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Pigeon inspired optimization approach to model prediction control for unmanned air vehicles

Rui Dou (School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics (BUAA), Beijing, China.)
Haibin Duan (Beihang University(BUAA))

Aircraft Engineering and Aerospace Technology

ISSN: 0002-2667

Article publication date: 4 January 2016



The purpose of this paper is to propose a novel concept of model prediction control (MPC) parameter optimization method, which is based on pigeon-inspired optimization (PIO) algorithm, with the objective of optimizing the unmanned air vehicles (UAVs) controller design progress.


The PIO algorithm is proposed for parameter optimization in MPC, which provides a new method to get the optimal parameter.


The PIO algorithm is a new swarm optimization method, which consists of two operators, so it can be better adapted for the optimal problems. The comparative consequences results with the particle swarm optimization (PSO) demonstrate the effectiveness of the PIO algorithm, and the superiority for global search is also verified in various cases.

Practical implications

PIO algorithm can be easily applied to practice and help the parameter optimization of the MPC.


In this paper, we first present the concept of using the PIO algorithm for parameter optimization in MPC so as to achieve the global best optimization. By using the PIO algorithm, the choice of the parameter could be easier and more effective. The authors also applied the algorithm to the designing of the MPC controller to optimize the response of the pitch rate of UAV.



This work was partially supported by Natural Science Foundation of China (NSFC) under grant # 61333004 and #61273054, Top-Notch Young Talents Program of China, and Aeronautical Foundation of China under grant #20135851042.


Dou, R. and Duan, H. (2016), "Pigeon inspired optimization approach to model prediction control for unmanned air vehicles", Aircraft Engineering and Aerospace Technology, Vol. 88 No. 1, pp. 108-116.



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