[go: up one dir, main page]

Patel, 2021 - Google Patents

Modeling and learning control system design for robots using deep learning techniques

Patel, 2021

View PDF
Document ID
1041453958213017440
Author
Patel R
Publication year

External Links

Snippet

In recent years, research and development in artificial intelligence dramatically impacted fields like health care, agriculture, and manufacturing. In the robotics field, innovation such as recognition, interaction, and manipulation made a breakthrough in" How a robot can help …
Continue reading at laurentian.scholaris.ca (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/32—Automatic controllers electric with inputs from more than one sensing element; with outputs to more than one correcting element
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition

Similar Documents

Publication Publication Date Title
Lenz et al. DeepMPC: Learning deep latent features for model predictive control.
Mohajerin et al. Multistep prediction of dynamic systems with recurrent neural networks
Cauligi et al. Coco: Online mixed-integer control via supervised learning
Hashemi et al. Multibody dynamics and control using machine learning
Zatout et al. Optimisation of fuzzy logic quadrotor attitude controller–particle swarm, cuckoo search and BAT algorithms
EP3424650B1 (en) A method and apparatus for performing control of a movement of a robot arm
Antsaklis Intelligent control
Jin et al. Task learning, intent prediction, and adaptive blended shared control with application to excavators
Rezaei-Shoshtari et al. Cascaded gaussian processes for data-efficient robot dynamics learning
Çallar et al. Hybrid learning of time-series inverse dynamics models for locally isotropic robot motion
Verma A survey on machine learning applied to dynamic physical systems
Patel Modeling and learning control system design for robots using deep learning techniques
Cursi et al. Augmenting loss functions of feedforward neural networks with differential relationships for robot kinematic modelling
Merva et al. Globally-Guided Geometric Fabrics for Reactive Mobile Manipulation in Dynamic Environments
Meimandi et al. RL-HAT: A New Framework for Understanding Human-Agent Teaming
Virani et al. Imitation of demonstrations using bayesian filtering with nonparametric data-driven models
de Silva Intelligent robotics-misconceptions, current trends, and opportunities
Truong et al. An efficient navigation framework for autonomous mobile robots in dynamic environments using learning algorithms
Rakhmatillaev et al. An integrative review of control strategies in robotics
Shetty et al. Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning
Acha Control of Leading-Edge Shock Train Using Deep Neural Network to Prevent Unstart
Waseem et al. Expert and Intelligent Systems for Robotic Manipulators Control: A Comprehensive Review
Efe et al. Variable structure systems theory based training strategies for computationally intelligent systems
Wang Data-driven trajectory tracking of manipulator with event-triggered model updating
Badoni Robotics controller: a literature survey