bluuebunny/crossref_metadata_2025 huggingface dataset:
Parallel Deep Learning Ensembles for Human Pose Estimation
Authors: Ren Hailin , Kumar Anil , Wang Xinran , Ben-Tzvi Pinhas
Published: November 2018

This paper presents an efficient method to detect human pose with monocular color imagery using a parallel architecture based on deep neural network. The network presented in this approach consists of two sequentially connected stages of 13 parallel CNN ensembles, where each ensemble is trained to detect one specific kind of linkage of the human skeleton structure. After detecting all skeleton linkages, a voting score-based post-processing algorithm assembles the individual linkages to form a complete human structure. This algorithm exploits human structural heuristics while assembling skeleton links and searches only for adjacent link pairs around the expected common joint area. The use of structural heuristics in the presented approach heavily simplifies the post-processing computations. Furthermore, the parallel architecture of the presented network enables mutually independent computing nodes to be efficiently deployed on parallel computing devices such as GPUs for computationally efficient training. The proposed network has been trained and tested on the COCO 2017 person-keypoints dataset and delivers pose estimation performance matching state-of-art networks. The parallel ensembles architecture improves its adaptability in applications aimed at identifying only specific body parts while saving computational resources.
Limit Cycle Behavior and Model Reduction of an Oscillating Fish-Like Robot
Authors: Pollard Beau , Fedonyuk Vitaliy , Tallapragada Phanindra
Published: November 2018

The design and control of underwater robots has to contend with the coupled robot-hydrodynamic interactions. A key aspect of this coupled dynamics is the interaction of the robot with the fluid via the vorticity that is created by the robot’s motion. In this paper we develop a simplified and very low dimensional model of this interaction. This is done recognizing that the vortex shedding is a nonholonomic constraint. We apply the harmonic balance approach to analyze and compare the limit cycle in the dynamics of the fish-shaped body propelled by a periodic input with that of a Chaplygin sleigh, a well known nonholonomic system. The dynamics on the limit cycles lead to a very low dimensional model of the swimming of the fish-shaped body that could be very useful from the perspective of controlling a swimming robot.
Scoop the Semi-Liquid Objects Using a Spoon-Equipped Robot Arm for Meal Support
Authors: Yoshioka Daiki , Ding Ming , Garcia Ricardez Gustavo Alfonso , Takamatsu Jun , Ogasawara Tsukasa
Published: November 2018

In this research, a scooping motion generation method is proposed to scoop the semi-liquid objects from different containers automatically for meal support purpose. A spoon equipped robot arm is used. Based on the pre-measured shape of the containers, the robot arm can move the spoon to trace the inner surface of containers continuously. We also control the rotation of the spoon to scoop more semi-liquid object every time by imitating human’s scooping motion. A scraping motion is also generated as the auxiliary operation to gather the remaining semi-liquid object, which can realize an increase in the scooping amount. In the experiment, we tested the generated scooping motion for two containers and four type of semi-liquid objects. The scooped amount and the scooping times are measured and compared. The result shows that about 85.9% object on average could be scooped out.
Analysis of Tire Relaxation Constants for Modeling Vehicle Traction Performance and Handling
Authors: Vantsevich Vladimir V. , Demkiv Lyubomyr I. , Klos Sviatoslav R.
Published: November 2018

Various vehicle and tire dynamics models with different levels of complexity have been developed and used for predicting and studying vehicle traction and handling. This paper offers an overview of research work that was done on the tire relaxation length and time constant in the longitudinal and lateral directions with regard to tire/vehicle traction and handling characteristics. Several analytical and empirical models of the tire relaxation length and time constant from technical literature are compared by applying these models to a tire and, thus, analyzing and comparing computational results that come out from the models.

Based on the overview and analysis of the longitudinal/lateral relaxation length and time constant data, the paper also provides an analysis of the impact of the tire normal reaction, tire inflation pressure, tire slippage and wheel actual velocity on the relaxation constants.

The paper concludes with recommendations on the analytical methods and experimental data of the tire relaxation length and time constant computing that are more suitable for real-time modeling of agile traction and handling characteristics and, thus, can be profound for agile control synthesis. Therefore, the core contribution of the paper is to provide a fundamental analysis of the relaxation length effect for its further incorporation in agile tire slippage control.
Strawberry Plant Localization via Relative Pixels in Sequential Images
Authors: Kong Xiangling , Xu Yunjun
Published: November 2018

When conducting precision operations, such as disease detection, weed removal, yield prediction, and harvesting, on plants such as strawberries and blueberries, it is necessary to know the exact location of each plant. To date, GPS and LiDAR based methods have been proposed, however these methods either cannot routinely store position data, are labor intensive, expensive, or bulky. In this study, a low cost and lightweight localization approach is proposed using relative pixel information of adjacent plants. The kinematic information of a scouting robot carrying the camera and the relative position information of adjacent plants are modeled. The centroids of strawberry plants are identified one by one via image processing technologies. An extended Kalman filter is then developed to estimate the relative positions of adjacent plants. The proposed strawberry plant localization algorithm is validated in a commercial farm. The method is low cost and can be used in routine localization operations in agricultural fields.
Image Quality-Driven Octorotor Flight Control via Reinforcement Learning
Authors: Li Qiang , Xu Yunjun
Published: November 2018

