bluuebunny/crossref_metadata_2025 huggingface dataset:
A Nonlinear Autoregressive With Exogenous Inputs Artificial Neural Network Model for Building Thermal Load Prediction
Authors: Yu Byeongho , Kim Dongsu , Cho Heejin , Mago Pedro
Published: November 2019

Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve building energy performance and efficiency. Regarding thermal load prediction, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time, but they may not provide accurate results for complex energy systems with intricate nonlinear dynamic behaviors. This study proposes an artificial neural network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of nonlinear autoregressive with exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models because the NARX concept can address nonlinear system behaviors effectively based on its recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using the field data of an academic campus building at Mississippi State University (MSU). Results show that the proposed NARX-ANN model can provide an accurate and robust prediction performance and effectively address nonlinear system behaviors in the prediction.
Engineer Energy Dissipation in 3D Graphene Nanolattice Via Reversible Snap-Through Instability
Authors: Ni Bo , Gao Huajian
Published: November 2019

Carbon micro/nanolattice materials, defined as three-dimensional (3D) architected metamaterials made of micro/nanoscale carbon constituents, have demonstrated exceptional mechanical properties, including ultrahigh specific strength, stiffness, and extensive deformability through experiments and simulations. The ductility of these carbon micro/nanolattices is also important for robust performance. In this work, we present a novel design of using reversible snap-through instability to engineer energy dissipation in 3D graphene nanolattices. Inspired by the shell structure of flexible straws, we construct a type of graphene counterpart via topological design and demonstrate its associated snap-through instability through molecular dynamics (MD) simulations. One-dimensional (1D) straw-like carbon nanotube (SCNT) and 3D graphene nanolattices are constructed from a unit cell. These graphene nanolattices possess multiple stable states and are elastically reconfigurable. A theoretical model of the 1D bi-stable element chain is adopted to understand the collective deformation behavior of the nanolattice. Reversible pseudoplastic behavior with a finite hysteresis loop is predicted and further validated via MD. Enhanced by these novel energy dissipation mechanisms, the 3D graphene nanolattice shows good tolerance of crack-like flaws and is predicted to approach a specific energy dissipation of 233 kJ/kg in a loading cycle with no permanent damage (one order higher than the energy absorbed by carbon steel at failure, 16 kJ/kg). This study provides a novel mechanism for 3D carbon nanolattice to dissipate energy with no accumulative damage and improve resistance to fracture, broadening the promising application of 3D carbon in energy absorption and programmable materials.
An Atomistic Perspective on the Effect of Strain Rate and Lithium Fraction on the Mechanical Behavior of Silicon Electrodes
Authors: Darbaniyan Faezeh , Yan Xin , Sharma Pradeep
Published: November 2019

The process of charging and discharging of lithium-ion batteries results in the periodic intercalation and ejection of lithium ions in the anode material. High-capacity anode materials that are of significant interest for next-generation batteries, such as silicon, undergo large deformation during this process. The ensuing electro-chemo-mechanical stresses and accompanying microstructural changes lead to a complex state of inelastic deformation and damage in the silicon electrode that causes a significant capacity loss within just a few cycles. In this study, we attempt to understand, from an atomistic viewpoint, the mechanisms underlying the plasticity behavior of Si-anode as a function of lithiation. Conventional molecular dynamics simulations are of limited use since they are restricted to loading rates in the order of 108 s−1. Practical charging-discharging rates are several orders of magnitude slower, thus precluding a realistic atomistic assessment of the highly rate-dependent mechanical behavior of lithiated silicon anodes via conventional molecular dynamics. In this work, we use a time-scaling approach that is predicated on the combination of a potential energy surface sampling method, minimum energy pathway, kinetic Monte Carlo, and transition state theory, to achieve applied strain rates as low as 1 s−1. We assess and compare the atomistic mechanisms of plastic deformation in three different lithium concentration structures: LiSi2, LiSi, and Li15Si4 for various strain-rates. We find that the strain rate plays a significant role in the alteration of the deformation and damage mechanisms including the evolution of the plastic deformation, nucleation of shear transformation zone, and void nucleation. Somewhat anomalously, LiSi appears to demonstrate (comparatively) the least strain rate sensitivity.
The Failure Theory for Isotropic Materials: Proof and Completion
Authors: Christensen Richard M.
Published: November 2019

This work represents the completion of the many developments in recent years on failure theory for homogeneous and isotropic materials. Presented here is the resulting failure formalism in final and technically complete form. Significant further results are also given for the verification of the failure formalism. The scope of this paper goes from the history of misguided failure theory investigations right up to the present final tested forms ready for applications. For every predicted failure level in terms of the stresses, there is an accompanying ductility level. This ranges from brittle failure up to fully ductile failure. The entire theory is calibrated by only two specified parameters (failure properties). Nothing else is needed. The seemingly interminable, actually centuries long search for the missing theory of failure has finally been brought to a resolute and successful conclusion.
Stall and Recovery Process of a Transonic Fan With and Without Inlet Distortion
Authors: Zhang Wenqiang , Vahdati Mehdi
Published: November 2019

