Consecutive Inertia Drift of Autonomous RC Car via Primitive-based Planning and Data-driven Control

Inertia drift is an aggressive transitional driving maneuver, which is challenging due to the high nonlinearity of the system and the stringent requirement on control and planning performance. This paper presents a solution for the consecutive inertia drift of an autonomous RC car based on primitive-based planning and data-driven control. The planner generates complex paths via the concatenation of path segments called primitives, and the controller eases the burden on feedback by interpolating between multiple real trajectories with different initial conditions into one near-feasible reference trajectory. The proposed strategy is capable of drifting through various paths containing consecutive turns, which is validated in both simulation and reality.Comment: 9 pages, 10 figures, to appear to IROS 202

Autonomous Vertical Recovery of Fixed Wing Unmanned Aerial Vehicles

As unmanned aerial vehicles (UAVs) prevail in commercial and first responder applications, the need for safer and more consistent recovery methods is growing. Standard aircraft landing manoeuvres are only possible with a suitable runway which is often unavailable outside of military applications. Alternative recovery approaches can be either contained within the aircraft, ie. parachute or be setup on the ground ie. net landing. By integrating the recovery system into the vehicle, the flight preparation can be streamlined due to the fact that setting up recovering devices is no longer required. The goal of this thesis is to investigate the application of an autonomous vertical landing capability for fixed wing UAVs using articulated motors to enter vertical flight. Using an experimental UAV design, the dynamics of the problem were investigated using recorded flight data. Implementing a decoupled control approach, the aircraft was stabilized to maintain a horizontal hover. Through the characterized plant model that defines the vertical descent behaviour, a control topology was developed and tested in simulation using an optimal control approach. The developed controller was tested on the experimental UAV to verify the vertical landing performance. It was determined that this approach was well suited to autonomous vertical recovery of a fixed wing UAV. In employing this approach to UAV recovery, operators can easily operate in theatres where space for conventional landing does not exist

