Fault Diagnosis and Fault-Tolerant Control of Unmanned Aerial Vehicles

With the increasing demand for unmanned aerial vehicles (UAVs) in both military and civilian applications, critical safety issues need to be specially considered in order to make better and wider use of them. UAVs are usually employed to work in hazardous and complex environments, which may seriously threaten the safety and reliability of UAVs. Therefore, the safety and reliability of UAVs are becoming imperative for development of advanced intelligent control systems. The key challenge now is the lack of fully autonomous and reliable control techniques in face of different operation conditions and sophisticated environments. Further development of unmanned aerial vehicle (UAV) control systems is required to be reliable in the presence of system component faults and to be insensitive to model uncertainties and external environmental disturbances. This thesis research aims to design and develop novel control schemes for UAVs with consideration of all the factors that may threaten their safety and reliability. A novel adaptive sliding mode control (SMC) strategy is proposed to accommodate model uncertainties and actuator faults for an unmanned quadrotor helicopter. Compared with the existing adaptive SMC strategies in the literature, the proposed adaptive scheme can tolerate larger actuator faults without stimulating control chattering due to the use of adaptation parameters in both continuous and discontinuous control parts. Furthermore, a fuzzy logic-based boundary layer and a nonlinear disturbance observer are synthesized to further improve the capability of the designed control scheme for tolerating model uncertainties, actuator faults, and unknown external disturbances while preventing overestimation of the adaptive control parameters and suppressing the control chattering effect. Then, a cost-effective fault estimation scheme with a parallel bank of recurrent neural networks (RNNs) is proposed to accurately estimate actuator fault magnitude and an active fault-tolerant control (FTC) framework is established for a closed-loop quadrotor helicopter system. Finally, a reconfigurable control allocation approach is combined with adaptive SMC to achieve the capability of tolerating complete actuator failures with application to a modified octorotor helicopter. The significance of this proposed control scheme is that the stability of the closed-loop system is theoretically guaranteed in the presence of both single and simultaneous actuator faults

Health-aware and fault-tolerant control of an octorotor UAV system based on actuator reliability

A major goal in modern flight control systems is the need for improving reliability. This work presents a health-aware and fault-tolerant control approach for an octorotor UAV that allows distributing the control effort among the available actuators based on their health information. However, it is worth mentioning that, in the case of actuator fault occurrence, a reliability improvement can come into conflict with UAV controllability. Therefore, system reliability sensitivity is redefined and modified to prevent uncontrollable situations during the UAV’s mission. The priority given to each actuator is related to its importance in system reliability. Moreover, the proposed approach can reconfigure the controller to compensate actuator faults and improve the overall system reliability or delay maintenance tasks.Peer ReviewedPostprint (published version

An Adaptive Fault-Tolerant Sliding Mode Control Allocation Scheme for Multirotor Helicopter Subject to Simultaneous Actuator Faults

This paper proposes a novel adaptive sliding mode based control allocation scheme for accommodating simultaneous actuator faults. The proposed control scheme includes two separate control modules with virtual control part and control allocation part, respectively. As a lowlevel control module, the control allocation/re-allocation scheme is used to distribute/redistribute virtual control signals among the available actuators under fault-free or faulty cases, respectively. In the case of simultaneous actuator faults, the control allocation and re-allocation module may fail to meet the required virtual control signal which will degrade the overall system stability. The proposed online adaptive scheme can seamlessly adjust the control gains for the high-level sliding mode control module and reconfigure the distribution of control signals to eliminate the effect of the virtual control error and maintain stability of the closed-loop system. In addition, with the help of the boundary layer for constructing the adaptation law, the overestimation of control gains is avoided, and the adaptation ceases once the sliding variable is within the boundary layer. A significant feature of this study is that the stability of the closed-loop system is guaranteed theoretically in the presence of simultaneous actuator faults. The effectiveness of the proposed control scheme is demonstrated by experimental results based on a modified unmanned multirotor helicopter under both single and simultaneous actuator faults conditions with comparison to a conventional sliding mode controller and a linear quadratic regulator scheme

Health-aware control of an octorotor UAV system based on actuator reliability

A major goal in modern flight control systems is the need of improving the reliability. This work presents a reliable control approach of an octorotor UAV that allows distributing the control effort among the actuators using health actuator information. The octorotor is an over-actuated system where the redundancy of the actuators allows the redistribution of the control effort among the existing actuators according to a given control strategy. The priority is given to each actuator according to the capabilities and reliability of this actuatorPeer ReviewedPostprint (author's final draft

Robust quasi-LPV model reference FTC of a quadrotor UAV subject to actuator faults

