bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical Simulation of Dynamic Bending Deflection of a Disc Cam Profile With Roller Follower System
Authors: Yousuf Louay S. , Dabool Yaakob K. H. 
Published: January 2021

Abstract
The bending deflection of the disc cam profile and the dynamic response of the follower were discussed and analyzed for three paths of contact. The objective of this paper was to study the influence of maximum contact pressure on the bending deflection of the cam profile. Numerical simulation was carried out using SolidWorks Software to simulate the follower displacement, velocity and acceleration. Finite element analysis was used taking into account the use of ANSYS package to calculate the bending deflection. The experiment setup had been done through an infrared camera device. The bending deflection of point (18) is bigger than the bending deflection of point (4) because of the bigness of radius of curvature of nose (2).
Adaptive Trajectory Tracking During Motorized and FES-Induced Biceps Curls via Integral Concurrent Learning
Authors: Allen Brendon C. , Stubbs Kimberly J. , Dixon Warren E. 
Published: January 2021

Abstract
A common rehabilitative technique for those with neuro-muscular disorders is functional electrical stimulation (FES) induced exercise such as FES-induced biceps curls. FES has been shown to have numerous health benefits, such as increased muscle mass and retraining of the nervous system. Closed-loop control of a motorized FES system presents numerous challenges since the system has nonlinear and uncertain dynamics and switching is required between motor and FES control, which is further complicated by the muscle having an uncertain control effectiveness. An additional complication of FES systems is that high gain feedback from traditional robust controllers can be uncomfortable to the participant. In this paper, data-based, opportunistic learning is achieved by implementing an integral concurrent learning (ICL) controller during a motorized and FES-induced biceps curl exercise. The ICL controller uses adaptive feedforward terms to augment the FES controller to reduce the required control input. A Lyapunov-based analysis is performed to ensure exponential trajectory tracking and opportunistic, exponential learning of the uncertain human and machine parameters. In addition to improved tracking performance and robustness, the potential of learning the specific dynamics of a person during a rehabilitative exercise could be clinically significant. Preliminary simulation results are provided and demonstrate an average position error of 0.14 ± 1.17 deg and an average velocity error of 0.004 ± 1.18 deg/s.
Passivity-Based Distributed Acquisition and Station-Keeping Control of a Satellite Constellation in Areostationary Orbit
Authors: Sin Emmanuel , Yin He , Arcak Murat 
Published: January 2021

Abstract
We present a distributed control law to assemble a cluster of satellites into an equally-spaced, planar constellation in a desired circular orbit about a planet. We assume each satellite only uses local information, transmitted through communication links with neighboring satellites. The same control law is used to maintain relative angular positions in the presence of disturbance forces. The stability of the constellation in the desired orbit is proved using a compositional approach. We first show the existence and uniqueness of an equilibrium of the interconnected system. We then certify each satellite and communication link is equilibrium-independent passive with respective storage functions. By leveraging the skew symmetric coupling structure of the constellation and the equilibrium-independent passivity property of each subsystem, we show that the equilibrium of the interconnected system is stable with a Lyapunov function composed of the individual subsystem storage functions. We further prove that the angular velocity of each satellite converges to the desired value necessary to maintain circular, areostationary orbit. Finally, we present simulation results to demonstrate the efficacy of the proposed control law in acquisition and station-keeping of an equally-spaced satellite constellation in areostationary orbit despite the presence of unmodeled disturbance forces.
Navigation and Obstacle Avoidance of Snake-Robot Guided by a Co-Robot UAV Visual Servoing
Authors: Haghshenas-Jaryani Mahdi , Sevil Hakki Erhan , Sun Liang 
Published: January 2021

Abstract
This paper presents the concept of teaming up snake-robots, as unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs) for autonomous navigation and obstacle avoidance. Snake robots navigate in cluttered environments based on visual servoing of a co-robot UAV. It is assumed that snake-robots do not have any means to map the surrounding environment, detect obstacles, or self-localize, and these tasks are allocated to the UAV, which uses visual sensors to track the UGVs. The obtained images were used for the geo-localization and mapping the environment. Computer vision methods were utilized for the detection of obstacles, finding obstacle clusters, and then, mapping based on Probabilistic Threat Exposure Map (PTEM) construction. A path planner module determines the heading direction and velocity of the snake robot. A combined heading-velocity controller was used for the snake robot to follow the desired trajectories using the lateral undulatory gait. A series of simulations were carried out for analyzing the snake-robot’s maneuverability and proof-of-concept by navigating the snake robot in an environment with two obstacles based on the UAV visual servoing. The results showed the feasibility of the concept and effectiveness of the integrated system for navigation.
Combined Trajectory Planning and Tracking for Autonomous Vehicles on Deformable Terrains
Authors: Dallas James , Weng Yifan , Ersal Tulga 
Published: January 2021