This article presents the design of a reinforcement learning method based flight controller to enhance the qualities of image taken from an octorotor platform. Concerning the effect of a low resolution and a high blur rate of target images on feature extraction and target detection, we started by analyzing the relationship between these two kinds of image qualities and altitude and velocity of the octorotor. This leads to the generation of corresponding control commands. We then applied a reinforcement learning technique to automatically design the altitude and velocity controllers of the octorotor. The image analysis and the control command generation algorithms are successfully tested on the octorotor platform, and the controllers demonstrate a satisfactory performance in simulations.
Path Control of a Heat Treatment Truck Considering Driver-Vehicle Interaction
Authors: Dai Andong , Xu Yunjun
Published: November 2018

Citrus greening (Huanglongbing) has caused significant financial loss in many states of the United States and worldwide. In recent years, a heat therapy method has been investigated to prolong the production period of the diseased citrus trees after infection. One crucial step in this heat treatment process is to precisely align the truck with the diseased tree to reduce the operation time during deployment of the treatment tent. In this study, a binocular vision system is used to detect the position of the diseased tree relative to the truck, and a direct based path planning method is used to generate a nominal, optimal path for the truck to follow. A driver’s eye perception model is derived, simulating the distortion due to the human eye’s perception of objects on the computer screen, which will be used in the truck controller. A linear quadratic controller is then designed to compensate for the error coming from the eye perception mismatches and sensor and actuator noise. The studied human augmented driving control system can significantly reduce the operation time as the driver doesn’t have to constantly get out of the cab to check the truck-tree alignment. Simulation results show the effectiveness of the proposed system.
Experimental Walking to Running Transition
Authors: Alcorta Salvador , Caruntu Dumitru I.
Published: November 2018

This work investigates the human gait transition, from walking to running and running to walking. The data obtained was later filtered to obtain the displacement recorded from the Greater Trochanter, Lateral Knee, the Tibia Tuberosity protrusion, Lateral Ankle, Lateral heel and the foot Toe. The results show a trend to predict the transition phase as a change when the relative phase from the markers is evaluated.
Optimal Energy Management in a Range Extender PHEV Using a Cascaded Dynamic Programming Approach
Authors: Oruganti Pradeep Sharma , Jung Daniel , Arasu Mukilan , Ahmed Qadeer , Rizzoni Giorgio
Published: November 2018

Dynamic programming is widely used to benchmark the performance of a hybrid electric vehicle. It is also well documented that it is a very computationally heavy procedure depending on the number of states and control inputs in the problem formulation. In this paper we investigate the possibility of reduction in the computational time by splitting the number of states and control inputs between two models and applying dynamic programming individually, using the output of one as an input to the other and hence cascading the two models. A range extended hybrid electric vehicle powertrain architecture is modeled with four states and four control inputs, which is considered as the full model. Further, the states and control inputs of the battery and engine are separated from the other states, splitting them between the two new DP models. The vehicle performance estimated from this ‘cascaded models approach’ is compared with that from the full model. Initial comparisons show a very good match with minor differences in performance and considerable a reduction in computation time from around 6 hours to around a minute.
Modeling and Dynamics Analysis of a Beam-Hoverboard Self-Transportation System
Authors: Mehrvarz Amin , Khodaei Mohammad Javad , Clark William , Jalili Nader
Published: November 2018

In recent years, self-balancing personal transportation devices have gained significant popularity, the most popular ones being the “Hoverboard” systems. These systems utilize the dynamics of an inverted pendulum to create stable lateral motion. In this paper, the dynamic behavior of a hoverboard system with an attached flexible beam is investigated. By introducing a flexible beam, the vibrational characteristics of the system created by both the rider and the environment can be measured and accounted for. The beam is a continuous system modeled as an n degree of freedom (DOF) inverted pendulum. The resulting system becomes an n+2 DOF (n DOF for the beam, one DOF for the rotation of the beam about the wheel axis, and one DOF for the horizontal motion of the system). A mathematical model is developed to simulate the vibrations of the beam when excited by a piezoelectric actuator at the base, and to simulate the horizontal motion necessary to balance the beam as it is excited.
Theoretical and Experimental Analysis of Coupled Flexural-Torsional Vibrations of Rotating Beams
Authors: Khodaei Mohammad Javad , Mehrvarz Amin , Candelino Nicholas , Jalili Nader
Published: November 2018

