bluuebunny/crossref_metadata_2025_split huggingface dataset:
‘Homeland farming’ or ‘rural emancipation’? The discursive overlap between populist and green parties in Hungary
Authors: Lubarda Balsa 
Published: November 2019

AbstractIn their attempts to associate the nationalistic ideology with speculative promises to emancipate the people from malevolent ‘outsiders’, right‐wing populists often engage with rural and agricultural topics. Meanwhile, green parties, commonly associated with the progressive ideas of environmentally friendly agriculture, occasionally employ the binary logic of agrarian populism. This paper has three objectives. First, to identify the discursive features of rural (right‐wing and agrarian) populism. Second, to examine how these discursive differences unfold in agricultural, party politics. Third, to examine the implications of overlapping ideas of populist and green parties for emancipatory rural politics. The study is predominantly based on the analysis of political discourses in Hungary: Jobbik (right‐wing populist) and LMP (green party), while the agricultural discourse of Fidesz, the ruling right‐wing populist party will serve as the background to the analysis. Particular attention is given to the aesthetic, symbolic, and material dimensions of land in discourse, including environmentally friendly farming and the EU’s Common Agricultural Policy (CAP).
Capital controls and the location of industry
Authors: Wiberg Magnus 
Published: November 2019

AbstractThis paper studies capital controls on the outflow of capital in a two‐region new economic geography model. Capital controls are set in a non‐cooperative or cooperative manner by social planners. Capital controls are relatively higher in the North in the non‐cooperative equilibrium. This leads to relatively more firms located in the region where more consumers reside under the non‐cooperative equilibrium. The locational bias towards the North in the non‐cooperative equilibrium becomes larger as trade barriers are reduced. That is, firms locate to the North at a relatively higher rate in the non‐cooperative equilibrium as trade is liberalised. Contrary to previous findings, it follows that global welfare is relatively higher in the non‐cooperative equilibrium, although the social planner sets capital controls by maximising joint regional welfare in the cooperative equilibrium.
Lifestyles of Populations of Extractive Reserves of Rondônia - Brazil
Authors: Haddad Rogério Delbone , Madi Rubens Riscala , Coelho Andressa Sales 
Published: November 2019

Extractive Reserves (Resex) are a category of sustainable use conservation unit that aims to protect the livelihoods and culture of their populations, and ensure the sustainable use of natural resources. This article aims to analyze a set of social, economic and environmental indicators aimed at the sustainable development of the populations, protected areas and the “Extractive Reserves” located in the State of Rondônia - Brazil under an ethnographic perspective. The methodological procedures were delineated in an epistemological perspective of ethnography recognition in 2017 and 2018 , where the different ways of life and the dynamics in the construction of spaces of these populations were observed and experienced, of (re)consideration of individuals in environmental, social and economic policies and to form the basis for the interdisciplinary approaches, in the study indicators and in the intervention and management of the territory and its resources. The results showed that the Resex populations need a broader view of the State and Society, because their lives are, with few perspectives, one can perceive the vulnerability of many in relation to poverty, lack of basic assistance and consequently the generation of social exclusion. In this sense, the analysis of the study indicators and the interconnections with authors on the socio-environmental and economic scenarios of the Amazon identified that the Resex public policies need to be rethought in order to build a fairer and more equitable society.
Data-Driven ILC for Trajectory Tracking in Nonlinear Dynamic Systems
Authors: Lee Yu-Hsiu , Tsao Tsu-Chin 
Published: November 2019

Abstract
The aim of this work is to propose a data-driven ILC algorithm that features fast convergence for nonlinear dynamic systems. This idea utilizes adaptive filtering that implicitly identifies the time-varying system inverse along the trajectory being tracked. By feeding the error signal through the copied inverse filter, it results in a rapidly convergent inversion-based ILC. This approach is compared to a nonlinear extension of the data-driven ILC that uses system adjoint as the learning filter. The developed algorithm is validated through simulation on a fully actuated 2 DOF Furuta pendulum.
Trajectory Tracking of 3D Autonomous Vehicles Using Backstepping Control
Authors: Fetzer Karl Ludwig , Nersesov Sergey G. , Ashrafiuon Hashem 
Published: November 2019

