Blessing or Curse: Impact of Algorithmic Trading Bots Invasion of the Cryptocurrency Market

In this paper, we investigate the impact of the absence of trading bots on human traders’ investment returns. Using comprehensive data set obtained from a large cryptocurrency exchange platform, we find that trading bots play a market-making role, and they boost human traders’ investment returns. We use the natural experiment setting that transforms a heterogenous market co-created with trading bots and human traders into a human-only financial market for empirical design. This paper extends the traditional investment decision under uncertainty by considering human attitudes toward algorithms while providing significant contributions to policymakers and regulators by providing empirical evidence on trading bots

An Autonomous Human Following Caddie Robot with High-Level Driving Functions

Nowadays, mobile robot platforms are utilized in various fields not only for transportation but also for other diverse services such as industrial, medical and, sports, etc. Mobile robots are also an emerging application as sports field robots, where they can help serve players or even play the games. In this paper, a novel caddie robot which can autonomously follow the golfer as well as provide useful information such as golf course navigation system and weather updates, is introduced. The locomotion of the caddie robot is designed with two modes: autonomous human following mode and manual driving mode. The transition between each mode can be achieved manually or by an algorithm based on the velocity, heading angle, and inclination of the ground surface. Moreover, the transition to manual mode is activated after a caddie robot has recognized the human intention input by hand. In addition, the advanced control algorithm along with a trajectory generator for the caddie robot are developed taking into consideration the locomotion modes. Experimental results show that the proposed strategies to drive various operating modes are efficient and the robot is verified to be utilized in the golf course. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.1

Design of Adaptive Sliding Mode Controller for Robust Yaw Stabilization of In-wheel-motor-driven Electric Vehicles

A robust yaw stability control system is designed to stabilize the vehicle yaw motion. Since the vehicles undergo changes in parameters and disturbances with respect to the wide range of driving condition, e.g., tire-road conditions, a robust control design technique is required to guarantee system stability. In this paper, a sliding mode control methodology is applied to make vehicle yaw rate to track its reference with robustness against model uncertainties and disturbances. A parameter adaptation law is applied to estimate varying vehicle parameters with respect to road conditions and is incorporated into sliding mode control framework. The control performance of the proposed control system is evaluated through computer simulation using CarSim vehicle model which proved to give a good description of the dynamics of an experimental in-wheel-motor-driven electric vehicle. Moreover, field tests were carried out to verify the effectiveness of the proposed adaptive sliding mode controlle