In this paper, the flexural-torsional vibrations of a segmented cantilever beam are considered both theoretically and experimentally under steady-state base rotation. While operating in this steady-state, a piezoelectric actuator is used to excite the beam at various test frequencies. Further, through preliminary investigations, it is demonstrated that accelerometer measurements are not suitable for such a testing apparatus, as these sensors add complex unmodeled dynamics and change the natural frequencies of vibration. The resulting unmodeled dynamics appear to be caused by a large initial deflection due to the added sensor mass, contradicting the conventional assumption that the beam is initially undeformed. This initial bending results in a Coriolis acceleration, and consequently produces a substantial deviation from the anticipated tip response. To further investigate the effect of base rotation on flexural vibrations, experiments were performed in the absence of piezoelectric excitation, both with and without the tip mass. For these conditions, the theory uniformly predicts no flexural or torsional vibrations, while the experimental results demonstrate significant vibrations in both cases. These discrepancies illuminate the presence of significant unmodeled dynamics that are neglected in the conventional mathematical modeling, potentially invalidating the classical simplifications when considering rotating beams.
Modeling of a Linear Power Generator Driven by a Pulse Detonation Engine
Authors: Dighe Umang , Lu Frank K.
Published: November 2018

A power generation technique utilizing a pulse detonation engine to drive a piston connected to a linear power generator is described. Coupled magnetic–electric–mechanical models for the mass–spring–damper system are developed. The system is characterized by nonlinear damping due to the rare earth permanent magnets used in the architecture of the generator and a piecewise continuous external forcing function from the pulsed detonation. An analytical model is built to simulate the system, calculate the electromagnetic forces and estimate the power output. The analytical model used in this concept is suitable for flat type linear generators.
Build Height Control in Directed Energy Deposition Using a Model-Based Feed-Forward Controller
Authors: Wang Qian , Li Jianyi , Nassar Abdalla R. , Reutzel Edward W. , Mitchell Wesley
Published: November 2018

Control of deposition geometry is critical for repair and fabrication of complex components through directed energy deposition (DED). However, current limited sensing technology is often one of the bottlenecks that make it difficult to implement a real-time, measurement-feedback control of build geometry. Hence, this paper proposes to implement the control trajectories from a model-based, simulated-output feedback controller (where the controller uses simulated rather than measured outputs for feedback) as a feed-forward controller in a real DED process. We illustrate the effectiveness of such feed-forward implementation of a model-based, simulated-output feedback controller in the height control of a L-shaped structure via varying laser power in a DED process. Experimental validation shows that by applying the proposed feed-forward controller for laser power, the resulting build has (30%–50%) increased accuracy in achieving the target build height than applying laser with constant power or experience-based, hatch-dependent laser power. Results in this paper indicate that applying a simulated-output feedback controller could be a practical alternative for the control of DED (or other additive manufacturing processes) before the sensing technologies are matured enough to support real-time, measurement-feedback controller.
Solid-State Battery Modeling Case Studies for the Analysis of a Micro-Robot Power System
Authors: Teichert Kendall , Oldham Kenn
Published: November 2018

Recent works by the authors have presented modeling and simulation techniques for analyzing repeated fast dynamic loading of thin-film solid-state batteries. These techniques were developed to address timescale issues in modeling battery discharge under these types of loads. In this paper, we explore various consequences of battery usage in potential micro-robotic applications based on use of these modeling techniques in a series of case studies. Impact of electrochemical versus simple lumped parameter battery modeling is first examined. This is followed by projection of battery performance in basic series versus boost converter arrangements to supply thin-film piezoelectric robotic actuators. Potentially advantageous battery and conversion circuit configurations are identified.
A Reduced Order Model for Spatiotemporal Dynamics and Control of Cardiac Alternans
Authors: Zhao Xiaopeng , Tolkacheva Elena G.
Published: November 2018

Sudden cardiac arrest, caused primarily by ventricular fibrillation, is one of the leading causes of mortality in the Western world. There is a compelling need for risk stratification to identify patients at risk for sudden cardiac arrest. Cardiac alternans, a recognized harbinger of sudden cardiac arrest, manifests as a beat-to-beat alternation in action potential duration (cellular level) or in electrocardiogram morphology (whole heart level). Although much progress has been made to understand the mechanisms of alternans, predicting and control of alternans, especially at the heart level, remain great challenges. Current approaches to predict cardiac alternans based on restitution properties of the heart are either too simple to be valid or too complex to be useful. In this work, we developed a reduced order model from the amplitude equation to investigate dynamics and control of alternans in cardiac fiber, i.e. beyond single cell level. Detailed bifurcation and stability analyses were carried out to illustrate complex spatiotemporal patterns of alternans and the limitations in feedback control due to spatial effect.
A Dynamics-Agnostic State Estimator for Unmanned Aerial Vehicles Using Ultra-Wideband Radios
Authors: Mueller Mark W.
Published: November 2018

