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Proceedings Papers

Decentralized Iterative Learning Cooperative Impedance Control for a Team of Robot Manipulators

Xu Jin
Proc. ASME. DSCC2020, Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions, V001T01A003, October 5–7, 2020
Paper No: DSCC2020-3117
https://doi.org/10.1115/DSCC2020-3117
View Paper titled, Decentralized Iterative Learning Cooperative Impedance Control for a Team of Robot Manipulators
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Topics: Manipulators, Teams, Errors, Iterative learning control, Robotics, Robots, Simulation
Proceedings Papers

Iterative Learning Control for Hybrid Systems

Kirti D. Mishra, K. Srinivasan
Proc. ASME. DSCC2020, Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions, V001T03A001, October 5–7, 2020
Paper No: DSCC2020-3191
https://doi.org/10.1115/DSCC2020-3191
View Paper titled, Iterative Learning Control for Hybrid Systems
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Topics: Iterative learning control, Design, Design methodology, Errors, Linear matrix inequalities, Motion control, Nonlinear systems
Proceedings Papers

Robust Iterative Learning Control for Interval Linear Systems

Kirti D. Mishra, K. Srinivasan
Proc. ASME. DSCC2020, Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions, V001T03A004, October 5–7, 2020
Paper No: DSCC2020-3267
https://doi.org/10.1115/DSCC2020-3267
View Paper titled, Robust Iterative Learning Control for Interval Linear Systems
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Topics: Iterative learning control, Linear systems, Errors, Transients (Dynamics), Computation, Control equipment, Design, Design methodology, Position control, Uncertainty
Proceedings Papers

Data-Driven ILC for Trajectory Tracking in Nonlinear Dynamic Systems

Yu-Hsiu Lee, Tsu-Chin Tsao
Proc. ASME. DSCC2019, Volume 2: Modeling and Control of Engine and Aftertreatment Systems; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Validation; Motion Planning and Tracking Control; Multi-Agent and Networked Systems; Renewable and Smart Energy Systems; Thermal Energy Systems; Uncertain Systems and Robustness; Unmanned Ground and Aerial Vehicles; Vehicle Dynamics and Stability; Vibrations: Modeling, Analysis, and Control, V002T14A001, October 8–11, 2019
Paper No: DSCC2019-8926
https://doi.org/10.1115/DSCC2019-8926
View Paper titled, Data-Driven ILC for Trajectory Tracking in Nonlinear Dynamic Systems
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Topics: Iterative learning control, Nonlinear dynamical systems, Trajectories (Physics), Algorithms, Filters, Errors, Filtration, Pendulums, Signals, Simulation
Proceedings Papers

A Preliminary Study on a Physical Model Oriented Learning Algorithm With Application to UAVs

Minghui Zheng, Zhu Chen, Xiao Liang
Proc. ASME. DSCC2019, Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles, V003T17A009, October 8–11, 2019
Paper No: DSCC2019-9186
https://doi.org/10.1115/DSCC2019-9186
View Paper titled, A Preliminary Study on a Physical Model Oriented Learning Algorithm With Application to UAVs
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Topics: Algorithms, Unmanned aerial vehicles, Errors, Iterative learning control, Dynamics (Mechanics), Feedforward control, Physics, Reliability, Signals
Proceedings Papers

A Coupled Adaptive Measurement Delay Estimation and Iterative Learning Control Algorithm

Florian Browne, George T.-C. Chiu, Neera Jain
Proc. ASME. DSCC2019, Volume 1: Advanced Driver Assistance and Autonomous Technologies; Advances in Control Design Methods; Advances in Robotics; Automotive Systems; Design, Modeling, Analysis, and Control of Assistive and Rehabilitation Devices; Diagnostics and Detection; Dynamics and Control of Human-Robot Systems; Energy Optimization for Intelligent Vehicle Systems; Estimation and Identification; Manufacturing, V001T09A005, October 8–11, 2019
Paper No: DSCC2019-9210
https://doi.org/10.1115/DSCC2019-9210
View Paper titled, A Coupled Adaptive Measurement Delay Estimation and Iterative Learning Control Algorithm
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Topics: Algorithms, Delays, Iterative learning control, Errors, Simulation results, Stability
Proceedings Papers

Neural-Network Based Iterative Learning Control of a Hybrid Exoskeleton With an MPC Allocation Strategy

