Prototyping a new car semi-active suspension by variational feedback controller

New suspension systems electronically controlled are presented and mounted on board of a real car. The system consists of variable semi-active magneto-rheological dampers that are controlled through an electronic unit that is designed on the basis of a new optimal theoretical control, named VFC-Variational Feedback Controller. The system has been mounted on board of a BMW Series 1 car, and a set of experimental tests have been conducted in real driving conditions. The VFC reveals, because of its design strategy, to be able to enhance simultaneously both the comfort performance as well as the handling capability of the car. Preliminary comparisons with several industrially control methods adopted in the automotive field, among them skyhook and groundhook, show excellent results

Autonomous frequency domain identification: Theory and experiment

The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability

Heart rate variability : a fractal analysis

Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 200

Use of an Least Mean Squares Filter in the Control of Optical Beam Jitter

The article of record as published may be found at http://dx.doi.org/10.2514/1.26778Meeting optical beam jitter requirements is becoming a challenging problem for several space programs. A laser beam jitter control test bed has been developed at the Naval Postgraduate School to develop improved jitter control techniques. Several control techniques, such as least means squares and linear–quadratic regulator were applied for jitter control. Enhancement in least means squares techniques to improve convergence rate was achieved by adding an adaptive biasfilter to the reference signal. In the experiments, the platform is vibrated at 50 and 87 Hz. In addition, a fast steering mirror is used to inject a random component of 200 Hz band-limited white noise. The experimental results demonstrated that the addition of the adaptive bias filter to the least means squares algorithm significantly increased the converging rate of the controller. To achieve the reduction of both sinusoidal and random jitter, a combination of least means squares/adaptive bias filter and linear–quadratic regulator is most effective. The least means squares/adaptive bias filter control is most effective for a sinusoidal jitter and the linear–quadratic regulator control for a random jitter

1st year EFAST annual report

The present report provides information about the activities conducted during the 1st year of the EFAST project. The first chapter is dedicated to describe the inquiries conducted at the beginning of the project and to briefly summarise the main results. The second chapter is dedicated to the first EFAST workshop where some of the leading scientists in the field of earthquake engineering have met to discuss about the need and the technologies related to earthquake engineering. The third chapter contains a state of the art and future direction in seismic testing and simulation. The final chapter is dedicated to describe the preliminary design of the web portal of the future testing facility.JRC.DG.G.5-European laboratory for structural assessmen

Perspectives of Imaging of Single Protein Molecules with the Present Design of the European XFEL. - Part I - X-ray Source, Beamlime Optics and Instrument Simulations

The Single Particles, Clusters and Biomolecules (SPB) instrument at the European XFEL is located behind the SASE1 undulator, and aims to support imaging and structure determination of biological specimen between about 0.1 micrometer and 1 micrometer size. The instrument is designed to work at photon energies from 3 keV up to 16 keV. This wide operation range is a cause for challenges to the focusing optics. In particular, a long propagation distance of about 900 m between x-ray source and sample leads to a large lateral photon beam size at the optics. The beam divergence is the most important parameter for the optical system, and is largest for the lowest photon energies and for the shortest pulse duration (corresponding to the lowest charge). Due to the large divergence of nominal X-ray pulses with duration shorter than 10 fs, one suffers diffraction from mirror aperture, leading to a 100-fold decrease in fluence at photon energies around 4 keV, which are ideal for imaging of single biomolecules. The nominal SASE1 output power is about 50 GW. This is very far from the level required for single biomolecule imaging, even assuming perfect beamline and focusing efficiency. Here we demonstrate that the parameters of the accelerator complex and of the SASE1 undulator offer an opportunity to optimize the SPB beamline for single biomolecule imaging with minimal additional costs and time. Start to end simulations from the electron injector at the beginning of the accelerator complex up to the generation of diffraction data indicate that one can achieve diffraction without diffraction with about 0.5 photons per Shannon pixel at near-atomic resolution with 1e13 photons in a 4 fs pulse at 4 keV photon energy and in a 100 nm focus, corresponding to a fluence of 1e23 ph/cm^2. This result is exemplified using the RNA Pol II molecule as a case study

