1-20 of 69
Artificial neural networks
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

A Novel Intelligent Learning Control Scheme for Discrete-Time Nonlinear Uncertain Systems in Multiple Environments

Jingting Zhang, Chengzhi Yuan, Paolo Stegagno
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, V001T01A002, October 5–7, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2020-3112
https://doi.org/10.1115/DSCC2020-3112
View Paper titled, A Novel Intelligent Learning Control Scheme for Discrete-Time Nonlinear Uncertain Systems in Multiple Environments
Open the PDF for A Novel Intelligent Learning Control Scheme for Discrete-Time Nonlinear Uncertain Systems in Multiple Environments in another window
Topics: Uncertain systems, Control equipment, Artificial neural networks, Dynamics (Mechanics), Pattern recognition, Real-time control, Simulation, Stability
Proceedings Papers

Visual Navigation of Wheeled Mobile Robots Using Deep Reinforcement Learning: Simulation to Real-Time Implementation

Ezebuugo Nwaonumah, Biswanath Samanta
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, V001T05A010, October 5–7, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2020-3279
https://doi.org/10.1115/DSCC2020-3279
View Paper titled, Visual Navigation of Wheeled Mobile Robots Using Deep Reinforcement Learning: Simulation to Real-Time Implementation
Open the PDF for Visual Navigation of Wheeled Mobile Robots Using Deep Reinforcement Learning: Simulation to Real-Time Implementation in another window
Topics: Artificial neural networks, Mobile robots, Navigation, Reinforcement learning, Robots, Simulation
Proceedings Papers

Sensing and Classification of Ambient Vortex Wake From the Kinematics of a Bioinspired Swimming Robot Using Neural Networks

Beau Pollard, Phanindra Tallapragada
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, V001T05A011, October 5–7, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2020-3282
https://doi.org/10.1115/DSCC2020-3282
View Paper titled, Sensing and Classification of Ambient Vortex Wake From the Kinematics of a Bioinspired Swimming Robot Using Neural Networks
Open the PDF for Sensing and Classification of Ambient Vortex Wake From the Kinematics of a Bioinspired Swimming Robot Using Neural Networks in another window
Topics: Artificial neural networks, Biomimetics, Kinematics, Robots, Vortices, Wakes, Flow (Dynamics), Fluid dynamics, Fluids, Pressure
Proceedings Papers

Combined Trajectory Planning and Tracking for Autonomous Vehicles on Deformable Terrains

James Dallas, Yifan Weng, Tulga Ersal
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, V001T15A001, October 5–7, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2020-3174
https://doi.org/10.1115/DSCC2020-3174
View Paper titled, Combined Trajectory Planning and Tracking for Autonomous Vehicles on Deformable Terrains
Open the PDF for Combined Trajectory Planning and Tracking for Autonomous Vehicles on Deformable Terrains in another window
Topics: Autonomous vehicles, Trajectories (Physics), Control equipment, Artificial neural networks, Vehicles, Roads, Computation, Engineering simulation, Failure, Military vehicles
Proceedings Papers

An Online Transfer Learning Approach for Identification and Predictive Control Design With Application to RCCI Engines

Yajie Bao, Javad Mohammadpour Velni, Mahdi Shahbakhti
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, V001T21A003, October 5–7, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2020-3210
https://doi.org/10.1115/DSCC2020-3210
View Paper titled, An Online Transfer Learning Approach for Identification and Predictive Control Design With Application to RCCI Engines
Open the PDF for An Online Transfer Learning Approach for Identification and Predictive Control Design With Application to RCCI Engines in another window
Topics: Design, Engines, Predictive control, Artificial neural networks, Combustion, Compression, Control equipment, Errors, Ignition, Stress
Proceedings Papers

Effect of Neural Network on Reduction of Noise for Edge Detection

Enqi Zhang, Diane L. Peters, James Z. Zhang
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, V001T21A004, October 5–7, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2020-3230
https://doi.org/10.1115/DSCC2020-3230
View Paper titled, Effect of Neural Network on Reduction of Noise for Edge Detection
Open the PDF for Effect of Neural Network on Reduction of Noise for Edge Detection in another window
Topics: Artificial neural networks, Edge detection, Noise (Sound), Noise control
Proceedings Papers