Abstract
In this work, a novel combined trajectory planner and tracking controller is developed for autonomous vehicles operating on off-road deformable terrains. Common approaches to trajectory planning and tracking often rely on model-dependent schemes, which utilize a simplified model to predict the impact of control inputs to future vehicle response. However, in an off-road context and especially on deformable terrains, accurately modeling the vehicle response for predictive purposes can be challenging due to the complexity of the tire-terrain interaction and limitations of state-of-the-art terramechanics models in terms of operating conditions, computation time, and continuous differentiability. To address this challenge and improve vehicle safety and performance through more accurate prediction of the plant response, in this paper, a nonlinear model predictive control framework is presented that accounts for terrain deformability explicitly using a neural network terramechanics model for deformable terrains. The utility of the proposed scheme is demonstrated on high fidelity simulations for a notional lightweight military vehicle on soft soil. It is shown that the neural network based controller can outperform a baseline Pacejka model based scheme by improving on performance metrics associated with the cost function. In more severe maneuvers, the neural network based controller can achieve sufficient fidelity as compared to the plant to complete maneuvers that lead to failure for the Pacejka based controller. Finally, it is demonstrated that the proposed framework is conducive to real-time implementability.
Effect of Electromechanical Coupling on Locally Resonant Metastructures for Simultaneous Energy Harvesting and Vibration Attenuation Applications
Authors: Bukhari Mohammad A. , Qian Feng , Barry Oumar R. , Zuo Lei 
Published: January 2021

Abstract
The study of simultaneous energy harvesting and vibration attenuation has recently been the focus in many acoustic meta-materials investigations. The studies have reported the possibility of harvesting electric power using electromechanical coupling; however, the effect of the electromechanical resonator on the obtained bandgap’s boundaries has not been explored yet. In this paper, we investigate metamaterial coupled to electromechanical resonators to demonstrate the effect of electromechanical coupling on the wave propagation analytically and experimentally. The electromechanical resonator is shunted to an external load resistor to harvest energy. We derive the analytical dispersion curve of the system and show the band structure for different load resistors and electromechanical coupling coefficients. To verify the analytical dispersion relations, we also simulate the system numerically. Furthermore, experiment is carried out to validate the analytical observations. The obtained observations can guide designers in selecting electromechanical resonator parameters for effective energy harvesting from meta-materials.
Formation Control for Underactuated Surface Vessel Networks
Authors: Wang Bo , Nersesov Sergey , Ashrafiuon Hashem 
Published: January 2021

Abstract
Developing distributed control algorithms for multi-agent systems is difficult when each agent is modeled as a nonlinear dynamical system. Moreover, the problem becomes far more complex if the agents do not have sufficient number of actuators to track any arbitrary trajectory. In this paper, we present the first fully decentralized approach to formation control for networks of underactuated surface vessels. The vessels are modeled as three degree of freedom planar rigid bodies with two actuators. Algebraic graph theory is used to model the network as a directed graph and employing a leader-follower model. We take advantage of the cascade structure of the combined nonlinear kinematic and dynamic model of surface vessels and develop a reduced-order error dynamic model using a state transformation definition. The error dynamics and consequently all system states are then stabilized using sliding mode control approach. It is shown that the stabilization of the reduced-order error dynamics guarantees uniform global asymptotic stability of the closed-loop system subject to bounded uncertainties. The proposed control method can be implemented in directed time-invariant communication networks without the availability of global position measurements for any of the vehicles participating in the network. An example of a a network of five surface vessels is simulated to verify the effective performance of the proposed control approach.
Energy-Optimal, Direct-Phase Control of Brushless Motors for Robotic Drives
Authors: Ghorbanpour Amin , Richter Hanz 
Published: January 2021

Abstract
In this work, simultaneous energy regeneration and motion control for robot manipulators with brushless direct current (BLDC) motors is considered. All joints of the robot are connected to regenerative drives powered from a single ultra-capacitor. A new voltage-based control method is developed to individually command each phase of the BLDC motor. Three independent regenerative drives are interconnected in a wye configuration, and each drives a phase of the motor. The objective is to determine the control inputs for each drive to minimize energy consumption from the ultra-capacitor for a given motion task. To this end, the problem is formulated as constrained quadratic optimization problem that gives the control inputs based on the desired torque generated by a virtual controller. An experimental evaluation is performed using a pendulum actuated by a BLDC motor. It is shown that the suggested control method can accomplish the motion task and it is capable of energy regeneration. The results show a reduction of about 40% in energy consumption for the condition of the study, relative to non-regenerative case.
Cooperative Localization of Vehicles in Three-Dimensional Space
Authors: Oliveros Juan Carlos , Ashrafiuon Hashem 
Published: January 2021

