2 research outputs found
Computationally Efficient Data-Driven MPC for Agile Quadrotor Flight
This paper develops computationally efficient data-driven model predictive
control (MPC) for Agile quadrotor flight. Agile quadrotors in high-speed
flights can experience high levels of aerodynamic effects. Modeling these
turbulent aerodynamic effects is a cumbersome task and the resulting model may
be overly complex and computationally infeasible. Combining Gaussian Process
(GP) regression models with a simple dynamic model of the system has
demonstrated significant improvements in control performance. However, direct
integration of the GP models to the MPC pipeline poses a significant
computational burden to the optimization process. Therefore, we present an
approach to separate the GP models to the MPC pipeline by computing the model
corrections using reference trajectory and the current state measurements prior
to the online MPC optimization. This method has been validated in the Gazebo
simulation environment and has demonstrated of up to reduction in
trajectory tracking error, matching the performance of the direct GP
integration method with improved computational efficiency.Comment: 6 pages, accepted in ACC 2023 (American Control Conference, 2023
Data-Based MHE for Agile Quadrotor Flight
This paper develops a data-based moving horizon estimation (MHE) method for
agile quadrotors. Accurate state estimation of the system is paramount for
precise trajectory control for agile quadrotors; however, the high level of
aerodynamic forces experienced by the quadrotors during high-speed flights make
this task extremely challenging. These complex turbulent effects are difficult
to model and the unmodelled dynamics introduce inaccuracies in the state
estimation. In this work, we propose a method to model these aerodynamic
effects using Gaussian Processes which we integrate into the MHE to achieve
efficient and accurate state estimation with minimal computational burden.
Through extensive simulation and experimental studies, this method has
demonstrated significant improvement in state estimation performance displaying
superior robustness to poor state measurements.Comment: 8 pages, accepted in IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS) 202