Including Image-Based Perception in Disturbance Observer for Warehouse Drones

Zhu Chen, Xiao Liang, Minghui Zheng
Proc. ASME. DSCC2020, Volume 2: Intelligent Transportation/Vehicles; Manufacturing; Mechatronics; Engine/After-Treatment Systems; Soft Actuators/Manipulators; Modeling/Validation; Motion/Vibration Control Applications; Multi-Agent/Networked Systems; Path Planning/Motion Control; Renewable/Smart Energy Systems; Security/Privacy of Cyber-Physical Systems; Sensors/Actuators; Tracking Control Systems; Unmanned Ground/Aerial Vehicles; Vehicle Dynamics, Estimation, Control; Vibration/Control Systems; Vibrations, V002T39A005, October 5–7, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2020-3284
https://doi.org/10.1115/DSCC2020-3284
View Paper titled, Including Image-Based Perception in Disturbance Observer for Warehouse Drones
Open the PDF for Including Image-Based Perception in Disturbance Observer for Warehouse Drones in another window
Topics: Unmanned aerial vehicles, Warehouses, Oscillations, Grasping, Signals, Artificial neural networks, Feedback, Feedforward control
Proceedings Papers

Cutting the Deployment Costs of Physics-Based MPC in Buildings by Simulation-Based Imitation Learning

Jan Drgona, Lieve Helsen, Draguna L. Vrabie
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, V001T09A001, October 5–7, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2020-3118
https://doi.org/10.1115/DSCC2020-3118
View Paper titled, Cutting the Deployment Costs of Physics-Based MPC in Buildings by Simulation-Based Imitation Learning
Open the PDF for Cutting the Deployment Costs of Physics-Based MPC in Buildings by Simulation-Based Imitation Learning in another window
Topics: Cutting, Physics, Simulation, Structures, Artificial neural networks, Computer software, Maintenance, Modeling, Optimal control, Predictive control
Proceedings Papers

Temperature Dependent Hysteresis Modeling of a Piezotube Actuator Using Elman Neural Network

Mohammad Al Janaideh, Mohammad Al Saaideh, Micky Rakotondrabe
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, V001T09A004, October 8–11, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2019-9184
https://doi.org/10.1115/DSCC2019-9184
View Paper titled, Temperature Dependent Hysteresis Modeling of a Piezotube Actuator Using Elman Neural Network
Open the PDF for Temperature Dependent Hysteresis Modeling of a Piezotube Actuator Using Elman Neural Network in another window
Topics: Actuators, Artificial neural networks, Modeling, Temperature, Displacement, Simulation results, Temperature effects
Proceedings Papers

Reference Governors Based on Online Learning of Maximal Output Admissible Set

Manuel Lanchares, Ilya Kolmanovsky, Anouck Girard, Denise Rizzo
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, V002T24A002, October 8–11, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2019-8950
https://doi.org/10.1115/DSCC2019-8950
View Paper titled, Reference Governors Based on Online Learning of Maximal Output Admissible Set
Open the PDF for Reference Governors Based on Online Learning of Maximal Output Admissible Set in another window
Topics: Governors, Approximation, Algorithms, Artificial neural networks, Electric vehicles, Engineering simulation, Simulation
Proceedings Papers

Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator

Alex Bertino, Mostafa Bagheri, Miroslav Krstić, Peiman Naseradinmousavi
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, V002T14A002, October 8–11, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2019-8951
https://doi.org/10.1115/DSCC2019-8951
View Paper titled, Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator
Open the PDF for Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator in another window
Topics: Manipulators, Path planning, Artificial neural networks, Computers, Errors, Trajectories (Physics)
Proceedings Papers

Adaptive NN Learning Control of Discrete-Time Nonlinear Uncertain Systems

Jingting Zhang, Chengzhi Yuan, Paolo Stegagno
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, V003T16A002, October 8–11, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2019-8923
https://doi.org/10.1115/DSCC2019-8923
View Paper titled, Adaptive NN Learning Control of Discrete-Time Nonlinear Uncertain Systems
Open the PDF for Adaptive NN Learning Control of Discrete-Time Nonlinear Uncertain Systems in another window
Topics: Uncertain systems, Tracking control, Control equipment, Artificial neural networks, Composite materials, Computer simulation, Nonlinear dynamics, Rendering
Proceedings Papers

Comparison of Adaptive Control Laws for Wind Rejection in Quadrotor UAVs

Johannes Verberne, Hever Moncayo
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, V003T16A004, October 8–11, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2019-8957
https://doi.org/10.1115/DSCC2019-8957
View Paper titled, Comparison of Adaptive Control Laws for Wind Rejection in Quadrotor UAVs
Open the PDF for Comparison of Adaptive Control Laws for Wind Rejection in Quadrotor UAVs in another window
Topics: Adaptive control, Unmanned aerial vehicles, Wind, Control algorithms, Artificial neural networks, Flight, Algorithms, Control equipment, Degrees of freedom, Energy consumption
Proceedings Papers

