Fine-Tuning of Feedback Gain Control for Hover Quad Copter Rotors by Stochastic Optimization Methods
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RESEARCH PAPER
Fine-Tuning of Feedback Gain Control for Hover Quad Copter Rotors by Stochastic Optimization Methods Abdullah Ates1
•
Baris Baykant Alagoz1 • Gurkan Kavuran2 • Celaleddin Yeroglu1
Received: 22 November 2018 / Accepted: 1 February 2020 Ó Shiraz University 2020
Abstract Three degree of freedom (3 DOF) Hover Quad Copter (HQC) platforms are implemented for various missions in diverse scales from the micro to macro platforms. As HQC platforms scale down, micro platform requires rather robust and effective control techniques. This study investigates applicability of some stochastic optimization methods for tuning feedback gain control of HQC rotors and compares optimization results with results of linear quadratic regulator (LQR) method that has been widely used analytical method for optimal feedback gain control of HQCs. This study considers the utilization of two stochastic methods for tuning of HQCs. These methods are stochastic multi-parameter divergence optimization method (SMDO) and discrete stochastic optimization method (DSO). These methods are employed to optimize feedback gain coefficients of an experimental HQC test platform. Simulation and experimental results of SMDO and DSO methods are reported and compared with results of LQR method. Keywords Quad copters Stochastic optimization Controller tuning Fine-tuning Flight control
1 Introduction Nowadays, HQC platforms have been received considerable attention due to their simple structure, low cost, superior performance and unique flight control mode in the control system literature (Chen et al. 2014) and control problems of HQC platforms have significance for performance and feasibility of these platforms in diverse applications. The control of four rotors of HQCs introduces many difficulties such as nonlinearities of dynamic responses, model uncertainties, altering air condition during flight, highly coupled control and external interferences. To deal with these problems, researchers have introduced various control techniques in the literature. For example, an experimental evaluation of the forward-propagating Riccati equation control via Quanser 3 DOF Hover & Abdullah Ates [email protected] 1
Computer Engineering Department, Engineering Faculty, Inonu University, Malatya, Turkey
2
Electrical Engineering Department, Faculty of Engineering and Nature Sciences, Malatya Turgut Ozal University, Malatya, Turkey
Quad rotor system was presented in (Prach et al. 2016). A linear model of 3 DOF Hover was proposed and controller was designed for efficient and practical tracking of desired reference trajectory (Amin and Li 2017). Another study proposed local fuzzy-polynomial observer discrete-time designs for the state estimation of a nonlinear 3DOF quad rotor electromechanical platform (Pitarch and Sala 2014). LQR weighting matrices were optimized for 3 DOF Hover simulation model with Darwinian Particle Swarm Optimization (DPSO) and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) methods (Ic¸en et al. 2017). Hence,
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