An Aerial Robot for Rice Farm Quality Inspection With Type-2 Fuzzy Neural Networks Tuned by Particle Swarm Optimization-Sliding Mode Control Hybrid Algorithm

Agricultural robots, or agrobots, have been increasingly adopted in every aspect of farming from surveillance to fruit harvesting in order to improve the overall productivity over the last few decades. Motivated by the compelling growth of the agricultural robots in modern farms, in this work, an autonomous quality inspection over rice farms is proposed by employing quadcopters. Real-time control of these vehicles, however, is still challenging as they exhibit a highly nonlinear behavior especially for agile maneuvers. What is more, these vehicles have to operate under uncertain working conditions such as wind and gust disturbances as well as positioning errors caused by inertial measurement units and global positioning system. To handle these difficulties, as a model-free and learning control algorithm, type-2 fuzzy neural networks (T2-FNNs) are designed for the control of a quadcopter. The novel particle swarm optimization-sliding mode control (PSO-SMC) theory-based hybrid algorithm is proposed for the training of the T2-FNNs. In particular, the continuous version of PSO is adopted for the identification of the antecedent part of the T2-FNNs while the SMC-based update rules are utilized for the online learning of the consequent part during control. In the virtual environment, the quadcopter is expected to perform an autonomous flight including agile maneuvers such as steep turning and sudden altitude changes over a rice terrace farm in Longsheng, China. The simulation results for the T2-FNNs are compared with the outcome of conventional proportional-derivative (PD) controllers for different case studies. The results show that our method decreases the trajectory tracking integral squared error by %26 over PD controllers in the ideal case, while this ratio goes up to %95 under uncertain working conditions

Agile load transportation systems using aerial robots

In this dissertation, we address problems that can occur during load transport using aerial robots, i.e., small scale quadrotors. First, detailed models of such transportation system are derived. These models include nonlinear models of a quadrotor, a model of a quadrotor carrying a fixed load and a model of a quadrotor carrying a suspended load. Second, the problem of quadrotor stabilization and trajectory tracking with changes of the center of gravity of the transportation system is addressed. This problem is solved using model reference adaptive control based on output feedback linearization that compensates for dynamical changes in the center of gravity of the quadrotor. The third problem we address is a problem of a swing-free transport of suspended load using quadrotors. Flying with a suspended load can be a very challenging and sometimes hazardous task as the suspended load significantly alters the flight characteristics of the quadrotor. In order to deal with suspended load flight, we present a method based on dynamic programming which is a model based offline method. The second investigated method we use is based on the Nelder-Mead algorithm which is an optimization technique used for nonlinear unconstrained optimization problems. This method is model free and it can be used for offline or online generation of the swing-free trajectories for the suspended load. Besides the swing-free maneuvers with suspended load, load trajectory tracking is another problem we solve in this dissertation. In order to solve this problem we use a Nelder-Mead based algorithm. In addition, we use an online least square policy iteration algorithm. At the end, we propose a high level algorithm for navigation in cluttered environments considering a quadrotor with suspended load. Furthermore, distributed control of multiple quadrotors with suspended load is addressed too. The proposed hierarchical architecture presented in this doctoral dissertation is an important step towards developing the next generation of agile autonomous aerial vehicles. These control algorithms enable quadrotors to display agile maneuvers while reconfiguring in real time whenever a change in the center of gravity occurs. This enables a swing-free load transport or trajectory tracking of the load in urban environments in a decentralized fashion