고성능 한계 핸들링을 위한 인휠모터 토크벡터링 제어

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 이경수.지난 10년 동안 차량 자세 제어시스템(ESC)은 치명적인 충돌을 방지하기 위해 많은 상용 차량에서 비약적으로 발전되고 개발되고 있다. 특히, 차량 자세 제어 시스템은 악천후로 인한 미끄러운 도로와 같은 위험한 도로에서 불안정한 차량 주행 조건에서 사고를 피하는데 큰 역할을 한다. 그러나, 최근의 경우, 고성능 차량 또는 스포츠카 등의 경우 제동제어의 빈번한 개입은 운전의 즐거움을 감소시키는 불만도 존재한다. 최근 차량의 전동화와 함께, 자량 자세 제어시스템의 작동 영역인 한계 주행 핸들링 조건에서 각 휠의 독립적인 구동을 적용 할 수 있는 시스템 중 하나인 인휠 모터 시스템을 사용하여 차량의 종, 횡방향 특성을 제어 가능하게 하는 토크 벡터링 제어기술에 대한 연구가 활발하다. 따라서, 본 연구에서는 차량의 선회 한계 핸들링 조건에서 안정성과 주행 다이나믹 성능을 향상시킬 수 있는 토크 벡터링 제어기를 제안하고자 한다. 먼저, 차량의 비선형 주행 구간인 한계 핸들링 조건에 대한 자동 드리프트 제어 알고리즘을 제안한다. 이 알고리즘을 이용하여 토크벡터링제어에 차량의 다이나믹한 주행모드에 대한 통찰력을 제공하고 미끄러운 도로에서 차량의 높은 슬립 각도의 안정성 제어를 제공 할 수 있다. 또한, 인휠 모터 시스템을 차량의 전륜에 2개 모터로 사용하여 차량 고유의 특성인 차량 언더스티어 구배를 직접적 제어를 수행하여, 차량의 핸들링 성능을 향상시켰다. 제어기의 채터링 효과를 줄이고 빠른 응답을 얻기 위해 새로운 과도 매개 변수가 이용하여 수식화하여 구성하였으며, 차량의 정상 상태 및 과도 특성 향상을 검증하기 위하여 ISO 기반 시뮬레이션 및 차량 실험을 수행하였다. 마지막으로 요 제어기와 횡 슬립 각도 제어기로 구성된 MASMC (Multiple Adaptive Sliding Mode Control) 접근 방식을 사용하는 4륜 모터 시스템을 사용한 동적 토크벡터링 제어를 수행하였다. 높은 비선형 특성을 가진 차량의 전후륜 타이어의 코너링 강성은 적응제어기법을 이용하여 예측하였다. 따라서, 안전모드와 다이나믹 모드를 구성하여, 운전자로 하여금 원하는 주행의 조건에 맞게 선택할 수 있는 알고리즘을 구현하였다. 이 MASMC 알고리즘은 향후 전동화 차량에 주행안정성 향상과 다이나믹한 주행의 즐거움을 주는 기술로써, 전차량 시뮬레이션을 이용하여 검증하였다.In the last ten decades, vehicle stability control systems have been dramatically developed and adapted in many commercial vehicles to avoid fatal crashes. Significantly, ESC (Electric Stability Control) system can help escape the accident from unstable driving conditions with dangerous roads such as slippery roads due to inclement weather conditions. However, for the high performed vehicle, frequent intervention from ESC reduces the pleasure of fun-to-drive. Recently, the development of traction control technologies has been taking place with that of the electrification of vehicles. The IWMs (In-Wheel Motor system), which is one of the systems that can apply independent drive of each wheel, for the limit handling characteristics, which are the operation areas of the ESC, is introduced for the control that enables the lateral characteristics of the vehicle dynamics. Firstly, the automated drift control algorithm can be proposed for the nonlinear limit handling condition of vehicles. This approach can give an insight of fun-to-drive mode to TV (Torque Vector) control scheme, but also the stability control of high sideslip angle of the vehicle on slippery roads. Secondly, using IWMs system with front two motors, understeer gradient of vehicle, which is the unique characteristics of vehicle can be used for the proposed control strategy. A new transient parameter is formulated to be acquired rapid response of controller and reducing chattering effects. Simulation and vehicle tests are conducted for validation of TV control algorithm with steady-state and transient ISO-based tests. Finally, dynamic torque vectoring control with a four-wheel motor system with Multiple Adaptive Sliding Mode Control (MASMC) approach, which is composed of a yaw rate controller and sideslip angle controller, is introduced. Highly nonlinear characteristics, cornering stiffnesses of front and rear tires are estimated by adaptation law with measuring data. Consequently, there are two types of driving modes, the safety mode and the dynamic mode. MASMC algorithm can be found and validated by simulation in torque vectoring technology to improve the handling performance of fully electric vehicles.Chapter 1 Introduction 7 1.1. Background and Motivation 7 1.2. Literature review 11 1.3. Thesis Objectives 15 1.4. Thesis Outline 15 Chapter 2 Vehicle dynamic control at limit handling 17 2.1. Vehicle Model and Analysis 17 2.1.1. Lateral dynamics of vehicle 17 2.1.2. Longitudinal dynamics of vehicle 20 2.2. Tire Model 24 2.3. Analysis of vehicle drift for fun-to-drive 28 2.4. Designing A Controller for Automated Drift 34 2.4.1. Lateral controller 35 2.4.2. Longitudinal Controller 37 2.4.3. Stability Analysis 39 2.4.4. Validation with simulation and test 40 Chapter 3 Torque Vectoring Control with Front Two Motor In-Wheel Vehicles 47 3.1. Dynamic Torque Vectoring Control 48 3.1.1. In-wheel motor system (IWMs) 48 3.1.2. Dynamic system modeling 49 3.1.3. Designing controller 53 3.2. Validation with Simulation and Experiment 59 3.2.1. Simulation 59 3.2.2. Vehicle Experiment 64 Chapter 4 Dynamic handling control for Four-wheel Drive In-Wheel platform 75 4.1. Vehicle System Modeling 76 4.2. Motion Control based on MASMC 78 4.2.1. Yaw motion controller for the inner ASMC 80 4.2.2. Sideslip angle controller for the outer ASMC 84 4.3. Optimal Torque Distribution (OTD) 88 4.3.1. Constraints of dynamics 88 4.3.2. Optimal torque distribution law 90 4.4. Validation with Simulation 91 4.4.1. Simulation setup 91 4.4.2. Simulation results 92 Chapter 5 Conclusion and Future works 104 5.1 Conclusion 104 5.2 Future works 106 Bibliography 108 Abstract in Korean 114박

