Title: LQG controller design for a quadrotor UAV based on particle swarm optimisation

Authors: Rabii Fessi; Soufiene Bouallègue

Addresses: Research Laboratory in Automatic Control (LA.R.A), National Engineering School of Tunis, University of Tunis EL Manar, BP 37, Le Belvédère, 1002 Tunis, Tunisia; National Engineering School of Gabès, University of Gabès, 6000 Gabès, Tunisia ' Research Laboratory in Automatic Control (LA.R.A), National Engineering School of Tunis, University of Tunis EL Manar, BP 37, Le Belvédère, 1002 Tunis, Tunisia; High Institute of Industrial Systems of Gabès, 6011 Gabès, Tunisia

Abstract: This paper deals with the modelling and the linear quadratic Gaussian (LQG) control design of a quadrotor unmanned aerial vehicle (UAV) using different particle swarm optimisation (PSO) variants. Such a PSO-designed LQG controller is optimised in order to stabilise the position and the heading of the studied vertical take-off and landing (VTOL) quadrotor. Both canonical and recent variants of PSO algorithm, with linearly decreasing of inertia weight (PSO-In) and perturbed updating strategy (PSO-gbest), are considered for the systematically design and tuning of the LQG weighting matrices. These effective control parameters of the LQG approach represent the decision variables of the PSO-based LQG optimisation problem. Such an optimisation problem is formulated to minimise various performance time-domain criteria, like the integral of absolute error (IAE) and the maximum overshoot (MO) index, under nonlinear constraints related to the step responses of the closed-loop quadrotor dynamics. All proposed PSO algorithms are compared with each other and with the well known harmony search algorithm (HSA) and water cycle algorithm (WCA) metaheuristics for the stabilisation problem of the position and heading dynamics of the VTOL drone. Demonstrative simulation results are carried out in order to show the effectiveness of the proposed PSO variants-tuned LQG control approach.

Keywords: quadrotor UAV; modelling; position and heading stabilisation; LQG weighting matrices tuning; particle swarm optimisation; PSO.

DOI: 10.1504/IJAAC.2019.101910

International Journal of Automation and Control, 2019 Vol.13 No.5, pp.569 - 594

Received: 12 Oct 2017
Accepted: 05 Mar 2018
Published online: 30 Aug 2019 *

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