[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Campos et al., 2000 - Google Patents

Backlash compensation in discrete time nonlinear systems using dynamic inversion by neural networks

Campos et al., 2000

View PDF
Document ID
4327578004334127473
Author
Campos J
Lewis F
Selmic R
Publication year
Publication venue
Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065)

External Links

Snippet

A dynamics inversion compensation scheme is designed for control of nonlinear discrete- time systems with input backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The …
Continue reading at citeseerx.ist.psu.edu (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
    • 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
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • 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

Similar Documents

Publication Publication Date Title
US7080055B2 (en) Backlash compensation with filtered prediction in discrete time nonlinear systems by dynamic inversion using neural networks
US6611823B1 (en) Backlash compensation using neural network
Lee et al. An adaptive neurocontroller using RBFN for robot manipulators
Yan et al. Barrier function-based adaptive neural network sliding mode control of autonomous surface vehicles
Wang et al. Synchronization control for bilateral teleoperation system with position error constraints: A fixed-time approach
Nguyen et al. Adaptive chattering free neural network based sliding mode control for trajectory tracking of redundant parallel manipulators
Zhu et al. Robust adaptive NN tracking control for MIMO uncertain nonlinear systems with completely unknown control gains under input saturations
Campos et al. Backlash compensation in discrete time nonlinear systems using dynamic inversion by neural networks
Bensidhoum et al. Adaptive P‐type iterative learning radial basis function control for robot manipulators with unknown varying disturbances and unknown input dead zone
Liu et al. Event-triggered adaptive fixed-time fuzzy control for uncertain nonlinear systems with unknown actuator faults
Ao et al. Finite-time leaderless consensus control of a group of Euler-Lagrangian systems with backlash nonlinearities
Campos et al. Backlash compensation with filtered prediction in discrete time nonlinear systems by dynamic inversion using neural networks
Jang Neural network saturation compensation for DC motor systems
Selmic et al. Deadzone compensation in nonlinear systems using neural networks
Emara et al. Robust robot control enhanced by a hierarchical adaptive fuzzy algorithm
Leeghim et al. Adaptive nonlinear control using input normalized neural networks
Yang et al. NN/RISE-based asymptotic tracking control of uncertain nonlinear systems
Xie et al. Observer based control of piezoelectric actuators with classical Duhem modeled hysteresis
Yazdanzad et al. Combined neural network feedforward and RISE feedback control structure for a 5 DOF Upper-limb exoskeleton robot with asymptotic tracking
Lewis et al. On adaptive critic architectures in feedback control
Campos et al. Backlash compensation in discrete time nonlinear systems using dynamic inversion by neural networks: A preliminary approach
Kharrat et al. Neural networks-based adaptive fault-tolerant control for a class of nonstrict-feedback nonlinear systems with actuator faults and input delay
Sadati et al. Adaptive neural network multiple models sliding mode control of robotic manipulators using soft switching
Campos et al. Backlash compensation with filtered prediction in discrete time nonlinear systems by dynamic inversion using neural networks
Campos et al. Backlash Compensation in Nonlinear Systems by Dynamic Inversion Using Neural Networks: Continuous and Discrete Time Approaches