bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biomechanical Comparison of a New Dynamic Ankle Orthosis to a Standard Ankle-Foot Orthosis During Walking
Authors: Chung Chloe L. , DiAngelo Denis J. , Powell Douglas W. , Paquette Max R. 
Published: November 2019

Abstract
Patients who sustain irreversible cartilage damage or joint instability from ankle injuries are likely to develop ankle osteoarthritis (OA). A dynamic ankle orthosis (DAO) was recently designed with the intent to offload the foot and ankle using a distractive force, allowing more natural sagittal and frontal plane ankle motion during gait. To evaluate its efficacy, this study compared ankle joint kinematics and plantar pressures among the DAO, standard double upright ankle-foot orthosis (DUAFO), and a nonorthosis control (CON) condition in healthy adults during walking. Ten healthy subjects (26 ± 3.8 yr; 69.6 ± 12.7 kg; and 1.69 ± 0.07 m) walked on a treadmill at 1.4 m/s in three orthosis conditions: CON, DAO, and DUAFO. Ankle kinematics were assessed using a three-dimensional (3D) motion capture system and in-shoe plantar pressures were measured for seven areas of the foot. DAO reduced hallux peak plantar pressures (PPs) compared to CON and DUAFO. PPs under toes 2–5 were smaller in DAO than DUAFO, but greater in DUAFO compared to CON. Early stance peak plantarflexion (PF) angular velocity was smaller in DAO compared to CON and DUAFO. Eversion (EV) ROM was much smaller in DUAFO compared to CON and DAO. Early stance peak eversion angular velocity was smaller in DAO and much smaller in DUAFO compared to CON. This study demonstrates the capacity of the DAO to provide offloading during ambulation without greatly affecting kinematic parameters including frontal plane ankle motion compared to CON. Future work will assess the effectiveness of the DAO in a clinical osteoarthritic population.
Numerical Investigation of Oxygen Permeation Through a Ba0.5Sr0.5Co0.8Fe0.2O3−δ Ion Transport Membrane With Impingement Flow
Authors: Ben-Mansour R. , Hamdy Mohamed , Sanusi Y. , Araoye A. , Habib M. A. , Mokheimer Esmail M. A. 
Published: November 2019

Abstract
Ion transport membrane (ITM) is considered to be one of the promising techniques for the separation of oxygen from the air for clean energy applications. Studying flow configurations of gases around Ba0.5Sr0.5Co0.8Fe0.2O3−δ (BSCF) membrane is presented and discussed in this paper. The effects of the sweep mass flow rate and impingement configurations for the gases flow in the feed and permeation sides have been investigated. In this regard, flow with single or double impingement and impingement with different angles have been simulated and analyzed in order to identify the configurations that would provide the maximum permeation flux. Results show that increasing the sweep flow rate, directly, increases the oxygen permeation flux. It is also found that, in case of single impingement, decreasing the distance between the nozzle and the membrane (H), directly, increases the oxygen permeation flux for constant sweep side nozzle (slot) width (D). The permeation flux increases from around 2.9–3.66 µmole/cm2 s for the ratio H:D from1:1 to 1:4 (i.e., decreasing H to one-fourth of its value). Results show that the double impingement flow gives lower results than the single impingements by about 35.7%. The results also revealed that the optimum configuration is the parallel flow with vacuum in the sweeping side, which gives the highest permeation flux with an increase of more than 41% from that of the parallel configuration with a sweeping gas. Using carbon dioxide as a sweeping gas is better than helium.
Adaptive Dynamics Learning for Small Fault Detection of Discrete-Time Nonlinear Uncertain Systems
Authors: Zhang Jingting , Yuan Chengzhi , Stegagno Paolo 
Published: November 2019

