Nonlinear Controller Design for UAVs with Time-Varying Aerodynamic Uncertainties

Unmanned Aerial Vehicles (UAVs) are here and they are here to stay. Unmanned Aviation has expanded significantly in recent years and research and development in the field of navigation and control have advanced beyond expectations. UAVs are currently being used for defense programs around the world but the range of applications is expected to grow in the near future, with civilian applications such as environmental and aerial monitoring, aerial surveillance and homeland security being some representative examples. Conventional and commercially available small-scale UAVs have limited utilization and applicability to executing specific short-duration missions because of limitations in size, payload, power supply and endurance. This fact has already marked the dawn of a new era of more powerful and versatile UAVs (e.g. morphing aircraft), able to perform a variety of missions. This dissertation presents a novel, comprehensive, step-by-step, nonlinear controller design framework for new generation, non-conventional UAVs with time-varying aerodynamic characteristics during flight. Controller design for such UAVs is a challenging task mainly due to uncertain aerodynamic parameters in the UAV mathematical model. This challenge is tackled by using and implementing μ-analysis and additive uncertainty weighting functions. The technique described herein can be generalized and applied to the class of non-conventional UAVs, seeking to address uncertainty challenges regarding the aircraft\u27s aerodynamic coefficients

Design, Implementation and Testing of Advanced Control Laws for Fixed-wing UAVs

The present PhD thesis addresses the problem of the control of small fixed-wing Unmanned Aerial Vehicles (UAVs). In the scientific community much research is dedicated to the study of suitable control laws for this category of aircraft. This interest is motivated by the several applications that these platforms can perform and by their peculiarities as dynamical systems. In fact, small UAVs are characterized by highly nonlinear behavior, strong coupling between longitudinal and latero-directional planes, and high sensitivity to external disturbances and to parametric uncertainties. Furthermore, the challenge is increased by the limited space and weight available for the onboard electronics. The aim of this PhD thesis is to provide a valid confrontation among three different control techniques and to introduce an innovative autopilot configuration suitable for the unmanned aircraft field. Three advanced controllers for fixed-wing unmanned aircraft vehicles are designed and implemented: PID with H1 robust approach, L1 adaptive controller and nonlinear backstepping controller. All of them are analyzed from the theoretical point of view and validated through numerical simulations with a mathematical UAV model. One is implemented on a microcontroller board, validated through hardware simulations and tested in flight. The PID with H1 robust approach is used for the definition of the gains of a commercial autopilot. The proposed technique combines traditional PID control with an H1 loop shaping method to assess the robustness characteristics achievable with simple PID gains. It is demonstrated that this hybrid approach provides a promising solution to the problem of tuning commercial autopilots for UAVs. Nevertheless, it is clear that a tradeoff between robustness and performance is necessary when dealing with this standard control technique. The robustness problem is effectively solved by the adoption of an L1 adaptive controller for complete aircraft control. In particular, the L1 logic here adopted is based on piecewise constant adaptive laws with an adaptation rate compatible with the sampling rate of an autopilot board CPU. The control scheme includes an L1 adaptive controller for the inner loop, while PID gains take care of the outer loop. The global controller is tuned on a linear decoupled aircraft model. It is demonstrated that the achieved configuration guarantees satisfying performance also when applied to a complete nonlinear model affected by uncertainties and parametric perturbations. The third controller implemented is based on an existing nonlinear backstepping technique. A scheme for longitudinal and latero-directional control based on the combination of PID for the outer loop and backstepping for the inner loop is proposed. Satisfying results are achieved also when the nonlinear aircraft model is perturbed by parametric uncertainties. A confrontation among the three controllers shows that L1 and backstepping are comparable in terms of nominal and robust performance, with an advantage for L1, while the PID is always inferior. The backstepping controller is chosen for being implemented and tested on a real fixed-wing RC aircraft. Hardware-in-the-loop simulations validate its real-time control capability on the complete nonlinear model of the aircraft adopted for the tests, inclusive of sensors noise. An innovative microcontroller technology is employed as core of the autopilot system, it interfaces with sensors and servos in order to handle input/output operations and it performs the control law computation. Preliminary ground tests validate the suitability of the autopilot configuration. A limited number of flight tests is performed. Promising results are obtained for the control of longitudinal states, while latero-directional control still needs major improvements

Dual observer based adaptive controller for hybrid drones

A biplane quadrotor (hybrid vehicle) benefits from rotary-wing and fixed-wing structures. We design a dual observer-based autonomous trajectory tracking controller for the biplane quadrotor. Extended state observer (ESO) is designed for the state estimation, and based on this estimation, a Backstepping controller (BSC), Integral Terminal Sliding Mode Controller (ITSMC), and Hybrid Controller (HC) that is a combination of ITSMC + BSC are designed for the trajectory tracking. Further, a Nonlinear disturbance observer (DO) is designed and combined with ESO based controller to estimate external disturbances. In this simulation study, These ESO-based controllers with and without DO are applied for trajectory tracking, and results are evaluated. An ESO-based Adaptive Backstepping Controller (ABSC) and Adaptive Hybrid controller (AHC) with DO are designed, and performance is evaluated to handle the mass change during the flight despite wind gusts. Simulation results reveal the effectiveness of ESO-based HC with DO compared to ESO-based BSC and ITSMC with DO. Furthermore, an ESO-based AHC with DO is more efficient than an ESO-based ABSC with DO.Web of Science71art. no. 4

