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"Flight control Simulation methods."
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Flight mechanics modeling and analysis
\"Flight Mechanics Modelling and Analysis comprehensively covers flight mechanics and flight dynamics using a systems approach. The book focuses on applied mathematics and control theory in its discussion of flight mechanics to build a strong foundation for solving design and control problems in the areas of flight simulation and flight data analysis. The second edition has been expanded to include two new chapters and coverage of aeroservoelastic topics and engineering mechanics, presenting more concepts of flight control and aircraft parameter estimation. The book is intended for senior undergraduate aerospace students taking Aircraft Mechanics, Flight Dynamics & Controls, and Flight Mechanics courses. It will also be of interest to research students and R&D project-scientists of the same disciplines. Including end-of-chapter exercises and illustrative examples with a MATLAB® based approach, the book also includes a Solutions Manual and Figure Slides for adopting instructors. Features : covers flight mechanics, flight simulation, flight testing, flight control, and aeroservoelasticity. Features artificial neural network- and fuzzy logic-based aspects in modelling and analysis of flight mechanics systems : aircraft parameter estimation, and reconfiguration of control. Focuses on a systems-based approach. Includes two new chapters, numerical simulation examples with MATLAB® based implementations, and end-of-chapter exercises. Includes a Solutions Manual and Figure Slides for adopting instructors\"-- Provided by publisher.
Book
Evaluating the effectiveness of flight simulator training on developing perceptual-motor skills among flight cadets: a pilot study
This study aimed to evaluate the impact of flight simulator training on the enhancement of perceptual-motor skills among flight cadets. Perceptual-motor skills act as a crucial link through which pilots translate their environmental perceptions into precise maneuvers, a capability that is particularly vital in dynamic and unpredictable flight environments. A total of forty cadets participated in the experiment and were randomly assigned to either the Traditional Training Group (TTG) or the Efficient Training Group (ETG). The TTG received individual training on training aircraft under the supervision of an instructor, while the ETG trained on expert aircraft using a full-scenario memory replay training system enhanced with multimodal information feedback. The simulations were conducted at the Aeronautical University simulation laboratory, configured as a self-developed Flight Skill Accelerated Training Simulator. Both groups completed eight weeks of simulated flight training, which included testing scenarios such as takeoff, flight control, landing, and carrier landing. Results indicated that the ETG outperformed the TTG in the takeoff, flight control, landing tasks, and carrier landing tasks. Furthermore, the ETG demonstrated a faster training pace across all tasks. These findings suggest that our independently developed accelerated flight skills training system can effectively expedite motor skill acquisition among flight cadets, enhance flight performance, and holds promising potential for broad application in various flight training contexts.
Journal Article
Autonomous aerial obstacle avoidance using LiDAR sensor fusion
The obstacle avoidance problem of unmanned aerial vehicle (UAV) mainly refers to the design of a method that can safely reach the target point from the starting point in an unknown flight environment. In this paper, we mainly propose an obstacle avoidance method composed of three modules: environment perception, algorithm obstacle avoidance and motion control. Our method realizes the function of reasonable and safe obstacle avoidance of UAV in low-altitude complex environments. Firstly, we use the light detection and ranging (LiDAR) sensor to perceive obstacles around the environment. Next, the sensor data is processed by the vector field histogram (VFH) algorithm to output the desired speed of drone flight. Finally, the expected speed value is sent to the quadrotor flight control and realizes autonomous obstacle avoidance flight of the drone. We verify the effectiveness and feasibility of the proposed method in 3D simulation environment.
Journal Article
Active control of flexible spacecraft in orbit based on partial differential equations
Flexible spacecraft possess the ability to adapt to complex environments and use energy more efficiently, offering enhanced flexibility and stability in space missions, particularly in tasks with significant external disturbances such as deep space exploration and satellite attitude control. However, vibration suppression in flexible spacecraft remains a critical challenge. This study addresses the problem of vibration suppression in flexible spacecraft systems under external disturbances and input constraints. First, a partial differential equation (PDE) with boundary initial conditions is derived using Hamilton’s principle, accurately describing the dynamic characteristics of the flexible structure. A backstepping controller based on the Nassbaum function and a disturbance observer is then designed to ensure system stability in the presence of input constraints and external disturbances. A Lyapunov function is constructed, and appropriate control parameters are selected to further guarantee system stability. Numerical simulations confirm the superiority of the proposed control method, with results showing an 80 % reduction in settling time and a 94 % decrease in peak overshoot compared to conventional PD control. The proposed scheme significantly enhances the performance and stability of flexible spacecraft systems, demonstrating its potential for improving spacecraft dynamics in challenging space environments.
