36,862 research outputs found
UAV Model-based Flight Control with Artificial Neural Networks: A Survey
No full textModel-Based Control (MBC) techniques have dominated flight controller designs for Unmanned Aerial Vehicles (UAVs). Despite their success, MBC-based designs rely heavily on the accuracy of the mathematical model of the real plant and they suffer from the explosion of complexity problem. These two challenges may be mitigated by Artificial Neural Networks (ANNs) that have been widely studied due to their unique features and advantages in system identification and controller design. Viewed from this perspective, this survey provides a comprehensive literature review on combined MBC-ANN techniques that are suitable for UAV flight control, i.e., low-level control. The objective is to pave the way and establish a foundation for efficient controller designs with performance guarantees. A reference template is used throughout the survey as a common basis for comparative studies to fairly determine capabilities and limitations of existing research. The end-result offers supported information for advantages, disadvantages and applicability of a family of relevant controllers to UAV prototypes
Using UAVs for Future Mission on Mars
No full textIn the past years, rovers, landers, and orbiters played a fundamental role in the frame of planetary exploration on
the Moon and on Mars. Today, considering Unmanned Aerial Vehicles (UAVs) become a promising challenge in
future scientific missions. Particularly, the successful land on Mars in 2021 of the NASA helicopter “Ingenuity” and
its successful flights on the Martian surface open the door to a new way of exploring Mars by overcoming the
limitations of unmanned ground vehicles. Indeed, UAVs represent efficient alternatives for in-deep monitoring of the
surface of Mars, also allowing for the collection of detailed image data while navigating in both close and wide
areas. These aspects are not always feasible when using surface vehicles, as they constantly deal with ground
obstacles that in turn cause the vehicle to move at a slow pace, thus limiting the ground crossing and the line of sight.
On the other hand, orbiters always provided aerial images of Mars, but with insufficient spatial resolution when
compared to the data collected by drones. Therefore, when operating on Mars, the drone must be able to perform
fully autonomous navigation tasks to efficiently explore and map the surrounding area. In the presented research, a
general trade-off between multiple rotorcraft architectures is presented and, after the design choice of the hexacopter
configuration, the hardware is modeled using both 3D CAD Solidworks and Blender 3.1 tools. Then, to simulate the
autonomous navigation task, a preliminary analysis is performed together with the validation of the path planning
method and the 2D and 3D mapping algorithms using the Robot Operating System (ROS) and the Gazebo tool. The
hexacopter functionalities along with the navigation sensors are then simulated and tested in a Mars-looking
environment for developing comprehensive navigation approaches for future exploration of Mars
Mars Sample Return Mission: Mars Ascent Vehicle Propulsion Design
No full textThe aim of this research is to analyze a potential Mars Sample Return (MSR) mission through the study of an optimized design of the Mars Ascent Vehicle (MAV) propulsion system. The main goal of the MSR mission is to return to Earth samples of rocks and dust collected by a rover operating on the surface of Mars, and conveyed to the MAV into an Orbit Sample (OS) canister. The MAV must accomplish an initial ascent phase from the Mars surface to a circular Low Mars Orbit (LMO) with a radius of 500 Km and 30° inclination, and then with its second stage it must circularize into the target LMO where it releases the OS payload. A combination of the MAV and a second vehicle, the Mars Earth Return Vehicle (MERV) orbiter, is required to fulfill the final return phase from Mars to the Earth. After completing three different phases of rendezvous operations, with a final Hohmann Transfer the MERV is able to bring the OS to Earth with its payload. A spreadsheet model enables the evaluation of two different MAV architecture: a two-stage solid rocket, and a two-stage hybrid rocket. The study is based on the main rocket science equations, including the Tsiolkovsky Rocket Equation that calculates the change in velocity Delta V for the two stages of the MAV and the amount of propellant needed for both stages. From the analysis it can be noted that the two-stage hybrid design has significant advantages, firstly in terms of Gross Lift Off Mass GLOM (270 Kg) when compared to the solid solution (355 Kg). The hybrid rocket also has lower mass by up to 60 Kg since it does not require a thermal igloo. Finally, the mass fractions for both stages are comparable, and the required Delta V for the hybrid stages are less than those needed for the solid, allowing considerable fuel savings. The hybrid solution is ultimately preferred, considering the best performance related to the thermal fuel properties enabling the MAV to safely operated in the harsh Martian environment
3D Real-Time Energy Efficient Path Planning for a Fleet of Fixed-Wing UAVs
No full textUAV path planning requires finding an optimal (or sub-optimal) collision free path in a cluttered environment, while taking into account geometric, physical and temporal constraints, eventually allowing UAVs to perform their tasks despite several uncertainty sources. This paper reviews the current state-of-the-art in path planning, and subsequently introduces a novel node-based algorithm based on the called EEA*. EEA* is based on the A* Search algorithm and aims at mitigating some of its key limitations. The proposed EEA* deals with 3D environments, it provides robustness quickly converging to the solution, it is energy efficient and it is realtime implementable and executable. Along with the proposed EEA*, a local path planner is developed to cope with unknown dynamic threats in the environment. Applicability and effectiveness is first demonstrated via simulated experiments using a fixed-wing UAV that operates in different mountain-like 3D environments in the presence of several unknown dynamic obstacles. Then, the algorithm is applied in a multi-agent setting with three UAVs that are commanded to follow their respective paths in a safe way. The energy efficiency of the EEA* algorithm has also been tested and compared with the conventional A* algorithm
