A Review of Quadrotor Unmanned Aerial Vehicles: Applications, Architectural Design and Control Algorithms

Modified super twisting control for robust trajectory tracking and hovering of quadrotor

This paper proposes a novel controller design using adaptation based modified super twisting control to facilitate trajectory tracking and hovering maneuvers for the quadrotor. The controller gains of the existing modified super twisting control require bounds on the disturbance for trajectory tracking and hovering of the quadrotor. In this paper, the controller gains are adapted using the proposed dynamic adaptation law without knowing the actual disturbance or their upper bounds. The controller is designed within a nonlinear framework without performing linearization of quadrotor dynamics, which enables the proposed controller to remain effective even when the states deviate significantly from their nominal values. The performance of trajectory tracking and hovering of the quadrotor in the presence of disturbance is demonstrated using numerical simulations. In order to assess the effectiveness of the controller, the performance of the adaptation based modified super twisting control is compared to the existing modified super twisting control, and the proposed controller outperforms the existing one.

Dual-loop control and state prediction analysis of QUAV trajectory tracking based on biological swarm intelligent optimization algorithm

Quadrotor unmanned aerial vehicles (QUAVs) have attracted significant research focus due to their outstanding Vertical Take-Off and Landing (VTOL) capabilities. This research addresses the challenge of maintaining precise trajectory tracking in QUAV systems when faced with external disturbances by introducing a robust, two-tier control system based on sliding mode technology. For position control, this approach utilizes a virtual sliding mode control signal to enhance tracking precision and includes adaptive mechanisms to adjust for changes in mass and external disruptions. In controlling the attitude subsystem, the method employs a sliding mode control framework that secures system stability and compliance with intermediate commands, eliminating the reliance on precise models of the inertia matrix. Furthermore, this study incorporates a deep learning approach that combines Particle Swarm Optimization (PSO) with the Long Short-Term Memory (LSTM) network to foresee and mitigate trajectory tracking errors, thereby significantly enhancing the reliability and safety of mission operations. The robustness and effectiveness of this innovative control strategy are validated through comprehensive numerical simulations.

An Augmented Reality Visualization System for Simulated Multirotor Aerial Vehicles

Multirotors Aerial Vehicles are special class of Unmanned Aerial Vehicles with many practical applications. The growing demand for this class of aircraft requires tools that speed up their development. Simulated environments have gained increasing importance, as they facilitate testing and prototyping solutions, where virtual environments allow real-time interaction with simulated models, with similar behavior to real systems. More recently, the use of Augmented Reality has allowed an increasing experience of immersion and integration between the virtual world and a real scenario. This work proposes the use of Augmented Reality technology and a simulated model of a multirotor to create an interactive flight environment, aiming to improve the user experience in the analysis of simulated models. For this purpose, a smartphone was adopted as a hardware platform, a game engine is used as a basis for the development of the Augmented Reality application, that represents a numerical simulation of the flight dynamics and the control system of a multirotor, and a game controller is adopted for user interaction. The resulting system demonstrates that Augmented Reality is a viable technology that can be used to increase the possibilities of evaluating simulated systems.

Comprehensive reconstructions and predictive control for quadrotor UAV information gathering tracking missions based on fully actuated system approaches

This paper introduces a novel approach to the comprehensive reconstruction and predictive control (PC) of the quadrotor UAV for information-gathering missions, employing fully actuated system (FAS) approaches. Unlike conventional PC methods applied to a quadrotor UAV with hybrid constraints, our work integrates reconstructions of the system model, hybrid constraints, and the receding horizon performance index into to an integrated tracking control scheme within the FAS-PC framework. Specifically, the under-actuated quadrotor UAV model is reconstructed into a full-actuated model to inject full-actuation properties. And the implicit hybrid constraints that arise from the model reconstruction are explicitly transformed and decoupled. Simultaneously, the cascaded predictive algorithm is established that the new time-varying input constraints are solved in each predictive horizon, and then the nonlinear optimization problem is decoupled into four linear convex optimization problems subject to the corresponding decoupled linear constraints and the pre-addressed input constraints. Within this framework, the intrinsic complexities, nonlinearities, and interdependencies of the quadrotor UAV system model, along with hybrid constraints and the optimization dilemma, are considerably diminished. This reduction significantly eases computational demands, enabling satisfactory real-time performance. Furthermore, the selection of predictive parameters guarantees the stability of the resultant tracking error closed-loop system. Finally, the efficacy of the proposed method is validated through two sets of flight missions, conducted via simulation and practical experimentation, respectively.

