Flight evaluations of sliding mode fault tolerant controllers

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordThis paper considers the development of fault tolerant controllers (FTC) and their application to aerospace system. In particular, given the extensive and growing literature in this area, this paper focusses on methods where the schemes have been implemented and flight tested. One thread of the fault tolerant control literature has involved sliding mode controllers. This paper considers a specific class of sliding mode FTC which incorporates control allocation to exploit over-actuation (which is typically present in aerospace systems). The paper describes implementations of these ideas on a small quadrotor UAV and also piloted flight tests on a full-scale twin-engined aircraft

Fault tolerant control of multi-rotor unmanned aerial vehicles using sliding mode based schemes

This thesis investigates fault-tolerant control (FTC) for the specific application of small multirotor unmanned aerial vehicles (Unmanned Aerial Vehicle (UAV)s). The fault-tolerant controllers in this thesis are based on the combination of sliding mode control with control allocation where the control signals are distributed based on motors' health level. This alleviates the need to reconfigure the overall structure of the controllers. The thesis considered both the over actuated (sufficient redundancy) and under-actuated UAVs. Three multirotor UAVs have been considered in this thesis which includes a quadrotor (4 rotors), an Octocopter (8 rotors) and a spherical UAV. The non-linear mathematical models for each of the UAVs are presented. One of the main contributions of this thesis is the hardware implementation of the sliding mode Fault Tolerant Control (FTC) scheme on an open-source autopilot microcontroller called Pixhawk for a quadrotor UAV. The controller was developed in Simulink and implemented on the microcontroller using the Matlab/Simulink support packages. A gimbal- based test rig was developed and built to offer a safe test bed for testing control designs. Actual flight tests were done which showed sound responses during fault-free and faulty scenarios. This work represents one of successful implementation work of sliding mode FTC in the literature. Another key contribution of this thesis is the development of the mathematical model of a unique spherical UAV with highly redundant control inputs. The use of novel 8 flaps and 2 rotors configuration of the spherical UAV considered in this thesis provides a unique fault tolerant capability, especially when combined with the sliding mode-based FTC scheme. A key development in the later chapters of the thesis considers fault-tolerant control strategy when no redundancy is available. Unlike many works which consider FTC on quadrotors in the literature (which can only handle faults), the proposed schemes in the later chapters also include cases when failures also occur converting the system to an under actuated system. In one chapter, a bespoke Linear Parameter Varying (LPV) based controller is developed for a reduced attitude dynamics system by exploiting non-standard equation of motions which relates to position acceleration and load factor dynamics. This is unique as compared to the typical Euler angle control (roll, pitch and yaw angle control). In the last chapter, a fault-tolerant control scheme which can handle both the over and under actuated system is presented. The scheme considers an octocopter and can be used in fault-free, faulty and failure conditions up to two remaining motors. The scheme exploits the differential flatness property, another unique property of multirotor UAVs. This allows both inner loop and outer loop controller to be designed using sliding mode (as opposed to many sliding mode FTC in the literature, which only considers sliding mode for the inner loop control)

Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers

Unmanned aerial vehicle (UAV) applications have evolved to a wide range of fields in the last decade. One of the main challenges in autonomous tasks is the UAV stability during maneuvers. Thus, attitude and position control play a crucial role in stabilizing the vehicle in the desired orientation and path. Many control techniques have been developed for this. However, proportional integral derivative (PID) controllers are often used due their structure and efficiency. Despite PID’s good performance, different requirements may be present at different mission stages. The main contribution of this research work is the development of a novel strategy based on a fuzzy-gain scheduling mechanism to adjust the PID controller to stabilize both position and altitude. This control strategy must be effective, simple, and robust to uncertainties and external disturbances. The Robot Operating System (ROS) integrates the proposed system and the flight control unit. The obtained results showed that the proposed approach was successfully applied to the trajectory tracking and revealed a good performance compared to conventional PID and in the presence of noises. In the tests, the position controller was only affected when the altitude error was higher, with an error of 2% lower.publishedVersio

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system

A Simple Three-phase Three-wire Voltage Disturbance Compensator

The two-leg four-switch VC-VSI with a direct load voltage controller has been developed as a three-phase multi- functional voltage disturbance compensator for a three-phase three-wire system. By controlling only two legs (two phases) of the inverter, the third leg automatically generates the correct third voltage. The VC-VSI is connected in series between the PCC and loads by three single-phase matching transformers. The transformer primary windings are connected in delta. The VC- VSI operates to directly control the load voltage rather than its output voltage using a simple PI controller. The VC-VSI is able to produce automatically a three-phase compensation voltage so that the load voltage is sinusoidal, symmetrical and constant at a nominal value. Computer simulation verifies that the VC-VSI is able to perform well as a voltage disturbance compensator, although there is still an insignificant deviation (around 1%). The THD of the load voltage reduces to 1.4%

