental impacts. In this project, we envisioned an intelligent agricultural management system based on deep reinforcement learning (RL) and crop simulations (e.g., using DSSAT), which can improve crop yields while reducing the use of resources (e.g., fertilizers, irrigation water) compared to expert-suggested management practices. We also leveraged imitation learning (IL) to train practically-implementable management policies that use only the state information that is easily accessible in the real world.