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

Nonlinear robust control of tail-sitter aircrafts in flight mode transitions

© 2018 Elsevier Masson SAS In this paper, a nonlinear robust controller is proposed to deal with the flight mode transition control problem of tail-sitter aircrafts. During the mode transitions, the control problem is challenging due to the high nonlinearities and strong couplings. The tail-sitter aircraft model can be considered as a nominal part with uncertainties including nonlinear terms, parametric uncertainties, and external disturbances. The proposed controller consists of a nominal H∞controller and a nonlinear disturbance observer. The nominal H∞controller based on the nominal model is designed to achieve the desired trajectory tracking performance. The uncertainties are regarded as equivalent disturbances to restrain their influences by the nonlinear disturbance observer. Theoretical analysis and simulation results are given to show advantages of the proposed control method, compared with the standard H∞control approach

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

Robust Control and Estimation for Unmanned Aerial Vehicles

In recent years, unmanned aerial vehicles (UAVs) have found applications in many diverse fields encompassing commercial, civil, and military sectors. These applications include surveillance, search and rescue operations, aerial photography, mapping of geographical areas, aerial cargo delivery, to name a few. This research addresses how to develop next-generation UAV systems, namely, effective modeling of UAVs, robust control techniques, and non-linear/robust state estimation. The first part addresses modeling and control of a six-degree-of-freedom unmanned aerial vehicle capable of vertical take-off and landing in the presence of wind disturbances. We design a hybrid vehicle that combines the benefits of both fixed-wing and rotary-wing UAVs. A non-linear model for the hybrid vehicle is built, combining rigid body dynamics, the aerodynamics of the wing, and the dynamics of the motor and propeller. Further, we design an H2 optimal controller to make the UAV robust to wind disturbances. It is easy to achieve robustness in this design framework with respect to wind gusts. The controller is determined by solving a convex optimization problem involving linear matrix inequalities and simulated with a non-linear hybrid UAV model developed in the first section, with a wind gust environment. Further, we compare its results against that of PID and LQR-based control. Our proposed controller results in better performance in terms of root mean squared errors and time responses during two scenarios: hover and level-flight. In the second part of the research, we discuss robust Proportional-Integral-Derivative (PID) control techniques for the quadcopters. PID control is the most commonly used algorithm for designing controllers for unmanned aerial vehicles (UAVs). However, tuning PID gains is a non-trivial task. A number of methods have been developed for tuning PID gains but these methods do not handle wind disturbances, which is a major concern for small UAVs. In this paper, we propose a new method for determining optimized PID gains in the H2 optimal control framework, which achieves improved wind disturbance rejection. The proposed method compares the classical PID control law with the H2 optimal controller to determine the H2 optimal PID gains and involves solving a convex optimization problem. The proposed controller is tested in two scenarios, namely, vertical velocity control, and vertical position control. The results are compared with the existing LQR based PID tuning method. A good performance of the controller requires an accurate estimation of states from noisy measurements. Therefore, the third part of the research concentrates on the accurate attitude estimation of UAVs. Most UAV systems use a combination of a gyroscope, an accelerometer, and a magnetometer to obtain measurements and estimate attitude. Under this paradigm of sensor fusion, the Extended Kalman Filter (EKF) is the most popular algorithm for attitude estimation in UAVs. In this work, we propose a novel estimation technique called extended H2 filter that can overcome the limitations of the EKF, specifically with respect to computational speed, memory usage, and root mean squared error. We formulate our attitude-estimation algorithm using two distinct coordinate representations for the vehicle's orientation: Euler angles and unit quaternions, each with its own sets of benefits and challenges. The H2 optimal filter gain is designed offline about a nominal operating point by solving a convex optimization problem, and the filter dynamics is implemented using the nonlinear system dynamics. This implementation of this H2 optimal estimator is referred as the extended H2 estimator. The proposed technique is tested on four cases corresponding to long time-scale motion, fast time-scale motion, transition from hover to forward flight for VTOL aircrafts and an entire flight cycle (from take-off to landing). Its results are compared against that of the EKF in terms of the aforementioned performance metrics

Control system design using evolutionary algorithms for autonomous shipboard recovery of unmanned aerial vehicles

The capability of autonomous operation of ship-based Unmanned Aerial Vehicles (UAVs) in extreme sea conditions would greatly extend the usefulness of these aircraft for both military and civilian maritime purposes. Maritime operations are often associated with Vertical Take-Off and Landing (VTOL) procedures, even though the advantages of conventional fixed-wing aircraft over VTOL aircraft in terms of flight speed, range and endurance are well known. In this work, current methods of shipboard recovery are analysed and the problems associated with recovery in adverse weather conditions are identified. Based on this analysis, a novel recovery method is proposed. This method, named Cable Hook Recovery, is intended to recover small to medium-size fixed-wing UAVs on frigate-size vessels. It is expected to have greater operational capabilities than the Recovery Net technique, which is currently the most widely employed method of recovery for similar class of UAVs, potentially providing safe recovery even in very rough sea and allowing the choice of approach directions. The recovery method is supported by the development of a UAV controller that realises the most demanding stage of recovery, the final approach. The controller provides both flight control and guidance strategy that allow fully autonomous recovery of a fixed-wing UAV. The development process involves extensive use of specially tailored Evolutionary Algorithms and represents the major contribution of this work. The Evolutionary Design algorithm developed in this work combines the power of Evolutionary Strategies and Genetic Programming, enabling automatic evolution of both the structure and parameters of the controller. The controller is evolved using a fully coupled nonlinear six-degree-of-freedom UAV model, making linearisation and trimming of the model unnecessary. The developed algorithm is applied to both flight control and guidance problems with several variations, from optimisation of a routine PID controller to automatic control laws synthesis where no a priori data available. It is demonstrated that Evolutionary Design is capable of not only optimising, but also solving automatically the real-world problems, producing human-competitive solutions. The designed UAV controller has been tested comprehensively for both performance and robustness in a nonlinear simulation environment and has been found to allow the aircraft to be recovered in the presence of both large external disturbances and uncertainty in the simulation models