bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Intelligent Learning Control Scheme for Discrete-Time Nonlinear Uncertain Systems in Multiple Environments
Authors: Zhang Jingting , Yuan Chengzhi , Stegagno Paolo 
Published: January 2021

Abstract
In this paper, we propose a novel intelligent control scheme for a class of discrete-time nonlinear uncertain systems operating under multiple environments/control situations. First, based on the deterministic learning theory, artificial neural networks (NNs) are employed to accurately learn/identify the uncertain system dynamics under each individual environment. The learned knowledge is then utilized to: (i) achieve improved control performance by developing a family of experience-based controllers (EBCs), each of which is tailored to an individual environment; and (ii) determine real-time activation of the EBCs by developing a pattern recognition mechanism for online identifying the active control situation. In addition, a robust quasi-sliding mode controller is further designed and embedded in the overall control scheme to guarantee system stability during the transition process among multiple environments. The novelty of the proposed control scheme lies in its intelligent capabilities of knowledge acquisition and re-utilization in real-time control, enabling self-adaption to uncertain changing control environments. A simulation example is included to verify the effectiveness of the proposed results.
A Control Algorithm Framework for Time-of-Arrival and Arrival Airspeed Control
Authors: Stephens Shawn S. , Kunz Donald L. , Baker William P. , Hess Joshuah A. , Casbeer David W. 
Published: January 2021

Abstract
This paper documents the framework for a control algorithm which commands an aircraft to arrive at a specified final location with constraints on arrival time, airspeed, and heading in an environment with steady state wind and turbulence. The path is defined as a variable-length racetrack, allowing for an accurate estimate of the arrival time which can be quickly changed by increasing or decreasing the size of the racetrack. The arrival time is estimated using Gaussian quadrature which allows for the inclusion of steady state wind into the arrival time estimate. The time-of-arrival error between the estimated and the desired arrival time is then minimized through real-time path and air-speed control. A sensitivity study was performed which varied aircraft performance, wind speed, wind direction, arrival time and magnitude of turbulence. The results show the controller achieved a time-of-arrival error of less than one second and arrival airspeed error less than four meters per second across all simulations with moderate turbulence and wind speed less than one third the desired airspeed of the vehicle.
Nonlinear Modeling and Analysis of Power Lines With Stockbridge Dampers Under Vortex-Induced Vibrations
Authors: Malla Arun L. , Gupta Sunit K. , Barry Oumar 
Published: January 2021

Abstract
Vortex-induced vibrations have a considerable effect on overhead power transmission lines, often leading to fatigue-failure. While nonlinear models exist for power lines, vibration dampers, and vortex-induced vibrations, no work combines the nonlinearities stemming from the cable, vibration damper, and fluid forces in a single model. As power transmission lines are a major component of modern infrastructure, a thorough understanding of the nonlinear dynamic interactions of conductors, dampers, and wind forces is crucial. This paper examines a conductor with attached Stockbridge dampers under vortex-induced vibration. Sources of nonlinearity in this system include mid-plane stretching of the conductor, equivalent cubic stiffness of the Stockbridge damper, and fluctuating lift force modeled as a Van der Pol oscillator. The equations of motion for the resulting system are discretized using Galerkin’s method and solved using the numerical continuation method. Through parametric analysis, the effects of factors such as damper position and mass ratio on system response are determined. Insight is gained on the combined nonlinear system and a strong foundation is formed for ongoing study.
Modelica-Based Control of a Delta Robot
Authors: Okasha Ahmed , Bortoff Scott A. 
Published: January 2021

Abstract
In this paper we derive a dynamic model of the delta robot and two formulations of the manipulator Jacobian that comprise a system of singularity-free, index-one differential algebraic equations that is well-suited for model-based control design and computer simulation. One of the Jacobians is intended for time-domain simulation, while the other is for use in discrete-time control algorithms. The model is well-posed and numerically well-conditioned throughout the workspace, including at kinematic singularities. We use the model to derive an approximate feedback linearizing control algorithm that can be used for both trajectory tracking and impedance control, enabling some assembly tasks involving contact and collisions. The model and control algorithms are realized in the open-source Modelica language, and a Modelica-based software architecture is described that allows for a seamless development process from mathematical derivation of control algorithms, to desktop simulation, and finally to laboratory-scale experimental testing without the need to recode any aspect of the control algorithm. Simulation and experimental results are provided.
Modeling and Analysis of the Effects of Startle Reaction on Group Coordination
Authors: Mwaffo Violet , Vernerey Franck 
Published: January 2021

