Enabling On-Chip Adaptive Linear Optimal Control via Linearized Gaussian Process

Abstract

Unpredictable and complex aerodynamic effects pose significant challenges to achieving precise flight control, emphasizing the necessity of adaptive control via data- driven models. Moreover, real hardware usually requires high-frequency and has limited on-chip computation, making it challenging to balance the model complexity and computational cost. To address these challenges, we incorporate a linearized Gaussian process (GP) to model the external aerodynamics and combine it with linear model predictive control, enabling real-time computability. More importantly, to compensate for the control performance sacrificed by GP linearization and reduce on-chip GP computations, we design active data collection strategies using Bayesian optimization with additive GP, reducing the performance sacrifice as much as possible. Specifically, we decompose the performance into force and trajectory partitions, where the force model is for the downstream controller, and the trajectory model is used to guide collection. Experimental results show that we can achieve comparable tracking errors with full GP (not real-time computable) while maintaining real-time computable on the real Crazyflies.