We present a state estimator for a UAV operating in an environment equipped with ultra-wideband radio beacons. The beacons allow the UAV to measure distances to known positions in the world. The estimator additionally uses the vehicle’s rate gyroscope and accelerometer, and crucially does not rely on any knowledge of the vehicle’s dynamic properties (e.g. mass, mass moment of inertia, aerodynamic properties). This makes the estimator especially useful in situations where the exact system parameters are unknown (e.g. due to unknown payloads), or where the environment is unpredictable (e.g. wind gusts). Experimental results demonstrate the approach’s efficacy, and demonstrate that the estimator can run on low-cost microcontrollers with typical sensors.
Proportional Navigation and Model Predictive Control of an Unmanned Autonomous Vehicle for Obstacle Avoidance
Authors: Shaw Ryan P. , Bevly David M.
Published: November 2018

This paper presents a new approach for the guidance and control of a UGV (Unmanned Ground Vehicle). An obstacle avoidance algorithm was developed using an integrated system involving proportional navigation (PN) and a nonlinear model predictive controller (NMPC). An obstacle avoidance variant of the classical proportional navigation law generates command lateral accelerations to avoid obstacles, while the NMPC is used to track the reference trajectory given by the PN. The NMPC utilizes a lateral vehicle dynamic model. Obstacle avoidance has become a popular area of research for both unmanned aerial vehicles and unmanned ground vehicles. In this application an obstacle avoidance algorithm can take over the control of a vehicle until the obstacle is no longer a threat. The performance of the obstacle avoidance algorithm is evaluated through simulation. Simulation results show a promising approach to conditionally implemented obstacle avoidance.
Model-Based Sparse Information Recovery by a Collaborative Sensor Management
Authors: Xiao Hui , Bar-Shalom Yaakov , Chen Xu
Published: November 2018

This paper considers the real-time recovery of a fast time series (e.g., updated every T seconds) by using sparsely sampled measurements from two sensors whose sampling intervals are much larger than T (e.g., MT and NT, where M and N are integers). Specifically, when the fast signal is an autoregressive process, we propose an online information recovery algorithm that reconstructs the dense underlying temporal dynamics fully, by systematically modulating the sensor speeds MT and NT, and by exploiting a model-based fusion of the sparsely collected data. We provide the collaborative sensing design, parametric analysis and optimization of the algorithm. Application to a closed-loop disturbance rejection problem reveals the feasibility to annihilate fast disturbance signals with the slow and not fully aligned sensor pair in real time, and in particular, the rejection of narrow-band disturbances whose frequencies are much higher than the Nyquist frequencies of the sensors.
Wide-Band Loop Shaping for Modulation of Energy Transmission in Nonminimum-Phase Systems
Authors: Jiang Tianyu , Tang Jiong , Chen Xu
Published: November 2018

Modulating the closed-loop transmission of energy in a wide frequency band without sacrificing overall system performance is a fundamental issue in a wide range of applications from precision control, active noise cancellation, to energy guiding. This paper introduces a loop-shaping approach to create such wideband closed-loop behaviors, with a particular focus on systems with nonminimum-phase zeros. Pioneering an integration of the interpolation theory with a model-based parameterization of the closed loop, the work proposes a filter design that matches the inverse plant dynamics locally and creates a framework to shape energy transmission with user defined performance metrics in the frequency domain. Application to laser-based powder bed fusion additive manufacturing validates the feasibility to compensate wide-band vibrations and to flexibly control system performance at other frequencies.
Open-Loop Minimum-Energy Maneuver of a Solar-Sail Using Magnetic Torques and Reaction Wheels
Authors: Ayoubi Mohammad A. , Naseradinmousavi Peiman
Published: November 2018

This paper presents an open-loop, minimum-energy control law for attitude maneuver of a solar-sail. The proposed control law utilizes the magnetic torques and reaction wheels to reorient the solar-sail for near-earth missions. Using the calculus of variations and Pontryagin’s minimum principle, the necessary and sufficient conditions were derived for the nonlinear model of the solar-sail. Then the optimal control problem was formulated as a two-point boundary-value problem and solved via the Shooting method. The computed control law can be utilized in a flight-computer and in a sampled-data feedback closed-loop system which can compensate for disturbance torques.
Road Condition Based Adaptive Model Predictive Control for Autonomous Vehicles
Authors: Wang Xin , Guo Longxiang , Jia Yunyi
Published: November 2018

