Dynamics modeling and advanced metaheuristics based LQG controller design for a Quad Tilt Wing UAV

This paper deals with the modelling and advanced metaheuristics-based tuning of a Linear Quadratic Gaussian (LQG) controller for a particular class of convertible Unmanned Aerial Vehicles (UAVs), called Quad Tilt-Wing (QTW). A dynamical model for the vertical, transition and horizontal flights is firstly derived using the Newton–Euler formalism. Since the main design parameters in the LQG control approach are the weighting matrices which are usually selected by classical and hard trials-errors based procedures, an optimization problem under operating constraints is formulated for systematically tuning these decision variables. Such an optimization-based LQG control problem is systematically solved using advanced and recent metaheuristics such as the Harmonic Search Algorithm, Water Cycle Algorithm (WCA) and Fractional Particle Swarm Optimization-based Memetic Algorithm (FPSOMA). To assess the performance of the proposed algorithms and conclude about their control parameters choices, an empirical comparison study is performed for solving different test functions from the single-objective optimization literature. The classical Particle Swarm Optimization and Ant Bee Colony algorithms are retained as comparison tools. Value- and Friedmans rank-based statistical analyses are presented. After that, experiments are conducted to achieve optimal coefficients of weighting matrices of LQG controllers in solving QTW position and heading stabilization problem. Demonstrative simulations show that the WCA, outperforming each one of other metaheuristics, provides the best results in terms of robustness and performance for the problem in hand. Results verify the efficiency the proposed metaheuristics-tuned LQG control structures.