Vahidreza Molazadeh, Qiang Zhang, Xuefeng Bao, Nitin Sharma
Proc. ASME. DSCC2019, Volume 1: Advanced Driver Assistance and Autonomous Technologies; Advances in Control Design Methods; Advances in Robotics; Automotive Systems; Design, Modeling, Analysis, and Control of Assistive and Rehabilitation Devices; Diagnostics and Detection; Dynamics and Control of Human-Robot Systems; Energy Optimization for Intelligent Vehicle Systems; Estimation and Identification; Manufacturing, V001T05A011, October 8–11, 2019
Paper No: DSCC2019-9191
https://doi.org/10.1115/DSCC2019-9191
View Paper titled, Neural-Network Based Iterative Learning Control of a Hybrid Exoskeleton With an MPC Allocation Strategy
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Topics: Artificial neural networks, Control equipment, Exoskeleton devices, Iterative learning control, Predictive control
Proceedings Papers

ILC With Time-Varying Delay Estimation: A Case Study on Twin Roll Strip Casting

Florian Browne, George Chiu, Neera Jain, Brad Rees
Proc. ASME. DSCC2018, Volume 2: Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems, V002T23A002, September 30–October 3, 2018
Paper No: DSCC2018-9028
https://doi.org/10.1115/DSCC2018-9028
View Paper titled, ILC With Time-Varying Delay Estimation: A Case Study on Twin Roll Strip Casting
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Topics: Casting, Delays, Iterative learning control, Strips, Algorithms, Errors, Simulation results, Stability
Proceedings Papers

Rapid Probe Engagement and Withdrawal With Online Minimized Probe-Sample Interaction Force in Atomic Force Microscopy

Jingren Wang, Qingze Zou
Proc. ASME. DSCC2018, Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare, V001T11A004, September 30–October 3, 2018
Paper No: DSCC2018-9156
https://doi.org/10.1115/DSCC2018-9156
View Paper titled, Rapid Probe Engagement and Withdrawal With Online Minimized Probe-Sample Interaction Force in Atomic Force Microscopy
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Topics: Atomic force microscopy, Probes, Trajectories (Physics), Adhesives, Damage, Deformation, Engineering design processes, Errors, Imaging, Iterative learning control
Proceedings Papers

A Plant-Inversion Based Switched Iterative Learning Control Scheme for a Special Class of Multivariable Systems

He Li, Douglas A. Bristow, Robert G. Landers
Proc. ASME. DSCC2018, Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare, V001T01A008, September 30–October 3, 2018
Paper No: DSCC2018-9069
https://doi.org/10.1115/DSCC2018-9069
View Paper titled, A Plant-Inversion Based Switched Iterative Learning Control Scheme for a Special Class of Multivariable Systems
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Topics: Iterative learning control, Errors, Algorithms, Dynamics (Mechanics), Modeling, Robustness, Stability, Uncertainty
Proceedings Papers

Improved Cross-Coupled Iterative Learning Control for Contouring NURBS Curves

Ashley Armstrong, Amy Wagoner Johnson, Andrew Alleyne
Proc. ASME. DSCC2018, Volume 2: Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems, V002T23A005, September 30–October 3, 2018
Paper No: DSCC2018-9145
https://doi.org/10.1115/DSCC2018-9145
View Paper titled, Improved Cross-Coupled Iterative Learning Control for Contouring NURBS Curves
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Topics: Iterative learning control, Errors, Trajectories (Physics), Additive manufacturing, Computer-aided design, Control equipment, Design, Engineering simulation, Manufacturing, Simulation
Proceedings Papers

Easy-to-Use 3D Printer for Fabrication of Biological Scaffolds

Ashley Armstrong, Andrew Alleyne, Amy Wagoner Johnson
Proc. ASME. DSCC2017, Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications, V002T16A002, October 11–13, 2017
Paper No: DSCC2017-5147
https://doi.org/10.1115/DSCC2017-5147
View Paper titled, Easy-to-Use 3D Printer for Fabrication of Biological Scaffolds
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Topics: Manufacturing, Bone, Feedforward control, Additive manufacturing, Bioengineering, Control equipment, Design, Dynamic models, Engineering disciplines, Feedback
Proceedings Papers

Disturbance and Performance-Weighted Iterative Learning Control With Application to Modulated Tool Path-Based Manufacturing

Christopher DiMarco, Christopher Vermillion, John C. Ziegert
Proc. ASME. DSCC2016, Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control, V002T22A009, October 12–14, 2016
Paper No: DSCC2016-9898
https://doi.org/10.1115/DSCC2016-9898
View Paper titled, Disturbance and Performance-Weighted Iterative Learning Control With Application to Modulated Tool Path-Based Manufacturing
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Topics: Iterative learning control, Manufacturing, Machine tools, Control equipment, Machining, Oscillations, Temperature, Finishes, Fluctuations (Physics), Hazards
Proceedings Papers