Journal Article
Civil Airplane Safety Awareness Technology Using Virtual Flight Method
Civil airplanes encounter unpredictable safety risks due to uncertain environmental disturbances, mechanical failures, and pilot mis-operations. This paper develops a virtual flight method (VFM) consisting of a series of techniques including flight motion simulation, flight command simulation, flight control simulation, and flight environment simulation. Moreover, a safety perception technique is established using fuzzy safety constraints, which transfers the decoupled analysis of micro-level aircraft state parameters to the coupled analysis of macro-level global system parameters. This integrated approach enables virtual flight operations and safety situation awareness for civil aircraft within the ‘Human–Machine–Environment’ triad under the influences of complex factors. The takeoff and climb scenario of the Cessna Citation 550 aircraft is selected as a case study to validate the feasibility of the proposed safety awareness technology. Results illustrate the capability to effectively capture the aircraft’s flight characteristics and safety status of the civil aircraft under various operational conditions. The safe operational envelope within specific scenarios is also determined.
Journal Article
Remote Controlled Autonomous Microgravity Lab Platforms for Drug Research in Space
Research conducted in microgravity conditions has the potential to yield new therapeutics, as advances can be achieved in the absence of phenomena such as sedimentation, hydrostatic pressure and thermally-induced convection. The outcomes of such studies can significantly contribute to many scientific and technological fields, including drug discovery. This article reviews the existing traditional microgravity platforms as well as emerging ideas for enabling microgravity research focusing on SpacePharma’s innovative autonomous remote-controlled microgravity labs that can be launched to space aboard nanosatellites to perform drug research in orbit. The scientific literature is reviewed and examples of life science fields that have benefited from studies in microgravity conditions are given. These include the use of microgravity environment for chemical applications (protein crystallization, drug polymorphism, self-assembly of biomolecules), pharmaceutical studies (microencapsulation, drug delivery systems, behavior and stability of colloidal formulations, antibiotic drug resistance), and biological research, including accelerated models for aging, investigation of bacterial virulence , tissue engineering using organ-on-chips in space, enhanced stem cells proliferation and differentiation.
Journal Article
Reinforcement learning for UAV flight controls: Evaluating continuous space reinforcement learning algorithms for fixed-wing UAVs
Flight controls are experiencing a major shift with the integration of reinforcement learning (RL). Recent studies have demonstrated the potential of RL to deliver robust and precise control across diverse applications, including the flight control of fixed-wing unmanned aerial vehicles (UAVs). However, a critical gap persists in the rigorous evaluation and comparative analysis of leading continuous-space RL algorithms. This paper aims to provide a comparative analysis of RL-driven flight control systems for fixed-wing UAVs in dynamic and uncertain environments. Five prominent RL algorithms that include Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO) and Soft Actor-Critic (SAC) are evaluated to determine their suitability for complex UAV flight dynamics, while highlighting their relative strengths and limitations. All the RL agents are trained in a same high fidelity simulation environment to control pitch, roll and heading of the UAV under varying flight conditions. The results demonstrate that RL algorithms outperformed the classical PID controllers in terms of stability, responsiveness and robustness, especially during environmental disturbances such as wind gusts. The comparative analysis reveals that the SAC algorithm achieves convergence in 400 episodes and maintains a steady-state error below 3%, offering the best trade-off among the evaluated RL algorithms. This analysis aims to provide valuable insight for the selection of suitable RL algorithm and their practical integration into modern UAV control systems.