DESIGN AND CONTROL OF A HUMMINGBIRD-SIZE FLAPPING WING MICRO AERIAL VEHICLE

Flying animals with flapping wings may best exemplify the astonishing ability of natural selection on design optimization. They evince extraordinary prowess to control their flight, while demonstrating rich repertoire of agile maneuvers. They remain surprisingly stable during hover and can make sharp turns in a split second. Characterized by high-frequency flapping wing motion, unsteady aerodynamics, and the ability to hover and perform fast maneuvers, insect-like flapping flight presents an extraordinary aerial locomotion strategy perfected at small size scales. Flapping Wing Micro Aerial Vehicles (FWMAVs) hold great promise in bridging the performance gap between engineered flying vehicles and their natural counterparts. They are perfect candidates for potential applications such as fast response robots in search and rescue, environmental friendly agents in precision agriculture, surveillance and intelligence gathering MAVs, and miniature nodes in sensor networks

Development of control and autonomy algorithms for docking to complex tumbling satellites

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.MIT Institute Archives copy: with DVD; divisional library copy with no DVD.Includes bibliographical references (p. 171-173).The capability of automated rendezvous and docking is a key enabling technology for many government and commercial space programs. Future space systems will employ a high level of autonomy to acquire, repair, refuel, and reconfigure satellites. Several programs have demonstrated a subset of the necessary autonomous docking technology; however, none has demonstrated online path planning in-space necessary for safe automated docking. Particularly, when a docking mission is sent to service an uncooperative spacecraft that is freely tumbling. In order to safely maneuver about an uncontrolled satellite, an online trajectory planning algorithm with obstacle avoidance employed in a GN&C architecture is necessary. The main research contributions of this thesis is the development of an efficient sub-optimal path planning algorithm coupled with an optimal feedback control law to successfully execute safe maneuvers for docking to tumbling satellites. First, an autonomous GN&C architecture is presented that divides the docking mission into four phases, each uniquely using the algorithms within to perform their objectives. For reasons of safety and fuel efficiency, a new sub-optimal spline-based trajectory planning algorithm with obstacle avoidance of the uncooperative spacecraft is presented. This algorithm is shown to be computationally efficient and computes desirable trajectories to a complex moving docking port of the tumbling spacecraft. As a realistic space system includes external disturbances and noises in sensor measurement and control actuation, a closed-loop form of control is necessary to maneuver the spacecraft. Therefore, several optimal feedback control laws are developed to track a trajectory provided by the path planner. Performance requirements for the tracking controllers are defined for the case of two spacecraft docking. With these requirements, the selection of a controller is narrowed down to a phase-plane switching between LQR and servo-LQR control laws.(cont.) The autonomous GN&C architecture with the spline-based path planning algorithm and phase-plane controller is validated with simulations and hardware experiments using the Synchronized Position Hold Engage and Reorient Satellites (SPHERES) testbed aboard the International Space Station (ISS). Utilizing the unique space environment provided by the ISS, the experiment is the first in-space demonstration of an online path planning algorithm. Both the flight and simulation tests successfully validated the capabilities of the autonomous control system to dock to a complex tumbling satellite. The contributions in this thesis advance and validate a GN&C architecture that builds on a legacy in autonomous docking of spacecraft.by Amer Fejzić.S.M