Abstract
In this paper, the authors derive backstepping control laws for tracking a time-based reference trajectory for a 3D model of an autonomous vehicle with two degrees of underactuation. Tracking all six degrees of freedom is made possible by a transformation that reduces the order of the error dynamics. Stability of the resulting error dynamics is proven and demonstrated in simulations.
Lane Keeping Control Using Finite Spectrum Assignment With Modeling Errors
Authors: Vörös Illés , Várszegi Balázs , Takács Dénes 
Published: November 2019

Abstract
Lane keeping control of the single track vehicle model with linear tire characteristics is analyzed in the presence of time delay. In order to compensate time delay, the predictor control approach called finite spectrum assignment is applied. This controller uses an internal model of the plant to predict current system states in spite of the time delay. The predictions are based on a simplified version of the vehicle model, neglecting tire dynamics. The predictive control approach is compared with traditional feedback control using analytically derived stability maps and numerical simulations. Robustness to parameter mismatches and numerical issues related to the implementation of the control law are also analyzed.
Engine Calibration Optimization Based on its Surrogate Models
Authors: Pal Anuj , Wang Yan , Zhu Ling , Zhu Guoming G. 
Published: November 2019

Abstract
Diesel engines are becoming increasingly complex to control and calibrate with the desire of improving fuel economy and reducing emissions (NOx and Soot) due to global warming and energy usage. With ever increased control features, it is becoming more and more difficult to calibrate engine control parameters using the traditional engine mapping based methods due to unreasonable calibration time required. Therefore, this research focuses on the problem of performing engine calibration within a limited budget by efficiently optimizing three control parameters: namely variable geometry turbocharger (VGT) position, exhaust gas recirculation (EGR) valve position, and start of injection (SOI). Engine performance in terms of fuel consumption (BSFC) and emissions (NOX) are considered as objective function here with the constraint on boost pressure and engine load (BMEP). Since the engine calibration process requires a large number of high-fidelity evaluations, surrogate modeling methods are used to perform calibration quickly with a significantly reduced computational budget. Kriging metamodeling is used for this work with Expected Improvement (EI) as acquisition function. Results show more than 60% decrease in computational cost with results close to actual near Pareto optimal set.
Learning Flatness-Based Controller Using Neural Network
Authors: Ren Hailin , Qi Jingyuan , Ben-Tzvi Pinhas 
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/10.1115/1.4046776/1082074/Learning-Flatness-Based-Controller-Using-Neural
Identification of Temperature Dynamics Using Subspace and Machine Learning Techniques
Authors: Haber Aleksandar , Pecora Francesco , Chowdhury Mobin Uddin , Summerville Melvin 
Published: November 2019

Abstract
Identification, estimation, and control of temperature dynamics are ubiquitous and challenging control engineering problems. The main challenges originate from the fact that the temperature dynamics is usually infinite dimensional, nonlinear, and coupled with other physical processes. Furthermore, the dominant system time constants are often long, and due to various time constraints that limit the measurement time, we are only able to collect a relatively small number of input-output data samples. Motivated by these challenges, in this paper we present experimental results of identifying the temperature dynamics using subspace and machine learning techniques. We have developed an experimental setup consisting of an aluminum bar whose temperature is controlled by four heat actuators and sensed by seven thermocouples. We address noise reduction, experiment design, model structure selection, and overfitting problems. Our experimental results show that the temperature dynamics of the experimental setup can be relatively accurately represented by low-order models.
Performance Study of a Bat Searching Algorithm From System Dynamics Perspective
Authors: Zhang Haopeng , Schutte Nathan 
Published: November 2019