Advanced Motion Control for Electric Vehicles Using Lateral Tire Force Sensors

The principal concern in motion control of electric vehicles is providing drivers with more enhanced safety by preventing risky driving situations. A close examination of accident data reveals that loss of the vehicle control is the main reason for most vehicle accidents. To help to prevent such accidents, vehicle motion control systems should be used, which require important information on vehicle state and tire-road conditions. Unfortunately, some critical parameters in vehicle motion control such as vehicle sideslip angle, roll angle, and tire-road friction coefficient are difficult to measure in a vehicle, due to both technical and economic reasons. Recently, automotive companies have looked at utilizing tire forces which are directly measured by lateral tire force sensors, e.g., MSHub unit invented by NSK Ltd. Since the vehicle motion is governed by tire forces generated by tire-road interaction, this tire force information can provide promising solutions for accurate state estimation and advanced motion control. In this thesis, several advanced methods for robustly estimating the vehicle state and controlling the vehicle motion are proposed based on lateral tire force sensors. These advanced methods include 1) novel algorithms for vehicle sideslip angle and roll angle estimation, 2) real-time algorithm to estimate tire-road condition, 3) advanced motion control algorithm based on vehicle sideslip angle estimation, 4) advanced motion control algorithm based on direct tire force control for vehicle safety enhancement. Robust estimation of vehicle states such as vehicle sideslip angle and roll angle is a challenging issue in vehicle motion control applications like yaw stability control and roll stability control. In this thesis, novel methods for estimating vehicle sideslip angle and roll angle are proposed using lateral tire force sensors. For vehicle sideslip angle estimation, two estimation algorithms based on a recursive least square (RLS) approach and an extended Kalman filter (EKF) technique are designed, respectively. For roll angle estimation, KF is designed using available sensor measurements and physical roll dynamics model. The effectiveness of proposed estimation methods is verified through field tests on an experimental electric vehicle. Experimental results demonstrate that the proposed estimation methods can accurately estimate the vehicle sideslip angle and roll angle. The robust design of motion controllers for vehicle stability enhancement is challenging due to nonlinear characteristics in vehicle and tire models, e.g., varying tire cornering stiffness with respect to road condition and tire force saturation. In this thesis, the two motion control algorithms are designed based on lateral tire force sensor application; 1) a motion control algorithm, based on vehicle sideslip angle estimation, is designed using two-degree-of-freedom (2-DOF) control methodology for yaw rate and vehicle sideslip tracking control and an adaptive feed-forward control technique is applied for improving control performances, 2) a novel motion control algorithm based on direct tire force control is designed. In this control algorithm, the real-time estimation of a critical vehicle state, i.e., a vehicle sideslip angle, is not required anymore. A robust control approach is applied in the design of the controllers for improved robustness to uncertainties in the vehicle and tire models. Moreover, a DOB is utilized to compensate for changes in the dynamic tire model as well as for mechanical disturbances in the actuators. Both control algorithms are implemented on an experimental electric vehicle with in-wheel motors and those control performances, e.g., yaw rate, vehicle sideslip angle tracking, and lateral tire force tracking ability, are verified through field tests. It is shown that proposed control algorithms based on lateral tire force sensors can contribute to improvement of the vehicle stability. This thesis proposes novel vehicle state estimation and motion control methods using lateral tire force sensors, and has discussed the practical application of lateral tire force sensors to motion control systems for future electric vehicles. Moreover, this thesis investigates important technologies for improving the motion control systems of electric vehicles not only based on theoretical approaches to vehicle, tire dynamics and estimation, and control design, but also implementation on experimental electric vehicles in real-time.報告番号: ; 学位授与年月日: 2012-09-27 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: ; 研究科・専攻: 工学系研究科電気系工学専

Application of Novel Lateral Tire Force Sensors to Vehicle Parameter Estimation of Electric Vehicles

This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented and further developed by NSK Ltd. For estimating the lateral vehicle velocity, tire force models considering lateral load transfer effects are used, and a recursive least square algorithm is adapted to identify the lateral vehicle velocity as an unknown parameter. Using the estimated lateral vehicle velocity, tire cornering stiffness, which is an important tire parameter dominating the vehicle’s cornering responses, is estimated. For the practical implementation, the cornering stiffness estimation algorithm based on a simple bicycle model is developed and discussed. Finally, proposed estimation algorithms were evaluated using experimental test data

Socialize Less Pay More: The Link Between Virtual Network Embeddedness and User Contributions

This study examines the impact of network structure on two important business outcomes of the PWYW (Pay-What-You-Want) live streaming platform, namely live chat, and tipping. Specifically, we investigate the role of network embeddedness on these outcomes by analyzing 9,524,420 transaction-broadcast records from 1,334,999 users of a leading PWYW live streaming platform in South Korea. Our analysis sheds light on how user contributions in the form of tipping and live chat change because of the extent of content consumption overlaps. Our preliminary results suggest that higher levels of network embeddedness lead to increased tipping and decreased live chatting. These findings have important implications for platform designers and streamers, as they highlight the need to consider consumption patterns across users in the design of PWYW live streaming platforms

