DECENTRALIZED ROBUST NONLINEAR MODEL PREDICTIVE CONTROLLER FOR UNMANNED AERIAL SYSTEMS

The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1 A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2 A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3 An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4 A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible

A review of variable-pitch propellers and their control strategies in aerospace systems

The relentless pursuit of aircraft flight efficiency has thrust variable-pitch propeller technology into the forefront of aviation innovation. This technology, rooted in the ancient power unit of propellers, has found renewed significance, particularly in the realms of unmanned aerial vehicles and urban air mobility. This underscores the profound interplay between visionary aviation concepts and the enduring utility of propellers. Variable-pitch propellers are poised to be pivotal in shaping the future of human aviation, offering benefits such as extended endurance, enhanced maneuverability, improved fuel economy, and prolonged engine life. However, with additional capabilities come new technical challenges. The development of an online adaptive control of variable-pitch propellers that does not depend on an accurate dynamic model stands as a critical imperative. Therefore, a comprehensive review and forward-looking analysis of this technology is warranted. This paper introduces the development background of variable-pitch aviation propeller technology, encompassing diverse pitch angle adjustment schemes and their integration with various engine types. It places a central focus on the latest research frontiers and emerging directions in pitch control strategies. Lastly, it delves into the research domain of constant speed pitch control, articulating the three main challenges confronting this technology: inadequacies in system modeling, the intricacies of propeller-engine compatibility, and the impact of external, time-varying factors. By shedding light on these multifaceted aspects of variable-pitch propeller technology, this paper serves as a resource for aviation professionals and researchers navigating the intricate landscape of future aircraft development

Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization

Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far more limited in their flight envelope as compared to experienced human pilots, thereby restricting the conditions UAVs can operate in and the types of missions they can accomplish autonomously. This paper proposes a deep reinforcement learning (DRL) controller to handle the nonlinear attitude control problem, enabling extended flight envelopes for fixed-wing UAVs. A proof-of-concept controller using the proximal policy optimization (PPO) algorithm is developed, and is shown to be capable of stabilizing a fixed-wing UAV from a large set of initial conditions to reference roll, pitch and airspeed values. The training process is outlined and key factors for its progression rate are considered, with the most important factor found to be limiting the number of variables in the observation vector, and including values for several previous time steps for these variables. The trained reinforcement learning (RL) controller is compared to a proportional-integral-derivative (PID) controller, and is found to converge in more cases than the PID controller, with comparable performance. Furthermore, the RL controller is shown to generalize well to unseen disturbances in the form of wind and turbulence, even in severe disturbance conditions.Comment: 11 pages, 3 figures, 2019 International Conference on Unmanned Aircraft Systems (ICUAS

Unmanned Aerial Systems: Research, Development, Education & Training at Embry-Riddle Aeronautical University

With technological breakthroughs in miniaturized aircraft-related components, including but not limited to communications, computer systems and sensors, state-of-the-art unmanned aerial systems (UAS) have become a reality. This fast-growing industry is anticipating and responding to a myriad of societal applications that will provide new and more cost-effective solutions that previous technologies could not, or will replace activities that involved humans in flight with associated risks. Embry-Riddle Aeronautical University has a long history of aviation-related research and education, and is heavily engaged in UAS activities. This document provides a summary of these activities, and is divided into two parts. The first part provides a brief summary of each of the various activities, while the second part lists the faculty associated with those activities. Within the first part of this document we have separated UAS activities into two broad areas: Engineering and Applications. Each of these broad areas is then further broken down into six sub-areas, which are listed in the Table of Contents. The second part lists the faculty, sorted by campus (Daytona Beach-D, Prescott-P and Worldwide-W) associated with the UAS activities. The UAS activities and the corresponding faculty are cross-referenced. We have chosen to provide very short summaries of the UAS activities rather than lengthy descriptions. If more information is desired, please contact me directly, or visit our research website (https://erau.edu/research), or contact the appropriate faculty member using their e-mail address provided at the end of this document

2008 and 2009 Research and Engineering Annual Report

Selected research and technology activities at NASA Dryden Flight Research Center are summarized. These activities exemplify the Center's varied and productive research efforts