Outdoor operations of multiple quadrotors in windy environment

Coordinated multiple small unmanned aerial vehicles (sUAVs) offer several advantages over a single sUAV platform. These advantages include improved task efficiency, reduced task completion time, improved fault tolerance, and higher task flexibility. However, their deployment in an outdoor environment is challenging due to the presence of wind gusts. The coordinated motion of a multi-sUAV system in the presence of wind disturbances is a challenging problem when considering collision avoidance (safety), scalability, and communication connectivity. Performing wind-agnostic motion planning for sUAVs may produce a sizeable cross-track error if the wind on the planned route leads to actuator saturation. In a multi-sUAV system, each sUAV has to locally counter the wind disturbance while maintaining the safety of the system. Such continuous manipulation of the control effort for multiple sUAVs under uncertain environmental conditions is computationally taxing and can lead to reduced efficiency and safety concerns. Additionally, modern day sUAV systems are susceptible to cyberattacks due to their use of commercial wireless communication infrastructure. This dissertation aims to address these multi-faceted challenges related to the operation of outdoor rotor-based multi-sUAV systems. A comprehensive review of four representative techniques to measure and estimate wind speed and direction using rotor-based sUAVs is discussed. After developing a clear understanding of the role wind gusts play in quadrotor motion, two decentralized motion planners for a multi-quadrotor system are implemented and experimentally evaluated in the presence of wind disturbances. The first planner is rooted in the reinforcement learning (RL) technique of state-action-reward-state-action (SARSA) to provide generalized path plans in the presence of wind disturbances. While this planner provides feasible trajectories for the quadrotors, it does not provide guarantees of collision avoidance. The second planner implements a receding horizon (RH) mixed-integer nonlinear programming (MINLP) model that is integrated with control barrier functions (CBFs) to guarantee collision-free transit of the multiple quadrotors in the presence of wind disturbances. Finally, a novel communication protocol using Ethereum blockchain-based smart contracts is presented to address the challenge of secure wireless communication. The U.S. sUAV market is expected to be worth $92 Billion by 2030. The Association for Unmanned Vehicle Systems International (AUVSI) noted in its seminal economic report that UAVs would be responsible for creating 100,000 jobs by 2025 in the U.S. The rapid proliferation of drone technology in various applications has led to an increasing need for professionals skilled in sUAV piloting, designing, fabricating, repairing, and programming. Engineering educators have recognized this demand for certified sUAV professionals. This dissertation aims to address this growing sUAV-market need by evaluating two active learning-based instructional approaches designed for undergraduate sUAV education. The two approaches leverages the interactive-constructive-active-passive (ICAP) framework of engagement and explores the use of Competition based Learning (CBL) and Project based Learning (PBL). The CBL approach is implemented through a drone building and piloting competition that featured 97 students from undergraduate and graduate programs at NJIT. The competition focused on 1) drone assembly, testing, and validation using commercial off-the-shelf (COTS) parts, 2) simulation of drone flight missions, and 3) manual and semi-autonomous drone piloting were implemented. The effective student learning experience from this competition served as the basis of a new undergraduate course on drone science fundamentals at NJIT. This undergraduate course focused on the three foundational pillars of drone careers: 1) drone programming using Python, 2) designing and fabricating drones using Computer-Aided Design (CAD) and rapid prototyping, and 3) the US Federal Aviation Administration (FAA) Part 107 Commercial small Unmanned Aerial Vehicles (sUAVs) pilot test. Multiple assessment methods are applied to examine the students’ gains in sUAV skills and knowledge and student attitudes towards an active learning-based approach for sUAV education. The use of active learning techniques to address these challenges lead to meaningful student engagement and positive gains in the learning outcomes as indicated by quantitative and qualitative assessments

Advances and Trends in Mathematical Modelling, Control and Identification of Vibrating Systems

This book introduces novel results on mathematical modelling, parameter identification, and automatic control for a wide range of applications of mechanical, electric, and mechatronic systems, where undesirable oscillations or vibrations are manifested. The six chapters of the book written by experts from international scientific community cover a wide range of interesting research topics related to: algebraic identification of rotordynamic parameters in rotor-bearing system using finite element models; model predictive control for active automotive suspension systems by means of hydraulic actuators; model-free data-driven-based control for a Voltage Source Converter-based Static Synchronous Compensator to improve the dynamic power grid performance under transient scenarios; an exact elasto-dynamics theory for bending vibrations for a class of flexible structures; motion profile tracking control and vibrating disturbance suppression for quadrotor aerial vehicles using artificial neural networks and particle swarm optimization; and multiple adaptive controllers based on B-Spline artificial neural networks for regulation and attenuation of low frequency oscillations for large-scale power systems. The book is addressed for both academic and industrial researchers and practitioners, as well as for postgraduate and undergraduate engineering students and other experts in a wide variety of disciplines seeking to know more about the advances and trends in mathematical modelling, control and identification of engineering systems in which undesirable oscillations or vibrations could be presented during their operation

