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Design of an Optimal Integral Backstepping Controller for a Quadcopter | ||
| Journal of Engineering | ||
| Article 1, Volume 24, Issue 5, 2018, Pages 46-65 | ||
| Authors | ||
| Laith Jasim Saud; Alaq Falah Hasan | ||
| Abstract | ||
| In this paper, an Integral Backstepping Controller (IBC) is designed and optimized for full control, of rotational and translational dynamics, of an unmanned Quadcopter (QC). Before designing the controller, a mathematical model for the QC is developed in a form appropriate for the IBC design. Due to the underactuated property of the QC, it is possible to control the QC Cartesian positions (X, Y, and Z) and the yaw angle through ordering the desired values for them. As for the pitch and roll angles, they are generated by the position controllers. Backstepping Controller (BC) is a practical nonlinear control scheme based on Lyapunov design approach, which can, therefore, guarantee the convergence of the position tracking error to zero. To improve controller capability in the steady state against disturbances, an integral action is used with the BC. To determine the optimal values of the IBC parameters, the Particle Swarm Optimization (PSO) is used. In the algorithm, the controller parameters are computed by minimizing a cost function that depends on the Integral Time Absolute Error (ITAE) performance index. Finally, different numerical simulations are provided in order to illustrate the performances of the designed controller. And for comparison purposes, a PID controller is designed and optimized using the PSO to control the quadcopter. The obtainediresults indicated a superiority in performance for the IBC over the PID controller based on some points among which are: a 13.3% and 30.5% lesser settling times for X and Y consequently, the ability to perform critical maneuvers that the quadcopter failed to do using the PID controller, and the capability of fast following up and conforming the changes of pitch ( | ||
| Keywords | ||
| quadcopter; integral backstepping control; particle swarm optimization; position control; attitude control | ||
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