Abstract
This paper addresses the problem of small fault detection for discrete-time nonlinear uncertain systems. The problem is challenging due to (i) the considered system is subject to unstructured nonlinear uncertain dynamics; and (ii) the faults are considered to be “small” in the sense that system states and control inputs in faulty mode remain close to those in normal mode. To overcome these challenges, a novel adaptive dynamics learning based fault detection scheme is proposed. Specifically, an adaptive dynamics learning approach is first proposed to achieve the locally-accurate approximation of the system uncertain dynamics. Then, based on the learned knowledge, a novel residual system is designed by using the absolute measures of the change of the system dynamics resulting from the fault effect. An adaptive threshold is developed based on the residual system for real-time decision making, i.e., the fault is claimed to be detected when the associated residual signal becomes larger than the adaptive threshold. Rigorous analysis is performed to deduce the small fault detectability condition, which is shown to be significantly relaxed compared to those of existing fault detection methods. Extensive simulations have also been conducted to demonstrate the effectiveness and advantages of the proposed approach.
Hierarchical Estimator of Dual Clutch Torques for a Power-Split Hybrid Electric Vehicle
Authors: Zhang Jianwu , Xu Defeng 
Published: November 2019

Abstract
For fast drive mode transitions by shifting clutches equipped in the dedicated compound power-split hybrid transmission, correct estimations of pressure and torque of the clutches are crucial for control strategies. A hierarchical estimator is proposed herein for individual estimation of the clutch torques, consisting of not only the reference layer containing the unknown input observer of vehicle resistance and the reduced-order observer of drive shaft torque, but also the estimation layer combining the unknown input observer with the reduced-order observer. The estimator is implemented to strike a balance between estimation accuracy in the steady state and real time response in the transient state. For validation of the estimator, simulations and real car tests are carried out in specific drive conditions. By numerical results, it’s demonstrated that excellent predictive abilities are found including reasonably small estimation error and adaptive capability and, as a result, shift to shift induced driveline oscillations and vehicle jerks are reduced significantly.
Periodic Tracking Control Using Gain-Scheduled Fourier Series-Based Internal Models
Authors: Caverly Dylan , Caverly Ryan James , Forbes James Richard 
Published: November 2019

Abstract
This paper presents a gain-scheduled controller composed of a number of positive real controllers that contain internal models of reference command signals. Using the internal model principle as inspiration, and the Passivity Theorem to assure input-output closed-loop stability, the proposed controller is designed to realize tracking while maintaining input-output stability of the closed-loop system. The gain-scheduled nature of the internal models allows for a number of internal models to be simultaneously implemented. In particular, by expressing a periodic reference command as a Fourier series, the first few Fourier modes can be included as internal models in the controllers to be gain-scheduled, which reduces steady-state tracking error. An example involving tracking the outlet temperature of a heat exchanger is presented, where the first nine Fourier modes of the reference signal are used as internal models to reduce tracking error.
Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator
Authors: Bertino Alex , Bagheri Mostafa , Krstić Miroslav , Naseradinmousavi Peiman 
Published: November 2019

Abstract
In this paper, we examine the autonomous operation of a high-DOF robot manipulator. We investigate a pick-and-place task where the position and orientation of an object, an obstacle, and a target pad are initially unknown and need to be autonomously determined. In order to complete this task, we employ a combination of computer vision, deep learning, and control techniques. First, we locate the center of each item in two captured images utilizing HSV-based scanning. Second, we utilize stereo vision techniques to determine the 3D position of each item. Third, we implement a Convolutional Neural Network in order to determine the orientation of the object. Finally, we use the calculated 3D positions of each item to establish an obstacle avoidance trajectory lifting the object over the obstacle and onto the target pad. Through the results of our research, we demonstrate that our combination of techniques has minimal error, is capable of running in real-time, and is able to reliably perform the task. Thus, we demonstrate that through the combination of specialized autonomous techniques, generalization to a complex autonomous task is possible.
A Strictly Defined Orthogonal Global Task Coordinate Frame and its Contouring Control Application on Biaxial Systems
Authors: Yang Can , Chen Zheng , Yao Bin , Helian Bobo 
Published: November 2019