Adaptive control of compliant robots with Reservoir Computing

In modern society, robots are increasingly used to handle dangerous, repetitive and/or heavy tasks with high precision. Because of the nature of the tasks, either being dangerous, high precision or simply repetitive, robots are usually constructed with high torque motors and sturdy materials, that makes them dangerous for humans to handle. In a car-manufacturing company, for example, a large cage is placed around the robot’s workspace that prevents humans from entering its vicinity. In the last few decades, efforts have been made to improve human-robot interaction. Often the movement of robots is characterized as not being smooth and clearly dividable into sub-movements. This makes their movement rather unpredictable for humans. So, there exists an opportunity to improve the motion generation of robots to enhance human-robot interaction. One interesting research direction is that of imitation learning. Here, human motions are recorded and demonstrated to the robot. Although the robot is able to reproduce such movements, it cannot be generalized to other situations. Therefore, a dynamical system approach is proposed where the recorded motions are embedded into the dynamics of the system. Shaping these nonlinear dynamics, according to recorded motions, allows for dynamical system to generalize beyond demonstration. As a result, the robot can generate motions of other situations not included in the recorded human demonstrations. In this dissertation, a Reservoir Computing approach is used to create a dynamical system in which such demonstrations are embedded. Reservoir Computing systems are Recurrent Neural Network-based approaches that are efficiently trained by considering only the training of the readout connections and retaining all other connections of such a network unchanged given their initial randomly chosen values. Although they have been used to embed periodic motions before, they were extended to embed discrete motions, or both. This work describes how such a motion pattern-generating system is built, investigates the nature of the underlying dynamics and evaluates their robustness in the face of perturbations. Additionally, a dynamical system approach to obstacle avoidance is proposed that is based on vector fields in the presence of repellers. This technique can be used to extend the motion abilities of the robot without need for changing the trained Motion Pattern Generator (MPG). Therefore, this approach can be applied in real-time on any system that generates a certain movement trajectory. Assume that the MPG system is implemented on an industrial robotic arm, similar to the ones used in a car factory. Even though the obstacle avoidance strategy presented is able to modify the generated motion of the robot’s gripper in such a way that it avoids obstacles, it does not guarantee that other parts of the robot cannot collide with a human. To prevent this, engineers have started to use advanced control algorithms that measure the amount of torque that is applied on the robot. This allows the robot to be aware of external perturbations. However, it turns out that, even with fast control loops, the adaptation to compensate for a sudden perturbation, is too slow to prevent high interaction forces. To reduce such forces, researchers started to use mechanical elements that are passively compliant (e.g., springs) and light-weight flexible materials to construct robots. Although such compliant robots are much safer and inherently energy efficient to use, their control becomes much harder. Most control approaches use model information about the robot (e.g., weight distribution and shape). However, when constructing a compliant robot it is hard to determine the dynamics of these materials. Therefore, a model-free adaptive control framework is proposed that assumes no prior knowledge about the robot. By interacting with the robot it learns an inverse robot model that is used as controller. The more it interacts, the better the control be- comes. Appropriately, this framework is called Inverse Modeling Adaptive (IMA) control framework. I have evaluated the IMA controller’s tracking ability on sev- eral tasks, investigating its model independence and stability. Furthermore, I have shown its fast learning ability and comparable performance to taskspecific designed controllers. Given both the MPG and IMA controllers, it is possible to improve the inter- actability of a compliant robot in a human-friendly environment. When the robot is to perform human-like motions for a large set of tasks, we need to demonstrate motion examples of all these tasks. However, biological research concerning the motion generation of animals and humans revealed that a limited set of motion patterns, called motion primitives, are modulated and combined to generate advanced motor/motion skills that humans and animals exhibit. Inspired by these interesting findings, I investigate if a single motion primitive indeed can be modulated to achieve a desired motion behavior. By some elementary experiments, where an MPG is controlled by an IMA controller, a proof of concept is presented. Furthermore, a general hierarchy is introduced that describes how a robot can be controlled in a biology-inspired manner. I also investigated how motion primitives can be combined to produce a desired motion. However, I was unable to get more advanced implementations to work. The results of some simple experiments are presented in the appendix. Another approach I investigated assumes that the primitives themselves are undefined. Instead, only a high-level description is given, which describes that every primitive on average should contribute equally, while still allowing for a single primitive to specialize in a part of the motion generation. Without defining the behavior of a primitive, only a set of untrained IMA controllers is used of which each will represent a single primitive. As a result of the high-level heuristic description, the task space is tiled into sub-regions in an unsupervised manner. Resulting in controllers that indeed represent a part of the motion generation. I have applied this Modular Architecture with Control Primitives (MACOP) on an inverse kinematic learning task and investigated the emerged primitives. Thanks to the tiling of the task space, it becomes possible to control redundant systems, because redundant solutions can be spread over several control primitives. Within each sub region of the task space, a specific control primitive is more accurate than in other regions allowing for the task complexity to be distributed over several less complex tasks. Finally, I extend the use of an IMA-controller, which is tracking controller, to the control of under-actuated systems. By using a sample-based planning algorithm it becomes possible to explore the system dynamics in which a path to a desired state can be planned. Afterwards, MACOP is used to incorporate feedback and to learn the necessary control commands corresponding to the planned state space trajectory, even if it contains errors. As a result, the under-actuated control of a cart pole system was achieved. Furthermore, I presented the concept of a simulation based control framework that allows the learning of the system dynamics, planning and feedback control iteratively and simultaneously

