Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Oct 17;24(20):6678.
doi: 10.3390/s24206678.

UAV Trajectory Tracking Using Proportional-Integral-Derivative-Type-2 Fuzzy Logic Controller with Genetic Algorithm Parameter Tuning

Oumaïma Moali  1 , Dhafer Mezghani  1 , Abdelkader Mami  1 , Abdelatif Oussar  2 , Abdelkrim Nemra  2
Affiliations

UAV Trajectory Tracking Using Proportional-Integral-Derivative-Type-2 Fuzzy Logic Controller with Genetic Algorithm Parameter Tuning

Oumaïma Moali et al. Sensors (Basel). .

Abstract

Unmanned Aerial Vehicle (UAV)-type Quadrotors are highly nonlinear systems that are difficult to control and stabilize outdoors, especially in a windy environment. Many algorithms have been proposed to solve the problem of trajectory tracking using UAVs. However, current control systems face significant hurdles, such as parameter uncertainties, modeling errors, and challenges in windy environments. Sensitivity to parameter variations may lead to performance degradation or instability. Modeling errors arise from simplifications, causing disparities between assumed and actual behavior. Classical controls may lack adaptability to dynamic changes, necessitating adaptive strategies. Limited robustness in handling uncertainties can result in suboptimal performance. Windy environments introduce disturbances, impacting system dynamics and precision. The complexity of wind modeling demands advanced estimation and compensation strategies. Tuning challenges may necessitate frequent adjustments, posing practical limitations. Researchers have explored advanced control paradigms, including robust, adaptive, and predictive control, aiming to enhance system performance amidst uncertainties in a scientifically rigorous manner. Our approach does not require knowledge of UAVs and noise models. Furthermore, the use of the Type-2 controller makes our approach robust in the face of uncertainties. The effectiveness of the proposed approach is clear from the obtained results. In this paper, robust and optimal controllers are proposed, validated, and compared on a quadrotor navigating an outdoor environment. First, a Type-2 Fuzzy Logic Controller (FLC) combined with a PID is compared to a Type-1 FLC and Backstepping controller. Second, a Genetic Algorithm (GA) is proposed to provide the optimal PID-Type-2 FLC tuning. The Backstepping, PID-Type-1 FLC, and PID-Type-2 FLC with GA optimization are validated and evaluated with real scenarios in a windy environment. Deep robustness analysis, including error modeling, parameter uncertainties, and actuator faults, is considered. The obtained results clearly show the robustness of the optimal PID-Type-2 FLC compared to the Backstepping and PID-Type-1 FLC controllers. These results are confirmed by the numerical index of each controller compared to the PID-type-2 FLC, with 12% for the Backstepping controller and 51% for the PID-Type-1 FLC.

Keywords: Type-1 FLC; Type-2 FLC; backstepping controller; genetic algorithm; parameter uncertainties; quadrotor; robustness analyses; wind gust.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
UAV classification.
Figure 2
Figure 2
Quadrotor configuration [18].
Figure 3
Figure 3
Control architecture of the quadrotor [21].
Figure 4
Figure 4
Global model proposed for controller system for quadrotor.
Figure 5
Figure 5
Type-1 fuzzy logic controller.
Figure 6
Figure 6
Membership function of Type-1-FLC: (a) first input (e); (b) second input (de).
Figure 7
Figure 7
Model of membership function for Type-2 FLC.
Figure 8
Figure 8
Type-2 fuzzy logic controller.
Figure 9
Figure 9
Membership function of Type-2-FLC: (a) first input (e); (b) second input (de).
Figure 10
Figure 10
Quadrotor control scheme with GA optimization.
Figure 11
Figure 11
Generic algorithm cycle.
Figure 12
Figure 12
Architecture of optimization strategy for Type-1 FLC and Type-2 FLC using GA.
Figure 13
Figure 13
GA optimization step diagram.
Figure 14
Figure 14
Trajectory of simulation.
Figure 15
Figure 15
Quadrotor commands of Backstepping control.
Figure 16
Figure 16
Motor velocities of Backstepping control.
Figure 17
Figure 17
x, y, z errors evolution of Backstepping control.
Figure 18
Figure 18
Quadrotor angles of Backstepping control.
Figure 19
Figure 19
Quadrotor commands of Type-1 FLC.
Figure 20
Figure 20
Motor velocities of Type-1 FLC.
Figure 21
Figure 21
x, y, z error evolution of Type-1 FLC.
Figure 22
Figure 22
Quadrotor angles of Type-1 FLC.
Figure 23
Figure 23
Quadrotor commands of Type-2 FLC.
Figure 24
Figure 24
Motor velocities of Type-2 FLC.
Figure 25
Figure 25
x, y, z error evolution of Type-2 FLC.
Figure 26
Figure 26
Quadrotor angles of Type-2 FLC.
Figure 27
Figure 27
Quadrotor trajectory for proposed controllers.
Figure 28
Figure 28
The errors of X, Y, and Z position using the PID-Type-1 FLC controller with GA.
Figure 29
Figure 29
Quadrotor angles of PID-Type-1 FLC controller with GA.
Figure 30
Figure 30
Quadrotor commands of PID-Type-1 FLC controller with GA.
Figure 31
Figure 31
Motor velocities of PID-Type-1 FLC controller with GA.
Figure 32
Figure 32
The errors of X, Y, and Z for the PID-Type-2 FLC controller with GA.
Figure 33
Figure 33
Quadrotor angles of PID-Type-2 FLC controller with GA.
Figure 34
Figure 34
Quadrotor commands of PID-Type-2 FLC controller with GA.
Figure 35
Figure 35
Motor velocities of PID-Type-2 FLC controller with GA.
Figure 36
Figure 36
Quadrotor trajectory for PID-Type-1 FLC and PID-Type-2 FLC controllers with GA optimization.
Figure 37
Figure 37
Wind velocity for scenario 2.
Figure 38
Figure 38
Quadrotor trajectory for scenario 2.
Figure 39
Figure 39
The errors of X, Y, and Z for PID-Type-2 FLC scenario 2.
Figure 40
Figure 40
The errors of X, Y, and Z for PID FLC scenario 2.
Figure 41
Figure 41
The errors of X, Y, and Z for Backstepping controller scenario 2.
See this image and copyright information in PMC

References

    1. Kayacan E., Maslim R. Type-2 Fuzzy Logic Trajectory Tracking Control of Quadrotor VTOL Aircraft with Elliptic Membership Functions. IEEE/ASME Trans. Mechatron. 2017;22:339–348. doi: 10.1109/TMECH.2016.2614672. - DOI
    1. Sattar M., Ismail A. Modeling and Fuzzy Logic Control of a Quadrotor UAV. Int. Res. J. Eng. Technol. (IRJET) 2017;4:1494–1498.
    1. Lungus M., Lungus R. Adaptive Backstepping Flight Control for a mini—UAV. Int. J. Adapt. Control Signal Process. 2013;27:635–650. doi: 10.1002/acs.2330. - DOI
    1. Krstic M., Kanellakopoulos I., Kokotovic P. Nonlinear and Adaptive Control Design. John Wiley & Sons, Inc.; Hoboken, NJ, USA: 1995.
    1. Polycarpou M. Stable adaptive neural control scheme for nonlinear systems. IEEE Trans. Autom. Control. 1996;41:447–451. doi: 10.1109/9.486648. - DOI