Nonlinear model order reduction and control of very flexible aircraft

In the presence of aerodynamic turbulence, very flexible aircraft exhibit large deformations and as a result their behaviour is characterised as intrinsically nonlinear. These nonlinear effects become significant when the coupling of rigid–body motion with nonlinear structural dynamics occurs and needs to be taken into account for flight control system design. However, control design of large–order nonlinear systems is challenging and normally, is limited by the size of the system. Herein, nonlinear model order reduction techniques are used to make feasible a variety of linear and nonlinear control designs for large–order nonlinear coupled systems. A series of two–dimensional and three–dimensional test cases coupled with strip aerodynamics and Computational– Fluid–Dynamics is presented. A systematic approach to the model order reduction of coupled fluid–structure–flight dynamics models of arbitrary fidelity is developed. It uses information on the eigenspectrum of the coupled-system Jacobian matrix and projects the system through a Taylor series expansion, retaining terms up to third order, onto a small basis of eigenvectors representative of the full–model dynamics. The nonlinear reduced–order model representative of the dynamics of the nonlinear full–order model is then exploited for parametric worst–case gust studies and a variety of control design for gust load alleviation and flutter suppression. The control approaches were based on the robust H∞ controller and a nonlinear adaptive controller based on the model reference adaptive control scheme via a Lyapunov stability approach. A two degree–of–freedom aerofoil model coupled with strip theory and with Computational–Fluid–Dynamics is used to evaluate the model order reduction technique. The nonlinear effects are efficiently captured by the nonlinear model order reduction method. The derived reduced models are then used for control synthesis by the H∞ and the model reference adaptive control. Furthermore, the numerical models developed in this thesis are used for the description of the physics of a wind–tunnel model at the University of Liverpool and become the benchmark to design linear and nonlinear controllers. The need for nonlinear control design was demonstrated for the wind–tunnel model in simulation. It was found that for a wind–tunnel model with a cubic structural nonlinearity in the plunge degree–of–freedom, conventional linear control designs were inadequate for flutter suppression. However, a nonlinear controller was found suitable to increase the flight envelope and suppress the flutter. A large body of work dealt with the development of a numerical framework for the simulation of the flight dynamics of very flexible aircraft. Geometrically–exact nonlinear beam structural models were coupled with the rigid–body, the flight dynamics degrees–of–freedom and the strip theory aerodynamics, for the description of the nonlinear physics of free–flying aircraft. The flexibility effects of these vehicles on the flight dynamic response is quantified. It is found that different angle of attack and control input rotation is needed to trim a flexible aircraft and that a rigid analysis is not appropriate. Furthermore, it is shown that the aircraft flexibility has an impact on the flight dynamic response and needs to be included. The fully coupled models are consequently reduced in size by the nonlinear model reduction technique for a cheaper and a simpler computation of a variety of linear and nonlinear automatic control designs that are applied on the full–order nonlinear models inside the developed framework for gust load alleviation. The approach is tested on a Global Hawk type unmanned aerial vehicle developed by DSTL, on a HALE full aircraft configuration, and on a very large flexible free–flying wing. A comparison of the developed control algorithms is carefully addressed with the adaptive controller achieving better gust loads alleviation in some cases. Finally, future possible implementations and ideas related to the nonlinear model order reduction and the control design of flexible aircraft are discussed