Road conditions are of critical importance for motion control problems of the autonomous vehicle. In the existing studies of Model Predictive Control (MPC), road condition is generally modeled with the system dynamics, sometimes simplified as common disturbances, or even ignored based on some assumptions. For most of such MPC formulations, the cost function is usually designed as fixed function and has no relations with the time-varying road conditions. In order to comprehensively deal with the uncertain road conditions and improve the overall control performance, a new model predictive control strategy based on a mechanism of adaptive cost function is proposed in this paper. The relation between the cost function and road conditions is established based on a set of priority policies which reflect the different cost requirements under different road grades and friction coefficients. The adaptive MPC strategy is applied to solve the longitudinal control problem of autonomous vehicles. Simulation studies are conducted on the MPC method with both the fixed cost function and the adaptive cost function. The results show that the proposed adaptive MPC approach can achieve a better overall control performance under different road conditions.
Navigation Strategies for a Multi-Robot Ground-Based Row Crop Phenotyping Platform
Authors: Gao Tianshuang , Emadi Hamid , Saha Homagni , Zhang Jiaoping , Lofquist Alec , Singh Arti , Ganapathysubramanian Baskar , Sarkar Soumik , Singh Asheesh , Bhattacharya Sourabh
Published: November 2018

In this work, we address the problem of deploying a multi-robot system for row crop phenotyping. We propose a sampling-based navigation algorithm for the team of robots to estimate the underlying spatial distribution in a field. We use Gaussian Process Models to predict the distribution of a scalar function in a field, and choose Mutual Information as a metric for selecting the future samples. With a row crop structure, we present a collision-free assignment and scheduling algorithm for the robots to reach the goal positions which minimizes the total traveling distance. The effectiveness of the proposed algorithm is demonstrated through simulations.
Transient Dynamics of Harmonic Devices Under Thermal Loading
Authors: Unamboowe Heshan , Shukla Amit
Published: November 2018

Curved beams are an essential structural form widely used in the engineering industry. When loaded past a critical point, they may buckle in a bifurcation or snap-through mode. Studies have shown that structural buckling problems are highly sensitive to slight changes in thermal properties. A shallow curved beam model subjected to a harmonic excitation is considered in this paper. A simplified, dimensionless one degree of freedom model is obtained following the works of [1]. Using this model, the transient dynamics of the system is analyzed for varying forcing parameters. The effect of the arch rise (λ) is also examined as the varying thermal properties effect the geometry of the beam system. Dynamic and transient dynamic responses of the system are obtained in the time domain for both constant and linearly varying λ. Wavelet analysis is utilized to analyze this data in the frequency domain and it provided an effective representation of the system responses that are localized in both time and frequency.
Omnidirectional Force Feedback for Teleoperation of Omnidirectional Wheeled Robots
Authors: Tyagi Rajat , Mascaro Stephen
Published: November 2018

The objective of this research is to improve the ability of a human operator to teleoperate an omnidirectional wheeled robot using omnidirectional force feedback. Omnidirectional wheeled robots offer improved mobility over conventional wheeled robots and can potentially benefit people requiring motorized transportation and industries where robotic vehicles must operate in confined spaces. However, omnidirectional robotic vehicles require more degrees of freedom to control due to the additional degrees of freedom inherent in the vehicle’s design. We hypothesize that providing force feedback to the operator through an omnidirectional joystick will allow the robot to assist the driver in navigating and avoiding collisions with obstacles in a manner that is natural to the operator, coordinating both translational and rotational degrees of freedom. This research is the first attempt to use omnidirectional 3-DOF (degree of freedom) force feedback to provide navigational assistance for a human to drive/teleoperate an omnidirectional vehicle. This paper presents experiments using a novel omnidirectional force-feedback joystick and force-feedback strategy to guide operators to navigate a virtual omnidirectional wheeled robot in real-time through a geometrically constrained virtual environment. Experimental results demonstrate that the 3-DOF force feedback significantly improves collision avoidance. Additionally, including rotational feedback and optimizing the center of compliance results in significantly smoother trajectories.
Robust UAV Attitude Estimation Using a Cascade of Nonlinear Observer and Linearized Kalman Filter
Authors: Gudmundsson Valthor , Kristinsson Haukur , Petersen Soren , Hasan Agus
Published: November 2018

This paper presents a new approach for Unmanned Aerial Vehicle (UAV) attitude estimation using a cascade of nonlinear observer and linearized Kalman filter. The nonlinear observer is globally asymptotically stable and is designed using linear matrix inequalities (LMI). The exogenous signal from the nonlinear observer is used to generate a linearized model for the Kalman filter. The method is implemented for attitude estimation of a quadcopter. The nonlinear model is derived from the Newton-Euler equations. The nonlinear model is locally Lipschitz due to the cross and dot products between the angular and linear velocity vectors. The attitude estimation from the dynamical system presented in this paper can be used as a module for fault detection. Simulations in Gazebo on a PX4 using Software In The Loop (SITL) shows the proposed method is able to estimate the attitude of a quadcopter accurately.
Parameter Selection of an LTV-MPC Controller for Vehicle Path Tracking Considering CPU Computational Load
Authors: Wang Zejiang , Bai Yunhao , Wang Junmin , Wang Xiaorui
Published: November 2018

