Online Parameter Estimation Within Trajectory Optimization for Dynamic Soaring

Abstract

We propose a novel control architecture incorporating online parameter estimation alongside MPC with application to dynamic soaring. Dynamic soaring is a process that has prompted recent research which allows a UAV to extract energy from wind gradients to stay aloft, thereby minimizing the need for propulsive action during flight. However, to properly execute dynamic soaring maneuvers sufficient information about the wind gradient and aerodynamic properties of the UAV is required. Much of the research on dynamic soaring has focused on offline trajectory optimization. Either no estimation of parameters is incorporated, or estimation serves to inform decisions based on an existing library of trajectories or neural networks trained on offline-generated data points. Other work has been done on online trajectory planning in the form of Model Predictive Control (MPC), but estimation is rarely incorporated. Our proposed algorithm combines parameter estimation with online trajectory optimization to create a risk-aware framework which is able to select control inputs to minimize the impact of uncertainty and learn the true values of environmental and aerodynamic parameters. We also show that our Sequential Monte Carlo-based online parameter estimation method performs similarly under identical conditions to Markov Chain Monte Carlo. We also establish deterministic MPC as a baseline and show that our algorithm performs similarly to deterministic MPC with known parameters. In a comparison of deterministic MPC with known parameters, deterministic MPC with unknown parameters, and our algorithm, we show the positive impact of learning on the ability of MPC to maintain energy levels throughout the soaring process. Finally, after a robustness analysis, we report good performance of the algorithm under a variety of conditions both generally and in comparison to deterministic MPC.