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

TL;DR

This paper presents a novel on-chip adaptive control method using linearized Gaussian processes combined with Bayesian optimization, enabling real-time high-frequency flight control for drones despite complex aerodynamic effects.

Contribution

The paper introduces a linearized Gaussian process model integrated with Bayesian optimization for real-time adaptive control in aerial vehicles, addressing computational and modeling challenges.

Findings

Achieves real-time high-frequency control on quadrotors.

Maintains acceptable tracking errors in complex aerodynamic conditions.

Demonstrates effectiveness through simulations and real-world experiments.

Abstract

Unpredictable and complex aerodynamic effects pose significant challenges to achieving precise flight control, such as the downwash effect from upper vehicles to lower ones. Conventional methods often struggle to accurately model these interactions, leading to controllers that require large safety margins between vehicles. Moreover, the controller on real drones usually requires high-frequency and has limited on-chip computation, making the adaptive control design more difficult to implement. To address these challenges, we incorporate Gaussian process (GP) to model the adaptive external aerodynamics with linear model predictive control. The GP is linearized to enable real-time high-frequency solutions. Moreover, to handle the error caused by linearization, we integrate end-to-end Bayesian optimization during sample collection stages to improve the control performance. Experimental results on both simulations and real quadrotors show that we can achieve real-time solvable computation speed with acceptable tracking errors.