비대칭 가변스팬 모핑 무인 항공기의 자체스케줄 파라미터 가변 제어

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2023. 2. 김유단.In this dissertation, a novel framework for flight control of a morphing unmanned aerial vehicle (UAV) is proposed. The proposed method uses asymmetric span morphing for lateral-directional motion control considering the dynamic characteristics of the morphing actuators while exploiting the advantages of symmetric span morphing for longitudinal flight performance enhancement. The proposed control system is self-scheduled based on linear parameter-varying (LPV) methods, which guarantees stability and performance for the variations of the morphing configuration and the flight condition. Therefore, the morphing UAV is allowed to swiftly metamorphose into the optimal configuration to maximize the system-level benefit according to the maneuvering command and the flight condition. First, a high-fidelity nonlinear model of an asymmetric variable-span morphing UAV is obtained from the NASA generic transport model. The impacts of morphing on the center of mass, inertia matrix, and aerodynamic coefficients are modeled based on the asymmetrically damaged wing model. The span variation ratios of the left and right wings are decomposed into symmetric and asymmetric morphing parameters, which are considered as the scheduling parameter and the control input, respectively. The nonlinear model is decoupled and linearized to obtain point-wise linear time-invariant (LTI) models for the longitudinal and lateral-directional motions throughout the grid points over the entire rectangularized scheduling parameter domain. The LPV model of the morphing UAV is derived for the longitudinal and lateral-directional motions by associating the family of LTI models through interpolation. Second, the longitudinal and lateral-directional control augmentation systems are designed based on LPV methods to track the normal acceleration command and the angle of sideslip and the roll rate commands, respectively. The inherent dynamic characteristics of the morphing actuator, such as low bandwidth, are considered in the control design procedure through a frequency-dependent weighting filter. The span morphing strategy to assist the intended maneuver is studied considering the impacts of morphing on various aspects. Numerical simulations are performed to demonstrate the effectiveness of the proposed control scheme for pushover-pullup maneuver and high-g turn. Finally, the longitudinal and lateral-directional autopilots are designed based on LPV methods to track the airspeed and altitude commands and the angle of sideslip and roll angle commands, respectively. A nonlinear guidance law is coupled with the autopilots to enable three-dimensional trajectory tracking. Numerical simulation results for the trajectory-tracking flight show that the proposed controller shows satisfactory performance, while the closed-loop system using the conventional gain-scheduled controller may lose stability when the scheduling parameter varies rapidly or widely.본 논문에서는 모핑 무인 항공기(unmanned aerial vehicle: UAV)의 비행 제어를 위한 새로운 프레임워크가 제안된다. 제안된 기법은 모핑 구동기의 동적 특성을 고려한 횡방향축(lateral-directional) 운동 제어를 위해 비대칭 스팬 모핑을 사용하고 종축(longitudinal) 비행 성능 향상을 위해 대칭 스팬 모핑의 이점을 활용한다. 또한 설계된 제어 시스템은 선형 파라미터 가변(linear parameter-varying: LPV) 기법을 기반으로 제어기 이득이 자체적으로 스케줄링 되며 모핑 형상 및 비행 조건의 임의의 변화에 대해 안정성과 성능을 엄밀하게 보장한다. 따라서 모핑 UAV는 기동 명령과 비행 조건에 따라 안정성을 상실할 우려 없이 시스템 수준의 이점을 극대화하는 동시에 내부 루프 안정화를 위한 제어에 기여하도록 최적의 형상으로 신속하게 변형될 수 있다. 첫째, NASA GTM(generic transport model)으로부터 비대칭 가변 스팬 모핑 UAV의 고충실도(high-fidelity) 비선형 모델이 획득된다. 