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Data-Driven Architecture to Increase Resilience In Multi-Agent Coordinated Missions

The rise in the use of Multi-Agent Systems (MASs) in unpredictable and changing environments has created the need for intelligent algorithms to increase their autonomy, safety and performance in the event of disturbances and threats. MASs are attractive for their flexibility, which also makes them prone to threats that may result from hardware failures (actuators, sensors, onboard computer, power source) and operational abnormal conditions (weather, GPS denied location, cyber-attacks). This dissertation presents research on a bio-inspired approach for resilience augmentation in MASs in the presence of disturbances and threats such as communication link and stealthy zero-dynamics attacks. An adaptive bio-inspired architecture is developed for distributed consensus algorithms to increase fault-tolerance in a network of multiple high-order nonlinear systems under directed fixed topologies. In similarity with the natural organisms’ ability to recognize and remember specific pathogens to generate its immunity, the immunity-based architecture consists of a Distributed Model-Reference Adaptive Control (DMRAC) with an Artificial Immune System (AIS) adaptation law integrated within a consensus protocol. Feedback linearization is used to modify the high-order nonlinear model into four decoupled linear subsystems. A stability proof of the adaptation law is conducted using Lyapunov methods and Jordan decomposition. The DMRAC is proven to be stable in the presence of external time-varying bounded disturbances and the tracking error trajectories are shown to be bounded. The effectiveness of the proposed architecture is examined through numerical simulations. The proposed controller successfully ensures that consensus is achieved among all agents while the adaptive law v simultaneously rejects the disturbances in the agent and its neighbors. The architecture also includes a health management system to detect faulty agents within the global network. Further numerical simulations successfully test and show that the Global Health Monitoring (GHM) does effectively detect faults within the network

UAV Path Planning and Obstacle Avoidance Based on Fuzzy Logic and Kinodynamic RRT Methods

Path Planning is one of the important problems to be explored in unmanned aerial vehicle (UAV) to find the optimal path between starting position and destination. The aim of path planning technique is not only to find the shortest path but also to provide the collision-free path for the UAV in unknown environment. Although there have been significant advances on the methods of path planning where the map of environment is known in advance, there are still some challenges to be addressed for dynamic autonomous navigation for the UAV in unknown environment. This thesis research proposes a new path planning method named Fuzzy Kinodynamic RRT for unmanned aerial vehicle flying in the unknown environment. This method generates a global path based on RRT [1] (Rapidly-exploring random tree) and utilizes fuzzy logic system to avoid obstacles in real time. A set of heuristics fuzzy rules are designed to lead the UAV away from unmodeled obstacles and to guide the UAV towards the goal. The rules are also tested in different scenarios, and they are all working efficiently both in simple and complicated cases. The UAV starts to fly along the path generated by RRT, and the fuzzy logic system is then activated when it comes across the obstacle. When the sensor detects no collision within a specific distance, the fuzzy system is turned off and the UAV flies back to the previous path towards the final destination. The simulations of the developed algorithm have been carried out in various scenarios, with the sensor to detect the obstacles. The numerical simulations show the satisfactory results in various scenarios for path planning that considerably reduces the risk of colliding with other stationary and moving obstacles. A more robust and efficient fuzzy logic controller which embeds the path planning is finally proposed and the simulation shows the satisfactory results in complicated environments