Abstract
In this paper, a strictly defined new orthogonal global task coordinate frame (NGTCF) based on the false position method is proposed for precision contouring control of biaxial systems. In contrast to the existed global task coordinate frame (GTCF), the value of the normal coordinate in NGTCF directly represents the contour error, rather than the first-order approximation. Moreover, different from the conventional GTCF just suitable for contours with explicit shape functions, the proposed NGTCF can be utilized in various complex contours. The false position method is adopted to calculate the curve coordinates of actual points in NGTCF. Then an adaptive robust controller (ARC) is designed to deal with the effects of strong coupling of the system dynamics in the task space and modeling uncertainties. The proposed NGTCF-based ARC contouring control strategy is tested on a linear motor driven biaxial industrial gantry. Experiments under different contouring tasks with high-speed and large-curvature are conducted to verify the effectiveness of the proposed method, and the experimental results confirm that the excellent contouring performance of the proposed approach can be achieved.
Agricultural Field Coverage Using Cooperating Unmanned Ground Vehicles
Authors: Faryadi Saba , Davoodi Mohammadreza , Mohammadpour Velni Javad 
Published: November 2019

Abstract
In this paper, a distributed algorithm with obstacle avoidance capability is presented to deploy a group of ground robots for field-based agriculture applications. To this end, the field (consisting of many plots) is first modeled as a directed graph, and the robots are deployed to collect data from some important areas of the field (e.g., areas with high water stress or biotic stress). The key idea is to formulate the underlying problem as a locational optimization problem and then find the optimal solution based on the Voronoi partitioning of the associated graph. The proposed partitioning method is validated through simulation studies, as well as experiments using a group of mobile robots.
Enhanced Tire Blowout Modeling Using Vertical Load Redistribution and Self-Alignment Torque
Authors: Li Ao , Chen Yan , Du Xinyu , Lin Wen-Chiao 
Published: November 2019

Abstract
Publisher’s Note:
This paper was selected for publication in ASME Letters in Dynamic Systems and Control.
https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046314/1074439/Enhanced-Tire-Blowout-Modeling-Using-Vertical-Load
A Numerical Study of an Electrically Assisted Boosting System for Turbocharged Diesel Engines
Authors: Men Yifan , Martz Jason B. , Curtis Eric , Zhu Guoming G. 
Published: November 2019

Abstract
Modern diesel engines are normally turbocharged in order to achieve desired fuel economy and meet emission requirements. The well-known “turbo-lag”, delayed engine torque response to driver’s demand, is the main disadvantage for turbocharged engines operated under transient conditions. In addition, at low engine speed, the peak engine output torque is heavily limited by the available turbine energy. As a result, turbocharged engines have degraded peak torque at low speed and slow transient responses in general. Various technologies (variable geometry turbine, electrically assisted turbocharger, hydraulically assisted turbocharger, etc.) have been developed to improve transient response and low-speed torque performance. This paper presents a numerical study of an electrically assisted boosting (eBoost) system for a turbocharged diesel engine through 1-D simulations. This study focuses on two main areas: the electrical compensation at steady-state and turbo-lag reduction under transient operation. It is shown that the eBoost system is capable of increasing engine fuel economy at mid-speed and greatly improving low-speed peak torque. In addition, the eBoost system improves engine transient performance by reducing response time up to 60%.
Influence of Speed Forecasting on the Performance of Ecological Adaptive Cruise Control
Authors: Hyeon Eunjeong , Kim Youngki , Prakash Niket , Stefanopoulou Anna G. 
Published: November 2019

Abstract
In congested urban conditions, the fuel economy of a vehicle can be highly affected by traffic flow and particularly, the immediately preceding (lead) vehicle. Thus, estimating the future trajectories of the lead vehicle is essential to optimize the following vehicle’s maneuvers for its fuel economy. This paper investigates the influence of speed forecasting on the performance of an ecological adaptive cruise control (eco-ACC) strategy for connected autonomous vehicles. The real-time speed predictor proposed in [1] is applied to forecast the future speed profiles of the lead vehicle over a short prediction horizon. Under the assumption that vehicle-to-vehicle (V2V) communications are available, V2V information from multiple lead vehicles is utilized in the prediction process. Eco-ACC is formulated in a model predictive control (MPC) framework to control the connected autonomous vehicle. The influence of the state prediction to the performance of eco-ACC in terms of fuel economy and acceleration is evaluated with different number of connected vehicles.
Shared Control Between Human Driver and Automation in Cooperative Driving Based on Game Theoretic Model Predictive Control
Authors: Ko Sangjin , Langari Reza 
Published: November 2019

