Practical Coordination of Multi-Vehicle Systems in Formation

This thesis considers the cooperation and coordination of multi vehicle systems cohesively in order to keep the formation geometry and provide the string stability. We first present the modeling of aerial and road vehicles representing different motion characteristics suitable for cooperative operations. Then, a set of three dimensional cohesive motion coordination and formation control schemes for teams of autonomous vehicles is proposed. The two main components of these schemes are i) platform free high level online trajectory generation algorithms and ii) individual trajectory tracking controllers. High level algorithms generate the desired trajectories for three dimensional leader-follower structured tight formations, and then distributed controllers provide the individual control of each agent for tracking the desired trajectories. The generic goal of the control scheme is to move the agents while maintaining the formation geometry. We propose a distributed control scheme to solve this problem utilizing the notions of graph rigidity and persistence as well as techniques of virtual target tracking and smooth switching. The distributed control scheme is developed by modeling the agent kinematics as a single-velocity integrator; nevertheless, extension to the cases with simplified kinematic and dynamic models of fixed-wing autonomous aerial vehicles and quadrotors is discussed. The cohesive cooperation in three dimensions is so beneficial for surveillance and reconnaissance activities with optimal geometries, operation security in military activities, more viable with autonomous flying, and future aeronautics aspects, such as fractionated spacecraft and tethered formation flying. We then focus on motion control task modeling for three dimensional agent kinematics and considering parametric uncertainties originated from inertial measurement noise. We design an adaptive controller to perform the three dimensional motion control task, paying attention to the parametric uncertainties, and employing a recently developed immersion and invariance based scheme. Next, the cooperative driving of road vehicles in a platoon and string stability concepts in one-dimensional traffic are discussed. Collaborative driving of commercial vehicles has significant advantages while platooning on highways, including increased road-capacity and reduced traffic congestion in daily traffic. Several companies in the automotive sector have started implementing driver assistance systems and adaptive cruise control (ACC) support, which enables implementation of high level cooperative algorithms with additional softwares and simple electronic modifications. In this context, the cooperative adaptive cruise control approach are discussed for specific urban and highway platooning missions. In addition, we provide details of vehicle parameters, mathematical models of control structures, and experimental tests for the validation of our models. Moreover, the impact of vehicle to vehicle communication in the existence of static road-side units are given. Finally, we propose a set of stability guaranteed controllers for highway platooning missions. Formal problem definition of highway platooning considering constant and velocity dependent spacing strategies, and formal string stability analysis are included. Additionally, we provide the design of novel intervehicle distance based priority coefficient of feed-forward filter for robust platooning. In conclusion, the importance of increasing level of autonomy of single agents and platoon topology is discussed in performing cohesive coordination and collaborative driving missions and in mitigating sensory errors. Simulation and experimental results demonstrate the performance of our cohesive motion and string stable controllers, in addition we discuss application in formation control of autonomous multi-agent systems

Guidance and control of an autonomous underwater vehicle

Merged with duplicate record 10026.1/856 on 07.03.2017 by CS (TIS)A cooperative project between the Universities of Plymouth and Cranfield was aimed at designing and developing an autonomous underwater vehicle named Hammerhead. The work presented herein is to formulate an advance guidance and control system and to implement it in the Hammerhead. This involves the description of Hammerhead hardware from a control system perspective. In addition to the control system, an intelligent navigation scheme and a state of the art vision system is also developed. However, the development of these submodules is out of the scope of this thesis. To model an underwater vehicle, the traditional way is to acquire painstaking mathematical models based on laws of physics and then simplify and linearise the models to some operating point. One of the principal novelties of this research is the use of system identification techniques on actual vehicle data obtained from full scale in water experiments. Two new guidance mechanisms have also been formulated for cruising type vehicles. The first is a modification of the proportional navigation guidance for missiles whilst the other is a hybrid law which is a combination of several guidance strategies employed during different phases of the Right. In addition to the modelling process and guidance systems, a number of robust control methodologies have been conceived for Hammerhead. A discrete time linear quadratic Gaussian with loop transfer recovery based autopilot is formulated and integrated with the conventional and more advance guidance laws proposed. A model predictive controller (MPC) has also been devised which is constructed using artificial intelligence techniques such as genetic algorithms (GA) and fuzzy logic. A GA is employed as an online optimization routine whilst fuzzy logic has been exploited as an objective function in an MPC framework. The GA-MPC autopilot has been implemented in Hammerhead in real time and results demonstrate excellent robustness despite the presence of disturbances and ever present modelling uncertainty. To the author's knowledge, this is the first successful application of a GA in real time optimization for controller tuning in the marine sector and thus the thesis makes an extremely novel and useful contribution to control system design in general. The controllers are also integrated with the proposed guidance laws and is also considered to be an invaluable contribution to knowledge. Moreover, the autopilots are used in conjunction with a vision based altitude information sensor and simulation results demonstrate the efficacy of the controllers to cope with uncertain altitude demands.J&S MARINE LTD., QINETIQ, SUBSEA 7 AND SOUTH WEST WATER PL

