Vol.27, No.3, 2021, pp.817-834, doi:10.32604/iasc.2021.015932
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ARTICLE
Multi-Model Fuzzy Formation Control of UAV Quadrotors
  • Abdul-Wahid A. Saif1, Mohammad Ataur-Rahman1, Sami Elferik1, Muhammad F. Mysorewala1, Mujahed Al-Dhaifallah1,*, Fouad Yacef2
1 Systems Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
2 Centre de Développement des Technologies Avancées, Algeria
* Corresponding Author: Mujahed Al-Dhaifallah. Email:
(This article belongs to this Special Issue: Recent Trends in Computational Methods for Differential Equations)
Received 14 December 2020; Accepted 14 January 2021; Issue published 01 March 2021
Abstract
In this paper, the formation control problem of a group of unmanned air vehicle (UAV) quadrotors is solved using the Takagi–Sugeno (T–S) multi-model approach to linearize the nonlinear model of UAVs. The nonlinear model sof the quadrotor is linearized first around a set of operating points using Taylor series to get a set of local models. Our approach’s novelty is in considering the difference between the nonlinear model and the linearized ones as disturbance. Then, these linear models are interpolated using the fuzzy T–S approach to approximate the entire nonlinear model. Comparison of the nonlinear and the T–S model shows a good approximation of the system. Then, a state-feedback controller is synthesized utilizing the parallel distributed compensation (PDC) concept. The linear quadratic regulator (LQR) controller is used to stabilize the system and obtain the desired response. This is followed by the formation control of a set of quadrotors using the leader–follower method. In this strategy, the potential field method is utilized to obtain the ideal shape formations. An attractive potential is generated such that the followers are attracted towards the leader, and a repulsive potential is generated that repels adjacent quadrotors to avoid collisions. Simulations are performed to evaluate the proposed method’s effectiveness in obtaining the desired shape formation for different cases. From the simulation results, we can see that the proposed formation control results in a good tracking response.
Keywords
Multi-models; nonlinear systems UA quadrotor; fuzzy logic control; formation control
Cite This Article
A. A. Saif, M. Ataur-Rahman, S. Elferik, M. F. Mysorewala, M. Al-Dhaifallah et al., "Multi-model fuzzy formation control of uav quadrotors," Intelligent Automation & Soft Computing, vol. 27, no.3, pp. 817–834, 2021.
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