무익기형 전기 추진 수직 이착륙기에 대한 다학제 해석 및 시뮬레이션 프레임워크

학위논문(박사) -- 서울대학교대학원 : 공과대학 항공우주공학과, 2023. 2. 이관중.A wingless-type electric vertical take-off and landing (eVTOL) is one of the representative aircrafts utilized logistics and delivery, search and rescue, military, agriculture, and inspection of structures. For a small unmanned aerial vehicles of the wingless-type eVTOL, a quadrotor is a representative configuration to operate those missions. For a large size of the wingless-type eVTOL, it is an aircraft for urban air mobility service (UAM) specialized for intracity point-to-point due to its advantages such as efficient hover performance, high gust resistance, and relatively low noisiness. The rotating speed of the multiple rotors in the wingless-type eVTOL has to be changed continuously to achieve stable flight. Moreover, the speed and the loaded torque of the motors also continuously change. Therefore, it is necessary to analyze the rotor thrust and torque with respect to the speed of each rotor as assigned by the controller to predict the flight performance of the wingless-type eVTOL. The electric power required by the motors is also necessary to be predicted based on the torque loaded to the motors to maintain the rotating speed. This study suggests a flight simulation framework based on these multidisciplinary analyses including control, rotor aerodynamics, and electric propulsion system analysis. Using the flight simulation framework, it is possible to predict the flight performance of the wingless-type eVTOL for given operating conditions. The flight simulation framework can predict the overall performance required to resist the winds and the corresponding battery energy of a quadrotor. Flight endurance of an industrial quadrotor was examined under light, moderate, and strong breeze modeled by von Kármán wind turbulence with Beaufort wind force scale. As a result, it is found that the excess battery energy is increased with ground speed, even under the same wind conditions. As the ground speed increases, the airspeed is increased, led to higher frame drag, position error, pitch angle, and required mechanical power, consequently. Moreover, the quadrotor is not operable beyond a certain wind and ground speed since the required rotational speed of rotors exceeds the speed limit of motors. The simulation framework can also predict the overall performance of a wingless eVTOL for UAM service. Because of its multiple rotors, rotor–rotor interference inevitably affects flight performance, mainly depending on inter-rotor distance and rotor rotation directions. In this case, there is an optimal rotation direction of the multiple rotors to be favorable in actual operation. In this study, it was proposed that a concept of rotor rotation direction that achieves the desirable flight performance in actual operation. The concept is called FRRA (Front rotors Retreating side and Rear rotors Advancing side). It was found that FRRA minimizes thrust loss due to rotor-rotor interference in high-speed forward flight. For a generic mission profile of UAM service, the rotation direction set by FRRA reduces the battery energy consumption of 7 % in comparison to the rotation direction of unfavorable rotor-rotor interference in operation.