Sıtkı Yenal Vural

State estimation and control for low-cost unmanned aerial vehicles

Introduction to Unmanned Aerial Vehicles.- Equations of Motion for the UAV.- Navigation Systems for UAV.- Estimation of the UAV Dynamics.- Estimation of the UAV Dynamics.- Estimation of the UAV Dynamics in the Presence of Sensor Faults.- Estimation of the UAV Dynamics in the Presence of Sensor/Actuator Faults.- Fault Detection, Isolation and Data Fusion for the UAV Air Data System.- Stability Analysis for the UAV.- Classical Controller Design for the UAV.- LQR Controller Design.- Fuzzy Logic Based Controller Design.

Introduction to Unmanned Aerial Vehicles

In this chapter, an introduction to unmanned aerial vehicles (UAVs) is given. It is discussed why UAVs are important and what are their advantages over conventional manned planes. A brief history of UAVs is also included.

Equations of Motion for an Unmanned Aerial Vehicle

This chapter initially gives information about the rigid body equations of motion of an aircraft and the small perturbation method utilized for linearizing these equations of motion. Then, the equations are specialized for unmanned aerial vehicles (UAVs) by taking the characteristics of a specific plane into consideration and they are made appropriate to use in the next phases of the book.

Estimation of Unmanned Aerial Vehicle Dynamics

In this chapter, the linear optimal Kalman filter (OKF) algorithm is presented. The OKF for unmanned aerial vehicle (UAV) state estimation is given. The stability of the OKF and the necessity for Kalman filter adaptation are discussed.

Linear Quadratic Regulator Controller Design

In this chapter, an optimal controller is designed using the linear quadratic regulator (LQR) method. In order to better evaluate the effect of disturbances on the obtained measurements, a Kalman filter is used. Initially, the controller is tested for a case where disturbances are absent. Then, a Kalman filter is designed and the system under disturbances is tested with the designed controller and filter. The results reveal the effectiveness of the Kalman filter and LQR controller.

Stability Analysis for Unmanned Aerial Vehicles

The stability of unmanned aerial vehicles (UAVs) is investigated in this chapter for controller design purposes. The transfer functions for the longitudinal and lateral motions of UAVs are introduced, and then stability analysis is performed by means of the characteristics equations.

Estimation of Unmanned Aerial Vehicle Dynamics in the Presence of Sensor/Actuator Faults

The process noise covariance adaptation procedure (Q-adaptation) for unmanned aerial vehicle (UAV) state estimation is introduced and the robust adaptive Kalman filter (RAKF) algorithm with R- and Q-adaptations against sensor/actuator failures is proposed. Thus, the filter proves to be robust against faults and, even in case of sensor/actuator failure, keeps providing accurate estimation results. The performance of the proposed RAKF is investigated via simulations for the state estimation procedure of the UAV. The presented RAKF ensures that the parameter estimation system of the UAV is not influenced by sensor/actuator failures and, therefore, autonomous missions can be performed successfully.

Navigation Systems for Unmanned Aerial Vehicles

In this chapter, navigation systems for unmanned aerial vehicles (UAVs) are given. In this context, inertial navigation, air data system, surface radar, satellite radio navigation systems, Doppler and altimeter radars, magnetic sensors, vision-based systems, and simultaneous localization and mapping are described. Moreover, measurement faults for these systems, when they are used in UAVs, are investigated and details for the fault modeling are presented.

Estimation of Unmanned Aerial Vehicle Dynamics in the Presence of Sensor Faults

In this chapter, a robust Kalman filter (RKF) with filter gain correction for the case of measurement malfunctions is introduced. By using a defined variable called the measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight and the estimations are corrected without affecting the characteristics of the accurate measurements. The RKF algorithms with single and multiple measurement noise scale factors (R-adaptation procedure) are proposed and applied for the state estimation process of the unmanned aerial vehicle (UAV) platform in the presence of measurement faults. The results of these algorithms are compared for different types of sensor faults and recommendations about their utilization are given. A remark on stability for the proposed RKFs is also included.

Fuzzy Logic-Based Controller Design

In this chapter, a fuzzy controller for unmanned aerial vehicle (UAV) flight dynamics is investigated. Fuzzy logic-based longitudinal and lateral controllers are designed. The fuzzy logic controller works well, even though no optimization technique is used to optimize the system and it was designed without dynamic model knowledge. In fuzzy logic controllers, a careful arrangement of membership functions may lead to better results. The stability of fuzzy controllers is also investigated. The effectiveness of different control methods for UAV flight control is compared in this chapter.