Geometric Active Disturbance Rejection Control for Autonomous Rotorcraft in Complex Atmospheric Environment

This dissertation presents several novel robust tracking control schemes of rotorcraft unmanned aerial vehicles under realistic atmospheric turbulence. To achieve fast converging and stable performance of the rotorcraft control scheme, a new H\ {o}lder-continuous differentiator, similar to the super-twisting algorithm used in the second-order sliding model control scheme, is proposed with guaranteed fast finite-time stability. Unlike the super-twisting algorithm, which uses a sliding-mode structure to achieve finite-time stability, the proposed differentiator maintains its fast finite-time stability with H\ {o}lder continuity, theoretically eliminating the harmful chattering phenomenon in practical control applications. Perturbation and noise robustness analyses are conducted for the proposed differentiator. The dissertation formulates the rotorcraft tracking control and disturbance estimation problems separately. The rotorcraft aerial vehicle is modeled as a rigid body with control inputs that actuate all degrees of freedom of rotational motion and only one degree of freedom of translational motion. The motion of the aircraft is globally represented on \TSE, which is the tangent bundle of the special Euclidean group \SE. The translational and attitude control schemes track the desired position and attitude on \SE. The disturbance estimation problem is formulated as an extended states observer on \TSE. Next, two rotorcraft control schemes on \SE with disturbance rejection mechanisms are presented. The proposed disturbance rejection control systems comprise two parts: an extended states observer for disturbance estimation and a tracking control scheme containing the disturbance rejection term to track the trajectory. The first disturbance rejection control scheme comprises an exponentially stable extended states observer and an asymptotically stable tracking control scheme. The second system comprises a fast finite-time stable extended state observer and a fast finite-time stable tracking control scheme. The fast finite-time stable extended state observer uses the \textup{H\ {o}}lder-continuous differentiator to estimate the resultant external disturbance force and disturbance torque acting on the vehicle. It ensures stable convergence of disturbance estimation errors in finite time when the disturbances are constant. Software-in-the-loop simulation is carried out for the active disturbance rejection control scheme with an open-source autopilot and a physics-based simulation tool. The simulation utilizes simulated wind gusts, propeller aerodynamics, actuator limitation, and measurement noise to validate the disturbance rejection control systems in a simulated environment with high fidelity. Two sets of flight experiments are conducted to investigate the autonomous rotorcraft flight control performance under turbulent income flows. A wind tunnel composed of fan arrays is involved in both experiments to provide different turbulent incoming flows by adjusting the duty of individual fans. The first set of experiments conducts income flow measurements for wind tunnel calibration. For the turbulent flows generated by different fan configurations, their steady velocity field and unsteady turbulence characteristics are measured by a pressure scanner and hot-wire anemometer. The second set of experiments involves flight tests of a rotorcraft within the turbulent environment measured and calibrated in the first experiment set. The proposed extended states observer is implemented onto a rotorcraft by customizing an open-source autopilot software. With this implementation, the flight control performance of the proposed disturbance rejection control schemes is presented and compared with the autopilot without customization. The experimental results show that the proposed disturbance rejection control scheme enhanced by the disturbance estimation schem

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Development of Self-Learning Type-2 Fuzzy Systems for System Identification and Control of Autonomous Systems