Journal Article
Robust performance comparison of PMSM for flight control applications in more electric aircraft
This paper describes a robust performance comparison of flight control actuation controllers based on a permanent magnet synchronous motor (PMSM) in more electric aircraft (MEA). Recently, the PMSM has become a favorite for the flight control applications of more electric aircraft (MEA) due to their improved efficiency, higher torque, less noise, and higher reliability as compared to their counterparts. Thus, advanced nonlinear control techniques offer even better performance for the control of PMSM as noticed in this research. In this paper, three nonlinear approaches i.e. Feedback Linearization Control (FBL) through the cancellation of the non-linearity of the system, the stabilization of the system via Backstepping Control (BSC) using the Lyapunov candidate function as well as the robust performance with chattering minimization by applying the continuous approximation based Sliding Mode Control (SMC) are compared with generalized Field-Oriented Controller (FOC). The comparison of FOC, FBL, BSC and SMC shows that the nonlinear controllers perform well under varying aerodynamic loads during flight. However, the performance of the sliding mode control is found superior as compared to the other three controllers in terms of better performance characteristics e.g. response time, steady-state error etc. as well as the control robustness in the presence of the uncertain parameters of the PMSM model and variable load torque acting as a disturbance. In essence, the peak of the tolerance band is less than 20% for all nonlinear and FOC controller, while it is less than 5% for SMC. Steady state error for the SMC is least (0.01%) as compared to other three controllers. Moreover, the SMC controller is able to withstand 50% parameter variation and loading torque of 10 N.m without significant changes in performance. Six simulation scenarios are used to analyze the performance and robustness which depict that the sliding mode controller performs well in terms of the desired performance for MEA application.
Journal Article
H.sub.2 controller design for a kestrel-inspired ornithopter operating in extreme weather
Unsteady atmospheric disturbances significantly compromise the flight stability of ornithopters, necessitating advanced turbulence-mitigation strategies. Drawing inspiration from the kestrel's covert feathers, this study presents the modeling, control synthesis, and performance evaluation of a kestrel-inspired ornithopter equipped with an active covert-feather-based Gust Mitigation System (GMS). A reduced-order multibody bond-graph model (BGM) is derived from the full flapping-wing dynamics, capturing the coupled aero-elastic interaction between the main body, rigid wings, propulsion system, and feather actuation mechanism. Stability analysis reveals the presence of unstable internal dynamics, motivating the design of an Hâ optimal controller to ensure robust stability and fast disturbance rejection. The controller's performance is evaluated against a Linear Quadratic Regulator (LQR) under vertical gust inputs ranging from 0 m/s to 20 m/s using MATLAB/Simulink simulations. Quantitative results indicate that the Hâ-augmented GMS installed ornithopter reduces gust-induced forces by up to 32% and achieving faster state convergence within 1.1 seconds. The simulation results exhibit close agreement with previously reported findings, validating the fidelity of the proposed model and control framework. This work represents the first complete kestrel-inspired ornithopter integrating a bio-inspired GMS with Hâ optimal control, offering a validated and scalable foundation for next-generation adaptive ornithopters capable of maintaining stability in unsteady atmospheric environments.
Journal Article
Robust finite-time anti-swing control for quadrotor slung-load system based on compensation function observer
In this work, a robust finite-time anti-swing controller is proposed for the quadrotor slung-load system subject to external disturbances. To stabilize the swing angles of the payload, an energy function incorporating both kinetic energy and potential energy is constructed such that the swing angles gradually converge to zero. Since the swing angle information is integrated into the energy function, the global convergence of the quadrotor slung-load system is improved. Meanwhile, a novel compensation function observer is designed to suppress unknown disturbances, thereby enhancing the robustness of the closed-loop system. Compared with the traditional extended state observer method, the developed compensation function observer achieves a faster convergence rate and higher observation accuracy with a smaller observer gain. To further improve the fast convergence performance of the entire closed-loop system, the finite-time control technique is adopted to design the robust anti-swing flight control scheme based on the Lyapunov stability theory and backstepping approach. This control scheme guarantees that all error signals of the quadrotor slung-load system are uniformly ultimately bounded. Finally, simulation verification is performed and comparison results are provided to illustrate the superior effectiveness of the presented control algorithm.
Journal Article