Citation: Kang, C.; Park, B.; Choi, J.
Scheduling PID Attitude and
Position Control Frequencies for
Time-Optimal Quadrotor Waypoint
Tracking under Unknown External
Disturbances. Sensors 2022, 22, 150.
https://doi.org/10.3390/s22010150
Academic Editors: Yangquan Chen,
Subhas Mukhopadhyay, Nunzio
Cennamo, M. Jamal Deen, Junseop
Lee and Simone Morais
Received: 16 November 2021
Accepted: 24 December 2021
Published: 27 December 2021
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Article
Scheduling PID Attitude and Position Control Frequencies for
Time-Optimal Quadrotor Waypoint Tracking under Unknown
External Disturbances
Cheongwoong Kang , Bumjin Park and Jaesik Choi *
Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST),
Daejeon 34141, Korea; cw.kang@kaist.ac.kr (C.K.); bumjin@kaist.ac.kr (B.P.)
* Correspondence: jaesik.choi@kaist.ac.kr
Abstract:
Recently, the use of quadrotors has increased in numerous applications, such as agriculture,
rescue, transportation, inspection, and localization. Time-optimal quadrotor waypoint tracking is
defined as controlling quadrotors to follow the given waypoints as quickly as possible. Although
PID control is widely used for quadrotor control, it is not adaptable to environmental changes, such
as various trajectories and dynamic external disturbances. In this work, we discover that adjusting
PID control frequencies is necessary for adapting to environmental changes by showing that the
optimal control frequencies can be different for different environments. Therefore, we suggest a
method to schedule the PID position and attitude control frequencies for time-optimal quadrotor
waypoint tracking. The method includes (1) a Control Frequency Agent (CFA) that finds the best
control frequencies in various environments, (2) a Quadrotor Future Predictor (QFP) that predicts the
next state of a quadrotor, and (3) combining the CFA and QFP for time-optimal quadrotor waypoint
tracking under unknown external disturbances. The experimental results prove the effectiveness of
the proposed method by showing that it reduces the travel time of a quadrotor for waypoint tracking.
Keywords:
waypoint tracking; external disturbance estimation; quadrotor control; reinforcement
learning; deep learning; artificial intelligence
1. Introduction
In recent years, the use of quadrotor has rapidly increased in various fields, includ-
ing agriculture [
1
,
2
], rescue [
3
,
4
], delivery [
5
], inspection [
6
], and map construction [
7
].
A quadrotor, or drone, shows a versatile ability for various kinds of missions because of the
simple structure, the simple use of control, and useful properties, such as VTOL (vertical
take-off and landing).
Quadrotor waypoint tracking is defined as controlling quadrotors to follow the given
waypoints. To complete time-critical missions, such as rescue and delivery, time-optimal
quadrotor waypoint tracking is essential. Although PID control [
8
] is widely used for
controlling robots, including quadrotors [
9
–
11
], it is hardly generalizable to various envi-
ronments, such as various trajectories and dynamic external disturbances, which cannot be
precisely modeled due to the uncertainties [
12
]. Existing PID control methods for quadro-
tors use a fixed position control frequency, where the position errors are fed to the PID
periodically. However, in this work, we discover that different trajectories require different
control frequencies to achieve time-optimal tracking control. We also show that different
control frequencies are required as the external disturbances change.
There have been numerous efforts for adaptive control in environmental changes,
including cascade control [
13
,
14
], finite-time control [
15
,
16
], coordinated control [
17
,
18
],
PID gain scheduling [
19
,
20
], backstepping control [
21
,
22
], sliding mode control [
23
,
24
],
external disturbance estimation [
25
,
26
], and compensation [
27
,
28
]. However, no existing
studies consider finding the best control frequencies for time-optimal tracking control.
Sensors 2022, 22, 150. https://doi.org/10.3390/s22010150 https://www.mdpi.com/journal/sensors
