Safe Controller Optimization for Quadrotors with Gaussian Processes

TL;DR

This paper introduces a safe optimization method using Gaussian processes to automatically tune quadrotor controllers, ensuring safety and efficiency without manual intervention.

Contribution

It applies the SafeOpt algorithm to control parameter tuning, guaranteeing safety during automatic optimization of quadrotor controllers.

Findings

Fast and safe automatic controller tuning demonstrated on quadrotors

No safety violations during the optimization process

Significant performance improvements achieved

Abstract

One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance. To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used. However, as these methods evaluate different controller parameters on the real system, safety-critical system failures may happen. In this paper, we overcome this problem by applying, for the first time, a recently developed safe optimization algorithm, SafeOpt, to the problem of automatic controller parameter tuning. Given an initial, low-performance controller, SafeOpt automatically optimizes the parameters of a control law while guaranteeing safety. It models the underlying performance measure as a Gaussian process and only explores new controller parameters whose performance lies above a safe performance threshold with high probability. Experimental results on a quadrotor vehicle indicate that the proposed method enables fast, automatic, and safe optimization of controller parameters without human intervention.