무익기형 전기 추진 수직 이착륙기는 택배 및 운송 서비스, 수색 및 구조, 국방, 농업, 구조물 점검과 같은 분야에서 대표적으로 이용되고 있는 항공기이다. 쿼드로터는 이러한 임무를 수행하기 위한 대표적인 소형 무익기형 전기 추진 수직 이착륙기이다. 대형 무익기형 전기 추진 수직 이착륙기는 효율적인 제자리 비행 성능, 높은 내풍성, 낮은 소음 공해와 같은 특징으로 인해 도심 내 운항 서비스를 위한 항공기로 활용되고 있다. 무익기형 전기 추진 수직 이착륙기의 여러 회전 날개는 안정된 비행을 유지하기 위해, 지속해서 회전 속도를 변화시킨다. 게다가, 모터의 회전 속도와 부하되는 토크 또한 지속적으로 변화한다. 그러므로 무익기형 전기 추진 수직 이착륙기의 비행 성능을 예측하기 위해, 제어기에서 각 회전 날개에 부여된 회전 속도에 따른 추력 및 토크를 해석해야 한다. 그리고 이러한 회전 날개의 회전 속도를 유지하기 위해 모터에 부하 되는 토크를 기반으로, 모터에서 요구되는 전력을 예측해야 한다. 본 논문에서는 제어, 회전 날개 공력, 전기 추진 시스템 해석이 포함된 다학제 해석 기반의 비행 시뮬레이션 프레임워크를 제시한다. 비행 시뮬레이션 프레임워크를 이용하여, 실제 운용 환경에서의 무익기형 전기 추진 수직 이착륙기 비행 성능을 예측할 수 있다. 비행 시뮬레이션 프레임워크를 활용하여 쿼드로터에 대해 외풍을 저항하기 위한 비행 전반적인 성능과 그에 따른 배터리 에너지 소모를 예측하였다. Von Kármán 외풍 난류와 Beaufort 외풍 강도 등급을 활용하여 남실바람, 건들바람, 된바람 환경에 대한 산업용 쿼드로터의 비행시간을 조사하였다. 그 결과, 동일한 외풍 환경일지라도 전진 비행 속도가 증가할수록 배터리 소요 에너지가 증가한다는 것을 밝혔다. 전진 비행 속도의 증가로 인해 쿼드로터에 유입되는 유속이 증가하여, 동체 항력, 위치 오차, 기수 내림 각도, 요구 기계 동력이 증가하였다. 그리고 특정 외풍 속도와 전진 속도 이상에서의 쿼드로터는 요구되는 회전 날개의 회전 속도가 모터의 회전 속도의 한계보다 높으므로 비행할 수 없었다. 또한, 비행 시뮬레이션 프레임워크를 활용하여 도심 운항 서비스용 무익기형 전기 추진 수직 이착륙기의 전반적인 비행 성능을 예측하였다. 여러 회전 날개의 특징으로 인해, 회전 날개 간 거리와 회전 날개의 회전 방향에 따라 회전 날개 간 간섭효과가 필연적으로 비행 성능에 영향을 미친다. 이때, 운용에 유리한 최적의 회전 날개 회전 방향이 존재한다. 본 논문에서 실제 운용에서 바람직한 비행 성능을 발휘하는 회전 날개의 회전 방향에 대한 개념인 FRRA를 제시하였다. FRRA는 전방 로터의 후퇴 측과 후방 로터의 전진 측이 일직선으로 정렬된 상태의 회전 방향이다. FRRA 회전 방향은 고속 전진 비행에서 회전 날개 간 간섭효과로 인한 추력 손실이 최소화된다. 회전 날개 간 간섭효과로 인해 불리한 비행 성능을 가지는 회전 방향 대비 FRRA 회전 방향은 도심 항공 교통 서비스에 대한 일반적인 운용에서 배터리 소모율이 7% 정도 감소하였다.Chapter 1. Introduction 1 1.1 Overview of wingless-type eVTOL 1 1.2 Previous studies about wingless-type eVTOL 6 1.2.1 Multidisciplinary analysis of control, aerodynamic, and EPS 6 1.2.2 External wind of wingless-type eVTOLs for small UAVs 9 1.2.3 Rotor-rotor interference of wingless-type eVTOLs for UAM 10 1.3 Motivation and scope of the dissertation 12 Chapter 2. Simulation Framework 16 2.1 Layout and analysis modules in simulation framework 16 2.1.1 Cascade PID control module 19 2.1.1 Aerodynamic analysis module 24 2.1.2 Electric propulsion system analysis module 30 2.1.3 6-DOF dynamics analysis module 33 2.2 Add-on modules for actual operation 37 2.2.1 Wind turbulence module 37 2.2.2 Rotor-rotor interference module 39 Chapter 3. Validation of Simulation Framework 44 3.1 Static thrust and torque on a single rotor test 44 3.2 Wind resistance test 46 3.3 Rotor-rotor interference of tandem rotors 52 3.4 Rotor-rotor interaction of a quadrotor in CFD 54 3.5 Investigation of rotor-rotor interference with respect to rotation directions in a quadrotor 58 Chapter 4. Flight Performance of Quadrotor under Wind Turbulence 65 4.1 Flight conditions 65 4.2 Wind turbulence conditions 66 4.3 Simulation results 69 Chapter 5. Flight Performance of Wingless-type eVTOL for UAM Service with Respect to the Rotor Rotation Directions 78 5.1 Hypothetical model of a wingless-type eVTOL for UAM service 78 5.2 Rotor rotation directions and aerodynamic performance 83 5.2.1 Hover flight 86 5.2.2 Forward flight at 100 km/h 88 5.2.3 Forward flight in the airspeed of 100 km/h with 30 yaw angle 93 5.3 Surrogate models including the rotor-rotor interaction effect 96 5.4 Simulation results 99 Chapter 6. Conclusion 112 6.1 Summary 112 6.2 Originalities of the dissertation 113 6.3 Future works 116 Appendix 118 References 127 국문 초록 144박