[go: up one dir, main page]

Sangiovanni et al., 2018 - Google Patents

Deep reinforcement learning based self-configuring integral sliding mode control scheme for robot manipulators

Sangiovanni et al., 2018

View PDF
Document ID
5016814287002439602
Author
Sangiovanni B
Incremona G
Ferrara A
Piastra M
Publication year
Publication venue
2018 IEEE conference on decision and control (CDC)

External Links

Snippet

This paper deals with the design of an intelligent self-configuring control scheme for robot manipulators. The scheme features two control structures: one of centralized type, implementing the inverse dynamics approach, the other of decentralized type. In both control …
Continue reading at re.public.polimi.it (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
    • G05B13/04—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
    • G05B13/042—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 in which a parameter or coefficient is automatically adjusted to optimise the performance
    • 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
    • G05B13/0265—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
    • G05B13/0275—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 fuzzy logic 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/42—Servomotor, servo controller kind till VSS
    • 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
    • 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
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • 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
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Carron et al. Data-driven model predictive control for trajectory tracking with a robotic arm
Amer et al. Adaptive fuzzy sliding mode control using supervisory fuzzy control for 3 DOF planar robot manipulators
Fu et al. Motion tracking control design for a class of nonholonomic mobile robot systems
Zhang et al. Neural network-based model-free adaptive near-optimal tracking control for a class of nonlinear systems
Jezernik et al. Neural network sliding mode robot control
Liu et al. Iterative convex optimization for model predictive control with discrete-time high-order control barrier functions
Luo et al. Chaos RBF dynamics surface control of brushless DC motor with time delay based on tangent barrier Lyapunov function
Lin et al. Sliding-mode-controlled slider-crank mechanism with fuzzy neural network
Li et al. Command filter-based adaptive fuzzy finite-time output feedback control of nonlinear electrohydraulic servo system
Sangiovanni et al. Deep reinforcement learning based self-configuring integral sliding mode control scheme for robot manipulators
CN113093538A (en) Non-zero and game neural-optimal control method of modular robot system
Fateh et al. Adaptive RBF network control for robot manipulators
Fateh et al. Decentralized direct adaptive fuzzy control of robots using voltage control strategy
Liu et al. Uncertainty observation-based adaptive succinct fuzzy-neuro dynamic surface control for trajectory tracking of fully actuated underwater vehicle system with input saturation
Rojko et al. Sliding-mode motion controller with adaptive fuzzy disturbance estimation
Curran et al. Dimensionality Reduced Reinforcement Learning for Assistive Robots.
Tzafestas Adaptive, robust, and fuzzy rule-based control of robotic manipulators
Li et al. H∞ output-feedback anti-swing control for a nonlinear overhead crane system with disturbances based on TS fuzzy model
Amer et al. Quasi sliding mode‐based single input fuzzy self‐tuning decoupled fuzzy PI control for robot manipulators with uncertainty
Yang et al. Self-evolving data cloud-based PID-like controller for nonlinear uncertain systems
Huber et al. Online trajectory optimization for nonlinear systems by the concept of a model control loop—Applied to the reaction wheel pendulum
Theodoridis A new adaptive neuro-fuzzy controller for trajectory tracking of robot manipulators
Taira et al. Adaptive control of underwater vehicle-manipulator systems using radial basis function networks
Lakhekar et al. Robust self tuning of fuzzy sliding mode control
CN118034356A (en) Fuzzy control method and system for TS of mechanical arm of underwater vehicle