The aim of this research is to study the stall and recovery behavior of a transonic fan stage with and without inlet distortion. For this purpose, simulations of the stall and recovery process of NASA stage 67 are performed with clean and distorted inflow conditions. The rotor is pushed into stall by closing the exit nozzle. It is shown that in both cases, stall is initiated via spike but the subsequent development of the stall differs. In the stable rotating stall, both cases contain one stall cell traveling at 63% shaft speed. During the recovery process, when the exit nozzle is gradually opened, the size of this stall cell reduces as the corrected mass flow increases. Although the fan stalls at a larger mass flow with inlet distortion, it recovers to a similar corrected mass flow as the case with clean inflow, which indicates that inlet distortion has minor effects on the recovery process for this blade. In spite of the lack of data, detailed analysis based on past experience and physical reasoning is used to demonstrate the validity on numerical simulations. The author appreciates that a validated computational fluid dynamics (CFD) study can provide instructive results to other researchers.
Bayesian Estimation of Snow-Avalanche Victim Pose: A Method to Assist Human and/or Robot First Responders to Quickly Locate a Buried Victim
Authors: Bourne Joseph R. , Leang Kam K.
Published: November 2019

Finding a victim buried in a snow avalanche as quickly as possible can significantly increase the victim’s survival rate. A body-pose estimation algorithm is described that quickly and efficiently estimates the victim’s pose (3D location and orientation) underneath the snow. The algorithm exploits non-parametric Bayesian estimation and considers the uncertainty in an avalanche transceiver’s magnetic-field measurement. Simulation results compare the performances between three victim-search methods: (1) naive raster-scanning search, (2) traditional industry-standard search along the measured magnetic field lines, and (3) search by the Bayesian-based technique. The results show that the Bayesian-based technique accurately determines the victim’s pose within two minutes. In contrast, the raster-scanning and magnetic-field-line following methods yield search times more than three to four times longer.
Sliding-Mode Control With Switching-Gain Adaptation for Trajectory Tracking of Underactuated Unmanned Surface Vessels
Authors: Yu Rui , Qi Haochen , Upadhyay Kamal , Zhou Hua
Published: November 2019

Robust trajectory tracking is a vitally important issue to underactuated unmanned surface vessels (USVs). In this paper, a sliding-mode control method with switching-gain adaptation is proposed to force an underactuated USV to track a predefined trajectory, despite the presence of parametric uncertainties and unknown environmental disturbances induced by the wave loads, the wind and the ocean currents. The proposed controller is designed using sliding-mode control (SMC) and backstepping and switching-gain adaptation techniques. In this methodology, SMC and backstepping technique are combined to enhance the robustness of the system, and the switching-gain adaptation law based on the ideal switching-gain is employed to alleviate the chattering problem. In addition, we have proved the global asymptotic stability of the closed-loop system under the discontinuous thruster forces or the varying uncertain terms related to the velocities and the environmental disturbances. Finally, numerical simulations are provided to demonstrate the robustness and effectiveness of the proposed method for trajectory tracking of underactuated USVs.
Optimal Transient Real-Time Engine-Generator Control in the Series-Hybrid Vehicle
Authors: Lock Jonathan , Arvidsson Rickard , McKelvey Tomas
Published: November 2019

We study the dynamic engine-generator optimal control problem with a goal of minimizing fuel consumption while delivering a requested average electrical power. By using an infinite-horizon formulation and explicitly minimizing fuel consumption, we avoid issues inherent with penalty-based and finite-horizon problems. The solution to the optimal control problem, found using dynamic programming and the successive approximation method, can be expressed as instantaneous non-linear state-feedback. This allows for trivial real-time control, typically requiring 10–20 CPU instructions per control period, a few bytes of RAM, and 5–20 KiB of nonvolatile memory. Simulation results for a passenger vehicle indicate a fuel consumption improvement in the region of 5–7% during the transient phase when compared with the class of controllers found in the industry. Bench-tests, where the optimal controller is executed in native hardware, show an improvement of 3.7%, primarily limited by unmodeled dynamics. Our specific choice of problem formulation, a guaranteed globally optimal solution, and trivial real-time control resolve many of the limitations with the current state of optimal engine-generator controllers.
Combined Energy and Comfort Optimization of Air Conditioning System in Connected and Automated Vehicles
Authors: Wang Hao , Amini Mohammad Reza , Song Ziyou , Sun Jing , Kolmanovsky Ilya
Published: November 2019