모핑이 질량 중심, 관성 행렬 및 공기역학 계수에 미치는 영향은 날개가 비대칭적으로 손상된 모델을 기반으로 도출된다. 좌우 날개의 스팬 변화율은 대칭 및 비대칭 모핑 파라미터로 분해되며, 두 모핑 파라미터는 각각 스케줄링 파라미터 및 제어 입력으로 간주된다. 비선형 모델을 종축 및 횡방향축 운동으로 분리하고 직사각형 형태의 스케줄링 파라미터 영역의 각 격자점에서 선형화함으로써 각 점에 대한 선형 시불변(linear time-invariant: LTI) 모델이 얻어진다. LTI 모델 집합에 보간(interpolation)을 적용하면 종축 및 횡방향축 운동에 대한 모핑 UAV의 LPV 모델이 얻어진다. 둘째, 수직 가속도(normal acceleration) 명령과 옆미끄럼각(angle of sideslip) 및 롤 각속도 명령 추종을 위해 LPV 기법을 기반으로 종축 및 횡방향축 제어 증강 시스템(control augmentation system)이 설계된다. 이때, 제어 설계 과정에서 주파수종속(frequency-dependent) 가중치 필터를 통해 낮은 대역폭(bandwidth)과 같은 모핑 구동기 고유의 동적 특성이 고려된다. 또한 비행 특성에 대한 모핑의 다양한 영향을 고려하여 실행하고자 하는 기동을 보조하기 위한 스팬 모핑 전략이 논의된다. Pushover-pullup 기동 및 high-g turn에 대한 수치 시뮬레이션 결과를 통해 제안된 기법이 타당함을 확인할 수 있다. 마지막으로, 대기속도(airspeed) 및 고도 명령과 옆미끄럼각 및 롤 각 명령을 추종하기 위해 LPV 기법을 기반으로 종축 및 횡방향축 자동 조종 장치(autopilot)가 설계된다. 이때, 3차원 경로 추종을 위해 비선형 유도 법칙이 자동 조종 장치와 결합된다. 경로 추종 비행에 대한 수치 시뮬레이션 결과를 통해 스케줄링 파라미터의 변화 속도가 빠르거나 변화의 폭이 넓은 경우 일반적인 이득스케줄 제어기는 안정성을 상실할 수 있는 반면 제안된 기법은 만족할 만한 성능을 유지함을 확인할 수 있다.1 Introduction 1 1.1 Background and Motivation 1 1.2 Literature Review 6 1.2.1 Fixed-Wing Aircraft Implementing Morphing Technologies 6 1.2.2 Flight Control of Morphing Aircraft 7 1.2.3 Gain Scheduling Approaches to Controller Design 7 1.3 Objectives and Contributions 9 1.3.1 Objectives 9 1.3.2 Contributions 9 1.4 Dissertation Outline 11 2 Mathematical Preliminaries 13 2.1 LPV Systems 15 2.1.1 Taxonomy of Dynamical Systems 15 2.1.2 Definition of LPV Systems 15 2.1.3 LPV Modeling by Linearization 20 2.2 Gain Self-Scheduled Induced L2-Norm Control of LPV Systems 25 2.2.1 Norms of Signals and Systems 25 2.2.2 Analysis of LPV Systems 26 2.2.3 LPV Controller Design 30 2.2.4 Software for Synthesis and Analysis 30 3 Asymmetric Variable-Span Morphing UAV Model 33 3.1 Nonlinear Model of a Morphing UAV 36 3.1.1 Nominal Model of a Baseline Model 36 3.1.2 Morphing UAV Model 41 3.2 Derivation of an LPV Model of a Morphing UAV 52 3.2.1 Trim Analysis and Scheduling Parameter Selection 52 3.2.2 Pointwise Linearization of a Nonlinear Model 55 3.2.3 Linear Parameter-Varying Modeling and Analysis 58 4 CAS Design Based on LPV Method for Morphing-Assisted Maneuvers 61 4.1 Longitudinal CAS Design for Normal Acceleration Control 65 4.1.1 Performance Specifications 65 4.1.2 Controller Synthesis and Analysis 68 4.2 Lateral-Directional CAS Design for Turn Coordination and Roll Rate Control 73 4.2.1 Performance Specifications 73 4.2.2 Controller Synthesis and Analysis 75 4.3 Span Morphing Strategy 83 4.3.1 Effects of Span Morphing 83 4.3.2 Criteria for Span Variation 85 4.4 Nonlinear Simulation of Morphing-Assisted Maneuvers 86 4.4.1 High-Fidelity Flight Dynamics Simulator 86 4.4.2 Push-over and Pull-up 86 4.4.3 High-g Turn 89 5 Autopilot Design Based on LPV Methods for Morphing-Assisted Flights 109 5.1 Longitudinal Autopilot Design for Airspeed and Altitude Control 111 5.1.1 Performance Specifications 111 5.1.2 Controller Synthesis and Analysis 113 5.2 Lateral-Directional Autopilot Design for Turn Coordination and Roll Angle Control 121 5.2.1 Performance Specifications 121 5.2.2 Controller Synthesis and Analysis 123 5.3 Nonlinear Guidance Law for Trajectory Tracking 131 5.4 Nonlinear Simulation of Morphing-Assisted Flights 132 5.4.1 Waypoint Following at Low Altitude 132 5.4.2 Circular Trajectory Tracking at High Altitude 132 5.4.3 Helical Ascent under Fast Morphing 132 5.4.4 Spiral Descent with Morphing Scheduling 139 6 Conclusion 147 6.1 Concluding Remarks 147 6.2 Future Work 148박

