US 5,335,164 AGranted

Method and apparatus for adaptive control

AssigneeUniversal Dynamics, Inc.

InventorsWilliam A. G. Gough, Jr., David L. Lyon

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Abstract

The present invention provides a method and apparatus for adaptive control which does not require a predetermined model of the process to be controlled. The method comprises the steps of 1) setting an initial state vector; 2) setting an initial parameter vector; 3) setting an initial prediction parameter gain; 4) set the initial covariance matrix; 5) performing a state update 6) estimating the model error; 7) updating the parameter vector, 8) updating the co-variance matrix, and 9) updating the controller output.

Claims7

Dependent claims

A method of generating a control signal for controlling a process, during each of a plurality of sampling instants, said process having at least one input variable and one output variable, the application of said control signal to said process constituting a control action causing a variation of said input variable, said method comprising:

a) providing an adaptive controller adapted to store a model of said process comprising a state vector and a model parameter vector and to generate said control action;
b) providing means for measuring said output variable of said process, generating a signal representing said measurement and communicating said measurement to said adaptive controller;
c) selecting a number n of orthonormal series functions to model said process;
d) providing a signal to define an initial state vector l(t).sub.t=O = ##EQU12## where d.sub.i is a constant, and storing said initial state vector in said adaptive controller;
e) providing a signal to define an initial predicted model parameter vector= ##EQU13## where h.sub.i is a constant, and storing said initial predicted model parameter vector in said adaptive controller;
f) performing an update of said state vector in said controller using said orthonormal series functions to provide a history of said control actions and any feedforward input variables up to that sampling instant;
g) measuring said process output variable and calculating a model error in the predicted model parameter vector by comparing said predicted model parameter vector to said measured process output variable;
h) updating the model parameter vector with said model error by a Kalman filter recursive least squares method;
i) calculating the change in controller output required to bring said process output variables to a desired value based on a predicted process output variable obtained from said updated model parameter vector and said updated state vector;
j) generating a control action based on said calculated required change in controller output;
k) repeating steps f) through j) at each sampling instant;
l) storing a reference model parameter vector in said controller during a period of satisfactory controller operation;
and
m) periodically replacing said model parameter vector in said controller with said reference vector if a steadily decreasing prediction parameter gain is detected.

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Dependencies are inferred from the claim text.

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Abstract

a) providing an adaptive controller adapted to store a model of said process comprising a state vector and a model parameter vector and to generate said control action;
b) providing means for measuring said output variable of said process, generating a signal representing said measurement and communicating said measurement to said adaptive controller;
c) selecting a number n of orthonormal series functions to model said process;
d) providing a signal to define an initial state vector l(t).sub.t=O = ##EQU12## where d.sub.i is a constant, and storing said initial state vector in said adaptive controller;
e) providing a signal to define an initial predicted model parameter vector= ##EQU13## where h.sub.i is a constant, and storing said initial predicted model parameter vector in said adaptive controller;
f) performing an update of said state vector in said controller using said orthonormal series functions to provide a history of said control actions and any feedforward input variables up to that sampling instant;
g) measuring said process output variable and calculating a model error in the predicted model parameter vector by comparing said predicted model parameter vector to said measured process output variable;
h) updating the model parameter vector with said model error by a Kalman filter recursive least squares method;
i) calculating the change in controller output required to bring said process output variables to a desired value based on a predicted process output variable obtained from said updated model parameter vector and said updated state vector;
j) generating a control action based on said calculated required change in controller output;
k) repeating steps f) through j) at each sampling instant;
l) storing a reference model parameter vector in said controller during a period of satisfactory controller operation;
and
m) periodically replacing said model parameter vector in said controller with said reference vector if a steadily decreasing prediction parameter gain is detected.

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