Article
Neural Network-Based Active Fault-Tolerant Control Design
for Unmanned Helicopter with Additive Faults
Sohrab Mokhtari
1,
*, Alireza Abbaspour
2
, Kang K. Yen
1
and Arman Sargolzaei
3
Citation: Mokhtari, S.; Abbaspour,
A.; Yen, K.K.; Sargolzaei, A. Neural
Network-Based Active Fault-Tolerant
Control Design for Unmanned
Helicopter with Additive Faults.
Remote Sens. 2021, 13, 2396. https://
doi.org/10.3390/rs13122396
Academic Editor: João Catalão
Fernandes
Received: 29 April 2021
Accepted: 15 June 2021
Published: 19 June 2021
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4.0/).
1
Electrical and Computer Engineering Department, Florida International University, Miami, FL 33174, USA;
yenk@fiu.edu
2
Functional Safety Engineer, Tusimple Co., San Diego, CA 92093, USA; Aabba014@fiu.edu
3
Mechanical Engineering Department, Tennessee Technological University, Cookeville, TN 38505, USA;
asargolzaei@tntech.edu
* Correspondence: somokhta@fiu.edu; Tel.: +1-305-680-4338
Abstract:
A novel adaptive neural network-based fault-tolerant control scheme is proposed for six
degree-of-freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate
actuators and sensors’ faults in real time. An adaptive observer-based on neural network (NN)
and extended Kalman filter (EKF) is designed, which incorporates the helicopter’s dynamic model
to detect faults in the actuators and navigation sensors. Based on the detected faults, an active
fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for
the occurred faults in real time. The simulation results showed that the proposed approach is able to
detect and mitigate different types of faults on the helicopter actuators, and the helicopter tracks the
desired trajectory without any interruption.
Keywords:
unmanned aerial vehicle (UAV); faulty sensors; fault detection and isolation; abrupt fault;
feedback linearization control
1. Introduction
In recent years, unmanned aerial vehicles (UAVs), especially unmanned helicopters,
have achieved significant improvement because of their remarkable vertical landing and
take-off capabilities, hovering, and maneuverability. The unmanned helicopters have a
complex and inherently unstable nonlinear dynamic system [
1
]. Therefore, they require an
efficient flight control mechanism to ensure a successful mission, such as fire suppression,
urban traffic control, mapping, and surveillance. To this, the proper application of sensors
in a UAV has a significant role. Sensor fusion is a solution to reduce the uncertainty in
the measurement of the UAV parameters [
2
]. However, UAVs’ nonlinear characteristic
implies the importance of mitigating any possible disturbance in the actuators and sensors’
application. Conventional controllers employ linearization methods to deal with the
nonlinear characteristics of the dynamic flight model [
3
,
4
]. Since the linearized model is
an approximate estimation of the nonlinear system, it cannot cover most of the nonlinear
characteristics of helicopter dynamics; thus, the derived control system is vulnerable
against the inherent uncertainties and nonlinearities in the helicopter behavior.
In the last decade, due to the striking development in computing power and technol-
ogy, more efficient strategies for designing helicopter flight control based on the nonlin-
ear dynamic model of systems have been introduced, such as differential programming
method [
5
,
6
]. Basically, a helicopter’s dynamic system is a multiple-input and multiple-
output (MIMO) subject to considerable external uncertainties [
7
]. Recently, many studies
have been applied on helicopter dynamics, e.g., a study by Abbaspour et al. focuses
on nonlinear optimized adaptive control of helicopter [
8
]. In their study, a six degree-
of-freedom (DoF) model of a helicopter is controlled using three time-scale separation
architecture through nonlinear dynamic inversion (NDI). Their study leveraged a neural
Remote Sens. 2021, 13, 2396. https://doi.org/10.3390/rs13122396 https://www.mdpi.com/journal/remotesensing