Abstract
Startle reaction is an alarm behavior observed in animal groups during anti-predatory response or fear-inducing stimulation. This behavior is characterized by spontaneous change in heading direction and increasing speed that can drastically affect group coordination. In this work, we leverage a mathematical model of fish social behavior to recreate startle reaction. Specifically, we model startle reaction through a biased jump diffusion process, where the jumps process captures sudden and fast changes of heading direction observed during this escaping behavior. Then, using extensive numerical simulations, we test their effects on group of fish including an informed individual prescribing the direction of motion and several followers by systematically varying the frequency and intensity of the sudden and fast turns introduced in the heading direction of a single individual. We demonstrate the emergence of novel form of leadership and phase transition between complete ordered states and disorganized states. In addition, we evidence that at specific range of frequencies and amplitudes, the initiation of this behavior might be utilized to divert group followers from their reference trajectory while keeping them in a synchronized state with the startling individual. Our findings offer a new paradigm to recreate the emergence of leadership applicable to divert or contain multi-vehicle systems.
Formation Control of Non-Holonomic Mobile Robots Moving on Slippery Surfaces
Authors: Mwaffo Violet , De Lellis Pietro , Humbert Sean 
Published: January 2021

Abstract
In this work, we analyze the decentralized formation control problem for a class of multi-robotic systems evolving on slippery surfaces. Grounded on experimental data of robots moving on a gravel surface inducing slippery, we show that a deterministic model cannot capture the uncertainties resulting from the kinematics of the robots while, instead, a model incorporating stochastic noise is capable of emulating such perturbations on wheel driving speed and turn rate. To account for these uncertainties, we consider a second order non-holonomic unicycle model to capture the full dynamics of individual vehicles where both actuation force and torque are subject to stochastic disturbances. Upon reducing the input-output dynamics of individual robot to a stochastic double integrator, we investigate the effects of these perturbations on the control input using concepts from stochastic stability theory and through numerical simulations. We demonstrated the applicability of the proposed scheme for formation control notably by providing sufficient conditions for exponential mean square convergence and we numerically determined the range of noise intensities for which team of robots can achieve formation stabilization. The promising findings from this work are expected to aid the design of robust control schemes for formation control of non-holonomic robots on off-road or un-paved surfaces.
Performance Evaluation of Suspended Energy Harvesting Backpack Using Half-Wave Mechanical Rectification
Authors: Mi Jia , Li Qiaofeng , Liu Mingyi , Li Xiaofan , Zuo Lei 
Published: January 2021

Abstract
Human beings are becoming more and more dependent on electronic devices, such as smart phones, smart watches, GPS, etc. This paper presents the design, modeling and testing of a novel suspended energy harvesting backpack using half-wave mechanical rectification. The proposed half-wave rectification mechanism can convert bidirectional linear vibration into unidirectional rotation with nonlinear inertia. Compared with full-wave mechanical rectification, the proposed half-wave rectification is designed only to convert the motion in one of the vibration directions while remaining idle in the other direction. Numerical simulation shows the proposed half-wave rectification based suspended energy harvesting backpack can obtain about two times of the average output power as the previous full-wave rectification design while also maintaining larger output power in the wideband frequency range. Bench test results indicate that the proposed half-wave rectification-based energy harvesting backpack can harvest 6.7 W (peak)/2.1 W (average) under 2 Hz and 6 mm excitation with a 31.8 kg payload, which is a significant improvement compared with 1.9 W(peak)/0.9 W (average) for the counterpart of full-wave rectification system. In addition, bench test results also validate the energy harvesting in wideband frequency range. Treadmill tests demonstrate an average power range of 1.2–11.0 W under walking speeds of 3.2–6.4 km/h with a 13.6 kg payload.
Physics-Informed Gaussian Process Based Optimal Control of Laser Powder Bed Fusion
Authors: Ren Yong , Wang Qian 
Published: January 2021

Abstract
Regulating the melt-pool size to a constant reference value during the build process is a challenging task in Laser Powder Bed Fusion additive manufacturing (LPBF-AM). This paper considers adjusting laser power to achieve a constant melt-pool volume during laser processing of a multi-track build under LPBF-AM. First, a Gaussian Process Regression (GPR) is applied to model the variation of the melt-pool volume along the deposition distance, with physics-informed input features. Then a constrained finite-horizon optimal control problem is formulated, with a quadratic cost function defined to minimize the difference between the melt-pool volume and a reference value. A projected gradient descent algorithm is applied to compute the sequence of laser power in the proposed optimal control problem. The GPR modeling of melt-pool dynamics is trained and tested using simulated data sets generated from a commercial finite-element based AM software, and the same commercial AM software is used to evaluate the control performance. Simulation results demonstrate the effectiveness of the proposed GPR modeling and optimal control in regulating melt-pool volume for building multi-track parts with LPBF-AM.
Contact-Rich Trajectory Generation in Confined Environments Using Iterative Convex Optimization
Authors: Zhao Wei-Ye , He Suqin , Wen Chengtao , Liu Changliu 
Published: January 2021

