| dc.description.abstract | In recent years, unmanned aerial vehicles (UAVs) have found applications in many diverse fields encompassing commercial, civil, and military sectors. These applications include surveillance, search and rescue operations, aerial photography, mapping of geographical areas, aerial cargo delivery, to name a few. This research addresses how to develop next-generation UAV systems, namely, effective modeling of UAVs, robust control techniques, and non-linear/robust state estimation.
The first part addresses modeling and control of a six-degree-of-freedom unmanned aerial vehicle capable of vertical take-off and landing in the presence of wind disturbances. We design a hybrid vehicle that combines the benefits of both fixed-wing and rotary-wing UAVs. A non-linear model for the hybrid vehicle is built, combining rigid body dynamics, the aerodynamics of the wing, and the dynamics of the motor and propeller. Further, we design an H2 optimal controller to make the UAV robust to wind disturbances. It is easy to achieve robustness in this design framework with respect to wind gusts. The controller is determined by solving a convex optimization problem involving linear matrix inequalities and simulated with a non-linear hybrid UAV model developed in the first section, with a wind gust environment. Further, we compare its results against that of PID and LQR-based control. Our proposed controller results in better performance in terms of root mean squared errors and time responses during two scenarios: hover and level-flight.
In the second part of the research, we discuss robust Proportional-Integral-Derivative (PID) control techniques for the quadcopters. PID control is the most commonly used algorithm for designing controllers for unmanned aerial vehicles (UAVs). However, tuning PID gains is a non-trivial task. A number of methods have been developed for tuning PID gains but these methods do not handle wind disturbances, which is a major concern for small UAVs. In this paper, we propose a new method for determining optimized PID gains in the H2 optimal control framework, which achieves improved wind disturbance rejection. The proposed method compares the classical PID control law with the H2 optimal controller to determine the H2 optimal PID gains and involves solving a convex optimization problem. The proposed controller is tested in two scenarios, namely, vertical velocity control, and vertical position control. The results are compared with the existing LQR based PID tuning method.
A good performance of the controller requires an accurate estimation of states from noisy measurements. Therefore, the third part of the research concentrates on the accurate attitude estimation of UAVs. Most UAV systems use a combination of a gyroscope, an accelerometer, and a magnetometer to obtain measurements and estimate attitude. Under this paradigm of sensor fusion, the Extended Kalman Filter (EKF) is the most popular algorithm for attitude estimation in UAVs. In this work, we propose a novel estimation technique called extended H2 filter that can overcome the limitations of the EKF, specifically with respect to computational speed, memory usage, and root mean squared error. We formulate our attitude-estimation algorithm using two distinct coordinate representations for the vehicle's orientation: Euler angles and unit quaternions, each with its own sets of benefits and challenges. The H2 optimal filter gain is designed offline about a nominal operating point by solving a convex optimization problem, and the filter dynamics is implemented using the nonlinear system dynamics. This implementation of this H2 optimal estimator is referred as the extended H2 estimator. The proposed technique is tested on four cases corresponding to long time-scale motion, fast time-scale motion, transition from hover to forward flight for VTOL aircrafts and an entire flight cycle (from take-off to landing). Its results are compared against that of the EKF in terms of the aforementioned performance metrics. | en |
