Mechatronic Systems and Control最新文献_第4页

Completing Complex Contact Tasks Using Integrated Active and Passive Compliant Control Methodologies 使用集成的主动和被动柔性控制方法完成复杂的接触任务

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Mechatronic Systems and Control Pub Date : 2019-11-26 DOI: 10.1115/dscc2019-9062

Adam Pettinger, M. Pryor

引用次数: 3

The Restricted Newton Method for Fast Nonlinear Model Predictive Control 快速非线性模型预测控制的约束牛顿法

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Mechatronic Systems and Control Pub Date : 2019-11-26 DOI: 10.1115/dscc2019-9067

A. Maitland, C. Jin, J. McPhee

引用次数: 1

Assembly Planning Using a Two-Arm System for Polygonal Furniture 基于双臂系统的多边形家具装配规划

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Mechatronic Systems and Control Pub Date : 2019-11-26 DOI: 10.1115/dscc2019-9173

S. T. Payne, C. Garrison, Steve Markham, Tucker Hermans, K. Leang

引用次数: 0

Adaptive Control of Large-Scale Soft Robot Manipulators With Unknown Payloads 具有未知载荷的大型软机械臂自适应控制

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Mechatronic Systems and Control Pub Date : 2019-11-26 DOI: 10.1115/dscc2019-9037

J. S. Terry, J. Whitaker, R. Beard, Marc D. Killpack

引用次数: 2

Active Vibration Rejection in Multi Actuator Drives: Data Driven Approach 主动振动抑制在多驱动器:数据驱动的方法

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Mechatronic Systems and Control Pub Date : 2019-11-26 DOI: 10.1115/dscc2019-8983

Prateek Shah, R. Horowitz

{"title":"Active Vibration Rejection in Multi Actuator Drives: Data Driven Approach","authors":"Prateek Shah, R. Horowitz","doi":"10.1115/dscc2019-8983","DOIUrl":"https://doi.org/10.1115/dscc2019-8983","url":null,"abstract":"\u0000 Multi Actuator Technology was unveiled in December 2017 by Seagate, a breakthrough that can double the data performance of the future generation hard disk drives. This technology will equip drives with dual actuators operating on the same pivot point. Each actuator will control half of the drives arms.\u0000 This new technology brings new control challenges with it. Since two actuators operate independently on the same pivot timber, the control forces and torques generated by one actuator can affect the operation of the other actuator. The independent functioning of the two actuators will lead to a scenario of one actuator in the track seeking mode while the other actuator is in the track following mode. It is expected that the track seeking actuator will impart vibration onto the track following actuator, worsening its performance drastically.\u0000 In this paper, we propose a methodology to estimate this imparted vibration and to design feedforward controllers for the voice coil motor and the micro actuator, of the track following actuator, to suppress the estimated vibration. The vibration estimation is performed using power spectral factorization techniques. Whereas, the feedforward controllers are designed using a mixed H2 – H∞ data driven methodology to obtain a robust design.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86659132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Performance Study of a Bat Searching Algorithm From System Dynamics Perspective 基于系统动力学的蝙蝠搜索算法性能研究

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Mechatronic Systems and Control Pub Date : 2019-11-26 DOI: 10.1115/dscc2019-9017

Haopeng Zhang, N. Schutte

引用次数: 0

Time Delay Control of a High-DOF Robot Manipulator Through Feedback Linearization Based Predictor 基于反馈线性化预测器的高自由度机械臂时延控制

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Mechatronic Systems and Control Pub Date : 2019-11-26 DOI: 10.1115/dscc2019-8915

M. Bagheri, P. Naseradinmousavi, M. Krstić

{"title":"Time Delay Control of a High-DOF Robot Manipulator Through Feedback Linearization Based Predictor","authors":"M. Bagheri, P. Naseradinmousavi, M. Krstić","doi":"10.1115/dscc2019-8915","DOIUrl":"https://doi.org/10.1115/dscc2019-8915","url":null,"abstract":"\u0000 We formulate a predictor-based controller for a high-DOF manipulator to compensate a time-invariant input delay during a pick-and-place task. Robot manipulators are widely used in tele-manipulation systems on the account of their reliable, fast, and precise motions while they are subject to large delays. Using common control algorithms on such delay systems can cause not only poor control performance, but also catastrophic instability in engineering applications. Therefore, delays need to be compensated in designing robust control laws. As a case study, we focus on a 7-DOF Baxter manipulator subject to three different input delays. First, delay-free dynamic equations of the Baxter manipulator are derived using the Lagrangian method. Then, we formulate a predictor-based controller, in the presence of input delay, in order to track desired trajectories. Finally, the effects of input delays in the absence of a robust predictor are investigated, and then the performance of the predictor-based controller is experimentally evaluated to reveal robustness of the algorithm formulated. Simulation and experimental results demonstrate that the predictor-based controller effectively compensates input delays and achieves closed-loop stability.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89030140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator 基于实验自主深度学习的七自由度机械臂三维路径规划