In this paper, we propose a combined energy and comfort optimization (CECO) strategy for the air conditioning (A/C) system of the connected and automated vehicles (CAVs). By leveraging the weather and traffic predictions enabled by the emerging CAV technologies, the proposed strategy is able to minimize the A/C system energy consumption while maintaining the occupant thermal comfort (OTC) within the comfort constraints, where the comfort is quantified by a modified predictive mean vote (PMV) model adapted for an automotive application. A general CECO problem is formulated and addressed using model predictive control (MPC) and weather/traffic previews. Depending on the ways of exploiting the preview information and enforcing the OTC constraint, different MPCs are developed based on solving different variations of the general CECO problem. The CECO-based MPCs are then tested in simulation using an automotive A/C system simulation model (CoolSim) as the virtual testbed. The simulation results show that, over SC03 driving cycle, the proposed CECO-based MPCs outperform the baseline cabin temperature tracking controller, reducing the A/C system energy consumption by up to 7.6%, while achieving better OTC according to the PMV-based metrics. This energy saving in A/C system translates to 3.1% vehicle fuel economy improvement. The trade-off between energy efficiency and OTC for different control scenarios is also highlighted.
A Probabilistic Technique for Allocation of Tasks With Priorities in a School of Autonomous Mobile Robots
Authors: Bakshi Soovadeep , Feng Tianheng , Yan Zeyu , Chen Dongmei
Published: November 2019

Using Autonomous Mobile Robots (AMRs) to collaboratively complete tasks has received a lot of attention in recent years from both industry and acedemia, especially in applications related to manufacturing automation. However, in spite of the technological progress, there are many challenges yet to be addressed in prioritized task allocation and scheduling of a school of AMRs in real time. This paper focuses on the real-time task allocation problem for a school of AMRs, i.e., given a prioritized task list and multiple AMRs, determining the set of tasks to be completed by each AMR. This paper proposes a probabilistic task allocation method which formulates the problem as a log-likelihood maximization problem, and uses a cyclic optimization scheme. This algorithm is shown to perform better when compared to commonly-used algorithms for asymmetric clustering. This proposed algorithm can be combined with scheduling methods to generate a ‘cluster-first, order-second’ solution to the multi-AMR task planning problem.
Multi-Objective Optimal Design of a PID Sliding Mode Controller With Three Different Reaching Laws
Authors: Xu Xiaotian , Sardahi Yousef , Boker Almuatazbellah
Published: November 2019

Sliding mode controllers (SMCs) are well-known nonlinear control techniques. The design of a SMC involves the selection of a sliding mode surface and reaching law. The constant, exponential, and power rate reaching laws are the most widely used. Selecting a reaching law is often based on the desired reaching time; that is how fast the state trajectory approaches the switching manifold. However, the selection of a reaching law does not only affect the reaching time (tr) but also other design specifications such as the settling time (ts), overshoot (Mp), and tracking error (JIAE). Indeed, the design of a closed-loop system usually involves multiple and often conflicting objectives. Therefore, a multi-objective optimal design approach that takes into consideration all the design requirements should be adopted. Furthermore, a systematic study is needed to evaluate and compare the performance of a SMC controller under these reaching laws in multi-objective settings. To this end, the problems of designing a PID (Proportional-Integral-Derivative) sliding mode controller applied to linear and nonlinear dynamic systems using the three reaching laws are formulated as multi-objective optimization problems (MOPs). The objective space includes tr, Mp, ts, and JIAE and the parameter space consists of the design gains of the reaching laws and the sliding mode surface. The non-dominated sorting genetic algorithm (NSGA – II) is used to solve the optimization problem. The solution of the MOP is a Pareto front of optimal design points. Therefore, comparing three Pareto fronts is not a straightforward task. As a result, sections of the Pareto fronts that satisfy some legitimate constraints on the objective space are extracted. Then, a comparison among these sections is conducted graphically. The results show that the exponential rate reaching law outperforms the other two laws in most of the objectives under investigation.
An Experimental Study of Longitudinal Tire Relaxation Constants for Vehicle Traction Dynamics Modeling
Authors: Vantsevich Vladimir V. , Demkiv Lyubomyr I. , Klos Sviatoslav R. , Misko Samuel R. , Moradi Lee
Published: November 2019

Existing literature on vehicle traction dynamics were reviewed for a variety of vehicle and tire dynamic models, some of which consider the pneumatic tires’ relaxation as a property of vehicle transient dynamics. In general, unlike the lateral relaxation counterpart, the longitudinal tire relaxation characteristics were mostly overlooked in tire transient dynamics modeling. As a continuation of the analytical study published in the 2018 DSCC Proceedings, the co-authors of this paper present an experimental study of the longitudinal tire relaxation characteristics of a Continental MPT 81 tire. Experimental results were obtained by conducting tests on an MTS Flat-Trac LTR tire testing machine. The experimental data is analyzed to investigate longitudinal tire relaxation characteristics as they relate to changes of tire conditions. The goal is to verify and refine the existing models suggested in the literature; as well as, discuss advantages and disadvantages of different test procedures and tire testing equipment.