There exist two intrinsic shortcomings on model predictive control (MPC) strategy, namely the extensive online calculation burden and the complex tuning process, which prevent MPC from being applied to a wider extent. To tackle these two drawbacks, different methods were proposed with majority of them treating these two issues independently. However, parameter tuning in fact has double-sided effects on both controller performance as well as real-time computational burden. Due to the lack of theoretical tools for globally analyzing the complex conflicts among MPC parameter tuning, controller performance optimization as well as computational burden easement, a look-up table based online parameter selection method is proposed in this paper to help a vehicle track its reference path under both the stability and computational capacity constraints. Matlab-CarSim conjoint simulation shows the effectiveness of the proposed strategy.
Differentiation of Collective Behavior Based on Automated Discovery of Dynamical Kinds
Authors: Hashimoto Amanda , Abaid Nicole , Roy Subhradeep , Jantzen Benjamin , Shea-Blymyer Colin
Published: November 2018

In this paper, we explore a model of collective behavior using EUGENE, an algorithm for automated discovery of so-called “dynamical kinds”. Two systems are of the same dynamical kind if their underlying causal dynamics are similar, as defined using dynamical symmetry. We apply EUGENE to simulation data from a model capable of generating a range of qualitatively different collective behaviors, from aligned motion to circular milling. These behaviors are measured using both global and local order parameters, and this data is analyzed with EUGENE. We find that EUGENE is capable of differentiating between these systems when global order parameters are used, and can only identify more coarse characteristics when local order parameters are considered.
Trajectory Tracking and Control for Nonholonomic Ground Vehicle: Preliminary and Experimental Test
Authors: Chen Yuanyan , Zhu J. Jim
Published: November 2018

A car-like ground vehicle is a nonlinear and underactuated system subject to nonholonomic constraints. Trajectory tracking control of such systems is a challenging problem. To this end, a trajectory tracking controller based on nonlinear kinematics and dynamics model of a ground vehicle by Trajectory Tracking Control (TLC) is presented in our previous work. In this paper, we present hardware validation of TLC controller design with vehicle parameters determination for a Radio Controlled (RC) scaled model vehicle, experimental implementation, and tuning procedure. Hardware testing results are presented to demonstrate the effectiveness of our design. The design can be readily scaled-up to full-size vehicles and adapted to different types of autonomous ground vehicles with only knowledge of the vehicle model parameters.
Semi-Active Control for Two-Body Ocean Wave Energy Converter by Using Hybrid Model Predictive Control
Authors: Xiong Qiuchi , Li Xiaofan , Martin Dillon , Guo Sijing , Zuo Lei
Published: November 2018

Model predictive control (MPC) has been considered as one important feed-forward optimal control strategy for ocean wave energy converter (WEC) targeted on power maximization. The capability of MPC to handle system constraints (ex. stroke, velocity, actuator limitations), and the availability to provide optimal solution for linear system provide potential for the implementation of such algorithm in the WEC control. However, currently, only active MPC control has been introduced for single and two-body WECs. Such control strategy may introduce negative power during the optimization process, since the power take-off (PTO) damping has no constraint. In this paper, we proposed a hybrid MPC strategy in limiting both the PTO damping force and PTO damping to avoid negative power generation during cost function minimization (negative power minimization) for the two-body WEC. The problem is formulated into a quadratic programming (QP) problem targeted at power maximization. However, the standard QP problem formulation cannot be directly applied to the semi-active control problem due to the PTO damping constraints. Therefore, the problem is reformulated as a Mixed-integer Quadratic Programming (MIQP) problem, which contains logical switch to select constraint matrices based on the sign of the relative velocity between the buoy and submerged body. The optimal solution is compared with those of the active MPC control strategy and the passive model with the same irregular wave input.
Large Signal Stability Analysis of a Hybrid AC/DC Microgrid With a Cascaded Control Inverter
Authors: Xu Hongru , Chen Yan , Keel Brian
Published: November 2018

The large signal stability analysis of a hybrid AC/DC microgrid based on a grid-connected inverter with cascaded control is discussed. The impacts of the connected inductor, capacitor, and the control parameters of the inverter on the DC link stability region are analyzed. To achieve these analyses, a dynamic model of the microgrid with the cascaded control inverter is first developed. A Lyapunov large-signal stability analysis tool is then applied to estimate the domain of attraction, which is the asymptotic stability region. Results show that DC side capacitor, the AC side grid filter, as well as the control gain, will have different influences on the stability regions of the DC link voltage. High fidelity simulations through PLECS are successfully applied to verify the asymptotic stability regions estimated from the Lyapunov large signal analysis method.
Modeling of Collective Cell Behaviors Interacting With Extracellular Matrix Using Dual Faceted Linearization
Authors: Mayalu Michaëlle N. , Kim Min-Cheol , Asada Haruhiko
Published: November 2018