Third International Symposium on Magnetic Suspension Technology

In order to examine the state of technology of all areas of magnetic suspension and to review recent developments in sensors, controls, superconducting magnet technology, and design/implementation practices, the Third International Symposium on Magnetic Suspension Technology was held at the Holiday Inn Capital Plaza in Tallahassee, Florida on 13-15 Dec. 1995. The symposium included 19 sessions in which a total of 55 papers were presented. The technical sessions covered the areas of bearings, superconductivity, vibration isolation, maglev, controls, space applications, general applications, bearing/actuator design, modeling, precision applications, electromagnetic launch and hypersonic maglev, applications of superconductivity, and sensors

Identification of Flight Vehicle Models Using Fuzzified Eigensystem Realization Algorithm

This paper presents a new approach with a fuzzified eigensystem realization algorithm for identification of flight vehicle models in low-speed wind tunnel (LSWT) and high-speed wind tunnel (HSWT). A variety of variables in model types and testing environment (such as angle-of-attack, sideslip angle, tunnel wind speed) and profile, elevator, and power system (motor and propeller) of mini unmanned aerial vehicle (mini-UAV) model are considered in a power-on mini-UAV testing system in LSWT and an Advisory Group for Aerospace Research and Development (AGARD) standard calibration model in HSWT. The method based on the fuzzy logic inference structure is simple and effective. The results obtained are compared to those obtained by the conventional wind tunnel testing method. To verify the effectiveness of the proposed methodology, simulations are conducted using real-world experimental results that demonstrate that the working performance of the proposed method correlates well as expected