Distributed coordination and control experiments on a multi-UAV testbed

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.Includes bibliographical references (p. 153-157).(cont.) of environmental disturbances and measurement noise.The product of this thesis is a robust planning system that is tolerant of the types of uncertainty experienced by real aircraft. This robustness has been demonstrated by more than 20 successful flights on a fully automated UAV testbed.This thesis presents the development and testing of a unique testbed consisting of a fleet of eight unmanned aerial vehicles (UAVs) that was designed as a platform for evaluating coordination and control algorithms. A hierarchical configuration of task assignment, trajectory design, and low-level, waypoint following, are used in a receding horizon framework to control the UAV system. Future UAV teams will have to autonomously demonstrate cooperative behaviors in dynamic and uncertain environments, and this testbed can be used to compare various control approaches to accomplish these coordinated missions. Flight demonstrations are made utilizing real-time mixed-integer linear programming techniques, exercising the algorithms in realistic environments with real-world disturbances. Large disturbance sources, computational delay and measurementnoise all represent significant error sources that reduce the ability of UAV teams to interact in a coordinated fashion by increasing uncertainty on higher planning levels. This thesis develops a method that explicitly accounts for this uncertainty by including feedback loops on the task assignment and trajectory design algorithms to prescribe added robustness for the uncertainty at each stage. This approach takes into account low level controller saturation limits that might cause infeasibilities in the plans created at the higher levels of the planning system. Detailed and realistic simulation environments are useful for large-scale multi-vehicle simulations, particularly when logistics prevent flight testing on that scale. This thesis validates one such hardware-in-the-loop simulation environment through the comparison of models obtained from experimentally collected flight data and detailed modelingby Ellis T. King.S.M

Development of a guidance, navigation and control architecture and validation process enabling autonomous docking to a tumbling satellite

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.Includes bibliographical references (p. 307-324).The capability to routinely perform autonomous docking is a key enabling technology for future space exploration, as well as assembly and servicing missions for spacecraft and commercial satellites. Particularly, in more challenging situations where the target spacecraft or satellite is tumbling, algorithms and strategies must be implemented to ensure the safety of both docking entities in the event of anomalies. However, difficulties encountered in past docking missions conducted with expensive satellites on orbit have indicated a lack of maturity in the technologies required for such operations. Therefore, more experimentation must be performed to improve the current autonomous docking capabilities. The main objectives of the research presented in this thesis are to develop a guidance, navigation and control (GN&C) architecture that enables the safe and fuel-efficient docking with a free tumbling target in the presence of obstacles and anomalies, and to develop the software tools and verification processes necessary in order to successfully demonstrate the GN&C architecture in a relevant environment. The GN&C architecture was developed by integrating a spectrum of GN&C algorithms including estimation, control, path planning, and failure detection, isolation and recovery algorithms.(cont.) The algorithms were implemented in GN&C software modules for real-time experimentation using the Synchronized Position Hold Engage and Reorient Experimental Satellite (SPHERES) facility that was created by the MIT Space Systems Laboratory. Operated inside the International Space Station (ISS), SPHERES allow the incremental maturation of formation flight and autonomous docking algorithms in a risk-tolerant, microgravity environment. Multiple autonomous docking operations have been performed in the ISS to validate the GN&C architecture. These experiments led to the first autonomous docking with a tumbling target ever achieved in microgravity. Furthermore, the author also demonstrated successful docking in spite of the presence of measurement errors that were detected and rejected by an online fault detection algorithm. The results of these experiments will be discussed in this thesis. Finally, based on experiments in a laboratory environment, the author establishes two processes for the verification of GN&C software prior to on-orbit testing on the SPHERES testbed.by Simon Nolet.Sc.D

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones