Lange et al., 1992 - Google Patents

Iterative self-improvement of force feedback control in contour tracking.

Lange et al., 1992

View PDF
Document ID
2690048095641997088
Author
Lange F
Hirzinger G
Publication year
Publication venue
ICRA

External Links

Snippet

Ahtract A very general 3 level learning method ir presented. aiming at self-improvernent of the parameters of a force feedback controller demonstrated in contour tracking tasks. It is assumed that no model is known a priori, neither of the robot nor of the contour to be …
Continue reading at www.robotic.dlr.de (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
    • 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
    • G05B19/19—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 characterised by positioning or contouring control systems, e.g. to control position from one programmed point to another or to control movement along a programmed continuous path
    • 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
    • G05B2219/41—Servomotor, servo controller till figures
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1628—Programme controls characterised by the control loop
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/1607—Calculation of inertia, jacobian matrixes and inverses

Similar Documents

Publication Publication Date Title
CN109465825B (en) RBF neural network self-adaptive dynamic surface control method for flexible joint of mechanical arm
Mitrovic et al. Adaptive optimal feedback control with learned internal dynamics models
CN110524544A (en) A kind of control method of manipulator motion, terminal and readable storage medium storing program for executing
CN111596545A (en) Self-adaptive fault-tolerant preset performance control method for multi-input multi-output mechanical system
EP1974869A1 (en) Apparatus and method for generating and controlling the motion of a robot
CN112417755A (en) Master-slave mode surgical robot track prediction control method
CN110053044B (en) Model-free self-adaptive smooth sliding mode impedance control method for clamping serial fruits by parallel robot
CN110597072A (en) Robot admittance compliance control method and system
Lange et al. Iterative self-improvement of force feedback control in contour tracking.
Hu et al. Impedance sliding mode control with adaptive fuzzy compensation for robot-environment interacting
Kang et al. Kinematic path‐tracking of mobile robot using iterative learning control
Izadbakhsh et al. Superiority of q-Chlodowsky operators versus fuzzy systems and neural networks: Application to adaptive impedance control of electrical manipulators
Hauck et al. What can be learned from human reach-to-grasp movements for the design of robotic hand-eye systems?
Xia et al. Hybrid force/position control of industrial robotic manipulator based on Kalman filter
Lange et al. Learning of a controller for non-recurring fast movements
Stulp et al. Reinforcement learning of impedance control in stochastic force fields
US5331265A (en) Method of executing sliding-mode control including twist feedback
Lee et al. Adaptive perturbation control with feedforward compensation for robot manipulators
Li et al. Model predictive control for constrained robot manipulator visual servoing tuned by reinforcement learning
Harokopos Optimal learning control of mechanical manipulators in repetitive motions
Moya et al. Robot control systems: A survey
EP0471077B1 (en) Sliding mode control system including feedback of amount of twist
Tokhi et al. Hybrid learning control schemes with acceleration feedback of a flexible manipulator system
Hirzinger et al. Towards a new generation of multisensory light-weight robots with learning capabilities
Hashimoto et al. Visual servoing with linearized observer