Development of a Customized Autopilot for Unmanned Helicopter Model Using Genetic Algorithm via the Application of Different Guidance Strategies

Document Type : Original Article

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Egyptian Armed Forces, Egypt.

Abstract

The objective of this paper is to develop a customized autopilot system that enables a helicopter model to carry out an autonomous flight using on-board microcontroller. The main goal of this project is to provide a comprehensive controller design methodology, Modeling, simulation, guidance and verification for an unmanned helicopter model. The autopilotsystem was designed to demonstrate autonomous maneuvers such as flying over the planned waypoints with constant forward speed and considerable steep maneuvers. For the controller design, the nonlinear dynamic model of the Remote Control helicopter was built by employing Lumped Parameter approach comprising of four different subsystems such as actuator dynamics, rotary wing dynamics, force and moment generation process and rigid body dynamics. The nonlinear helicopter mathematical model was then linearized using small perturbation theory for stability analysis and linear feedback control system design. The linear state feedback for the stabilization and control of the helicopter was derived using Pole Placement Method. The overall dynamic system control with output feedback was computed using Genetic Algorithm. Series of Matlab-Simulink models and guidance algorithms were presented in this work to simulate and verify the autopilot system performance. The proposed autopilot has shown acceptable capability of stabilizing and controlling the helicopter during tracking the desired waypoints. This paper is presenting a detailed comparison study for two different guidance strategies. The first strategy is concerning the difference between the desired heading or elevation referred to the next waypoint and the actual heading or elevation of the unmanned helicopter model. The second strategy is concerning the relative distance between the actual and the desired trajectories. In other words the first method is tracking the waypoints while the second one is tracking the trajectory. In this work a comparison study was conducted through the mentioned strategies simulation to show the significant differences in the output performance. Some performance indexes were presented to evaluate the system performance errors and the control effort needed for both strategies using the same desired trajectory and the same waypoints.

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