Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion

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.

Data-Driven Suboptimal Scheduling of Switched Systems

“…where k is the iteration number. The cost-to-go is solved in policy evaluation (7) and the switching policy is updated in policy improvement (8) iteratively. The stability and convergence are stated in the following theorem which has been proved in [21,38].…”

“…Theorem 1. For the system (1) with the cost (2), if the value function sequence {V k } ∞ k=0 and the switching policy sequence {v k } ∞ k=0 are generated through (7) and (8) initiating from a stabilizing initial policy v 0 , the value functions {V k } ∞ k=0 converge to the optimal value function V * , and the switching policies {v k } ∞ k=0 stabilize the system in Ω.…”

“…For system (1) with cost (2) under Assumptions 1 and 3, if the value function sequence {V k } ∞ k=0 and the switching policy sequence {v k } ∞ k=0 are generated through (7) and (8) initiating from an initial admissible policy v 0 , for ∀ε > 0 and all x ∈ Ω, there existsN > 0 such that for ∀N >N, when δt approaches to zero, the following inequalities hold:…”

“…The switching among subsystems complicates the control problems and many of the problems remain to be open such as the optimal control problems. Optimal control [7,8] problems of switched systems have attracted considerable attention over the past few years. Thereinto, the optimal scheduling problem of switched systems is investigated in this paper.…”

Indoor Mapping Guidance Algorithm of Rotary-Wing UAV Including Dead-End Situations

“…Research on Unmanned Aerial Vehicles (UAVs) has been conducted in recent years due to the various applications [1,2,3,4,5]. A high-fidelity UAV is able to deal with advanced missions including surveillance, reconnaissance, exploration, mapping, delivery, and disaster assistance in dangerous environments.…”