안전한 재구성 로봇 시스템: 설계, 프로그래밍 및 반응형 경로계획

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 기계항공공학부, 2020. 8. 박종우.The next generation of robots are being asked to work in close proximity to humans. At the same time, the robot should have the ability to change its topology to flexibly cope with various tasks. To satisfy these two requirements, we propose a novel modular reconfi gurable robot and accompanying software architecture, together with real-time motion planning algorithms to allow for safe operation in unstructured dynamic environments with humans. Two of the key innovations behind our modular manipulator design are a genderless connector and multi-dof modules. By making the modules connectable regardless of the input/output directions, a genderless connector increases the number of possible connections. The developed genderless connector can transmit as much load as necessary to an industrial robot. In designing two-dof modules, an offset between two joints is imposed to improve the overall integration and the safety of the modules. To cope with the complexity in modeling due to the genderless connector and multi-dof modules, a programming architecture for modular robots is proposed. The key feature of the proposed architecture is that it efficiently represents connections of multi-dof modules only with connections between modules, while existing architectures should explicitly represent all connections between links and joints. The data structure of the proposed architecture contains properties of tree-structured multi-dof modules with intra-module relations. Using the data structure and connection relations between modules, kinematic/dynamic parameters of connected modules can be obtained through forward recursion. For safe operation of modular robots, real-time robust collision avoidance algorithms for kinematic singularities are proposed. The main idea behind the algorithms is generating control inputs that increase the directional manipulability of the robot to the object direction by reducing directional safety measures. While existing directional safety measures show undesirable behaviors in the vicinity of the kinematic singularities, the proposed geometric safety measure generates stable control inputs in the entire joint space. By adding the preparatory input from the geometric safety measure to the repulsive input, a hierarchical collision avoidance algorithm that is robust to kinematic singularity is implemented. To mathematically guarantee the safety of the robot, another collision avoidance algorithm using the invariance control framework with velocity-dependent safety constraints is proposed. When the object approached the robot from a singular direction, the safety constraints are not satis ed in the initial state of the robot and the safety cannot be guaranteed using the invariance control. By proposing a control algorithm that quickly decreases the preparatory constraints below thresholds, the robot re-enters the constraint set and avoids collisions using the invariance control framework. The modularity and safety of the developed reconfi gurable robot is validated using a set of simulations and hardware experiments. The kinematic/dynamic model of the assembled robot is obtained in real-time and used to accurately control the robot. Due to the safe design of modules with o sets and the high-level safety functions with collision avoidance algorithms, the developed recon figurable robot has a broader safe workspace and wider ranger of safe operation speed than those of cooperative robots.다음 세대의 로봇은 사람과 가까이에서 협업할 수 있는 기능을 가져야한다. 그와 동시에, 로봇은 다양하게 변하는 작업에 대해 유연하게 대처할 수 있도록 자신의 구조를 바꾸는 기능을 가져야 한다. 