Abstract
Shared control is a control framework in which control action is shared between a human driver and an automation. In this work, the shared control is studied based on game theoretical approach of distributed model predictive control (DMPC). The solution of cooperative game is derived under DMPC framework, and then realistic driving situation is studied in cooperative game framework. Shared control strategy for fully mixed driving authority is proposed considering collision probability based on TTC (time to collision) and tracking error. Simulations were conducted using Matlab/Simulik in cooperative driving. The simulation scenarios consist of safety critical situation and non-safety critical situation. In safety critical situation, an automation takes more control authority to avoid a collision, and in non-safety critical situation a human driver takes more control authority. The simulation results show that the control authority is shared continuously by the proposed shared control strategy.
Influence of Tire Inflation Pressure on Vehicle Dynamics and Handling Performance
Authors: Wu Jianyang , Wang Zhenpo , Wang Junmin 
Published: November 2019

Abstract
Tire inflation pressure affects both tire longitudinal and lateral stiffness and thus may impose a considerable influence on vehicle dynamics and handling performance. This paper presents a comprehensive study revealing the effects of tire pressure variations and their distribution among four tires on vehicle dynamics and handling performance. An extended Magic Formula tire model and a modified UniTire model involving tire inflation pressure are employed to describe the tire longitudinal and lateral forces, respectively. Two groups of vehicle maneuvers are simulated in CarSim: a single lane change maneuver with braking and a double lane change maneuver, to exhibit the effects of tire inflation pressure. Various tire pressure variations including all four tires at same and different pressures are examined. A vehicle dynamics, lateral motion stability index, and driver steering workload are utilized to quantify the influence of tire pressure variations and distributions. Analyses on the simulation results indicate that: 1) a front tire pressure reduction induces vehicle understeering tendency and a larger steering angle; 2) a rear tire pressure reduction causes oversteering characteristics and a sacrifice on vehicle stability with a larger vehicle sideslip angle; 3) all-tire inflation pressure decrease will increase driver’s steering workload; and 4) lower rear-tire inflation pressure can promote the combined performance of vehicle path-tracking and driver’s steering workload.
Modeling Actuation of Ionomer Cilia in Salt Solution Under an External Electric Field
Authors: Boldini Alain , Rosen Maxwell , Cha Youngsu , Porfiri Maurizio 
Published: November 2019

Abstract
Publisher’s Note:
This paper was selected for publication in ASME Letters in Dynamic Systems and Control.
https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046366/1074565/Modeling-Actuation-of-Ionomer-Cilia-in-Salt
Geometric Trajectory Planning for Robot Motion Over a 3D Surface
Authors: Hosseini Jafari Bashir , Walker Nolan , Smaldone Ronald , Gans Nicholas 
Published: November 2019

Abstract
Mapping a desired 2D pattern onto a curved surface has many applications. This includes motion planning for mobile robots to perform coverage path planing, robot end effector trajectory design for tasks such as printing, depositing, wielding on a 3D surface. This problem becomes more difficult if we want the mapped pattern to keep the properties of the original pattern (i.e, least possible mapping distortion), and pass over some desired points and/or remain bounded in a specific region on the surface. In this paper, we apply surface parameterization and mapping distortion analysis, which is rarely used in robot motion planning works, to map a pattern onto 3D surface. To meet additional goals such as passing over certain points, a planar mapping determined by constrained optimization is employed on the original pattern. Our focus is on printing/depositing materials on curved surfaces, and simulations and experiments are provided to confirm the performance of the approach.
A Hybrid Electro-Thermal Energy Storage System for High Ramp Rate Power Applications
Authors: Laird Cary E. , Alleyne Andrew G. 
Published: November 2019