Cells interacting over an extracellular matrix (ECM) exhibit emergent behaviors, which are often observably different from single-cell dynamics. Fibroblasts embedded in a 3-D ECM, for example, compact the surrounding gel and generate an anisotropic strain field, which cannot be observed in single cell-induced gel compaction. This emergent matrix behavior results from collective intracellular mechanical interaction and is crucial to explain the large deformations and mechanical tensions that occur during embryogenesis, tissue development and wound healing. Prediction of multi-cellular interactions entails nonlinear dynamic simulation, which is prohibitively complex to compute using first principles especially as the number of cells increase. Here, we introduce a new methodology for predicting nonlinear behaviors of multiple cells interacting mechanically through a 3D ECM. In the proposed method, we first apply Dual-Faceted Linearization to nonlinear dynamic systems describing cell/matrix behavior. Using this unique linearization method, the original nonlinear state equations can be expressed with a pair of linear dynamic equations by augmenting the independent state variables with auxiliary variables which are nonlinearly dependent on the original states. Furthermore, we can find a reduced order latent space representation of the dynamic equations by orthogonal projection onto the basis of a lower dimensional linear manifold within the augmented variable space. Once converted to latent variable equations, we superpose multiple dynamic systems to predict their collective behaviors. The method is computationally efficient and accurate as demonstrated through its application for prediction of emergent cell induced ECM compaction.
New Algorithm to Design Real Time Optimal and Robust Ultrafiltration Rates in Chronic Kidney Disease to Prevent Cardiovascular Morbidity and Mortality
Authors: Abohtyra Rammah M. , Chait Y.
Published: November 2018

Hemodialysis (HD) is a necessary treatment for end-stage kidney disease (ESKD) patients in order to prevent cardiovascular morbidity and mortality that may be related to the hemodynamic effects of rapid ultrafiltration. Despite significant advances in HD technology, only half of ESKD patients treated with HD survive more than 3 years. Fluid management remains one of the most challenging aspects of HD care, with serious implications for morbidity and mortality.

In this paper, we develop a novel algorithm to design real time optimal, robust ultrafiltration rates based on actual HD data to identifying the parameters of a fluid volume model of an individual patient during HD. Our design achieves, if exists, an optimal ultrafiltration profile for the identified nominal model under maximum ultrafiltration and hematocrit constraints and guarantees that these constraints are satisfied over a pre-defined set of parameter uncertainty. We demonstrate the robust performance of our algorithm through a combination of clinical data and simulations.
A Novel Current-Interference Scanning Method for Detection of Abnormal Tissues
Authors: Lee Kok-Meng , Li Junwei , Bai Kun
Published: November 2018

This paper presents a current-interference scanning (CIS) method for detecting abnormal tissues (such as breast and lung tumors) characterized by a significantly higher electrical conductivity than healthy tissues. The CIS method overcomes several limitations encountered in existing screening techniques based on electrical impedance tomography (EIT), which usually suffer from poor spatial resolution due to the limited number of electrodes that can be attached on human body. In addition, the reconstructions of the impedance image in EIT are often poorly conditioned due to its uneven sensitivity to different areas and ill posed for limited information. In this paper, the theoretical basis of a CIS method is analytically derived, which uses two high-frequency sinusoidal currents to create a low-frequency current-interference area moving in two orthogonal directions. The effectiveness of the CIS method and its feasibility for detecting relatively large different electrical conductivities in human tissues are illustrated numerically and experimentally.
Ergodic Exploration for Adaptive Sampling of Water Columns Using Gliding Robotic Fish
Authors: Ennasr Osama , Mamakoukas Giorgos , Murphey Todd , Tan Xiaobo
Published: November 2018

In recent years, gliding robotic fish have emerged as promising mobile platforms for underwater sensing and monitoring due to their notable energy efficiency and maneuverability. For sensing of aquatic environments, it is important to use efficient sampling strategies that incorporate previously observed data in deciding where to sample next so that the gained information is maximized. In this paper, we present an adaptive sampling strategy for mapping a scalar field in an underwater environment using a gliding robotic fish. An ergodic exploration framework is employed to compute optimal exploration trajectories. To effectively deal with the challenging complexity of finding optimum three-dimensional trajectories that are feasible for the gliding robotic fish, we propose a novel strategy that combines a unicycle model-based 2D trajectory optimization with spiral-enabled water column sampling. Gaussian process (GP) regression is used to infer the field values at unsampled locations, and to update a map of expected information density (EID) in the environment. The outputs of GP regression are then fed back to the ergodic exploration engine for trajectory optimization. We validate the proposed approach with simulation results and compare its performance with a uniform sampling grid.
A Comprehensive Framework for Simulating Dynamics of an Off-Road Vehicle in Unconstructed Environments
Authors: Karimi Shahab , Vahidi Ardalan , Jayakumar Paramsothy
Published: November 2018