이러한 두 가지 요구조건을 만족시키기 위해, 본 논문에서는 새로운 모듈라 로봇 시스템과 프로그래밍 아키텍쳐를 제시하고, 사람이 존재하는 동적 환경에서 안전한 로봇의 운용을 위한 실시한 경로 계획 알고리즘을 제시한다. 개발된 모듈라 로봇의 두 가지 핵심적인 혁신성은 무성별 커넥터와 다자유도 모듈에서 찾을 수 있다. 입력/출력 방향에 상관 없이 모듈이 연결될 수 있도록 함으로써, 무성별 커넥터는 결합 가능한 경우의 수를 늘릴 수 있다. 개발된 무성별 커넥터는 산업용 로봇에서 요구되는 충분한 부하를 견딜 수 있도록 설계되었다. 2 자유도 모듈의 설계에서 두 축 사이에 오프셋을 가지도록 함으로써 전체적인 완성도 및 안전도를 증가시켰다. 무성별 커넥터와 다자유도 모듈로 인한 모델링의 복잡성에 대응하기 위해, 일반적인 모듈라 로봇을 위한 소프트웨어 아키텍쳐를 제안하였다. 기존 모듈라 로봇의 연결을 나타내는 방법이 모든 링크와 조인트 사이의 연결 관계를 별도로 나타내야하는 것과 다르게, 제안된 아키텍쳐는 모듈들 사이의 연결관계만을 나타냄으로써 효율적인 다자유도 모듈의 연결관계를 나타낼 수 있다는 것을 특징으로 한다. 이를 위해 트리 구조를 가지는 일반적인 다자유도 모듈의 성질을 나타내는 데이터 구조를 정의하였다. 모듈들 사이의 연결관계 및 데이터 구조를 이용하여, 정확한 기구학/동역학 모델 파라미터를 얻어내는 순방향 재귀 알고리즘을 구현하였다. 모듈라 로봇의 안전한 운용을 위해, 기구학적 특이점에 강건한 실시간 충돌회피 알고리즘을 제안하였다. 방향성 안전도를 줄이는 방향의 제어 입력을 생성하여 물체 방향으로의 로봇 방향성 매니퓰러빌리티를 증가시키는 것이 제안한 알고리즘의 핵심이다. 기존의 방향성 안전도가 기구학적 특이점 근처에서 원하지 않는 성질을 가지는 것과는 반대로, 제안한 기하학적 안전도는 전체 조인트 공간에서 안정적인 제어 입력을 생성한다. 이 기하학적 안전도를 이용하여, 기구학적 특이점에 강건한 계층적 충돌회피 알고리즘을 구현하였다. 수학적으로 로봇의 안전도를 보장하기 위해, 상대속도에 종속적인 안전 제약조건을 가지는 불변 제어 프레임워크을 이용하여 또 하나의 충돌 회피 알고리즘을 제안하였다. 물체가 특이점 방향으로부터 로봇에 접근할 때, 로봇의 초기 상태에서 안전 제약조건을 만족시키지 못하게 되어 불변제어를 적용할 수 없게 된다. 준비 제약조건을 빠르게 임계점 아래로 감소시키는 알고리즘을 적용함으로써, 로봇은 제약조건 집합에 다시 들어가고 불변 제어 방법을 이용하여 충돌을 회피할 수 있게 된다. 개발된 재구성 로봇의 모듈라리티와 안전도는 일련의 시뮬레이션과 하드웨어 실험을 통해 검증되었다. 실시간으로 조립된 로봇의 기구학/동역학 모델을 얻어내 정밀 제어에 사용하였다. 안전한 모듈 디자인과 충돌 회피 등의 고차원 안전 기능을 통하여, 개발된 재구성 로봇은 기존 협동로봇보다 넓은 안전한 작업공간과 작업속도를 가진다.1 Introduction 1 1.1 Modularity and Recon gurability 1 1.2 Safe Interaction 4 1.3 Contributions of This Thesis 9 1.3.1 A Recon gurable Modular Robot System with Bidirectional Modules 9 1.3.2 A Modular Robot Software Programming Architecture 10 1.3.3 Anticipatory Collision Avoidance Planning 11 1.4 Organization of This Thesis 14 2 Design and Prototyping of the ModMan 17 2.1 Genderless Connector 18 2.2 Modules for ModMan 21 2.2.1 Joint Modules 21 2.2.2 Link and Gripper Modules 25 2.3 Experiments 26 2.3.1 System Setup 26 2.3.2 Repeatability Comparison with Non-recon gurable Robot Manipulators 28 2.3.3 E ect of the O set in Two-dof Modules 30 2.4 Conclusion 32 3 A Programming Architecture for Modular Recon gurable Robots 33 3.1 Data Structure for Multi-dof Joint Modules 34 3.2 Automatic Kinematic Modeling 37 3.3 Automatic Dynamic Modeling 40 3.4 Flexibility in Manipulator 42 3.5 Experiments 45 3.5.1 System Setup 46 3.5.2 Recon gurability 46 3.5.3 Pick-and-Place with Vision Sensors 48 3.6 Conclusion 49 4 A Preparatory Safety Measure for Robust Collision Avoidance 51 4.1 Preliminaries on Manipulability and Safety 52 4.2 Analysis on Reected Mass 56 4.3 Manipulability Control on S+(1;m) 60 4.3.1 Geometry of the Group of Positive Semi-de nite Matrices 60 4.3.2 Rank-One Manipulability Control 63 4.4 Collision Avoidance with Preparatory Action 65 4.4.1 Repulsive and Preparatory Potential Functions 65 4.4.2 Hierarchical Control and Task Relaxation 67 4.5 Experiments 70 4.5.1 Manipulability Control 71 4.5.2 Collision Avoidance 75 4.6 Conclusion 82 5 Collision Avoidance with Velocity-Dependent Constraints 85 5.1 Input-Output Linearization 87 5.2 Invariance Control 89 5.3 Velocity-Dependent Constraints for Robot Safety 90 5.3.1 Velocity-Dependent Repulsive Constraints 90 5.3.2 Preparatory Constraints 92 5.3.3 Corrective Control for Dangerous Initial State 93 5.4 Experiment 95 5.5 Conclusion 98 6 Conclusion 101 6.1 Overview of This Thesis 101 6.2 Future Work 104 Appendix A Appendix 107 A.1 Preliminaries on Graph Theory 107 A.2 Lie-Theoretic Formulations of Robot Kinematics and Dynamics 108 A.3 Derivatives of Eigenvectors and Eigenvalues 110 A.4 Proof of Proposition Proposition 4.1 111 A.5 Proof of Triangle Inequality When p = 1 114 A.6 Detailed Conditions for a Danger Field 115 Bibliography 117 Abstract 127Docto