Linear Quadratic Regulator: A Simple Thrust Vector Control System for Rockets
No full textThe paper focuses on developing, tuning, and testing a controller for a two-stage finless rocket during its boost phase that is based on the Linear Quadratic Regulator (LQR) optimal control method. This is accomplished by deriving and adopting a simplified rigid body rocket model that represents accurately its physical properties and the corresponding aerodynamic forces acting on the rocket system during the flight phase. The launcher is commanded through the control input thrust gimbal angle δ to the desired altitude using the implemented LQR-based controller. Emphasis is given to the Thrust Vector Control (TVC) system, and to the minimization of the drift caused by wind gust disturbance phenomena, which may result in a sideway motion of the rocket, and, consequently, in deviating from its desired trajectory; this is addressed, and it is overcome by considering the output parameters expressed in terms of the pitch angle, pitch rate (or angular body rate) and drift. The linearized state-space model is validated for analysis and design compensation of the pitch control logic of the ascent flight control system. The derived algorithm is, then, implemented in a Matlab/Simulink setting to demonstrate that the LQR controller provides closed-loop dynamic tracking, while the tuning of the LQR controller through the weighting matrices Q and R allows for simulating and testing how the variation of the gain directly impacts the performance of the closed-loop system and, in turn, the controller
A Survey of Artificial Neural Networks with Model-based Control Techniques for Flight Control of Unmanned Aerial Vehicles
No full textThrust Vector Controller Comparison for a Finless Rocket
No full textThe paper focuses on comparing applicability, tuning, and performance of different controllers implemented and tested on a finless rocket during its boost phase. The objective was to evaluate the advantages and disadvantages of each controller, such that the most appropriate one would then be developed and implemented in real-time in the finless rocket. The compared controllers were Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), and Proportional Integral Derivative (PID). To control the attitude of the rocket, emphasis is given to the Thrust Vector Control (TVC) component (sub-system) through the gimballing of the rocket engine. The launcher is commanded through the control input thrust gimbal angle δ, while the output parameter is expressed in terms of the pitch angle θ. After deriving a linearized state–space model, rocket stability is addressed before controller implementation and testing. The comparative study showed that both LQR and LQG track pitch angle changes rapidly, thus providing efficient closed-loop dynamic tracking. Tuning of the LQR controller, through the Q and R weighting matrices, illustrates how variations directly affect performance of the closed-loop system by varying the values of the feedback gain (K). The LQG controller provides a more realistic profile because, in general, not all variables are measurable and available for feedback. However, disturbances affecting the system are better handled and reduced with the PID controller, thus overcoming steady-state errors due to aerodynamic and model uncertainty. Overall controller performance is evaluated in terms of overshoot, settling and rise time, and steady-state error
Urban monitoring of smart communities using UAS
No full textUnmanned Aircraft Systems (UAS) have become prevalent for a wide spectrum of civilian applications. Support tools and technologies for UAS-based monitoring of smart cities and communities are under development, where Unmanned Aerial Vehicles (UAVs) are the main means of implementation. UAVs provide the eye-in-the-sky alternative to ground-based monitoring, contributing to safety, early anomaly detection and possibly prediction, and improving everyday quality of life with little disruption of, and interference with, humans. This paper presents a simulated real-world environment and accurate model of the city of Turin (Italy) and implements it in the Gazebo software physics simulator, with the aim of monitoring the city. The environment allows for piloting and navigating UAVs with on-board cameras and other onboard sensors through a Ground Control Station (GCS), or through manual direct piloting. Simulated scenarios illustrate monitoring over the perimeter of the urban area, autonomous flight, partially autonomous flight and manual piloting of the UAV. It is expected that obtained results will pave the way to developing complex simulated world environments where candidate scenarios will be developed and executed before real-world testing
A Comparative Study on Differential Evolution with Other Heuristic Methods for Continuous Optimization
No full textIn this paper, we describe an optimization method based on differential evolution (DE). It shows good convergence properties with few parameters. However, the appropriate selection of the parameters is a difficult task. Hence, we here analyze the performance indexes of the DE algorithm to set the control parameters. Moreover, to identify the best parameter intervals, the DE approach is first compared to two different Particle Swarm Optimization (PSO) algorithms and then to a recent adaptive genetic algorithm (DABGA). The optimization of benchmark functions shows that the DE algorithm performs better than PSO and DABGA methods
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