Arbitrary-Order Iterative Learning Control Considering H ∞ Synthesis

Minghui Zheng, Cong Wang, Liting Sun, Masayoshi Tomizuka
Proc. ASME. DSCC2016, Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control, V002T22A010, October 12–14, 2016
Paper No: DSCC2016-9902
https://doi.org/10.1115/DSCC2016-9902
View Paper titled, Arbitrary-Order Iterative Learning Control Considering H ∞ Synthesis
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Topics: Iterative learning control, Design, Filters, Algorithms, Errors, Feedforward control, Impulse (Physics), Optimal control, Semiconductor wafers, Signals
Proceedings Papers

Optimization Based Weighting Matrices Design for Norm Optimal Iterative Learning Control

Xinyi Ge, Jeffrey L. Stein, Tulga Ersal
Proc. ASME. DSCC2016, Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control, V002T28A002, October 12–14, 2016
Paper No: DSCC2016-9758
https://doi.org/10.1115/DSCC2016-9758
View Paper titled, Optimization Based Weighting Matrices Design for Norm Optimal Iterative Learning Control
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Topics: Design, Iterative learning control, Optimization, Errors, Steady state, Uncertainty, Modeling, Robustness, Simulation
Proceedings Papers

Towards an Optimal Learning for Robust Iterative-Based Intelligent PID

Elmira Madadi, Dirk Söffker
Proc. ASME. DSCC2016, Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation, V001T02A003, October 12–14, 2016
Paper No: DSCC2016-9712
https://doi.org/10.1115/DSCC2016-9712
View Paper titled, Towards an Optimal Learning for Robust Iterative-Based Intelligent PID
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Topics: Control equipment, Design, Iterative learning control
Proceedings Papers

A Task-Level Iterative Learning Control Algorithm for Accurate Tracking in Manipulators With Modeling Errors and Stringent Joint Position Limits

Pranav A. Bhounsule, Abhishek A. Bapat, Katsu Yamane
Proc. ASME. DSCC2016, Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control, V002T27A002, October 12–14, 2016
Paper No: DSCC2016-9759
https://doi.org/10.1115/DSCC2016-9759
View Paper titled, A Task-Level Iterative Learning Control Algorithm for Accurate Tracking in Manipulators With Modeling Errors and Stringent Joint Position Limits
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Topics: Algorithms, Errors, Iterative learning control, Manipulators, Modeling, End effectors, Kinematics, Robots, Redundancy (Engineering), Stability
Proceedings Papers

Model-Free Approaches Applied to the Control of Nonlinear Systems: A Brief Survey With Special Attention to Intelligent PID Iterative Learning Control

Elmira Madadi, Dirk Söffker
Proc. ASME. DSCC2015, Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems, V001T03A004, October 28–30, 2015
Paper No: DSCC2015-9721
https://doi.org/10.1115/DSCC2015-9721
View Paper titled, Model-Free Approaches Applied to the Control of Nonlinear Systems: A Brief Survey With Special Attention to Intelligent PID Iterative Learning Control
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Topics: Iterative learning control, Nonlinear systems, Control equipment, Design, Simulation results
Proceedings Papers

Compensating Flexibility in Servo Systems Using Iterative Learning Control

Cong Wang, Zining Wang, Cheng Peng, Yu Zhao, Masayoshi Tomizuka
Proc. ASME. DSCC2015, Volume 3: Multiagent Network Systems; Natural Gas and Heat Exchangers; Path Planning and Motion Control; Powertrain Systems; Rehab Robotics; Robot Manipulators; Rollover Prevention (AVS); Sensors and Actuators; Time Delay Systems; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamics Control; Vibration and Control of Smart Structures/Mech Systems; Vibration Issues in Mechanical Systems, V003T40A001, October 28–30, 2015
Paper No: DSCC2015-9659
https://doi.org/10.1115/DSCC2015-9659
View Paper titled, Compensating Flexibility in Servo Systems Using Iterative Learning Control
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Topics: Iterative learning control, Servomechanisms, Torque, Engines, Errors, Gears, Motors, Robust control, Uncertainty, Vibration
Proceedings Papers

An Iterative Learning Control Approach to Multi-Agent Formations

Michael Quann, Kira Barton
Proc. ASME. DSCC2015, Volume 3: Multiagent Network Systems; Natural Gas and Heat Exchangers; Path Planning and Motion Control; Powertrain Systems; Rehab Robotics; Robot Manipulators; Rollover Prevention (AVS); Sensors and Actuators; Time Delay Systems; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamics Control; Vibration and Control of Smart Structures/Mech Systems; Vibration Issues in Mechanical Systems, V003T37A009, October 28–30, 2015
Paper No: DSCC2015-9872
https://doi.org/10.1115/DSCC2015-9872
View Paper titled, An Iterative Learning Control Approach to Multi-Agent Formations
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Topics: Iterative learning control, Control equipment, Trajectories (Physics), Dynamics (Mechanics), Engineering simulation, Simulation, Stability