Nothing Special   »   [go: up one dir, main page]

Kebria et al., 2021 - Google Patents

Stable neural adaptive filters for teleoperations with uncertain delays

Kebria et al., 2021

Document ID
18075287363509136259
Author
Kebria P
Khosravi A
Nahavandi S
Publication year
Publication venue
IEEE Robotics and Automation Letters

External Links

Snippet

Uncertainties in communication networks negatively affect the performance and usability of teleoperation systems, specially, in time-critical applications such as telesurgery. There already exist different methods to tackle this problem using filtering and learning approaches …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive 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
    • G05B13/027Adaptive 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
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive 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
    • G05B13/042Adaptive 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
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive 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
    • G05B13/0285Adaptive 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 and fuzzy logic
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive 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
    • G05B13/048Adaptive 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 using a predictor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • G05B2219/39Robotics, robotics to robotics hand
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00Programme-control systems
    • G05B19/02Programme-control systems electric
    • G05B19/418Total 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]
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00Systems involving the use of models or simulators of said systems
    • G05B17/02Systems involving the use of models or simulators of said systems electric
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run

Similar Documents

Publication Publication Date Title
Kebria et al. Adaptive type-2 fuzzy neural-network control for teleoperation systems with delay and uncertainties
Gueaieb et al. A robust hybrid intelligent position/force control scheme for cooperative manipulators
Pezzato et al. A novel adaptive controller for robot manipulators based on active inference
Sun et al. Type-2 fuzzy modeling and control for bilateral teleoperation system with dynamic uncertainties and time-varying delays
Li et al. Adaptive fuzzy control for synchronization of nonlinear teleoperators with stochastic time-varying communication delays
Karakasoglu et al. Identification and decentralized adaptive control using dynamical neural networks with application to robotic manipulators
Chiou et al. An adaptive fuzzy controller for robot manipulators
Refoufi et al. Control of a manipulator robot by neuro-fuzzy subsets form approach control optimized by the genetic algorithms
Kim et al. A novel neuro-fuzzy controller for autonomous underwater vehicles
Duburcq et al. Online trajectory planning through combined trajectory optimization and function approximation: Application to the exoskeleton Atalante
Ganjefar et al. A Lyapunov stable type-2 fuzzy wavelet network controller design for a bilateral teleoperation system
Kebria et al. Stable neural adaptive filters for teleoperations with uncertain delays
Chen Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems
Ngo et al. Robust adaptive self-organizing wavelet fuzzy CMAC tracking control for de-icing robot manipulator
Li et al. Distributed neural-network-based cooperation control for teleoperation of multiple mobile manipulators under round-robin protocol
Jalali et al. Design Parallel Linear PD Compensation by Fuzzy Sliding Compensator for Continuum Robot
Izadbakhsh et al. Superiority of q-Chlodowsky operators versus fuzzy systems and neural networks: Application to adaptive impedance control of electrical manipulators
Grandesso et al. CACTO: Continuous actor-critic with trajectory optimization—towards global optimality
Kebria et al. Robust adaptive synchronisation of a single-master multi-slave teleoperation system over delayed communication
Krug et al. Representing movement primitives as implicit dynamical systems learned from multiple demonstrations
Lin et al. Hybrid adaptive fuzzy controllers with application to robotic systems
Nazmara et al. Exponentially convergence for the regressor-free adaptive fuzzy impedance control of robots by gradient descent algorithm
Garcia-Rodriguez et al. Normal and tangent force neuro-fuzzy control of a soft-tip robot with unknown kinematics
Stulp et al. Reinforcement learning of impedance control in stochastic force fields
Kiguchi et al. Generation of efficient adjustment strategies for a fuzzy-neuro force controller using genetic algorithms–application to robot force control in an unknown environment