Improving Engagement Assessment by Model Individualization and Deep Learning

This dissertation studies methods that improve engagement assessment for pilots. The major work addresses two challenging problems involved in the assessment: individual variation among pilots and the lack of labeled data for training assessment models. Task engagement is usually assessed by analyzing physiological measurements collected from subjects who are performing a task. However, physiological measurements such as Electroencephalography (EEG) vary from subject to subject. An assessment model trained for one subject may not be applicable to other subjects. We proposed a dynamic classifier selection algorithm for model individualization and compared it to other two methods: base line normalization and similarity-based model replacement. Experimental results showed that baseline normalization and dynamic classifier selection can significantly improve cross-subject engagement assessment. For complex tasks such as piloting an air plane, labeling engagement levels for pilots is challenging. Without enough labeled data, it is very difficult for traditional methods to train valid models for effective engagement assessment. This dissertation proposed to utilize deep learning models to address this challenge. Deep learning models are capable of learning valuable feature hierarchies by taking advantage of both labeled and unlabeled data. Our results showed that deep models are better tools for engagement assessment when label information is scarce. To further verify the power of deep learning techniques for scarce labeled data, we applied the deep learning algorithm to another small size data set, the ADNI data set. The ADNI data set is a public data set containing MRI and PET scans of Alzheimer\u27s Disease (AD) patients for AD diagnosis. We developed a robust deep learning system incorporating dropout and stability selection techniques to identify the different progression stages of AD patients. The experimental results showed that deep learning is very effective in AD diagnosis. In addition, we studied several imbalance learning techniques that are useful when data is highly unbalanced, i.e., when majority classes have many more training samples than minority classes. Conventional machine learning techniques usually tend to classify all data samples into majority classes and to perform poorly for minority classes. Unbalanced learning techniques can balance data sets before training and can improve learning performance

