Adaptive Control for Quadrotor UAVs Drones

Related Resources: Aerospace Design Engineering

Adaptive Control for Quadrotor UAVs Drones

Applications and Design
Power Transmission and Technology

Adaptive Control for Quadrotor UAVs
Stephen K Armah* and Sun Yi
Department of Mechanical Engineering, North Carolina A&T State University, USA
13 Pages

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Abstract:

Design of effective control for unmanned aerial vehicles (UAVs) requires consideration of several sources of uncertainty. These undesired uncertainties affect the flight stability and performance in an unpredictable manner. The existence of transmission delays caused by wireless communication and payload variation are among such critical challenges. Adaptive control (AC) can lead to high performance tracking in the presence of uncertainties. This paper presents the application of model reference adaptive control (MRAC) to quadrotor types of UAVs considering the time delay in the altitude control system. MATLAB system identification tool is applied to obtain the altitude motion model, without time delay, for the quadrotor. Proportional-plus-velocity (PV) and PV-MRAC altitude control systems are designed, by incorporating an estimated constant time delay. The designed controllers are validated using simulations and flight tested in an indoor environment.

Introduction:

The application of autonomous control techniques to quadrotor types of unmanned aerial vehicles (UAVs) has been the focus of active research during the past decades. Design of control for aircraft requires several important considerations. There are numerous sources of uncertainty. For example, devices are ageing and wearing (e.g., actuator degradation), external disturbances, control input saturation, payload fluctuations, and potentially uncertain time delays in processing or communication [1]. These undesired nonlinearities affect the flight stability and performance of the controlled systems. Adaptive control (AC) is a candidate to resolve the issues, because of its ability to generate high performance tracking in the presence of uncertainties [1].