In particular, the paper investigates the longitudinal tire relaxation constant variation due to changes of wheel velocity, tire inflation pressure, and sine oscillations of tire slippage in the time and frequency domains. The paper concludes on the influence of the longitudinal tire relaxation constants on the tire/vehicle traction dynamics modeling.
Comparison of Position Control With and Without Friction on a Foot Interface
Authors: Rudolph Brandon , Winck Ryder C.
Published: November 2019

A foot interface may one day control a third arm to assist the hands with a difficult task, but the interface needs to be easy to use. Developing a good foot interface is challenging because of the need to provide support for the leg, allow the user to disengage with the interface without causing unwanted motion, and make it easy for the user to hold a set position. The addition of friction in the interface can enable the device to meet these goals without negatively affecting performance. Although teleoperation is a well explored area of research, relatively little research has been done that examines the effects of friction on the control interface. This paper presents an experiment in which two foot control interfaces are compared. One device uses friction and the other has no added friction, so there is little resistance to motion in any direction. The experiment uses a reaching task and a path-following task to compare the interfaces. The only statistically significant performance differences were that the friction interface reduced the time needed to stop at a target and reduced excess movement when stopping at a target. Also, subjects indicated a preference for the friction interface. The results show that friction can be added to a foot interface to support the device and user and provide some positive gains in performance.
IMC-Based Calibration of the Boost Pressure Controller in an Electrically Assisted Turbocharged Gasoline Engine
Authors: Xiao Baitao , Kelly Tyler , Stolzenfeld Timothy , Lu Chenliu , Bell Dave , Jankovic Mrdjan , Buckland Julia , Rollinger John
Published: November 2019

In this work, a systematic approach is developed to calibrate a feedback controller for boost pressure control of an electrically assisted turbocharged gasoline engine. The information from the experiments indicates the system can be approximated by a Gain-Integrator-Delay (GID) model which can be robustly identified. Two controllers are designed for two different types of inner loop control (torque/speed) of the electrically assisted turbocharger. The underlying calibration methodology is based on Internal Model Control (IMC). The application of IMC leads to controllers that can be naturally mapped to a classic feedback controller. The plant model is obtained by characterizing the boost system with relay feedback experiments. The calibration methodology as well as the controller designs are demonstrated with a validated simulation platform and good performance is observed.
Completing Complex Contact Tasks Using Integrated Active and Passive Compliant Control Methodologies
Authors: Pettinger Adam , Pryor Mitch
Published: November 2019

In this paper we introduce the Generalized Contact Control Framework (GCCF) implemented on a compliant robotic manipulator. We demonstrate that the combined joint compliance and GCCF-based compliance control enable the completion of complex contact tasks in uncertain environments, where complex refers to the need to meet different contact force requirements involving multiple steps and output axes. Operating in uncertain environments means limited knowledge of the location or material properties of contact objects. The demonstrated tasks include opening a pill bottle and rigidly connecting to a purely mechanical tool changer. The GCCF simplifies the definition and modification of contact control parameters and allows for on-the-fly definition and completion of new tasks. Unlike hybrid force/impedance controllers, we do not need to define large damping and stiffness matrices, and we decouple the joint level control gains from the compliance control. The result is a robotic manipulator that can dynamically switch between unconstrained motion and contact tasks and provides a lot of versatility to perform a wide variety of tasks.
Hierarchical Nonlinear Moving Horizon Estimation of Vehicle Lateral Speed and Road Friction Coefficient
Authors: Jin Chi , Maitland Anson , McPhee John
Published: November 2019

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/10.1115/1.4046395/1074688/Hierarchical-Nonlinear-Moving-Horizon-Estimation
Control-Oriented Model-Based Burn Duration and Ignition Timing Prediction With Recursive-Least-Square Adaptation for Closed-Loop Combustion Phasing Control of a Spark Ignition Engine
Authors: Wang Xin , Khameneian Amir , Dice Paul , Chen Bo , Shahbakhti Mahdi , Naber Jeffrey D. , Archer Chad , Qu Qiuping , Glugla Chris , Huberts Garlan
Published: November 2019

