Abstract
In this paper, an infinite-horizon adaptive linear quadratic tracking (ALQT) control scheme is designed for optimal attitude tracking of a quadrotor unmanned aerial vehicle (UAV). The proposed control scheme is experimentally validated in the presence of real-world uncertainties in quadrotor system parameters and sensor measurement. The designed control scheme guarantees asymptotic stability of the close-loop system with the help of complete controllability of the attitude dynamics in applying optimal control signals. To achieve robustness against parametric uncertainties, the optimal tracking solution is combined with an online least squares based parameter identification scheme to estimate the instantaneous inertia of the quadrotor. Sensor measurement noises are also taken into account for the on-board Inertia Measurement Unit (IMU) sensors. To improve controller performance in the presence of sensor measurement noises, two sensor fusion techniques are employed, one based on Kalman filtering and the other based on complementary filtering. The ALQT controller performance is compared for the use of these two sensor fusion techniques, and it is concluded that the Kalman filter based approach provides less mean-square estimation error, better attitude estimation, and better attitude control performance.
Highlights
Unmanned aerial vehicle (UAV) systems, quadrotor UAV systems, have been popular in various autonomous surveillance and transportation applications in recent years
The proposed adaptive controller is designed by an indirect approach and combined with the LS based parameter identification (PI) to eliminate the influences of inertial uncertainties
The Kalman filter has been designed for canceling noise effects on the attitude estimation data to provide more reliable feedback to the controller and it is compared with the Complementary filter
Summary
Unmanned aerial vehicle (UAV) systems, quadrotor UAV systems, have been popular in various autonomous surveillance and transportation applications in recent years. Robotics and control researchers have been interested in improving quadrotor UAV systems with regard to path planning, tracking, stability and autonomous motion capability in simultaneous localization and mapping (SLAM) tasks for difficult missions such as defense patrol duties, agricultural activities, surveillance, and rescue [1,2,3,4,5,6]. One of the main control interests for quadrotors UAV is optimization of time and energy (battery) consumption by designing optimal path planning and optimal tracking control. For such optimal attitude tracking, Ref. [1] has designed a linear quadratic regulation (LQR) based attitude stabilization
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