Abstract
In this paper, the performance of a bat searching algorithm is studied from system dynamics point of view. Bat searching algorithm (BA) is a recently developed swarm intelligence based optimization algorithm which has shown great success when solving complicated optimization problems. Each bat in the BA has two main states: velocity and position. The position represents the solution of the optimization problems while the velocity represents the searching direction and step size during each iteration. Due to the nature of the update equations, the dynamics of the bats are formulated as a group of second-order discrete-time systems. In this paper, the performance of the algorithm is analyzed based on the nature of the responses in the second-order systems. The over-damped response, under-damped responses are studied and the parameters requirements are derived. Moreover, unstable scenarios of the bats are also considered when examining the performance of the algorithm. Numerical evaluations are conducted to test different choices of the parameters in the BA.
Dynamics of a Vibration Driven Bristlebot
Authors: Gandra Chandravamsi , Tallapragada Phanindra 
Published: November 2019

Abstract
Vibration driven robots such as the so called bristlebot and kilobot utilize periodic forced vibration of an internal mass to achieve directed locomotion. These robots are supported on an elastic element such as bristles or cilia and contain an internal mass that is driven to oscillate at a high frequency. Besides well known applications in investigating swarming behavior, such robots have potential applications in rescue operations in rubble, inspections of pipes and other inaccessible confined areas and in medical devices where conventional means of locomotion is ineffective. Bristlebot or its commercially available variants such as hexbugs are popular toy robots. Despite the apparent simplicity of these robots, their dynamic behavior is very complex. Vibration robots have attracted surprisingly few analytical models, those models that exist can only explain some regimes of locomotion. In this paper, a wide range of motion dynamics of a bristlebot is explored using a mathematical model which accounts for slip-stick motion of the bristles with the substrate. Analytical conditions for the system to exhibit a particular type of motion are formulated and the system of equations defining the motion are solved numerically using these conditions. The numerical simulations show transitions in the kinds of locomotion of a bristlebot as a function of the forcing frequency. These different kinds of locomotion include stick-slip and pure slip motions along with the important phenomenon of the reversal of the direction of motion of the robot. In certain ranges of frequencies, the robot can lose contact with the ground and ‘jump’. These different regimes of locomotion are a result of the nonlinear vibrations of the robot and the friction between the robot’s bristles and the ground. The results of this paper can potentially lead to more versatile vibration robots with predictable and controllable dynamics.
Following Fast-Dynamic Targets With Only Slow and Delayed Visual Feedback: A Kalman Filter and Model-Based Prediction Approach
Authors: Xiao Hui , Chen Xu 
Published: November 2019

Abstract
Although visual feedback has enabled a wide range of robotic capabilities such as autonomous navigation and robotic surgery, low sampling rate and time delays of visual outputs continue to hinder real-time applications. When partial knowledge of the target dynamics is available, however, we show the potential of significant performance gain in vision-based target following. Specifically, we propose a new framework with Kalman filters and multirate model-based prediction (1) to reconstruct fast-sampled 3D target position and velocity data, and (2) to compensate the time delay for general robotic motion profiles. Along the path, we study the impact of modeling choices and the delay duration, build simulation tools, and experimentally verify different algorithms with a robot manipulator equipped with an eye-in-hand camera. The results show that the robot can track a moving target with fast dynamics even if the visual measurements are slow and incapable of providing timely information.
Output Feedback Control Synthesis for a Helicopter Using Explicit Nonlinear Model Predictive Control, Dynamic Inversion and Extended High Gain Observers
Authors: Seo Joohwan , Choi Jongeun 
Published: November 2019