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Mechatronic Systems and Control Pub Date : 2019-11-26 DOI: 10.1115/dscc2019-8951

Alex Bertino, M. Bagheri, M. Krstić, P. Naseradinmousavi

{"title":"Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator","authors":"Alex Bertino, M. Bagheri, M. Krstić, P. Naseradinmousavi","doi":"10.1115/dscc2019-8951","DOIUrl":"https://doi.org/10.1115/dscc2019-8951","url":null,"abstract":"\u0000 In this paper, we examine the autonomous operation of a high-DOF robot manipulator. We investigate a pick-and-place task where the position and orientation of an object, an obstacle, and a target pad are initially unknown and need to be autonomously determined. In order to complete this task, we employ a combination of computer vision, deep learning, and control techniques. First, we locate the center of each item in two captured images utilizing HSV-based scanning. Second, we utilize stereo vision techniques to determine the 3D position of each item. Third, we implement a Convolutional Neural Network in order to determine the orientation of the object. Finally, we use the calculated 3D positions of each item to establish an obstacle avoidance trajectory lifting the object over the obstacle and onto the target pad. Through the results of our research, we demonstrate that our combination of techniques has minimal error, is capable of running in real-time, and is able to reliably perform the task. Thus, we demonstrate that through the combination of specialized autonomous techniques, generalization to a complex autonomous task is possible.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77559539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Amplitude-Frequency Response of Superharmonic Resonance of Third Order of Electrostatically Actuated MEMS Cantilever Resonators 静电驱动MEMS悬臂谐振器三阶超谐波共振的幅频响应

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Mechatronic Systems and Control Pub Date : 2019-11-26 DOI: 10.1115/dscc2019-9172

D. Caruntu, Christian Reyes

{"title":"Amplitude-Frequency Response of Superharmonic Resonance of Third Order of Electrostatically Actuated MEMS Cantilever Resonators","authors":"D. Caruntu, Christian Reyes","doi":"10.1115/dscc2019-9172","DOIUrl":"https://doi.org/10.1115/dscc2019-9172","url":null,"abstract":"\u0000 This work deals with amplitude frequency response of MEMS cantilever resonators undergoing superharmonic resonance of third order. The cantilever resonator is parallel to a ground plate and under alternating current (AC) voltage that excites the cantilever into vibrations. The driving frequency of the AC voltage is near one sixth of the first natural frequency of the cantilever beam resulting into superharmonic resonance of third order. The cantilever beam is modeled using Euler-Bernoulli beam theory. The electrostatic force is modeled using Palmer’s formula to include the fringe effect. In order to investigate the amplitude frequency behavior of the system reduced order models (ROMs) are developed. Three methods are used to solve these ROMs they are 1) the method of multiple scales (MMS) for ROM with one mode of vibration, 2) homotopy analysis method (HAM) for ROM with one mode of vibration, and 3) direct numerical integration for 2 modes of vibration Reduced Order Model (2T ROM) producing time responses of the tip of the cantilever resonator. In this work the limitations of MMS and HAM are highlighted when considering large voltage values i.e hard excitations. For large voltage values MMS and HAM cannot accurately predict the amplitude frequency response; the results from 2T ROM time responses disagree significantly with the MMS and HAM solutions. The effect of voltage on the frequency response is investigated. As the voltage values in the system increase the responses shift to lower frequencies and larger amplitudes.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87073083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Reflective Learning Strategy for Persistent Autonomy of Aerial Manipulators 空中机械臂持续自主的自反思学习策略

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Mechatronic Systems and Control Pub Date : 2019-11-26 DOI: 10.1115/dscc2019-9086

Xu Zhou, Jiucai Zhang, Xiaoli Zhang

{"title":"Self-Reflective Learning Strategy for Persistent Autonomy of Aerial Manipulators","authors":"Xu Zhou, Jiucai Zhang, Xiaoli Zhang","doi":"10.1115/dscc2019-9086","DOIUrl":"https://doi.org/10.1115/dscc2019-9086","url":null,"abstract":"\u0000 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.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90265030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0