Optimization of UAV robust control using genetic algorithm

A hybrid methodology combining the use of a robust LQR servomechanism (RSLQR) and a genetic algorithm (GA) for the design of the flight control system (FCS) of a lightweight unmanned aerial vehicle is the subject of this paper. The objective is to develop a systematic design approach based on a proven technique that provides improved time response and robust steady-state performance of the control system, so as to reduce the burden of trial-and-error procedures. The design of the inner loops of the UAV autopilot is formulated as an optimization problem where the GA is used to determine the weights of the RSLQR synthesis. The process is aimed at maximizing a weighted sum of an appropriately defined multi-objective fitness function, evaluated through a series of nonlinear simulations, so as to fully engage the control system in complex maneuvers, such as combined changes in altitude and heading at different flight speeds. The performance of the proposed control design approach is evaluated using analytical tools for linear systems, software-in-the-loop simulations, and Monte Carlo campaigns. The comparison between the new controller and a classical FCS with internal PID loops on attitude angles for stability and control augmentation is analyzed and discussed using an accurate vehicle model with an extended Kalman filter for output reconstruction.