Abstract
Control design for a helicopter is a challenging problem because of its non-affine inputs, complicated dynamics and it is an under-actuated system. To solve a control problem of the helicopter under model uncertainties and disturbance present environments, an Explicit Nonlinear Model Predictive Control (ENMPC), a dynamic inversion and an Extended High-Gain Observers (EHGO) are combined in a multi-time-scale fashion. The multi-time scaled structrue and the ENMPC provides the framework of the control design, the dynamic inversion deals with non-affine control inputs, and the EHGO estimates the unmeasured states and uncertainties. In addition, a discretization scheme using the saturation and adding low pass filters to the control inputs is presented. Finally, the numerical simulation of a fixed sampling period has been carried out to demonstrate the validity of the proposed multi-time-scale control design and the discretization scheme.
Optimal Lane Management in Heterogeneous Traffic Network
Authors: Karimi Shahri Pouria , Chintamani Shindgikar Shubhankar , HomChaudhuri Baisravan , Ghasemi Amir H. 
Published: November 2019

Abstract
This paper aims to determine an optimal allocation of autonomous vehicles in a multi-lane heterogeneous traffic network where the road is shared between autonomous and human-driven vehicles. The fundamental traffic diagram for such heterogeneous traffic networks is developed wherein the capacity of the road is determined as a function of the penetration rate and the headways of autonomous and human-driven vehicles. In this paper, we define two cost functions to maximize the throughput of the network and minimize the variation between flow rates. To solve the proposed optimization problem, an exhaustive search optimization approach is performed. Several numerical examples are presented to highlight the different influence of different design parameters on the allocation of autonomous vehicles.
The Restricted Newton Method for Fast Nonlinear Model Predictive Control
Authors: Maitland Anson , Jin Chi , McPhee John 
Published: November 2019

Abstract
We introduce the Restricted Newton’s Method (RNM), a basic optimization method, to accelerate model predictive control turnaround times. RNM is a hybrid of Newton’s method (NM) and gradient descent (GD) that can be used as a building block in nonlinear programming. The two parameters of RNM are the subspace on which we restrict the Newton steps and the maximal size of the GD step. We present a convergence analysis of RNM and demonstrate how these parameters can be selected for MPC applications using simple machine learning methods. This leads to two parameter selection strategies with different convergence behaviour. Lastly, we demonstrate the utility of RNM on a sample autonomous vehicle problem with promising results.
Design of a Kangaroo Inspired Hopping Robot for Unrestricted Locomotion and Controller Development
Authors: Curtis Austin , Mynderse James , Vejdani Hamid 
Published: November 2019

Abstract
Inspired by the agility and maneuverability of running kangaroos, a prototype robot was developed using a reduced order model to constrain the system. Both passive and active models were used to understand the relationship between system parameters and gait performance. A frequency response experiment was performed on the prototype to quantify the relationship between design parameters and system responses. Additionally, preliminary tail controllers were tested. Based on the results of the initial platform, a new robot was designed and built as a platform for the study of three dimensional hopping.
Integrated Forward and Reverse Trajectory Tracking Control for Car-Like Ground Vehicle
Authors: Chen Yuanyan , Zhu J. Jim , Lin Letian 
Published: November 2019

Abstract
Conventional automatic trajectory tracking control technics for car-like ground vehicles typically decompose the controller into separate longitudinal driving control and lateral-directional steering control, owing to the nonholonomic kinematic constraint, highly nonlinear dynamics and control under-actuation of such vehicles. However, such decoupled control techniques inevitably impose operational constraints on agile maneuvers that may be critical in evading impending collisions, preventing loss-of-control of the vehicle, and special maneuvers that are needed for law enforcement missions. Thus, integrated three-Degree-of-Freedom (3DOF) tracking control of car-like ground vehicles are highly desirable but remains a challenging problem. There also appears to be a lack of research on automated reverse driving. In our previous work [ASME DSCC2017-5372, DSCC2018-9148], design and hardware validation test results of an integrated 3DOF trajectory tracking controller based on nonlinear kinematics and dynamics vehicle model using Trajectory Linearization Control (TLC) for forward driving have been reported. The present paper supplements that work with design and hardware validation test results on vehicle backward driving at fast and low speeds. The reverse driving control incurs minimal alteration to the original design with minimal tuning efforts due to the model-based TLC control approach, and it should be readily scaled-up to full-size vehicles and adapted to different types of autonomous ground vehicles with the knowledge of vehicles’ kinematics and dynamics parameters.
Diesel Engine Characterization and Performance Scaling via Brake Specific Fuel Consumption Map Dimensional Analysis
Authors: Pelletier Evan , Brennan Sean 
Published: November 2019

