Method for model-free control of general discrete-time systems
AssigneeThe Johns Hopkins University
InventorsJames C. Spall, John A. Cristion
Life of this patent
Estimating the term…
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).
Claims10
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.
Dependencies are inferred from the claim text.