タイヤ横力センサを利用した電気自動車のモーションコントロール

The principal concern in motion control of electric vehicles is providing drivers with more enhanced safety by preventing risky driving situations. A close examination of accident data reveals that loss of the vehicle control is the main reason for most vehicle accidents. To help to prevent such accidents, vehicle motion control systems should be used, which require important information on vehicle state and tire-road conditions. Unfortunately, some critical parameters in vehicle motion control such as vehicle sideslip angle, roll angle, and tire-road friction coefficient are difficult to measure in a vehicle, due to both technical and economic reasons. Recently, automotive companies have looked at utilizing tire forces which are directly measured by lateral tire force sensors, e.g., MSHub unit invented by NSK Ltd. Since the vehicle motion is governed by tire forces generated by tire-road interaction, this tire force information can provide promising solutions for accurate state estimation and advanced motion control. In this thesis, several advanced methods for robustly estimating the vehicle state and controlling the vehicle motion are proposed based on lateral tire force sensors. These advanced methods include 1) novel algorithms for vehicle sideslip angle and roll angle estimation, 2) real-time algorithm to estimate tire-road condition, 3) advanced motion control algorithm based on vehicle sideslip angle estimation, 4) advanced motion control algorithm based on direct tire force control for vehicle safety enhancement. Robust estimation of vehicle states such as vehicle sideslip angle and roll angle is a challenging issue in vehicle motion control applications like yaw stability control and roll stability control. In this thesis, novel methods for estimating vehicle sideslip angle and roll angle are proposed using lateral tire force sensors. For vehicle sideslip angle estimation, two estimation algorithms based on a recursive least square (RLS) approach and an extended Kalman filter (EKF) technique are designed, respectively. For roll angle estimation, KF is designed using available sensor measurements and physical roll dynamics model. The effectiveness of proposed estimation methods is verified through field tests on an experimental electric vehicle. Experimental results demonstrate that the proposed estimation methods can accurately estimate the vehicle sideslip angle and roll angle. The robust design of motion controllers for vehicle stability enhancement is challenging due to nonlinear characteristics in vehicle and tire models, e.g., varying tire cornering stiffness with respect to road condition and tire force saturation. In this thesis, the two motion control algorithms are designed based on lateral tire force sensor application; 1) a motion control algorithm, based on vehicle sideslip angle estimation, is designed using two-degree-of-freedom (2-DOF) control methodology for yaw rate and vehicle sideslip tracking control and an adaptive feed-forward control technique is applied for improving control performances, 2) a novel motion control algorithm based on direct tire force control is designed. In this control algorithm, the real-time estimation of a critical vehicle state, i.e., a vehicle sideslip angle, is not required anymore. A robust control approach is applied in the design of the controllers for improved robustness to uncertainties in the vehicle and tire models. Moreover, a DOB is utilized to compensate for changes in the dynamic tire model as well as for mechanical disturbances in the actuators. Both control algorithms are implemented on an experimental electric vehicle with in-wheel motors and those control performances, e.g., yaw rate, vehicle sideslip angle tracking, and lateral tire force tracking ability, are verified through field tests. It is shown that proposed control algorithms based on lateral tire force sensors can contribute to improvement of the vehicle stability. This thesis proposes novel vehicle state estimation and motion control methods using lateral tire force sensors, and has discussed the practical application of lateral tire force sensors to motion control systems for future electric vehicles. Moreover, this thesis investigates important technologies for improving the motion control systems of electric vehicles not only based on theoretical approaches to vehicle, tire dynamics and estimation, and control design, but also implementation on experimental electric vehicles in real-time.University of Tokyo (東京大学

Wheel Slip Control for Improving Traction-Ability and Energy Efficiency of a Personal Electric Vehicle

In this paper, a robust wheel slip control system based on a sliding mode controller is proposed for improving traction-ability and reducing energy consumption during sudden acceleration for a personal electric vehicle. Sliding mode control techniques have been employed widely in the development of a robust wheel slip controller of conventional internal combustion engine vehicles due to their application effectiveness in nonlinear systems and robustness against model uncertainties and disturbances. A practical slip control system which takes advantage of the features of electric motors is proposed and an algorithm for vehicle velocity estimation is also introduced. The vehicle velocity estimator was designed based on rotational wheel dynamics, measurable motor torque, and wheel velocity as well as rule-based logic. The simulations and experiments were carried out using both CarSim software and an experimental electric vehicle equipped with in-wheel-motors. Through field tests, traction performance and effectiveness in terms of energy saving were all verified. Comparative experiments with variations of control variables proved the effectiveness and practicality of the proposed control design