Abstract
The goal of this work is to develop easily generalized models of heavy duty truck engine maps that allow for approximate comparisons of engine performance, thus enabling fuel efficient matching of engines to a set of corresponding loads and routes. This is achieved by applying dimensional analysis to create a uniformly applicable, dimensionless Brake Specific Fuel Consumption (BSFC) map that fits the behavior of a wide range of diesel engines. A commonality between maps was found to occur when engine data is scaled by specific dimensional parameters that target data consistency among the primary operating points across engines. This common map highlights observable trends in engine performance based on the influence of these same parameters being scaled across engines. The resulting dimensionless engine map fits the minimum BSFC regions of four diesel engines to within 2.5 percent.
Instantaneous Center of Rotation-Based Master-Slave Kinematic Modeling and Control
Authors: Ramanathan Vikram , Zelenak Andy , Pryor Mitch 
Published: November 2019

Abstract
This article presents a novel kinematic model and controller design for a mobile robot with four Centered Orientable Conventional (COC) wheels. When compared to non-conventional wheels, COC wheels perform better over rough terrain, are not subject to vertical chatter and offer better braking capability. However, COC wheels are pseudo-omnidirectional and subject to nonholonomic constraints. Several established modeling and control techniques define and control the Instantaneous Center of Rotation (ICR); however, this method involves singular configurations that are not trivial to eliminate. The proposed method uses a novel ICR-based kinematic model to avoid these singularities, and an ICR-based nonlinear controller for one ‘master’ wheel. The other ‘slave’ wheels simply track the resulting kinematic relationships between the ‘master’ wheel and the ICR. Thus, the nonlinear control problem is reduced from 12th to 3rd-order, becoming much more tractable. Simulations with a feedback linearization controller verify the approach.
Analytical Statistical Study of Linear Parallel Feedforward Compensators for Nonminimum-Phase Systems
Authors: Noury Keyvan , Yang Bingen 
Published: November 2019

Abstract
In this work, a new parallel feedforward compensator for the feedback loop of a linear nonminimum-phase system is introduced. Then, analytical statistical arguments between the existing developed methods and the innovated method are brought. The compelling arguments suggest the parallel feedforward compensation with derivative (PFCD) method is a strong method even though at its first survey it seems to be optimistic and not pragmatic. While most of the existing methods offer an optimal integral of squared errors (ISE) for the closed-loop response of the nominal plant, the PFCD offers a finite ISE; in reality, typically, the nominal plant is not of main concern in the controller design and the performance in the presence of mismatch model, noise, and disturbance has priority. In this work, there are several arguments brought to bold the importance of the innovated PFCD design. Also, there is a closed-loop design example to show the PFCD effectiveness in action.
Configuration Optimization for End-Point Stabilization of Redundant Manipulators With Base Flexibility
Authors: LiBretto Michael , Cho Yong K. , Ahn Yong Han , Han Chang Soo , Ueda Jun 
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/10.1115/1.4046684/1081845/Configuration-Optimization-for-End-Point
Adaptive Single Action Control Policies for Linearly Parameterized Systems
Authors: Ennasr Osama , Mamakoukas Giorgos , Castaño Maria , Coleman Demetris , Murphey Todd , Tan Xiaobo 
Published: November 2019

Abstract
This paper presents an adaptive, needle variation-based feedback scheme for controlling affine nonlinear systems with unknown parameters that appear linearly in the dynamics. The proposed approach combines an online parameter identifier with a second-order sequential action controller that has shown great promise for nonlinear, underactuated, and high-dimensional constrained systems. Simulation results on the dynamics of an underwater glider and robotic fish show the advantages of introducing online parameter estimation to the controller when the model parameters deviate from their true values or are completely unknown.