Abstract
Effective trajectory planning and cooperative control of multi-agent systems require accurate localization of the agents to perform collaborative missions. Accurate localization may be achieved by Global Positioning System (GPS) and simultaneous localization and mapping. However, GPS signals and fixed features may not be readily available, particularly in remote and unstructured environments. Under these circumstances, Cooperative Localization (CL) has been proposed as a short-term solution that can significantly improve vehicle pose estimation. CL algorithms have been developed and tested mainly on mobile robots and planar vehicles due to complexities of three-dimensional (3D) motion. In this paper, we present a CL algorithm for multi-agent systems comprised of 3D vehicles. Each vehicle’s pose is represented by three position and three orientation variables. Quaternions are employed to represent orientation and avoid singularities associated with Euler angle. Vehicle kinematic velocity relations are used to model vehicle dynamics with respect to a fixed reference frame. It is assumed a vehicle can take relative pose measurements of other neighboring vehicles within an ad hoc network of agents. We then designate the observed as target vehicles and the observers as base vehicles and determine the linearized output matrix of the relative measurements for Extended Kalman Filter (EKF) application. Simulations are presented to discuss the advantages and shortcomings of the algorithm.
A Switched Adaptive Model for Layer-to-Layer Selective Laser Melting With Varying Laser Paths
Authors: Wang Xin , Bristow Douglas A. , Landers Robert G. 
Published: January 2021

Abstract
Selective Laser Melting (SLM) is an additive manufacturing technique that uses a moving laser source to melt and solidify an area of a powder bed by scanning over the area with a laser beam, thus, fabricating a solid part layer by layer. Although SLM can print complex geometries that are difficult to achieve by machining, quality and repeatability of a printed part are still two challenges that must be addressed. These challenges arise due to the complex physical transformation of the metal powder and the lack of mature process control schemes.
Researchers often use optical sensors for the feedback control of SLM processes. Since fast motion of laser beams may not allow real time feedback control given the large quantity of data to be processed, some researchers have applied layer-to-layer control, i.e., collecting data during the fabrication of an entire layer and then updating process parameter profiles for the next layer. In this paper, by specifying the input and output as laser power and peak temperature, a parameter adaption model is used to estimate the unknown input-output model, which is difficult to evaluate analytically due to complex process physics. In addition, laser paths in SLM usually vary to create more isotropic parts than can be achieved with constant laser paths. The variation of laser paths results in varying local thermal histories. To handle the common situation where laser paths are varying from layer to layer, a switched model is designed and trained by a Parameter Adaption Algorithm (PAA). In a simulation of an overhang part with a constant cross-section and varying laser paths, the switched adaptive model shows the ability to achieve a desired output profile and also better performance if more switched gains are utilized.
Modeling and Simulation of Aircell Actuator Seat Cushion With Pneumatic Line Lag and Capacitative Effects
Authors: Nuthi Pavan , Wijesundara Muthu B. J. 
Published: January 2021

Abstract
This article presents the development of a hybrid model with pneumatic lag effects for the smart seat cushion which can be extended to any active aircell cushion. Smart seat cushion is one such rehabilitation device which reduces the risk of pressure injury formation by automatically performing repositioning exercises for wheelchair users. Operation of the smart seat cushion device can be greatly enhanced by accurate prediction of pneumatic line lag during its various modes of operation. The proposed model combines a linear model from literature to capture the effects of pneumatic line lag and a bang-bang scheduling controller to enable efficient use of pneumatic components in the smart seat cushion device. The resulting simulation is validated by conducting multiple runs of uniform inflation experiment with the device, and comparing the closed loop behavior of the model. The hybrid modeling allows accurate prediction of pneumatic line lag in the smart seat cushion during various modes of redistribution and offloading. The modeling accuracy and the prediction of switching times for discrete states can be improved by data sampled at a higher rate from the device.
On the Ergodicity of an Autonomous Robot for Efficient Environment Explorations
Authors: Kabir Rabiul Hasan , Lee Kooktae 
Published: January 2021

Abstract
This paper addresses the autonomous robot ergodicity problem for efficient environment exploration. The spatial distribution as a reference distribution is given by a mixture of Gaussian and the mass generation of the robot is assumed to be skinny Gaussian. The main problem to solve is then to find out proper timing for the robot to visit as well as leave each component-wise Gaussian for the purpose of achieving the ergodicity. The novelty of the proposed method is that no approximation is required for the developed method. Given the definition of the ergodic function, a convergence condition is derived based on the timing analysis. Also, a formal algorithm to achieve the ergodicity is provided. To support the validity of the proposed algorithm, simulation results are provided.
Optimal Path Planning for a Team of Heterogeneous Drones to Monitor Agricultural Fields
Authors: Faryadi Saba , Davoodi Mohammadreza , Mohammadpour Velni Javad 
Published: January 2021

Abstract
In this work, we investigate the problem of finding the minimum coverage time of an agricultural field using a team of heterogeneous unmanned aerial vehicles (UAVs). The aerial robotic system is assumed to be heterogeneous in terms of the equipped cameras’ field of view, flight speed, and battery capacity. The coverage problem is formulated as a vehicle routing problem (VRP) [1] with two significant extensions. First, the field is converted into a graph, including nodes and edges generated based on sweep direction and the minimum length of UAVs’ footprints. Second, the underlying optimization problem accounts for aerial vehicles having different sensor footprints. A series of simulation experiments are carried out to demonstrate that the proposed strategy can yield a satisfactory monitoring performance and offer promise to be used in practice.
Mobile Sensing of Multi-Dimensional Dynamic Field via Compressed Sensing
Authors: Li Tianwei , Zou Qingze 
Published: January 2021

Abstract
In this paper, we consider to measure time-varying dynamic signals at discrete locations by using a single mobile sensor The challenge arises as the mobile sensor is required to transit between the sampling locations, resulting in intermittent measurements at each location, and the time-varying dynamic signals must be recovered from the intermittent measured data. In this work, we propose to tackle this single mobile sensing of multi-location dynamic signals through the compressed sensing framework. Both the temporal-spatial coupling arising from the random sampling requirement and the mobility limitation of the sensor in transition between sampling locations are addressed through a simulated annealing based optimization approach. Simulation results are presented and discussed to illustrate the proposed method.
Model-Based Dynamic In-Cylinder Air Charge, Residual Gas and Temperature Estimation for a GDI Spark Ignition Engine Using Cylinder, Intake and Exhaust Pressures
Authors: Khameneian Amir , Wang Xin , Dice Paul , Shahbakhti Mahdi , Naber Jefferey D. , Archer Chad , Moilanen Peter , Glugla Chris , Huberts Garlan 
Published: January 2021

Abstract
The in-cylinder trapped air, residual gas, and temperature directly impact Spark Ignition (SI) engine operation and control. However, estimation of these variables dynamically is difficult. This study proposes a dynamic cycle-by-cycle model for estimation of the in-cylinder mixture temperature at different events such as Intake Valve Closed (IVC), as well as mass of trapped air and residual gas. In-cylinder, intake and exhaust pressure traces are the primary inputs to the model. The mass of trapped residual gas is affected by valve overlap increase due to the exhaust gas backflow. Of importance to engines with Variable Valve Timing (VVT), the compressible ideal-gas flow correlations were applied to predict the exhaust gas backflow into the cylinder. Furthermore, 1D GT-Power Three Pressure Analysis (TPA) was used to calibrate and validate the designed model under steady-state conditions. To minimize the calibration efforts, Design of Experiments (DOE) analysis methodology was used. The transient behavior of the model was validated using dynamometer dynamic driving cycle. The cycle-based output parameters of the developed model are in good agreement with transient experimental data with minimal delay and overshoot. The predicted parameters follow the input dynamics propagated in the in-cylinder, intake and exhaust pressure traces with a 1.5% average relative steady-state prediction error.
Data-Driven Drop Formation Modeling in Nanoliter Drop-on-Demand Inkjet Printing
Authors: Wang Jie , Chiu George T.-C. 
Published: January 2021

Abstract
Drop formation models are essential for the control of drop-on-demand (DoD) inkjet printing process. Traditional numerical models are difficult to implement in real-time control. In this paper, an equivalent circuit model (ECM) is developed to motivate the implementation of a data-driven ARX model combined with a drop volume adjuster to estimate drop volume. Drop pinch-off instants and drop velocities are modeled in polynomials with respect to the parameters of the drive waveform. All models are validated through 10-fold cross validation. The simulation of the drop volume model shows good agreement with experimental results.
Dynamic Modeling of a Steerable Drifter
Authors: Gaskell Eric , Tan Xiaobo 
Published: January 2021

Abstract
Drifters are energy-efficient platforms for monitoring rivers and oceans. Prior work largely focused on free-floating drifters that drift passively with flow and have little or no controllability. In this paper we propose steerable drifters that use multiple rudders for modulating the hydrodynamic forces and thus maneuvering. A dynamic model for drifters with multiple rudders is presented. Simulation is conducted to examine the behavior of the drifter in two different flow conditions, uniform flow and parabolic flow. When there is no difference in relative flow between the rudders, as in uniform flow, the drifter can only be controlled until its velocity approaches that of the water. However, when present, local flow differentials can be exploited to initiate motion lateral to the ambient flow and control the trajectory of the drifter to some degree. The motion of the drifter is further classified as belonging to one of three major modes, rotational, oscillatory, and stable. The behavior of the drifter in a simulated river was mapped for different rudder angles. Identifying the parameters that induce each mode lays the groundwork for developing a feedback control scheme for the drifter.