Research Article
Flight control law of unmanned
aerial vehicles based on robust
servo linear quadratic regulator
and Kalman filtering
Yongfeng Zhi
1
, Gaoshang Li
1
, Qun Song
1
,KeYu
2
and Jun Zhang
1
Abstract
A new flight control law for unmanned aerial vehicles based on robust servo linear quadratic regulator control and Kalman
filtering is proposed. This flight control law has a simple structure with high dependability in engineering. The pitch angle
controller, which is designed based on the robust servo linear quadratic regulator control, is given to show the flight
control law. Simulation results show that the pitch angle controller works well under noise-free conditions. Finally,
Kalman filtering is applied to the pitch angle controller under noisy conditions, and the simulation results show that the
proposed method reduces the influence of noise.
Keywords
Robust servo LQR control, Kalman filtering, unmanned aerial vehicle
Date received: 29 June 2016; accepted: 19 November 2016
Topic: Special Issue – Intelligent Flight Control for Unmanned Aerial Vehicles
Topic Editor: Mou Chen
Introduction
Unmanned aerial vehicles (UAVs) have shown their great
value in recent years because of their small volume, light-
weight, and low cost. As is known, dynamic modeling and
computation of aerodynamic coefficients are the founda-
tions of designing flight control laws. Thus, in the study of
Jang and Tomlin,
1
a nonlinear model was linearized to
design a controller for UAVs. There are two different ways
to design a flight control law: one based on classical control
theory and the other on modern control theory. Propor-
tional–integral–derivative (PID) control, which is a classi-
cal form of control, has been applied to flight control law to
improve the response time of the systems.
2
This kind of
classical control method has a simple structure that is easy
to engineer, but it is difficult for it to meet UAVs perfor-
mance requirements because of its single-input, single-
output characteristic. Compared to classical control,
modern control theory further improves the performance
of the control system. Cooperative receding horizon
control,
3
adaptive control system design,
4
and decentra-
lized nonlinear control
5
have been applied on automatic
control system. In the study of Wei and Cassandras,
3
a
receding horizon controller suitable for dynamic and uncer-
tain environments is proposed to maximize the total reward
accumulated over a given time interval. In the study by
Fradkov and Andrievsky,
4
a combined adaptive control law
for UAVs is proposed and improves the performance of the
control system on different flight conditions. In the study of
Singh et al.,
5
a decentralized nonlinear robust control
1
School of Automation, Northwestern Polytechnical University, Xi’an,
China
2
Science and Technology on Aircraft Control Laboratory, FACRI, Xi’an,
China
Corresponding author:
Yongfeng Zhi, School of Automation, Northwester n Pol ytechnical
University, You Yi Xi Lu 127 Hao, Xi’an, Shaanxi 710072, China.
Email: zyfnwpu@126.com
Internation al Journal of Advanced
Robotic Systems
January-February 2017: 1–7
ª The Author(s) 2017
DOI: 10.1177/1729881416686952
journals.sagepub.com/home/arx
Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License
(http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without
further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/
open-access-at-sage).