Abstract
Applying intelligent robot arms in dynamic uncertain environments (i.e., flexible production lines) remains challenging, which requires efficient algorithms for real time trajectory generation. The motion planning problem for robot trajectory generation is highly nonlinear and nonconvex, which usually comes with collision avoidance constraints, robot kinematics and dynamics constraints, and task constraints (e.g., following a Cartesian trajectory defined on a surface and maintain the contact). The nonlinear and nonconvex planning problem is computationally expensive to solve, which limits the application of robot arms in the real world. In this paper, for redundant robot arm planning problems with complex constraints, we present a motion planning method using iterative convex optimization that can efficiently handle the constraints and generate optimal trajectories in real time. The proposed planner guarantees the satisfaction of the contact-rich task constraints and avoids collision in confined environments. Extensive experiments on trajectory generation for weld grinding are performed to demonstrate the effectiveness of the proposed method and its applicability in advanced robotic manufacturing.
Position and Attitude Control of Underactuated Drones Using the Adaptive Function Approximation Technique
Authors: Shamshirgaran Azin , Ebeigbe Donald , Simon Dan 
Published: January 2021

Abstract
Despite the popularity of drones and their relatively simple operation, the underlying control algorithms can be difficult to design due to the drones’ underactuation and highly nonlinear properties. This paper focuses on position and orientation control of drones to address challenges such as path and edge tracking, and disturbance rejection. The adaptive function approximation technique control method is used to control an underactuated and nonlinear drone. The controller utilizes reference attitude signals, that are derived from a proportional derivative (PD) linear feedback control methodology. To avoid analytic expressions for the reference attitude velocities, we employ a continuous-time Kalman filter based on a model of the measurement signal — which is derived by passing the reference attitude position through a low-pass signal differentiator — as a second-order Newtonian system. Stability of the closed loop system is proven using a Lyapunov function. Our design methodology simplifies the control process by requiring only a few tuning variables, while being robust to time-varying and time-invariant uncertainties with unknown variation bounds, and avoids the requirement for the knowledge of the dynamic equation that governs the attitude of the drone. Three different scenarios are simulated and our control method shows better accuracy than the proportional-derivative controller in terms of edge tracking and disturbance rejection.
Computationally Efficient Urea-Dosing Controllers for Urea-SCR
Authors: Ganne Rajakumar , Jain Kaushal K. , Meckl Peter H. , Angre Harshil , Hiremath Jagdish R. 
Published: January 2021

Abstract
This paper presents two non-model-based reference-shaping and a model-based predictive urea-dosing controller for the Urea-SCR system. An ideal urea-dosing controller would minimize both tailpipe NOx and NH3 slip. However, this is not possible because of the trade-off between deNOx and NH3 slip. This trade-off is used to clearly define a control objective in terms of NH3 slip. Three controllers are then developed to meet this control objective such that they are all computationally inexpensive. The three controllers are then tested for three very different drive cycles. Simulation results show that the performance of the non-model based reference-shaping controllers is subjected to manual tuning of their variables. In contrast, the predictive controller, which is the highlight of this paper, can adapt to various drive cycles without compromising on the computational cost.
A Numerical Investigation of an Eddy Current Sensor for Detecting Small Defects in Metal Additive Manufacturing
Authors: Guo Zhengya , Lee Kok-Meng , Hao Bingjie , Xiong Zhenhua 
Published: January 2021

Abstract
This paper presents a parametric study of an eddy current (EC) sensor that measures the magnetic flux density (MFD) for detecting lack-of-fusion defects commonly found in metal additive manufacturing (Metal-AM). In this study, the EC-sensing system is simulated using a two-stage method that decomposes the EC-based detection of a defective conductor into two subproblems; the first analytically solves for the EC assuming no defects, and the second solves for the EC perturbation in the focused regions near the defects using a distributed current source method. Based on the proposed EC model, the effects of geometrical parameters on the sensitivity of an EC-sensing system were analyzed and verified by comparing with finite-element analysis (FEA). The study leads to the identification of two key parameters that significantly affect the sensitivity and accuracy of an EC sensor for detecting small defects, which are the locations and axes of the MFD sensors relative to the coil. The ECD distributions are simulated for two EC-sensor design scenarios: fixed at specified locations, and scanning over the entire specimen. Both DCS-based and FEA results match excellently well when images are at a fixed senor location. When scanning, DCS-based images are much smoother and require significantly less time to scan as compared to FEA that requires remeshing between steps and exhibits significant numerical noise, demonstrating the accuracy and efficiency of the proposed numerical model.
Optimal Control of a Multirotor Unmanned Aerial Vehicle Based on a Multiphysical Model
Authors: Michel Nicolas , Kong Zhaodan , Lin Xinfan 
Published: January 2021

