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

Wang et al., 2019 - Google Patents

Approximate neural optimal control with reinforcement learning for a torsional pendulum device

Wang et al., 2019

Document ID
16524379032482156664
Author
Wang D
Qiao J
Publication year
Publication venue
Neural Networks

External Links

Snippet

A torsional pendulum device containing hyperbolic tangent input nonlinearities can be formulated as a nonaffine system. Unlike basic affine systems, the optimal feedback control of complex nonaffine plants is difficult but quite important. In this paper, the approximate …
Continue reading at www.sciencedirect.com (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
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • 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
    • 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
    • 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
    • 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
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design

Similar Documents

Publication Publication Date Title
Wang et al. Approximate neural optimal control with reinforcement learning for a torsional pendulum device
Jiang et al. Value iteration and adaptive optimal output regulation with assured convergence rate
Luo et al. Policy gradient adaptive dynamic programming for data-based optimal control
Zhao et al. Observer based adaptive dynamic programming for fault tolerant control of a class of nonlinear systems
Song et al. Off-policy actor-critic structure for optimal control of unknown systems with disturbances
Xu et al. A novel model-free adaptive control design for multivariable industrial processes
Wang et al. Data-based adaptive critic designs for nonlinear robust optimal control with uncertain dynamics
Polyakov et al. Finite-time and fixed-time stabilization: Implicit Lyapunov function approach
Wang et al. Adaptive neural tracking control for a class of nonlinear systems with dynamic uncertainties
Wu et al. Simultaneous policy update algorithms for learning the solution of linear continuous-time H∞ state feedback control
Wang et al. Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems
Zhao et al. Observer-critic structure-based adaptive dynamic programming for decentralised tracking control of unknown large-scale nonlinear systems
Moodi et al. On observer-based controller design for Sugeno systems with unmeasurable premise variables
Yang et al. Observer-based decentralized adaptive NNs fault-tolerant control of a class of large-scale uncertain nonlinear systems with actuator failures
Jiang et al. H∞ control with constrained input for completely unknown nonlinear systems using data-driven reinforcement learning method
Wang et al. Event-based constrained robust control of affine systems incorporating an adaptive critic mechanism
Wang et al. Backstepping-based Lyapunov function construction using approximate dynamic programming and sum of square techniques
Zhao et al. Adaptive optimal output regulation of linear discrete-time systems based on event-triggered output-feedback
Song et al. Optimal control laws for time-delay systems with saturating actuators based on heuristic dynamic programming
Kamalapurkar et al. Concurrent learning-based approximate optimal regulation
Mu et al. Adaptive tracking control for a class of continuous-time uncertain nonlinear systems using the approximate solution of HJB equation
Fan et al. Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints
Yan et al. Error bound analysis of $ Q $-function for discounted optimal control problems with policy iteration
Wang et al. Adaptive finite-time prescribed performance control of switched nonlinear systems with unknown actuator dead-zone
Zhang et al. Neurodynamic programming and tracking control scheme of constrained-input systems via a novel event-triggered PI algorithm