In homogeneous spark-ignition (SI) engines, ignition timing is used to control the combustion phasing (crank angle of fifty percent of fuel burned, CA50), which affects fuel economy, engine torque output, and emissions. This paper presents a model-based adaptive ignition timing prediction strategy using a control-oriented dynamic combustion model for real-time closed-loop combustion phasing control. The combustion model predicts the burn duration from ignition timing to CA50 (ΔθIGN-CA50) at Intake Valve Closing (IVC) for the upcoming cycle based on current engine operating conditions, including variable valve timing, predicted ignition timing, air-fuel ratio, engine speed, and engine load. To maintain the accuracy of combustion model and ignition timing prediction during the engine lifetime, a Recursive-Least-Square (RLS) with Variable Forgetting Factor (VFF) based adaptation algorithm is developed to handle both short term (operating-point-dependent) and long term (engine aging) model errors. Due to short term model errors and stochastic characteristics of cycle-to-cycle combustion variations, large model errors may occur during severe transient operating conditions (tip-in/tip-out), which can result in wrong adjustments and excessive adaptations. Since on-road SI engines are always operating in transient conditions, the ‘Heavy Transient Detection’ algorithm is developed to avoid fault adaptation and assist the adaptation algorithm to be stable. On-road vehicle testing data is used to evaluate the performance of the entire model-based adaptive burn duration and ignition timing prediction algorithm. With only 64 calibration points, a mean ignition timing prediction error of 0.2 Crank Angle Degree (CAD) and average iteration number of 2 shows the capability of adaptive ignition timing prediction, a significant reduction of calibration efforts, and potential of real-time application of the developed adaptive ignition timing prediction algorithm.
Generalization of Spectral Methods for High-Cycle Fatigue Analysis to Accommodate Non-Stationary Random Processes
Authors: Khalil Mohamed , Wüchner Roland , Bletzinger Kai-Uwe
Published: November 2019

Estimation of material fatigue life is an essential task in many engineering fields. When non-proportional loads are applied, the methodology to estimate fatigue life grows in complexity. Many methods have been proposed to solve this problem both in time and frequency domains. The former tends to give more accurate results, while the latter seems to be more computationally favorable. Until now, the focus of frequency-based methods has been limited to signals assumed to follow a stationary statistic process. This work proposes a generalization to the existing methods to accommodate non-stationary processes as well. A sensitivity analysis is conducted on the influence of the formulation’s hyper-parameters, followed by a numerical investigation on different signals and various materials to assert the robustness of the method.
Optimal Speed Control for a Connected and Autonomous Electric Vehicle Considering Battery Aging and Regenerative Braking Limits
Authors: Shao Yunli , Sun Zongxuan
Published: November 2019

This work develops a comprehensive optimal speed control framework for connected and autonomous electric vehicles considering both battery aging effects and regenerative braking limits. With the battery aging consideration, the energy benefits can be achieved together with a satisfactory battery life. The regenerative braking limits ensure the optimal control law is realistic and can be implemented in practice (e.g. there should be no regen when battery is almost full). The target vehicle intelligently controls the vehicle speed and car-following distance based on predicted traffic conditions using real-time information enabled by connectivity. The traffic prediction is based on a traffic flow model and can be implemented in a mixed-traffic scenario where both connected vehicles and non-connected vehicles share the road. The optimal control problem is formulated, simplified and discretized with minimal computational burden. It can be solved in real-time using an efficient nonlinear programming solver and is implemented in the model predictive control (MPC) fashion. The average computational time of the optimization is 0.54 seconds for a 15-second prediction horizon. A representative traffic scenario is evaluated in simulation where the target vehicle follows a vehicle platoon with 50% penetration rate of connectivity to pass a signalized roadway. The results show that 9.1% energy benefits can be obtained. The performance is satisfactory compared to 14.3% benefits with perfect traffic prediction.
Design and Control of a Power-Electronic Interface for Regenerative Suspension Systems
Authors: Algethami Abdullah A. , Kim Won-jong
Published: November 2019

Recently, the automobile industry has begun applying an increasing number of systems to recycling wasted energy. One area that demands further research is the recycling and storing of energy in car suspension systems, especially in terms of developing an electronic interface to keep energy flowing bidirectionally. An electronic interface was designed to facilitate control of regenerative forces and store energy after the rectification process. The electronic interface was designed to be a symmetrical-bridgeless boost converter, due to this mechanism having few components and requiring little control effort. The converter was created such that it kept the current and voltage in phase for the maximum power factor. The input into this controller was the generator voltage used to determine the polarity of the pulse-width modulation, considering external road disturbances. Thus, this combination of converter and controller was able to replace an active controller. Variable resistance could be further controlled to manipulate the suspension damping force.
Self-Reflective Learning Strategy for Persistent Autonomy of Aerial Manipulators
Authors: Zhou Xu , Zhang Jiucai , Zhang Xiaoli
Published: November 2019

Autonomous aerial manipulators have great potentials to assist humans or even fully automate manual labor-intensive tasks such as aerial cleaning, aerial transportation, infrastructure repair, and agricultural inspection and sampling. Reinforcement learning holds the promise of enabling persistent autonomy of aerial manipulators because it can adapt to different situations by automatically learning optimal policies from the interactions between the aerial manipulator and environments. However, the learning process itself could experience failures that can practically endanger the safety of aerial manipulators and hence hinder persistent autonomy. In order to solve this problem, we propose for the aerial manipulator a self-reflective learning strategy that can smartly and safely finding optimal policies for different new situations. This self-reflective manner consists of three steps: identifying the appearance of new situations, re-seeking the optimal policy with reinforcement learning, and evaluating the termination of self-reflection. Numerical simulations demonstrate, compared with conventional learning-based autonomy, our strategy can significantly reduce failures while still can finish the given task.
Online Nonlinear System Identification With Parameter Constraints: Application to Automotive Engine Systems
Authors: Chen Kaian , Li Zhaojian , Wang Yan , Wang Jing , Wu Kai , Filev Dimitar P.
Published: November 2019

