초록 ▼

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 para

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 between 2 and 10; 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.

대표청구항 ▼

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 sai

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 1(t)t=0= [Figure] where d1 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= [Figure] where h1 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 where said change in controller output DDu(t)=[(h)(YSP-y(t)-CPTSADP(t)-CFF1TSADlFF1(t)- CFF2TSADlFF2(t)-CFF3TSADFF3(t)-CSTSADlS(t)- bFF1(t)DFF1(t)-bFF2(t)DFF2(t)-bFF3(t)DFF3(t)-bS(t)Dl(t)]bP-1(t) where (h) is a constant YSP is the set point y(t) is the process value at time t dgbp(t)=CpT(t)[AgdbFFx(t)=CFFxT(t) [AgdbS(t)=CST(t)[Ad1 Dx(t)=FFx(t)-FFx(t-1) 1(t)=proxied stochastic error=(0.5)1(t-1)+(0.5) (estimated error(t), D1s(t)=AD1s(t-1)+b1(t).