Abstract
The practice of hybridizing energy storage systems is vital to high ramp rate power applications, in which energy storage systems are constrained by strict power and energy requirements. Hybrid energy storage is typically studied in the electrical and thermal domains separately, but due to the inherent link between electrical and thermal energy domains, it is necessary to examine hybrid energy storage in both domains simultaneously. In this paper, a combined electro-thermal energy storage system is modeled and simulated. Equivalent circuit and lumped-parameter models are used to facilitate control design. PI controllers are designed for both the electrical and thermal domains to demonstrate the ability to perform multi-domain energy management.
Multiple-Scale Analysis of a Novel Piezoelectric Energy Harvester With a Tunable Potential Function
Authors: Qian Feng , Abaid Nicole , Zuo Lei 
Published: November 2019

Abstract
Publisher’s Note:
This paper was selected for publication in ASME Letters in Dynamic Systems and Control.
https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046961/1082714/Multiple-Scale-Analysis-of-a-Tunable-Bi-Stable
Behavior Identification and Prediction for a Probabilistic Risk Framework
Authors: Gill Jasprit S. , Pisu Pierluigi , Krovi Venkat N. , Schmid Matthias J. 
Published: November 2019

Abstract
Operation in a real world traffic requires the ability to plan motion in complex environments (multiple moving participants) from autonomous vehicles. Navigation through such environments necessitates the provision of the right search space for the trajectory or maneuver planners so that the safest motion for the ego vehicle can be identified. Analyzing risks based on the predicted trajectories of all traffic participants (given the current state of the environment and its participants) aids in the proper formulation of this search space. This study introduces a fresh taxonomy of safety and risk that an autonomous vehicle should be capable of handling. It formulates a reference system architecture for implementation as well as describes a novel way of identifying and predicting the behaviors of other traffic participants utilizing classic Multi Model Adaptive Estimation (MMAE). Detailed simulation results and a discussion about the associated tuning of the implemented model conclude this work.
Chemical-Source Localization Using a Swarm of Decentralized Unmanned Aerial Vehicles for Urban/Suburban Environments
Authors: Steiner Jake A. , Bourne Joseph R. , He Xiang , Cropek Donald M. , Leang Kam K. 
Published: November 2019

Abstract
In this paper, a decentralized chemical-source localization method is presented. In a real-world scenario, many challenges arise, including sporadic chemical measurements due to the complex interactions between the unmanned aerial vehicles (UAVs), the ambient air, and obstacles. The localization method is split into two phases: a search phase, where the agents cover the area and look for an initial chemical reading; followed by a convergence phase, where UAV agents utilize a particle swarm optimization (PSO) algorithm to locate the source of the chemical leak. The decentralized source-localization method enables a swarm of UAVs to safely travel in a complex environment and avoid obstacles and other agents while searching for the leaking source. The method is validated in simulation using realistic dynamic chemical plumes and through outdoor flight tests using a swarm of UAVs. The results demonstrate the feasibility of the approach.
Design and Verification of a Small-Scale District Heating Network Experiment
Authors: Krieger Melissa , Stecklair Dylan , Peluso Stephen , Stockar Stephanie 
Published: November 2019

Abstract
The ability to combine energy sources with intermittent performance with more consistent form of power is crucial for increasing the penetration of renewable energy sources in the electricity and heat generation sector. In this scenario, district heating networks are a promising solution but, to benefit the most from this technology, control tools must be developed with the objective of optimizing the heating load to each of the buildings in the network, while rejecting external disturbances. One of the main challenges for control design and verification is the limited access to data and experimental platforms. In addition, real systems are subjected to a large number of exogenous inputs and tests repeatability for benchmarking is a challenge. To overcome this limitation, a scaled experimental set up has been developed. This paper discusses the design of the experimental setup of a simple heat distribution network as well as the derivation, calibration and validation of a simulation model. Simulation results show that the model error in predicting temperature is always below 1 %.