In this paper, we treat the problem of online nonlinear system identification with parameter constraints. This approach is based upon our prior work on nonlinear system identification that exploits evolving Spatial-Temporal Filters (STF) to dynamically decompose system’s input/output space into a nonlinear combination of weighted local models. We extend the nonlinear system identification framework with the capability of dealing with linear equality and inequality parameter constraints. We leverage the gradient projection method in the local model parameter estimation process to inherently enforce the parameter constraints while retaining optimality. We apply the proposed algorithm to a turbo-charged gasoline engine system and promising results are demonstrated by experimental data.
Lifetime Optimization for a Grid-Friendly DC Fast Charge Station With Second Life Batteries
Authors: D’Arpino Matilde , Cancian Massimo
Published: November 2019

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/10.1115/1.4046579/1075680/Lifetime-Optimization-for-a-Grid-Friendly-DC-Fast
On the Dynamics and Control of a Full Wrist Exoskeleton for Tremor Alleviation
Authors: Wang Jiamin , Barry Oumar , Kurdila Andrew J. , Vijayan Sujith
Published: November 2019

This paper introduces a novel wearable full wrist exoskeleton designed for the alleviation of tremor in patients suffering from Parkinson’s Disease and Essential Tremor. The design introduces a structure to provide full observation of wrist kinematics as well as actuation in wrist flexion/extension and radial/ulnar deviation. To examine the feasibility of the design, the coupled dynamics of the device and the forearm is modeled via a general multibody framework. The dynamic analysis considers human motion, wrist stiffness, and tremor dynamics. The analysis of the model reveals that the identification of the wrist kinematics is indispensable for the controller design. Nonlinear regression based on the Levenberg-Marquardt algorithm has been applied to estimate the unknown parameters in a kinematic structural function designed to approximate the wrist kinematics, which leads to the construction of the control system framework. Finally, several simulation cases are demonstrated to conclude the study.
Cooperative Transport of a Payload With Offset CG Using Multiple UAVs
Authors: Barawkar Shraddha , Kumar Manish
Published: November 2019

Cooperative transportation by multiple Unmanned Aerial Vehicles (UAVs) has been a topic of interest amongst robotics researchers since a decade. Researchers have developed different control schemes to address some of the issues related to cooperative transport. However, most of the existing control strategies assume a stationary center of gravity (CG) coinciding with the geometric center of the payload. In real world applications such as package delivery or human transport, position of CG, in general, would not be at geometric center, or would not be even known a – priori. This paper proposes a Proportional, Integral and Derivative (PID) controller to address the issue of control when the CG is offset during transportation of a common payload using multiple UAVs. The proposed PID scheme is centralized in nature in that it provides the same control action to all UAVs. Using extensive numerical simulations, the paper shows that such a scheme is shown to work effectively irrespective of the location of CG on the payload. The control scheme is also independent of the payload geometry and number of UAVs employed for cooperative transport. The work presented in this paper provides the groundwork to develop better control strategies to solve the problem of multi-UAV cooperative transport with a – priori unknown CG.
A Model Predictive Approach for the Coordination of Powertrain Control Systems
Authors: Stockar Stephanie , Rostiti Cristian , Canova Marcello , Prucka Michael
Published: November 2019

The design, calibration and integration of powertrain control algorithms has become significantly more complex in recent years, as the automotive industry faces increasing challenges in meeting consumer requirements and government regulations. Traditionally, the powertrain control engineering design process develops the engine and transmission controllers independently and then integrates them after an initial calibration. This process can lead to suboptimal performance and requires additional calibration and verification steps to improve the coordination of the various subsystems.

This paper proposes a novel approach to achieve a systematic, high-level coordination, and optimization of the control strategy in an automotive powertrain system that will reduce overall calibration effort. Optimized set-points for engine and transmission controls are generated based on joint optimization of fuel consumption and drivability using Model Predictive Control to manage both continuous and discrete control variables. Simulation results confirm the control decisions made by the proposed coordinator match a well-calibrated production ECU with little tuning effort.
A Real-Time Pressure Wave Model for Predicting Engine Knock
Authors: Li Ruixue C. , Zhu Guoming G.
Published: November 2019