Surface Recognition for Cars: A Comprehensive Approach for Neural Networks

Ashkan Pourkand, Chris White, Naghmeh Zamani, David Grow
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, V003T18A008, October 8–11, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2019-9148
https://doi.org/10.1115/DSCC2019-9148
View Paper titled, Surface Recognition for Cars: A Comprehensive Approach for Neural Networks
Open the PDF for Surface Recognition for Cars: A Comprehensive Approach for Neural Networks in another window
Topics: Artificial neural networks, Automobiles, Vehicles, Accelerometers, Pavement, Asphalt, Braking, Concretes, Encryption, Feature extraction
Proceedings Papers

Machine-Learning Based Thermal-Geometric Predictive Modeling of Laser Powder Bed Fusion Additive Manufacturing

Yong Ren, Qian Wang, Pan Michaleris
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, V001T10A001, October 8–11, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2019-8995
https://doi.org/10.1115/DSCC2019-8995
View Paper titled, Machine-Learning Based Thermal-Geometric Predictive Modeling of Laser Powder Bed Fusion Additive Manufacturing
Open the PDF for Machine-Learning Based Thermal-Geometric Predictive Modeling of Laser Powder Bed Fusion Additive Manufacturing in another window
Topics: Additive manufacturing, Lasers, Machine learning, Modeling, Temperature, Simulation, Algorithms, Artificial neural networks, Computation, Physics
Proceedings Papers

Feature Learning Using Deep Neural Networks for Fault Diagnosis in Electromechanical Systems

Mehrdad Heydarzadeh, Alireza Mohammadi, Shahin Hedayati Kia, Mehrdad Nourani, Humberto Henao, Gérard-André Capolino
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, V001T06A003, October 8–11, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2019-8942
https://doi.org/10.1115/DSCC2019-8942
View Paper titled, Feature Learning Using Deep Neural Networks for Fault Diagnosis in Electromechanical Systems
Open the PDF for Feature Learning Using Deep Neural Networks for Fault Diagnosis in Electromechanical Systems in another window
Topics: Artificial neural networks, Electromechanical systems, Fault diagnosis, Bearings, Algorithms, Mechanical drives, Errors, Gears, Hardware, Machinery
Proceedings Papers

Convolutional Neural Networks for Environmentally Aware Locomotion Mode Recognition of Lower-Limb Amputees

Gholamreza Khademi, Dan Simon
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, V001T07A005, October 8–11, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2019-9180
https://doi.org/10.1115/DSCC2019-9180
View Paper titled, Convolutional Neural Networks for Environmentally Aware Locomotion Mode Recognition of Lower-Limb Amputees
Open the PDF for Convolutional Neural Networks for Environmentally Aware Locomotion Mode Recognition of Lower-Limb Amputees in another window
Topics: Artificial neural networks, Prostheses, Design, Sensors, Stairs, Algorithms, Control equipment, Image formation, Intelligent control systems, Measurement units and standards
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
Publisher: American Society of Mechanical Engineers
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
Open the PDF for Neural-Network Based Iterative Learning Control of a Hybrid Exoskeleton With an MPC Allocation Strategy in another window
Topics: Artificial neural networks, Control equipment, Exoskeleton devices, Iterative learning control, Predictive control
Proceedings Papers

An End-to-End Fully Automatic Bay Parking Approach for Autonomous Vehicles

Rui Li, Weitian Wang, Yi Chen, Srivatsan Srinivasan, Venkat N. Krovi
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, V002T15A004, September 30–October 3, 2018
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2018-9126
https://doi.org/10.1115/DSCC2018-9126
View Paper titled, An End-to-End Fully Automatic Bay Parking Approach for Autonomous Vehicles
Open the PDF for An End-to-End Fully Automatic Bay Parking Approach for Autonomous Vehicles in another window
Topics: Vehicles, Artificial neural networks, Computers, Control equipment
Proceedings Papers

A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning

Liting Sun, Cheng Peng, Wei Zhan, Masayoshi Tomizuka
Proc. ASME. DSCC2018, Volume 3: Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations: Modeling, Analysis, and Control, V003T37A012, September 30–October 3, 2018
Publisher: American Society of Mechanical Engineers
Paper No: DSCC2018-9249
https://doi.org/10.1115/DSCC2018-9249
View Paper titled, A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning
Open the PDF for A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning in another window
Topics: Artificial neural networks, Collisions (Physics), Control equipment, Engineering simulation, Highways, Optimization, Predictive control, Safety, Simulation, Vehicles