Onboard Time-Optimal Control for Tiny Quadcopters

J.M. Westenberger, C. de Wagter, G.C.H.E. de Croon

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Time-optimal model predictive control is important for achieving fast racing drones but is computationally intensive and thereby rarely used onboard small quadcopters with limited computational resources. In this work, we simplify the optimal control problem (OCP) of the position loop for several maneuvers by exploiting the fact that the solution resembles a so-called ’bang-bang’ in the critical direction, where only the switching time needs to be found. The noncritical direction uses a ’minimum effort’ approach. The control parameters are obtained by means of bisection search schemes on an analytical path prediction model. The approach is compared with a classical PID controller and theoretical time-optimal trajectories in simulations. We explain the effects of the OCP simplifications and introduce a method of mitigating one of these effects. Finally, we have implemented the ’bang-bang’ controller as a model predictive controller (MPC) onboard a Parrot Bebop and performed indoor flights to compare the controller’s performance to a PID controller. We show that the light novel controller outperforms the PID controller in waypoint-towaypoint flight while requiring only minimal knowledge of the quadcopter’s dynamics.
Original languageEnglish
Title of host publicationProceedings of the 12th International Micro Air Vehicle Conference
EditorsJose Martinez-Carranza
Pages93-100
Number of pages8
Publication statusPublished - 2021
Event12th International Micro Air Vehicle Conference - Puebla, Mexico
Duration: 17 Nov 202119 Nov 2021
Conference number: 12

Conference

Conference12th International Micro Air Vehicle Conference
Abbreviated titleIMAV 2021
Country/TerritoryMexico
CityPuebla
Period17/11/2119/11/21

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