This paper proposes a control-oriented pressure wave model, utilizing outputs of a reaction-based two-zone engine combustion model developed earlier, to accurately predict the key knock characteristics. The model can be used for model-based knock prediction and control. An in-cylinder pressure wave model of oscillation magnitude decay is proposed and simplified to describe pressure oscillations due to knock combustion, and the boundary and initial conditions of the pressure wave model at knock onset are provided by the two-zone reaction-based combustion model. The proposed pressure wave model is calibrated using experimental data, and the chemical kinetic-based Arrhenius integral (ARI) and maximum amplitude of pressure oscillations (MAPO) are used as the evaluation criteria for predicting knock onset and intensity, and the knock frequency is studied with the fast Fourier transform (FFT). The calibrated model is validated for predicting knock onset timing, knock intensity and frequency. Simulation results are compared with the experimental ones to demonstrate the capability of predicting engine knock characteristics by the proposed model.
Auto-Tuning Control of PEM Water Electrolyzer
Authors: Keow Alicia , Chen Zheng
Published: November 2019

Proton exchange membrane (PEM) electrolyzers with the ability to produce gases at a pressure suitable for direct metal hydride storage are desirable because they do not require the use of compressors and other auxiliary components. Direct storage into metal hydride cylinders is made feasible when the pressure and flow rate of hydrogen is controlled. The nonlinear dynamics of the PEM electrolyzer change with temperature and pressure, both of which change with the hydrogen production rate, and are thus difficult to estimate. Therefore, a model-free, relay-feedback, auto-tuning approach is used to tune a proportional integral (PI) controller. This allows for the determination of the voltage supply to the electrolyzer by tracking the current set-point and correlating it to the hydrogen production rate. A gain scheduling approach is used to record the tuned controller’s parameters at different set-points, minimizing the frequency of tuning the device. A self-assessment test is used to determine situations where the auto-tuner should activate to update the PI parameters, thus, allowing for the system to operate without supervision. The auto-tuning PI control is successfully tested with a PEM electrolyzer setup. Experimental results showed that an auto-tuner can tune the controller parameters and produce favorable transient behaviors, allowing for a degree of adaptability for variations in system set-points.
The Effects of Nylon Polymer Threads on Strain-Loading Hysteresis Behavior of Supercoiled Polymer (SCP) Artificial Muscles
Authors: Konda Revanth , Zhang Jun
Published: November 2019

Supercoiled polymers (SCP) actuator, as a recently discovered artificial muscle, has attracted a lot of attention as a compliant and compact actuation mechanism. SCP actuators can be fabricated from nylon polymer threads, and generates up to 20% strain under thermal activation. A common challenge, however, is to accurately and efficiently estimate the performance of SCP actuators considering their significant hysteresis among loading, strain, and power input. Previous studies adopted either linear models that failed to capture the hysteresis or phenomenological models that required tedious procedures for identification and implementation. In this paper, a physics-inspired model is presented to efficiently capture and estimate SCP actuators’ strain – loading hysteresis by analyzing the properties of nylon threads from which they are fabricated. The strains of SCP actuators are found to be linear to that of the nylon threads under the same loading conditions. An efficient approach is proposed to characterize and estimate the strain – loading hysteresis of SCP actuators fabricated with different numbers of nylon threads. A helical spring model is adopted to obtain the stiffness of SCP actuators with different configurations. Experimental validation involving two-ply, four-ply, and six-ply nylon threads and SCP actuators are provided to confirm the effectiveness of the proposed model.
Learning Hyperparameters in Efficient Spatial Model by Robotic Sensors
Authors: Jeong Jinho , Jeon Soo , Choi Jongeun
Published: November 2019

Recently, a new class of spatial models over a continuum domain that builds on hidden Gaussian Markov Random Fields (GMRFs) was proposed for resource-constrained networked mobile robots dealing with non-stationary physical processes. The hidden GMRF was realized with respect to a proximity graph over a surveillance region. In this paper, we investigate learning strategies based on the maximum likelihood (ML) and the maximum a posteriori (MAP) estimators to find the locational generating points for the spatial model so that mobile robots can efficiently make the prediction. Some promising simulation results and future research directions are discussed.
Comparison of Estimation Techniques for the Crankshaft Dynamics of an Opposed Piston Engine
Authors: Drallmeier Joseph A. , Siegel Jason B. , Stefanopoulou Anna G.
Published: November 2019

This study provides a comparison of a linear and unscented Kalman filter to estimate the dynamics of a two-stroke opposed piston engine. These engines maintain several advantages over conventional internal combustion engines including reduced heat transfer, higher power to weight ratio, and a larger expansion ratio. Additionally, a crank angle phasing is introduced between the two cranks to improve scavenging efficiency. However, the coupling of the two crankshafts through a large geartrain can cause high amounts of noise and vibration harshness (NVH) and mechanical efficiency losses. By removing the geartrain and controlling the decoupled crankshafts with motor-generators, the NVH can be minimized while maintaining the benefits of crank angle phasing. To control the input torque of the motors to the engine, accurate estimations of the crankshaft position and dynamics are necessary. While the unscented Kalman filter exhibits lower estimation error, the filter is sensitive to model uncertainty relating to cylinder pressure, demanding further investigation of the robustness of the nonlinear filter.
Dynamic Design Optimization for Thermal Management: A Case Study on Shell and Tube Heat Exchangers
Authors: Nash Austin L. , Jain Neera
Published: November 2019