Vehicle dynamics analysis becomes more demanding for off-road vehicles’ mobility in unconstructed environments. Significant vehicle orientation changes, extreme changes in ground elevation, and uneven ground profiles at tire-road contact regions, etc. must be taken into account. In addition, the simulation computations should strike a balance between the speed and the accuracy of the results. In this paper, a model with fourteen degrees of freedom is used for vehicle dynamics analysis. Integrated within the model, a comprehensive tire model and a system of instantaneous rotation matrices are programmed to address the effect of more extreme ground profiles on the vehicle dynamics. Additionally, an iterative algorithm is developed to explore and determine the tire-road contact point. The results of simulation for two random scenarios are validated versus a commercial vehicle dynamics software showing consistency of results.
The Effects of Switching Time on Shared Human-Robot Control
Authors: Belotti Roberto , von Ellenrieder Karl
Published: November 2018

In human-robot shared control, control authority is shared between human operators and automatic systems. Switching from one state to another can make the overall system unstable, even though the stability in each state is guaranteed. This issue is investigated in simulation using a Lane Keeping Assist System (LKAS), which guides a vehicle along a lane, while allowing lane changes, if desired by the human. An interface allows a human to input a steering control signal with a joystick and provides visual feedback of lane-position. The total steering command is the combination of the LKAS control signal and the human steering input. System performance is explored as the vehicle switches among different levels of cooperation between the human and the automatic driving system. The minimum time permitted between lane changes is an important parameter. As this time is decreased, user intent and the automatic controller are in conflict more often, resulting in larger control efforts from both user and automatic controller.
Path Planning for Autonomous Car Parking
Authors: Lin Letian , Zhu J. Jim
Published: November 2018

The path planning problem for autonomous car parking has been widely studied. However, it is challenging to design a path planner that can cope with parking in tight environment for all common parking scenarios. The important practical concerns in design, including low computational costs and little human’s knowledge and intervention, make the problem even more difficult. In this work, a path planner is developed using a novel four-phase algorithm. By using some switching control laws to drive two virtual cars to a target line, a forward path and a reverse path are obtained. Then the two paths are connected along the target line. As illustrated by the simulation results, the proposed path planning algorithm is fast, highly autonomous, sufficiently general and can be used in tight environment.
Disturbance Observer-Based Motion Control of Small Autonomous Underwater Vehicles
Authors: Wang Bingheng , Mihalec Marko , Gong Yongbin , Pompili Dario , Yi Jingang
Published: November 2018

This paper presents a trajectory-tracking method using disturbance observer-based model predictive control (MPC) for small autonomous underwater vehicles (AUV). The goal of the work is to design a robust motion controller for AUVs under the system constraints and unknown disturbances such as hydrodynamics and ocean currents. Super-twisting-algorithm (STA) is employed to design the disturbance observer and its output is used and included in the feedback linearization law to compensate for the disturbances. The control inputs are generated using the MPC design with the nominal linearized model. Simulation results are included to validate the effectiveness of the control design and also compare with the traditional MPC motion control.
Performance Evaluation of Train Suspension Energy Harvesting Shock Absorber on Railway Vehicle Dynamics
Authors: Pan Yu , Guo Sijing , Jiang Ruijin , Xu Yong , Tu Zhiwen , Zuo Lei
Published: November 2018

Railway transportation has been increasingly significant for modern society in recent decades. To enable smart technology, such as health monitoring and electromagnetic braking for railway vehicles, a mechanical motion rectifier (MMR) based energy harvesting shock absorber (EHSA) has been proposed and proved to be capable of scavenging energy from the train suspension vibration. When installed on the train, MMR-EHSA works as a tunable damper in parallel with an inerter. This new suspension form brings great potential for further optimization of suspension dynamics but is rarely researched before. In this paper, the influence of the energy harvesting shock absorber (EHSA) on the railway vehicle dynamics performance is studied. A ten-degree of freedom vehicle model is established, with MMR shock absorber’s nonlinearity taken into account, with the purpose to analyze the influence of the EHSA on the ride comfort and wheel-rail vertical forces. Simulations are conducted by replacing the traditional shock absorber from train secondary suspension with the EHSA. Results show that EHSA could respectively harvest 180 W and 40 W average power at AAR 6th and 5th rail irregularity. In addition, compared with the traditional shock absorber, the MMR-EHSA can provide a higher ride comfort for passengers and slightly reduce the wheel-rail contact force.
Foveation Control of a Robotic Eye Using Deep Reinforcement Learning
Authors: Rajendran Sunil Kumar , Wei Qi , Zhang Feitian
Published: November 2018

Deficit of the extraocular muscle is known as a key cause of ocular motility disorders that affect eye movement and complicate daily activities of millions of people in the US. A physical model mimicking the biomechanics of the oculomotor plant can improve the understanding of functionality and control of extraocular muscles and provide a tool for researchers to gain insights into binocular misalignment. This paper will present, for the first time, the design and development of a robotic eye system driven by antagonistic super coiled polymer (SCP) based artificial muscles and the motion control design by leveraging machine learning techniques. The dynamic model of the robotic eye will be presented. Deep reinforcement learning is used for control design of the robotic eye system, demonstrated by simulation of one-dimensional foveation control.