Robust Finite-Time Tracking Control Based on Disturbance Observer for an Uncertain Quadrotor under External Disturbances

In this paper, a robust flight control system is proposed for an autonomous quadrotor to quickly and accurately achieve the targeted trajectories. A novel supertwisting nonsingular terminal sliding mode control (STNTSMC) has been developed to ensure that the tracking errors vanish in a short finite-time. A nonlinear disturbance observer (DO) is incorporated into the control system to estimate the unknown external perturbations and to strengthen the system’s robustness. The Lyapunov theory is used to verify the closed-loop stability of the synthesized controller. Moreover, processor-in-the-loop (PIL) implementations are performed to validate the efficacy of the suggested method. The merit of the proposed DO-STNTSMC is evaluated under multiple flight scenarios. The obtained results demonstrate that the proposed controller has a highly reduced tracking error and strong robustness against random parameter changes and external disturbances, compared to conventional nonsingular terminal sliding mode control. Finally, experimental tests are conducted to validate the performance of the suggested method.

Evaluating the Forest Ecosystem through a Semi-Autonomous Quadruped Robot and a Hexacopter UAV

Accurate and timely monitoring is imperative to the resilience of forests for economic growth and climate regulation. In the UK, forest management depends on citizen science to perform tedious and time-consuming data collection tasks. In this study, an unmanned aerial vehicle (UAV) equipped with a light sensor and positioning capabilities is deployed to perform aerial surveying and to observe a series of forest health indicators (FHIs) which are inaccessible from the ground. However, many FHIs such as burrows and deadwood can only be observed from under the tree canopy. Hence, we take the initiative of employing a quadruped robot with an integrated camera as well as an external sensing platform (ESP) equipped with light and infrared cameras, computing, communication and power modules to observe these FHIs from the ground. The forest-monitoring time can be extended by reducing computation and conserving energy. Therefore, we analysed different versions of the YOLO object-detection algorithm in terms of accuracy, deployment and usability by the EXP to accomplish an extensive low-latency detection. In addition, we constructed a series of new datasets to train the YOLOv5x and YOLOv5s for recognising FHIs. Our results reveal that YOLOv5s is lightweight and easy to train for FHI detection while performing close to real-time, cost-effective and autonomous forest monitoring.

Synthesized Landing Strategy for Quadcopter to Land Precisely on a Vertically Moving Apron

Quadcopter unmanned aerial vehicles have become increasingly popular for various real-world applications, and a significant body of literature exists regarding the improvement of their flight capabilities to render them fully autonomous. The precise landing onto moving platforms, such as ship decks, is one of the remaining challenges that is largely unresolved. The reason why this operation poses a considerable challenge is because landing performance is considerably degraded by the ground effect or external disturbances. In this paper, we propose a synthesized landing algorithm that allows a quadcopter to land precisely on a vertically moving pad. Firstly, we introduce a disturbance observer-based altitude controller that allows the vehicle to perform robust altitude flight in the presence of external disturbances and the ground effect, strictly proving the system’s stability using Lyapunov’s theory. Secondly, we derive an apron state estimator to provide information on the landing target’s relative position. Additionally, we propose a landing planner to ensure that the landing task is completed in a safe and reliable manner. Finally, the proposed algorithms are implemented in an actual quadcopter, and we demonstrate the effectiveness and applicability of our method through real flight experiments.

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.