Identification of Flight Vehicle Models Using Eigensystem Realization Algorithm

本論文的目的是在探討特徵系統實現理論運用於飛行載具氣動力模式之識別。本文採用線性及非線性之參數系統識別，以求得低速風洞試驗及高速風洞試驗下之載具氣動力參數。 文中設計之線性系統參數識別運用模糊特徵值系統整合風洞試驗系統中之各種變數，包括風洞風速、模型測試攻角、側滑角、水平尾角度、翼型及載具動力(馬達轉速及螺槳型式)等，與測試環境關聯性之相關實用模糊規則定義，進行小型動力無人飛行載具(power-on mini-UAV)及AGARD標準模型進行氣動力參數系統識別。本論文將模擬結果與小型動力無人飛行載具於低速風洞及AGARD標準模型於高速風洞吹試之數據進行比對，證實所提方法之效益。 由於風洞試驗中之各相關變數為高度非線性，故本研究亦運用多層遞迴式神經網路架構結合非線性特徵系統識別方法來探討飛行載具之氣動力參數。本非線性系統最大效益為可以直接引用該構型之線性特徵系統所求得歸屬函數值，定義為非線性之識別系統之初始權重，再利用遞迴式神經網路架構來學習最佳權重值，不需藉實際風洞吹試就能得到近似之飛行載具氣動力參數。 模擬結果初步地證實本線性系統與非線性系統之特徵系統實現理論均達到以較少之實際風洞試驗結果來有效地識別系統參數的目的。我們利用現有輸入參數及不同識別變數延伸到多層遞迴式神經網路架構，估計最佳氣動力參數，此不但可以減少實際風洞吹試需求，更能節約測試資源及冗長時間。This dissertation presents a new approach to deal with system identification for flight vehicle models using the eigensystem realization algorithm. The system identification is used to achieve the desired parameters of unmanned aerial vehicles (UAVs) in low-speed wind tunnel (LSWT) test and high-speed wind tunnel (HSWT) test. The linear system identification applies a fuzzified eigensystem realization algorithm (fuzzified-ERA) for identification of the flight vehicle models in LSWT and HSWT. A variety of variables in model types and testing environment, such as tunnel wind speed, angle-of-attack, sideslip angle, elevator, mini-UAV model profile, and power system (motor and propeller) are considered in a power-on mini-UAV testing system in LSWT and an Advisory Group for Aerospace Research and Development (AGARD) standard calibration model in HSWT. The method based on the fuzzy logic inference structure is simple and effective. The results obtained are compared to those obtained by the conventional wind tunnel testing method. To verify effectiveness of the proposed methodology, simulations are conducted using the real-world experimental data that demonstrate the working performance of the proposed method correlates well as expected. The relationship of variables for flight vehicles in wind tunnel test is highly nonlinear. To fulfill aerodynamic parameter identification of flight vehicle models, an eigensystem realization algorithm identification method of ERA based on a nonlinear multilayer recurrent neural network (MRNN) is also proposed. ERA is a mathematical method, which purpose is to use measurements observed over time, containing random variations and other inaccuracies from the input data, and produce values that tend to be closer to the true values. For the MRNN, it is included to estimate the optimal parameters of the nonlinear flight vehicle model. We apply the results of linear ERA membership function as the initial weights of the nonlinear ERA in RNN model parameter identification and determine the optimal weights to identify aerodynamic coefficients of flight vehicles with less testing. Simulation results preliminarily validate that the method resulted from linear and nonlinear system with eigensystem realization algorithms. Considering the practical usefulness, the approaches presented in this dissertation efficiently help aerodynamicists selecting the optimal design parameters to meet the desired goal and reduce the cost for conducting real-world wind tunnel testing for flight vehicles.中文摘要 I Abstract II Contents V List of Tables VII List of Figures VIII Chapter 1 Introduction 1 1.1 Motivations 1 1.2 Literature Review 4 1.2.1 Review of Eigenvalue Realization Algorithms (ERAs) 4 1.2.2 Review of Nonlinear Identification Based on Multilayer Recurrent Neural Network (MRNN) 6 1.3 Contributions of the Dissertation 7 1.4 Organization of the Dissertation 8 Chapter 2 Wind Tunnel System Descriptions 9 2.1 Low Speed Wind Tunnel 9 2.2 High Speed Wind Tunnel 11 2.3 Wind Tunnel Forces and Moments 12 2.4 Wind Tunnel Data Corrections 19 2.4.1 Balance Corrections 19 2.4.2 Wall Corrections 21 2.5 Measurement Errors 21 Chapter 3 Linear ERA-Based Model Identification 22 3.1 ERA 22 3.2 Fuzzified Eigenvalue Realization Algorithm (Fuzzified -ERA) 24 3.2.1 Fuzzy Logic Design 24 3.2.2 Fuzzy Correlation Coefficient 36 3.2.3 Fuzzified-ERA 37 3.3 Results and Discussions 39 3.3.1 Verification of Different Wind Tunnel Speed and Rotational Speed in LSWT 40 3.3.2 Verification of Different Angle of Elevator in LSWT 45 3.3.3 Verification of Different Wind Tunnel Speed in Higher-Order in LSWT 50 3.3.4 Verification of Different Wind Tunnel Speed in HSWT 56 3.4 Concluding Remarks 60 Chapter 4. Nonlinear Identification Based on Multilayer Recurrent Neural Network 61 4.1 Nonlinear RNN System Description 61 4.2 Nonlinear Parameter Identification Description 62 4.2.1 Learning Algorithm of RNN 67 4.2.2 Systemic error threshold 69 4.2.3 Learning rate 71 4.3 Nonlinear MRNN in ERA-Based Identification Algorithm 71 4.3.1 Nonlinear MRNN Description 72 4.3.2 Learning Algorithm of MRNN 75 4.3.3 Main Results 77 4.4 Results and Discussions 78 4.4.1 Verification of linear and nonlinear systems with RNN in HSWT 81 4.4.2 Verification of single variable with RNN in HSWT 84 4.4.3 Verification of linear and nonlinear systems with MRNN in LSWT 87 4.4.4 Verification of multivariable in MRNN with LSWT 92 4.5 Concluding Remarks 97 Chapter 5. Conclusions and Future Works 98 5.1 Conclusions 98 5.2 Future Works 99 Bibliography 100 Publication List 10