Abstract
Electric multirotor aircraft with vertical-take-off-and-landing capabilities are emerging as a revolutionary transportation mode. This paper studies optimal control of a multirotor unmanned aerial vehicle based on a system-level multiphysical model. The model considers aerodynamics of the rotor-propeller assembly, electro-mechanical dynamics of the motor and motor controller, and rigid-body dynamics of the vehicle, as control based on a system-level model incorporating all these dynamics and their coupling is missing in literature. A forward flight operation is considered for time-optimal and energy-optimal control, as well as battery voltages of 25 V and 21 V. Energy-optimal control is shown to reduce the energy required for the operation by 38.5% at 25 V, while reducing the battery voltage increases the minimum operation time by 19.8%. The energy-optimal cruise velocity is also examined, demonstrating that the optimal velocity predicted without considering rotor aerodynamics uses 35.2% more energy per meter travelled than is required at the true optimal velocity.
NEMS Circular Plates Under Hard Electrostatic Excitations: Amplitude-Frequency Response of Superharmonic Resonance of Second Order to Include Casimir Effect
Authors: Caruntu Dumitru I. , Beatriz Julio , Huerta Benjamin 
Published: January 2021

Abstract
This work deals with the frequency-amplitude response of the superharmonic resonance of second order of electrostatically actuated clamped NEMS circular plate resonators. The NEMS system consists of a circular plate parallel to a ground plate. Hard excitations (large AC voltage) due to the electrostatic force of frequency near one fourth of the natural frequency of the plate resonator leads the plate into a superharmonic resonance of second order. Hard excitations are excitations significant enough to produce resonance although far from the primary resonance zone. There is no DC component in the voltage applied. For the partial differential equation of motion two reduced order models are developed. The first one uses one mode of vibration and it is solved using the Method of Multiple Scales (MMS), and the frequency-amplitude response is predicted. Hard excitations were modeled by keeping the first term of the Taylor polynomial of the electrostatic force as a large term. The second model uses two modes of vibration, and it is solved using numerical integration. This produces time responses of the resonator. In this work, the quantum dynamics effect such as Casimir effect is considered significant. The two branches, one unstable and one stable, with a saddle node bifurcation point are predicted. Both methods are in agreement for amplitudes up to 0.7 of the gap. The effect of damping and voltage on the frequency response are reported.
Control-Oriented Modeling of Cycle-to-Cycle Combustion Variability at the Misfire Limit in SI Engines
Authors: Maldonado Bryan P. , Kaul Brian C. 
Published: January 2021

Abstract
A control-oriented model is presented that can capture the prior-cycle correlation of combustion cycles during conditions with high levels of exhaust gas recirculation (EGR). Combustion events are modeled in discrete time and the dynamic evolution is captured by the residual air, fuel, and inert gas trapped in the combustion chamber. The mathematical formulation of the model is presented together with the calibration procedure to emulate a particular engine operating condition. A cycle-to-cycle system identification methodology is described which allows regressing model parameters from experimental data. Simulations are presented and compared to real engine measurements to show the modeling potential for analysis and control of combustion events.
The Effect of Simultaneous Auditory and Visual Sensing Cues in a Two-Dimensional Vicsek Model
Authors: Lemus Jeremy , Roy Subhradeep 
Published: January 2021

Abstract
Collective behavior refers to the group-level organization arising from interaction among individuals. To exchange information the individuals rely on their sensing modalities. Experimental studies provide evidence that the integration of information from multiple sensory modalities can influence animal navigation and social communication. In this paper, we present a modified Vicsek model with a composite sensing scheme that combines both auditory and visual sensing cues through the set of sensory neighbors. We investigate the combined effect of auditory and visual sensing on the group behavior compared to pure vision and audition using numerical simulation. We observe that taking the advantage of composite modality, the particles get access to more information that enables them to form a single large, cohesive, and perfectly aligned group using a narrow sensing region, which is possible in either vision or audition only using a wider sensing region.
Optimal Data-Driven Modeling-Free Differential-Inversion-Based Iterative Control: A Wafer Stage Example
Authors: Zhang Zezhou , Zou Qingze 
Published: January 2021

Abstract
In this paper, an optimal data-driven modeling-free differential-inversion-based iterative control (OMFDIIC) method is proposed for both high performance and robustness in the presence of random disturbances. Achieving high accuracy and fast convergence is challenging as the system dynamics behaviors vary due to the external uncertainties and the system bandwidth is limited. The aim of the proposed method is to compensate for the dynamics effect without modeling process and achieve both high accuracy and robust convergence, by extending the existed modeling-free differential-inversion-based iterative control (MFDIIC) method through a frequency- and iteration-dependent gain. The convergence of the OMFDIIC method is analyzed with random noise/disturbances considered. The developed method is applied to a wafer stage, and shows a significant improvement in the performance.