Modelling and control of dynamic systems are faced by multiple technical challenges, mainly due to the nature of uncertain complex, nonlinear, and time-varying systems. Traditional modelling techniques require a complete understanding of system dynamics and obtaining comprehensive mathematical models is not always achievable due to limited knowledge of the systems as well as the presence of multiple uncertainties in the environment. As universal approximators, fuzzy logic systems (FLSs), neural networks (NNs) and neuro-fuzzy systems have proved to be successful computational tools for representing the behaviour of complex dynamical systems. Moreover, FLSs, NNs and learning-based techniques have been gaining popularity for controlling complex, ill-defined, nonlinear, and time-varying systems in the face of uncertainties. However, fuzzy rules derived by experts can be too ad-hoc, and the performance is less than optimum. In other words, generating fuzzy rules and membership functions in fuzzy systems is a potential challenge especially for systems with many variables. Moreover, under the umbrella of FLSs, although type-1 fuzzy logic control systems (T1-FLCs) have been applied to control various complex nonlinear systems, they have limited capability to handle uncertainties. Aiming to accommodate uncertainties, type-2 fuzzy logic control systems (T2-FLCs) were established. This thesis aims to address the shortcomings of existing fuzzy techniques by utilisation of type-2 FLCs with novel adaptive capabilities. The first contribution of this thesis is a novel online system identification technique by means of a recursive interval type-2 Takagi-Sugeno fuzzy C-means clustering technique (IT2-TS-FC) to accommodate the footprint-of-uncertainties (FoUs). This development is meant to specifically address the shortcomings of type-1 fuzzy systems in capturing the footprint-of-uncertainties such as mechanical wear, rotor damage, battery drain and sensor and actuator faults. Unlike previous type-2 TS fuzzy models, the proposed method constructs two fuzzifiers (upper and lower) and two regression coefficients in the consequent part to handle uncertainties. The weighted least square method is employed to compute the regression coefficients. The proposed method is validated using two benchmarks, namely, real flight test data of a quadcopter drone and Mackey-Glass time series data. The algorithm has the capability to model uncertainties (e.g., noisy dataset). The second contribution of this thesis is the development of a novel self-adaptive interval type-2 fuzzy controller named the SAF2C for controlling multi-input multi-output (MIMO) nonlinear systems. The adaptation law is derived using sliding mode control (SMC) theory to reduce the computation time so that the learning process can be expedited by 80% compared to separate single-input single-output (SISO) controllers. The system employs the `Enhanced Iterative Algorithm with Stop Condition' (EIASC) type-reduction method, which is more computationally efficient than the `Karnik-Mendel' type-reduction algorithm. The stability of the SAF2C is proven using the Lyapunov technique. To ensure the applicability of the proposed control scheme, SAF2C is implemented to control several dynamical systems, including a simulated MIMO hexacopter unmanned aerial vehicle (UAV) in the face of external disturbance and parameter variations. The ability of SAF2C to filter the measurement noise is demonstrated, where significant improvement is obtained using the proposed controller in the face of measurement noise. Also, the proposed closed-loop control system is applied to control other benchmark dynamic systems (e.g., a simulated autonomous underwater vehicle and inverted pendulum on a cart system) demonstrating high accuracy and robustness to variations in system parameters and external disturbance. Another contribution of this thesis is a novel stand-alone enhanced self-adaptive interval type-2 fuzzy controller named the ESAF2C algorithm, whose type-2 fuzzy parameters are tuned online using the SMC theory. This way, we expect to design a computationally efficient adaptive Type-2 fuzzy system, suitable for real-time applications by introducing the EIASC type-reducer. The proposed technique is applied on a quadcopter UAV (QUAV), where extensive simulations and real-time flight tests for a hovering QUAV under wind disturbances are also conducted to validate the efficacy of the ESAF2C. Specifically, the control performance is investigated in the face of external wind gust disturbances, generated using an industrial fan. Stability analysis of the ESAF2C control system is investigated using the Lyapunov theory. Yet another contribution of this thesis is the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). T2-EFCS has two phases, namely, the structure learning and the parameters learning. The structure of T2-EFCS does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one rule. The rules are then added and pruned in an online fashion to achieve the desired set-point. The proposed technique is applied to control an unmanned ground vehicle (UGV) in the presence of multiple external disturbances demonstrating the robustness of the proposed control systems. The proposed approach turns out to be computationally efficient as the system employs fewer fuzzy parameters while maintaining superior control performance

Force-Canceling Mixer Algorithm for Vehicles with Fully-Articulated Radially Symmetric Thruster Arrays

A new type of fully-holonomic aerial vehicle is identified and developed that can optionally utilize automatic cancellation of excessive thruster forces to maintain precise control despite little or no throttle authority. After defining the physical attributes of the new vehicle, a flight control mixer algorithm is defined and presented. This mixer is an input/output abstraction that grants a flight control system (or pilot) full authority of the vehicle\u27s position and orientation by means of an input translation vector and input torque vector. The mixer is shown to be general with respect to the number of thrusters in the system provided that they are distributed in a radially symmetric array. As the mixer is designed to operate independently of the chosen flight control system, it is completely agnostic to the type of control methodology implemented. Validation of both the vehicle\u27s holonomic capabilities and efficacy of the flight control mixing algorithm are provided by a custom MATLAB-based rigid body simulation environment

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection