Tracking a Moving Target by a Fixed-wing UAV Based on Sliding Mode Control

<p>This paper presents a 3D guidance scheme for<br>\nUnmanned Aerial Vehicles (UAVs). The proposed guidance scheme<br>\nis based on the sliding mode approach using nonlinear sliding<br>\nmanifolds. Generalized 3D kinematic equations are considered<br>\nhere during the design process to cater for the coupling between<br>\nlongitudinal and lateral motions. Sliding mode based guidance<br>\nscheme is then derived for the multiple-input multiple-output<br>\n(MIMO) system using the proposed nonlinear manifolds. Instead of<br>\ntraditional sliding surfaces, nonlinear sliding surfaces are proposed<br>\nhere for performance and stability in all flight conditions. In the<br>\nreaching phase control inputs, the bang-bang terms with signum<br>\nfunctions are accompanied with proportional terms in order to reduce<br>\nthe chattering amplitudes. The Proposed 3D guidance scheme is<br>\nimplemented on a 6-degrees-of-freedom (6-dof) simulation of a UAV<br>\nand simulation results are presented here for different 3D trajectories<br>\nwith and without disturbances.</p>

The problem of neural adaptive distributed formation control is investigated for quadrotor multiple unmanned aerial vehicles (UAVs) subject to unmodeled dynamics and disturbance. The quadrotor UAV system is divided into two parts: the position subsystem and the attitude subsystem. A virtual position controller based on backstepping is designed to address the coupling constraints and generate two command signals for the attitude subsystem. By establishing the communication mechanism between the UAVs and the virtual leader, a distributed formation scheme, which uses the UAVs' local information and makes each UAV update its position and velocity according to the information of neighboring UAVs, is proposed to form the required formation flight. By designing a neural adaptive sliding mode controller (SMC) for multi-UAVs, the compound uncertainties (including nonlinearities, unmodeled dynamics, and external disturbances) are compensated for to guarantee good tracking performance. The Lyapunov theory is used to prove that the tracking error of each UAV converges to an adjustable neighborhood of zero. Finally, the simulation results demonstrate the effectiveness of the proposed scheme.

This dissertation presents a comprehensive approach to addressing the challenges associated with the control and navigation of Unmanned Aerial Vehicles (UAVs), specifically focusing on robustness under GPS-denied, turbulent, and dark indoor conditions with various disturbances. Initial efforts involved designing and theoretically validating Sliding Mode Controllers (SMC) with Disturbance Observers using MATLAB/SIMULINK, optimized using offline Particle Swarm Optimization (PSO). In order to evaluate their performance, a lightweight simulator was developed to simulate both quadcopter and hexacopter configurations under the PX4 architecture. This simulator served to validate and compare the optimized SMC against PSO-optimized PID controllers. The results of these comparisons demonstrated the superior adaptability of the SMC, especially in environments characterized by significant sensor noise and disturbances. Subsequent practical indoor tests were conducted with real quadcopters, which provided additional verification of the robustness and reliability of the enhanced control theory with the actual UAVs under dynamic disturbances. To further evaluate the compatibility and adaptability of the SMC, this dissertation establishes a framework for interfacing with high-performance STMH7 chips, serving as the UAV microcontroller unit (MCU). This framework facilitates the connection of various sensors and provides guidance for constructing different quadcopter frames. Moreover, it enables the comparison of different flight controllers on distinct air-frames. Concurrently, the optimized SMC allows for remote dynamic parameter tuning during flight. In pursuit of improved localization capacity, the dissertation also explores the integration of an Intel Realsense D435i camera with an Inertial Measurement Unit (IMU) for Visual-Inertial Odometry (VIO) fusion within the Robot Operating System (ROS) environment. This integration employs an onboard companion computer for real-time VIO estimation and flight command. The effectiveness of the VIO algorithm is verified through both simulations and real-world experiments, highlighting its ability to enhance UAV localization and navigation. Additionally, a visual inertial odometry method for UAV Simultaneous Localization and Mapping (vSLAM) is tested and validated in both simulated and real drone environments. Furthermore, the dissertation introduces an innovative hybrid filtered multi-directional radar inertial odometry solution denoted as Hybrid-MRIO. This Error State Extended Kalman Filter (ES-EKF) based Radar Inertial Odometry (RIO) approach is implemented within the open-source PX4 autopilot system, enhancing UAV navigation accuracy and performance, and serves as a robust navigation alternative, particularly in challenging environments where traditional navigation systems prove ineffective, such as dark or smoky conditions. This solution integrates data from multiple software synchronized high-resolution Frequency-Modulated Continuous-Wave (FMCW) mmWave radars with an IMU. A subsequent 4D (x, y, z, doppler) mmWave radar SLAM (rSLAM) system has been developed, including three modules: front-end, loop detection module, and back-end. In the front-end, radar ego-velocity is utilized for states estimation and dynamic object removal, and a point cloud registration-based approach known as APDGICP (Adaptive Probability Distribution-GICP) is employed for keyframe detection. The loop detection module utilizes the intensity scan context to identify potential loop closure candidates. In the back-end, a pose graph is constructed, integrating RIO estimated odometry and identified loop closures for better localization. To develop fully-autonomous UAVs with their decision making modules, this dissertation details the development of a custom AI-powered fully-autonomous quadcopter equipped with companion computers and stereo IR cameras. Additionally, this dissertation discusses a swarm of small Unmanned Aerial Systems (SUAS) interconnected through a software-defined communications network. These drones demonstrated their capacity to independently/collaboratively execute a wide range of tasks, including target search, detection, identification, classification, tracking, and following, both in simulated and real-world scenarios. The system exhibits advanced collision avoidance capabilities and the ability to strategically respond to dynamic scenarios, such as changes in target behavior, highlighting its effectiveness in managing dynamic and challenging situations without the need for continuous human intervention.--Author's abstract

Small Unmanned Aerial Vehicle(UAV) is well suited for missions that are dangerous to perform with human pilots. Control effort in the form of Automatic Flight Control System(AFCS) for achieving the desired flight profile against any unexpected disturbance or parameter variations is needed in such UAV's. Most of the UAV dynamics uses Euler rate method for axis transformation. Alternate computational methods have been evolved to compute and derive parameters from the available sensors. This paper attempts to employ quaternion based closed loop attitude control. The major problem encountered with the axis transformation with Euler rates is the singularity at 90° theta which can be directly removed when quaternion based control is employed. One of the major advantages in employing quaternion based closed loop control is that, it is independent of the error due to sensors. Hence this paper attempts to employ quaternion based control in a low speed subsonic UAV platform. The main aim of this paper is to introduce quaternion based feedback approach into the nonlinear UAV simulation model with Linear Quadratic Regulator (LQR) and Sliding Mode Controller (SMC).Unexpected disturbance and parameter variation can be tolerated by using robust fuzzy SMC. Simulation results show the effectiveness of the projected method.

Active fault-tolerant control of quadrotor UAVs with nonlinear observer-based sliding mode control validated through hardware in the loop experiments

Quadrotor unmanned aerial vehicles (UAV) are underactuated as although UAV has six degrees of freedom, only four control inputs are available. Under actuation limits the mobility of the UAV, as it cannot independently track a desired position and attitude trajectory in space. Thrust vectoring can be used to make the UAV into a fully actuated system. The most efficient method for thrust vectoring is by tilting the motors using actuators. The tilting mechanism will provide additional four control inputs, making the system overactuated. In this paper, a sliding mode controller is proposed for the simultaneous tracking of attitude and position of the overactuated quadrotor. Further a Takagi-Sugeno based fuzzy gain scheduling system is proposed for minimizing the chattering in the control signals. Finally, the performance of the designed control system is evaluated using simulation results.

Aiming at the emergency braking and rapid response of the Unmanned Aerial Vehicle (UAV) operating on the ground, a novel brake control strategy is proposed in this paper, adopting an integrated design of pressure and velocity based on sliding mode control. The strategy takes the air travel in the operation of the all-electric brake system into consideration. In this paper, the controller of an UAV Hardware-in-the-Loop (HIL) test system will be used to verify the feasibility of the novel brake control strategy and the simulation results are adopted to validate the superiority and feasibility of the brake control strategy for UAV all-electric brake system.

The proliferation of unmanned aerial vehicles (UAVs) has driven the emergence of urban air mobility (UAM) as a solution to alleviate the increasing demand for UAVs applications in various domains. The concept of UAM envisions a future where UAVs play a pivotal role in short-distance transportation, delivery services, surveillance, emergency rescue, and more. However, the rapid growth of UAVs poses significant challenges arising from the need to ensure the safe coexistence of UAVs with other manned/unmanned aerial vehicles. To ensure the safe integration of UAVs into low-altitude urban airspace and support high-density on-demand mobility, this research focuses on handling the safety-centric challenges related to UAV collision risk assessment, obstacle avoidance, and conflict resolution in complex urban environments.
\nOne of the main challenges is the intrusion of UAVs into airport runways. Airports serve as critical hubs for civil aircraft takeoff and landing operations. However, the increasing presence of UAVs has raised concerns about intruding into airports, leading to flight delays and temporary runway closures. To quantify the potential interference of UAVs on civil aircraft, a collision-course based trajectory planning model is proposed to evaluate the collision risk between manned civil aircraft and intruding UAVs. In this risk assessment model, the trajectory of the intruding UAV is formulated using a collision-course concept, taking into account the limited trajectory information available about intruding UAVs. The planned trajectory of the UAVs is then used to predict its relative position concerning civil aircraft. To account for the possible disturbances of the relative distance, a stochastic position prediction model based on Brownian motion is developed. The collision risk is assessed based on this stochastic kinematic model, providing a quantitative measure of the potential collisions. The proposed model is validated through extensive simulations involving different UAV initial positions, position updates, and different sizes of collision zone tests, as well as comparative tests with the Monte-Carlo algorithm, demonstrating its robustness in assessing the collision risk of the airport restricted area. To facilitate the application, the model is finally used to develop a platform to generate runway heatmaps, accounting for airport runway risk distributions. 
\nAnother safety-centric challenge is ensuring autonomous obstacle avoidance and conflict resolution of UAVs. To enable UAV autonomous safe operations in uncontrolled urban low-altitude airspace, a comprehensive collision-free path planning framework is developed to address the avoidance of both static obstacles and dynamic threats. Firstly, to handle static obstacles, a novel algorithm of 3D voxel jump point research is introduced to generate a global reference path for UAV navigation to avoid static obstacles, particularly for a free flight at different altitudes. Additionally, to optimize the reference path for specific mission requirements, techniques such as trajectory de-diagonalization, reconstruction, and smoothness are proposed to reduce the risk of static collisions, shorten the fly range, and enable UAVs to negotiate turns more efficiently. Secondly, real-time conflict resolution actions based on the Markov decision process are developed to avoid local dynamic threats encountered along the optimized desired path. This allows the host UAV to dynamically adjust its course to a resolution trajectory while maintaining its planned speed without hovering to avoid the potential collision with the approaching intruders. By avoiding near mid-air collisions (NMAC) and preventing the UAV from hovering, the proposed method ensures the timely completion of UAV missions. Simulation results demonstrate that the developed method successfully achieves UAV autonomous global path planning and local real-time conflict resolution in complex urban low-altitude environments, thereby avoiding both static obstacles and dynamic threats. 
\nMoreover, to bridge the research gap of UAV hard conflict resolution in mitigating NMAC risks arising from local dynamic threats, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. To improve UAV operating environments in urban airspace, a concept of tiered aerial passages is first designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity and optimizes the utilization of urban airspace. For UAV tiered aerial passage operations, a situational NMAC risk assessment module coupled with a conflict resolution decision-making module is formulated, enabling the host UAV to evaluate and respond to real-time conflicts, ensuring avoidance of potential collisions. To assess NMAC risks, a general risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the overtaking, approaching head-on, and converging UAV encounters are analyzed, and situational risk potential scores are created using the Gaussian kernel function in the CRF algorithm, considering advisory, caution, and warning risk alert levels. To resolve conflict, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed scheme is validated by simulating geometric encounter situations and comparative tests with the previous hard resolution technique. The results indicate that the proposed method outperforms UAV NMAC risk assessment and conflict resolution. Even in the most dangerous unacceptable resolution decision-making scenarios across three conflict encounters, the proposed algorithm achieves NMAC rates of 0.036, 0.070, and 0.043, which are lower than the corresponding NMAC rates associated with the hard resolution approach. Together with the acceptable and tolerable scenarios, the proposed negotiation resolution effectively tailors different risk requirements to accommodate diverse UAV missions. 
\nWith a primary focus on ensuring the safety of the UAM architecture, this research endeavors to assess UAV NMAC risks and efficiently manage potential conflicts. The proposed work are thoroughly validated to demonstrate their effectiveness in real-world scenarios. As a result, this study significantly contributes to the establishment of a robust UAM architecture, facilitating the realization of safe and reliable UAM.

Chapter 9 - Fast terminal sliding mode controller for high speed and complex maneuvering of unmanned aerial vehicles

In this brief, finite-time backstepping sliding mode control technique is investigated to handle with the yaw control of non-affine nonlinear unmanned aerial vehicle (UAV) helicopters with actuator faults and unknown disturbances. Using a taylor series expansion technique, the control input in non-affine nonlinear system is presented as an explicit expression, which is convenient for controller design. Based on it, nonlinear disturbance observer is designed to estimate unknown compound disturbance. Furthermore, finite-time convergence backstepping sliding mode controller is designed to force tracking errors to small bounded sets in finite time, in which dynamic surface control avoids explosion of complexity and the sliding mode variable enhances system robustness. Then, the Lyapunov function is employed to verify the stability of the control system. Finally, the validity of proposed scheme adopted in yaw-channel dynamic model of UAV helicopters under actuator faults is demonstrated via simulation results.

This paper presents an adaptive attitude control strategy based on a finite-time convergent differentiator for an unmanned aerial vehicle (UAV) using a sliding mode control combined with an auxiliary system with finite time differentiator as an estimator. Initially, the sliding mode control approach is proposed to stabilize the quadrotor by controlling the rotation angles under the external disturbance, uncertainties and actuator saturation compensator. Then, a finite-time differentiator is designed and included in the system feedback to ensure an accurate estimation of the full state even in the presence of noise, to address the parametric selection drawback a fuzzy system is proposed for the purpose of tuning the differentiator parameters. The stability of the overall system is proved by the Lyapunov theory and the simulation results shown the effectiveness of the proposed control are presented.

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

Fixed-time anti-saturation and fault-tolerant control for quadrotor UAVs using event-trigger adaptive barrier sliding mode control.

Growing demand for warehouse automation requires Unmanned Aerial Vehicles (UAVs), particularly quadcopters, to operate autonomously with a high level of precision and reliability. However, indoor localization poses unique challenges due to the absence of Global Positioning System (GPS) signals, making alternative sensors and robust control strategies essential. This study proposes an indoor UAV navigation system that integrates camera and LiDAR sensors with Fuzzy–Sliding Mode Control (Fuzzy-SMC) to enhance stability and reduce the chattering effects commonly associated with Sliding Mode Control. In the proposed method, the camera provides better accuracy for real-time position tracking compared to LiDAR, while fuzzy logic adaptively adjusts the Sliding Mode Control parameters, which serve as the main controller for stabilizing the quadcopter’s nonlinear dynamics. Research methodology includes mathematical modeling of the UAV quadcopter, the design of the Fuzzy-SMC controller, and simulation-based testing for trajectory tracking in indoor environments. Results show that the developed system achieves high accuracy, with error values ranging from 0 to 4.044%, remaining below the acceptable threshold of 5%. These findings demonstrate that integration of a camera with Fuzzy-SMC provides an effective and reliable solution for indoor quadcopter UAV navigation, while future research will focus on optimizing the fuzzy rule base and conducting hardware validation in real warehouse scenarios.

URLLC in UAV-enabled multicasting systems: A dual time and energy minimization problem using UAV speed, altitude and beamwidth