A solution for fault tolerant control (FTC) of a quadrotor unmanned aerial vehicle (UAV) is proposed. It relies on model reference-based control, where a reference model generates the desired trajectory. Depending on the type of reference model used for generating the reference trajectory, and on the assumptions about the availability and uncertainty of fault estimation, different error models are obtained. These error models are suitable for passive FTC, active FTC and hybrid FTC, the latter being able to merge the benefits of active and passive FTC while reducing their respective drawbacks. The controller is generated using results from the robust linear parameter varying (LPV) polytopic framework, where the vector of varying parameters is used to schedule between uncertain linear time invariant (LTI) systems. The design procedure relies on solving a set of linear matrix inequalities (LMIs) in order to achieve regional pole placement and H8 norm bounding constraints. Simulation results are used to compare the different FTC strategies.Peer ReviewedPostprint (published version

Design, Development and Implementation of Intelligent Algorithms to Increase Autonomy of Quadrotor Unmanned Missions

This thesis presents the development and implementation of intelligent algorithms to increase autonomy of unmanned missions for quadrotor type UAVs. A six-degree-of freedom dynamic model of a quadrotor is developed in Matlab/Simulink in order to support the design of control algorithms previous to real-time implementation. A dynamic inversion based control architecture is developed to minimize nonlinearities and improve robustness when the system is driven outside bounds of nominal design. The design and the implementation of the control laws are described. An immunity-based architecture is introduced for monitoring quadrotor health and its capabilities for detecting abnormal conditions are successfully demonstrated through flight testing. A vision-based navigation scheme is developed to enhance the quadrotor autonomy under GPS denied environments. An optical flow sensor and a laser range finder are used within an Extended Kalman Filter for position estimation and its estimation performance is analyzed by comparing against measurements from a GPS module. Flight testing results are presented where the performances are analyzed, showing a substantial increase of controllability and tracking when the developed algorithms are used under dynamically changing environments. Healthy flights, flights with failures, flight with GPS-denied navigation and post-failure recovery are presented

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ

Design and Implementation of an Artificial Neural Network Controller for Quadrotor Flight in Confined Environment

Quadrotors offer practical solutions for many applications, such as emergency rescue, surveillance, military operations, videography and many more. For this reason, they have recently attracted the attention of research and industry. Even though they have been intensively studied, quadrotors still suffer from some challenges that limit their use, such as trajectory measurement, attitude estimation, obstacle avoidance, safety precautions, and land cybersecurity. One major problem is flying in a confined environment, such as closed buildings and tunnels, where the aerodynamics around the quadrotor are affected by close proximity objects, which result in tracking performance deterioration, and sometimes instability. To address this problem, researchers followed three different approaches; the Modeling approach, which focuses on the development of a precise dynamical model that accounts for the different aerodynamic effects, the Sensor Integration approach, which focuses on the addition of multiple sensors to the quadrotor and applying algorithms to stabilize the quadrotor based on their measurements, and the Controller Design approach, which focuses on the development of an adaptive and robust controller. In this research, a learning controller is proposed as a solution for the issue of quadrotor trajectory control in confined environments. This controller utilizes Artificial Neural Networks to adjust for the unknown aerodynamics on-line. A systematic approach for controller design is developed, so that, the approach could be followed for the development of controllers for other nonlinear systems of similar form. One goal for this research is to develop a global controller that could be applied to any quadrotor with minimal adjustment. A novel Artificial Neural Network structure is presented that increases learning efficiency and speed. In addition, a new learning algorithm is developed for the Artificial Neural Network, when utilized with the developed controller. Simulation results for the designed controller when applied to the Qball-X4 quadrotor are presented that show the effectiveness of the proposed Artificial Neural Network structure and the developed learning algorithm in the presence of variety of different unknown aerodynamics. These results are confirmed with real time experimentation, as the developed controller was successfully applied to Quanser’s Qball-X4 quadrotor for the flight control in confined environment. The practical challenges associated with the application of such a controller for quadrotor flight in confined environment are analyzed and adequately resolved to achieve an acceptable tracking performance

Model-based Design Development and Control of a Wind Resistant Multirotor UAV

Multirotor UAVs have in recent years become a trend among academics, engineers and hobbyists alike due to their mechanical simplicity and availability. Commercial uses range from surveillance to recreational flight with plenty of research being conducted in regards to design and control. With applications towards search and rescue missions in mind, the main objective of this thesis work is the development of a mechanical design and control algorithm aimed at maximizing wind resistance. To these ends, an advanced multirotor simulator, based on helicopter theory, has been developed to give an accurate description of the flight dynamics. Controllers are then designed and tuned to stabilize the attitude and position of the UAV followed by a discussion regarding disturbance attenuation. In order to study the impact of different design setups, the UAV model is constructed so that physical properties can be scaled. Parameter influence is then investigated for a specified wind test using a Design of Experiments methodology. These results are combined with a concept generation process and evaluated with a control engineering approach. It was concluded that the proposed final design should incorporate a compact three-armed airframe with six rotors configured coaxially