We present a new methodology for designing a heat exchanger that explicitly considers both static and transient performance characteristics. The proposed approach leverages 1) a highly detailed, albeit static model that captures the complex nonlinear relationship between heat exchanger geometry and heat transfer coefficients, and 2) a reduced-order dynamic model of the heat exchanger that approximates the geometry detailed in the static model. In order to optimize the component design for both static and transient performance metrics, pole locations of the corresponding linearized model are penalized in the cost function of the proposed optimization algorithm in order to move dominant poles further into the left half complex plane. Through a simulated case study for a shell and tube heat exchanger, we demonstrate how the proposed algorithm exploits the trade off between static design metrics, including mass and footprint, and the rate at which heat is removed from the primary fluid.
Sub-Optimal Tracking in Switched Systems With Controlled Subsystems and Fixed-Mode Sequence Using Approximate Dynamic Programming
Authors: Sardarmehni Tohid , Song Xingyong
Published: November 2019

Optimal tracking in switched systems with controlled subsystem and Discrete-time (DT) dynamics is investigated. A feedback control policy is generated such that a) the system tracks the desired reference signal, and b) the optimal switching time instants are sought. For finding the optimal solution, approximate dynamic programming is used. Simulation results are provided to illustrate the effectiveness of the solution.
Modelling and Simulation of Perching With a Quadrotor Aerial Robot With Passive Bio-Inspired Legs and Feet
Authors: Dunlop David J. , Minor Mark A.
Published: November 2019

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/10.1115/1.4046778/1082076/Modeling-and-Simulation-of-Perching-With-a
Modeling Height Profile for Drop-on-Demand Print of UV Curable Ink
Authors: Wu Yumeng , Chiu George
Published: November 2019

This paper proposes a height profile model for drop-on-demand printing of UV curable ink. Existing models include superposition of single drops, numerical models, and graphic-based model. They are either too complicated or over simplified. Graphic model intends to find a sweet spot in between, however, accuracy is marginally improved from superposition model while it demands more computation. The proposed model aims to achieve the same as graphic model by introducing volume and area propagation matrices to reflect the localized ink flow from higher location to the lower, while avoiding the detailed physics behind it. This model assumes a constant volume and area propagation of subsequent drop due to height profile difference. It is validated with experiments on single drop, 2-drop and 3-drop line printing. Stability of this model is analyzed.. Using root mean square (RMS) error as benchmark, proposed model achieves 6.6% along the center row and 7.4% overall, better than existing models.
Electrocardiography with a Smartphone
Authors: Thoms Lars-Jochen , Colicchia Giuseppe , Watzka Bianca , Girwidz Raimund
Published: November 2019

Myocardial contraction is triggered whenever a wave of electrical impulses passes through the heart. The pattern of electrical excitation spreads coordinately over the structure of the heart. This results in a measurable change in potential across the surface of the body of a subject. The recording of the resultant signal from specific body points is known as an electrocardiogram (ECG). Students can directly record an ECG signal from the headset input of a smartphone with an electric lead and a suitable sound app. This allows interdisciplinary teaching by applying physics concepts to physiology.
Superposition of oscillation on the Metapendulum: Visualization of energy conservation with the smartphone
Authors: Weiler David , Bewersdorff Arne
Published: November 2019

The Metapendulum is a combination of both the simple gravity pendulum and the spring pendulum. Both excite each another and, therefore, can be used as an example of mechanical resonance phenomena. The data of a smartphone’s accelerometer can be used to record the acceleration of the pendulum during the oscillation movement. With that, the resonance within the system can be visualized.
Texas Sport Leadership Consultants, LLC: A Case of Cases
Authors: Sparvero Emily , Griffiths Randall , Tingle Jacob
Published: November 2019

This immersive, multi-case experience consists of four distinct cases and one meta-case that require students to engage with several organizational behavior topics. First, the meta-case takes the form of Texas Sport Leadership Consultants (TSLC), a fictitious company which consults with local sport management professionals in a variety of contexts. Students participate as temporary members of the company to analyze the issues and challenges associated with working as a member of TSLC. TSLC work groups are hired by four different clients, each of whom has a unique organizational behavior challenge. These clients include: (1) a combat-oriented sport company; (2) a company that provides luxury sport experiences for business travelers; (3) Division II athletic directors; and (4) a minor league hockey team. The clients face challenges related to mission and vision, group dynamics, change leadership, and power and politics. Student groups analyze the case and provide recommendations, which are presented as the basis for discussion among TSLC colleagues.
Australian blazes will ‘reframe our understanding of bushfire’
Authors: Pickrell John
Published: November 2019

Fire scientist David Bowman discusses the unfolding crisis.