Method for model-free control of general discrete-time systems
First Claim
1. A method for approximating a controller for a nonlinear, discrete-time, closed-loop, stochastic system wherein the nonlinear functions governing the system dynamics and measurement process are unknown, the method comprising the steps of:
- obtaining information about the system;
inputting the information about the system into a data processing means;
approximating the controller using the data processing means and the information about the system comprising the steps of;
selecting a single function approximator to approximate directly the controller;
estimating the unknown parameters of the single function approximator in the controller using a stochastic approximation algorithm; and
using the single function approximator to approximate the controller, wherein an output of the single function approximator is the controller; and
using the controller to control the system.
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Abstract
A method of developing a controller for general (nonlinear) discrete-time systems, where the equations governing the system are unknown and where a controller is estimated without building or assuming a model for the system. The controller is constructed through the use of a function approximator (FA) such as a neural network or polynomial. This involves the estimation of the unknown parameters within the FA through the use of a stochastic approximation that is based on a simultaneous perturbation gradient approximation, which requires only system measurements (not a system model).
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Citations
10 Claims
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1. A method for approximating a controller for a nonlinear, discrete-time, closed-loop, stochastic system wherein the nonlinear functions governing the system dynamics and measurement process are unknown, the method comprising the steps of:
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obtaining information about the system; inputting the information about the system into a data processing means; approximating the controller using the data processing means and the information about the system comprising the steps of; selecting a single function approximator to approximate directly the controller; estimating the unknown parameters of the single function approximator in the controller using a stochastic approximation algorithm; and
using the single function approximator to approximate the controller, wherein an output of the single function approximator is the controller; andusing the controller to control the system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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Specification