Optimal slip control for tractors with feedback of drive torque

Traction efficiency of tractors barely reaches 50 % in field operations. On the other hand, modern trends in agriculture show growth of the global tractor markets and at the same time increased demands for greenhouse gas emission reduction as well as energy efficiency due to increasing fuel costs. Engine power of farm tractors is growing at 1.8 kW per year reaching today about 500 kW for the highest traction class machines. The problem of effective use of energy has become crucial. Existing slip control approaches for tractors do not fulfil this requirement due to fixed reference set-point. The present work suggests an optimal control scheme based on set-point optimization and on assessment of soil conditions, namely, wheel-ground parameter identification using fuzzy-logic-assisted adaptive unscented Kalman filter.:List of figures VIII List of tables IX Keywords XI List of abbreviations XII List of mathematical symbols XIII Indices XV 1 Introduction 1 1.1 Problem description and challenges 1 1.1.1 Development of agricultural industry 1 1.1.2 Power flows and energy efficiency of a farm tractor 2 1.2 Motivation 9 1.3 Purpose and approach 12 1.3.1 Purpose and goals 12 1.3.2 Brief description of methodology 14 1.3.2.1 Drive torque feedback 14 1.3.2.2 Measurement signals 15 1.3.2.3 Identification of traction parameters 15 1.3.2.4 Definition of optimal slip 15 1.4 Outline 16 2 State of the art in traction management and parameter estimation 17 2.1 Slip control for farm tractors 17 2.2 Acquisition of drive torque feedback 23 2.3 Tire-ground parameter estimation 25 2.3.1 Kalman filter 25 2.3.2 Extended Kalman filter 27 2.3.3 Unscented Kalman filter 27 2.3.4 Adaptation algorithms for Kalman filter 29 3 Modelling vehicle dynamics for traction control 31 3.1 Tire-soil interaction 31 3.1.1 Forces in wheel-ground contact 32 3.1.1.1 Vertical force 32 3.1.1.2 Tire-ground surface geometry 34 3.1.2 Longitudinal force 36 3.1.3 Zero-slip condition 37 3.1.3.1 Soil shear stress 38 3.1.3.2 Rolling resistance 39 3.2 Vehicle body and wheels 40 3.2.1 Short description of Multi-Body-Simulation 40 3.2.2 Vehicle body and wheel models 42 3.2.3 Wheel structure 43 3.3 Stochastic input signals 45 3.3.1 Influence of trend and low-frequency components 47 3.3.2 Modelling stochastic signals 49 3.4 Further components and general view of tractor model 53 3.4.1 Generator, intermediate circuit, electrical motors and braking resistor 53 3.4.2 Diesel engine 55 4 Identification of traction parameters 56 4.1 Description of identification approaches 56 4.2 Vehicle model 58 4.2.1 Vehicle longitudinal dynamics 58 4.2.2 Wheel rotational dynamics 59 4.2.3 Tire dynamic rolling radius and inner rolling resistance coefficient 60 4.2.4 Whole model 61 4.3 Static methods of parameter identification 63 4.4 Adaptation mechanism of the unscented Kalman filter 63 4.5 Fuzzy supervisor for the adaptive unscented Kalman filter 66 4.5.1 Structure of the fuzzy supervisor 67 4.5.2 Stability analysis of the adaptive unscented Kalman filter with the fuzzy supervisor 69 5 Optimal slip control 73 5.1 Approaches for slip control by means of traction control system 73 5.1.1 Feedback compensation law 73 5.1.2 Sliding mode control 74 5.1.3 Funnel control 77 5.1.4 Lyapunov-Candidate-Function-based control, other approaches and choice of algorithm 78 5.2 General description of optimal slip control algorithm 79 5.3 Estimation of traction force characteristic curves 82 5.4 Optimal slip set-point computation 85 6 Verification of identification and optimal slip control systems 91 6.1 Simulation results 91 6.1.1 Identification of traction parameters 91 6.1.1.1 Comparison of extended Kalman filter and unscented Kalman filter 92 6.1.1.2 Comparison of ordinary and adaptive unscented Kalman filters 96 6.1.1.3 Comparison of the adaptive unscented Kalman filter with the fuzzy supervisor and static methods 99 6.1.1.4 Description of soil conditions 100 6.1.1.5 Identification of traction parameters under changing soil conditions 101 6.1.2 Approximation of characteristic curves 102 6.1.3 Slip control with reference of 10% 103 6.1.4 Comparison of operating with fixed and optimal slip reference 104 6.2 Experimental verification 108 6.2.1 Setup and description of the experiments 108 6.2.2 Virtual slip control without load machine 109 6.2.3 Virtual slip control with load machine 113 7 Summary, conclusions and future challenges 122 7.1 Summary of results and discussion 122 7.2 Contributions of the dissertation 123 7.3 Future challenges 123 Bibliography 125 A Measurement systems 137 A.1 Measurement of vehicle velocity 137 A.2 Measurement of wheel speed 138 A.3 Measurement or estimation of wheel vertical load 139 A.4 Measurement of draft force 140 A.5 Further possible measurement systems 141 B Basic probability theoretical notions 142 B.1 Brief description of the theory of stochastic processes 142 B.2 Properties of stochastic signals 144 B.3 Bayesian filtering 145 C Modelling stochastic draft force and field microprofile 147 D Approximation of kappa-curves 152 E Simulation parameters 15