ACTUATOR FAULT DETECTION, ISOLATION AND FAULT TOLERANT CONTROL OF A HEXACOPTER UAV

Abstract

Unmanned Aerial Vehicles (UAVs) continue to penetrate diverse sectors, including agriculture, disaster management, communication, transportation, and defense. The robustness and reliability of their operation have become paramount due to the increasing ubiquity of these systems across numerous sectors. UAVs can undergo faults in their sensors, actuators (motors), and even structure - all of these have different levels of severity and effects on the system. Motor faults can be of three main types, namely, Motor Locking, Loss of Efficiency (LoE), and Loss of Actuation (LoA). Motor locking is a case where the actuator keeps spinning at a given RPM and cannot be changed, whereas LoE is a case where the actuator degrades over time and is not able to generate enough torque due to lower RPM. In this thesis, however, we focus on LoA, where an actuator stops working completely (gets stuck at zero RPM) and can result in catastrophic failure in the UAV. This work delves into the dynamics of UAVs under actuator fault conditions, examines the resulting instability issues, and explores potential methods for identification and control reconfiguration for safe operation. Once an actuator failure occurs, the Fault Detection & Isolation (FDI) module should be able to locate the fault and pinpoint it with high accuracy and in a very short time, to allow the UAV to stabilize the fault. Various approaches can be used for the FDI system, namely, rule-based, model-based, and data-driven. Implementation of these methods can be relying on RPM sensors, battery monitors, and software-only solutions. We propose a data-driven software module for this purpose, since it is UAV frame agnostic, does not require any additional hardware (software-only), and can be run with minimal setup. The proposed Rotation Forest based classifier can detect, classify, and report the fault within 120-250 milliseconds of occurrence of the fault. This is an acceptable delay, since we have observed that the vehicle cannot handle delays upwards of 500 milliseconds in simulations. Once a fault is reported by the FDI module, the Fault Tolerant Control (FTC) module reconfigures the control system to stabilize the vehicle and continue the mission, or prevent a crash by peforming a safe landing. This thesis focuses on cases of complete actuator failure in Hexacopter UAVs, specifically, for single motor failure scenarios. The proposed FTC module performs UAV stabilization using control reconfiguration, after fault occurrence. We perform a thorough analysis for the FDI and FTC modules separately, extensively in simulation, and demonstrate the same in real flight tests. We also combine the two modules and analyze the response in the simulation. In real flights, the classifier (FDI module) responds to the fault in 2-5 sensor data samples (at a 60ms rate per sample) and has a high true positive rate of 92.6%. Also, in real flights, the performance of the FTC module is measured in terms of tracking error, percentage overshoot, and settling time. Integration of the FDI and FTC modules is performed and simulation results are presented showing satisfactory operation of each of these modules with guaranteed stable flight.