Flight control of very flexible unmanned aerial vehicles

This thesis aims to investigate the flight control of a very flexible ying wing model already developed in the literature. The model was derived from geometrically nonlinear beam theory using intrinsic degrees of freedom and linear unsteady aerodynamics, which resulted in a coupled structural dynamics, aerodynamics, and flight dynamics description. The scenarios of trajectory tracking and autonomous landing in the presence of wind disturbance are considered in control designs. Firstly, the aeroelastic and trajectory control of this very flexible ying wing model is studied. The control design employs a two-loop PI/LADRC (proportional integral/linear active disturbance rejection control) and H1 control scheme, based on a reduced-order linear model. The outer loop employs the PI/LADRC technique to track the desired flight paths and generate attitude commands to the inner loop, while the inner loop uses H1 control to track the attitude command and computes the corresponding control inputs. The particle swarm optimization algorithm is employed for parameter optimization in the H1 control design to enhance the control effectiveness and robustness. Simulation tests conducted on the full-order nonlinear model show that the designed aeroelastic and trajectory control system achieves good performance in aspects of tracking effectiveness and robustness against disturbance rejection. Secondly, the preview-based autonomous landing control of the very flexible ying wing model using light detection and ranging (Lidar) wind measurements is studied. The preview control system follows the above two-loop control structure and is also designed based on the reduced-order linear model. The outer loop emxv ploys the same LADRC and PI algorithms to track the reference landing trajectory and vertical speed, respectively. But the inner loop is extended to introduce Lidar wind measurements at a distance in front of the aircraft, employing H1 preview control to improve disturbance rejection performance during landing. Simulation results based on the full-order nonlinear model show that the preview-based landing control system is able to land the aircraft safely and effectively, which also achieves better control performance than a baseline landing control system (without preview) with respect to landing effectiveness and disturbance rejection. Finally, the data-driven flight control of the very flexible ying wing model using Model-Free Adaptive Control (MFAC) scheme to reduce the dependence of control design on system modeling is studied. A cascaded proportional-derivative MFAC (PD-MFAC) approach is proposed to accommodate the MFAC scheme in a flight control problem, which shows better control performance over the original MFAC algorithm. Based on the PD-MFAC approach, the data-driven flight control system is developed to achieve gust load alleviation and trajectory tracking. Simulation results based on the full-order nonlinear model show that the proposed data-driven flight control system is able to properly regulate all the rigid-body and flexible modes with better effectiveness and robustness (against disturbance rejection and modeling uncertainties), compared to a baseline H1 flight control system

Aeronautical engineering: A continuing bibliography with indexes (supplement 247)

This bibliography lists 437 reports, articles, and other documents introduced into the NASA scientific and technical information system in December, 1989. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Investigations of Model-Free Sliding Mode Control Algorithms including Application to Autonomous Quadrotor Flight

Sliding mode control is a robust nonlinear control algorithm that has been used to implement tracking controllers for unmanned aircraft systems that are robust to modeling uncertainty and exogenous disturbances, thereby providing excellent performance for autonomous operation. A significant advance in the application of sliding mode control for unmanned aircraft systems would be adaptation of a model-free sliding mode control algorithm, since the most complex and time-consuming aspect of implementation of sliding mode control is the derivation of the control law with incorporation of the system model, a process required to be performed for each individual application of sliding mode control. The performance of four different model-free sliding mode control algorithms was compared in simulation using a variety of aerial system models and real-world disturbances (e.g. the effects of discretization and state estimation). The two best performing algorithms were shown to exhibit very similar behavior. These two algorithms were implemented on a quadrotor (both in simulation and using real-world hardware) and the performance was compared to a traditional PID-based controller using the same state estimation algorithm and control setup. Simulation results show the model-free sliding mode control algorithms exhibit similar performance to PID controllers without the tedious tuning process. Comparison between the two model-free sliding mode control algorithms showed very similar performance as measured by the quadratic means of tracking errors. Flight testing showed that while a model-free sliding mode control algorithm is capable of controlling realworld hardware, further characterization and significant improvements are required before it is a viable alternative to conventional control algorithms. Large tracking errors were observed for both the model-free sliding mode control and PID based flight controllers and the performance was characterized as unacceptable for most applications. The poor performance of both controllers suggests tracking errors could be attributed to errors in state estimation, which effectively introduce unknown dynamics into the feedback loop. Further testing with improved state estimation would allow for more conclusions to be drawn about the performance characteristics of the model-free sliding mode control algorithms