Improved cuckoo search approach based optimal proportional-derivative parameters for quadcopter flight control

ABSTRACT

This paper proposes an improved cuckoo search (CS) algorithm for an optimal flight of an unmanned quadcopter using proportional-derivative (PD) controllers. In order to improve the optimisation capability of the standard CS, a new initialisation strategy of nests increases the exploitation search. The choice of an enhanced fitness function consisting of the weighted sum of the integral squared errors for longitude, latitude, altitude and attitude gives better performance. Then, the calculation of the velocity of displacements is inspired by the global nests’ intelligence search or the oriented CS capacity. Results and comparisons to the genetic algorithms, the particle swarm optimisation, the cooperative particle swarm optimisation-cuckoo search and the hybrid neural network PD techniques confirm that PD controllers tuned using the improved CS are efficient for quadrotor full control and trajectory tracking.

KEYWORDS:

• UAV
• quadcopter
• quadrotor
• non-linear
• intelligent
• CS
• cuckoo search
• PSO
• particle swarm optimisation
• control
• trajectory tracking

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Nada El Gmili

Nada El gmili (Ph. D., Eng.) was graduated from the Cadi Ayyad University and laureate of the ENSA Oujda, Morocco. She has previous teaching experience at the FST Marrakesh, Cadi Ayyad University, Morocco from 2017 to 2020, and more recently she became a professor at FST Mohammedia, Hassan II University, Casablanca, Morocco since 2020. She has published, in the last few years, a good number of papers in journals (indexed in the international Thomson Reuters and Scopus databases) and conferences, most of which are related to robotics, automatics, embedded systems, and more specifically artificial intelligence applications to optimize the performance of complex unmanned aerial vehicles' flight.

Mostafa Mjahed

Mostafa Mjahed received his 3ème cycle Doctorate in HEP from the University of Clermont Ferrand, France, in 1987, and a Ph.D. degree in Control and Artificial Intelligence from the University of Cadi Ayyad, Marrakech, in 2003. In 1989, he joined the Ecole Royale de l’Air, Marrakech, Morocco, as an associate professor in the Department of Mathematics and Systems. From 2003, he has been a professor in the same institute and department. His current research interests are conventional and AI-based flight control, pattern recognition, and classification by GA, PSO, and NN.

Abdeljalil Elkari

Abdeljalil El Kari received his Ph.D. degree from the University of Bordeaux, in 1993. In 1994, he joined Cadi Ayyad University, Faculty of Science and Technology, Marrakesh, Morocco, where he currently is a full professor with the Department of physics. In 2002, he obtained a Ph.D. degree from Cadi Ayyad University and Reims Champagne-ArdenneUniversity. He is a researcher member of the Electric Systems and Telecommunications Laboratory and is responsible of electrical engineering master. His mainareasofinterestinresearchareAutomatic, Robotics, Control Systems Engineering and Artificial Intelligence. He has so far published over 20 papers in different journals and conferences.

Hassan Ayad

Hassan Ayad obtained his doctorate thesis in 1993 from the University of Le Havre, France, and the Ph.D. degree from Cadi Ayyad University in 2007. Since 1993, he is a professor at the faculty of science and Technology of Marrakesh, responsible of the Physical department. He is a researcher member of the Electric Systems and Telecommunications Laboratory. He is the author of more than 20 articles and more than 20 international communications. His research interests include artificial intelligence, automatic control, intelligent control, intelligent systems, mechatronics, and robotics.