US 6,556,980 B1 · Model-free adaptive control for industrial processes

Abstract

An enhanced model-free adaptive controller is disclosed, which consists of a linear dynamic neural network that can be easily configured and put in automatic mode to control simple to complex processes. Two multivariable model-free adaptive controller designs are disclosed. An enhanced anti-delay model-free adaptive controller is introduced to control processes with large time delays. A feedforward/feedback model-free adaptive control system with two designs is introduced to compensate for measurable disturbances.

Claims18

Dependent claims

A controller for a process having a process output which is controlled by a control signal applied to an input of said process, said controller including a neural network comprising:

a) an error input representative of the difference between a predetermined setpoint and said process output;
b) a normalization unit for normalizing said error input to a predetermined range of values;
c) a scaling function for scaling said normalized error input to produce a value E1 of the form E 1 = K c T c ⁢ N ⁡ ( e ⁡ ( t ) ) ,
or an equivalent thereof, in which Kc is the controller gain;
Tc is the user-selected time constant of said process;
N(.) is the normalization function of said normalization unit;
and e(t) is the value of said error input at any given time;
d) a layer of input neurons having as their inputs successively time-delayed values of E1;
e) a layer of hidden neurons each having as its output the sum of individually weighted ones of said successively time-delayed values of E1;
f) an output neuron having as its output the sum of a first function of the individually weighted outputs of said hidden neurons;
and
g) a control output which is at least in part the denormalized value of a second function of the output of said output neuron.

9 more elements

A controller for a process having a process output which is controlled by a control signal applied to an input of said process, said controller comprising:

a) an error input connected to receive an error signal representative of the difference between a predetermined setpoint signal and said process output;
and
b) a dynamic neural network connected to said error input and arranged to produce a control signal for application to said process input, said control signal being such as to cause said process output to change and thereby reduce said error signal;
c) said neural network having a piecewise linear activation function.

1 more element

A multivariable model-free adaptive process control system, comprising:

a) a plurality of processes, each process having a first process output responsive to a control signal and a sub-process having an output which is additively combined with said first process output of another of said plurality of processes to form a second process output of said other process;
b) a plurality of predetermined setpoints;
c) a plurality of controllers;
d) a plurality of compensators;
e) each of said controllers having an iteratively adjusted first control output which is a function of an error signal representing the difference between said second process output of one of said plurality of processes and a corresponding one of said setpoints;
f) each said compensator having said first control output of a corresponding controller as its input, the output of said compensator being additively combined with said first control output of another of said controllers to form a second control output which is the input to one each of said processes and sub-processes;
g) said controllers each including a neural network with an input layer including a plurality of input neurons arranged to receive normalized, scaled and delayed forms of said error signal, a hidden layer including a plurality of hidden neurons each arranged to sum the signals received by each of said input neurons weighted by an individual first weighting factor, and an output neuron which sums the individually weighted outputs of hidden neurons, the weighting factors for said hidden neuron outputs being iteratively adjusted, and an activation function f(x) of the form
f(x)=0, if x<−{fraction (b/a)}
f(x)=ax+b, if −{fraction (b/a)}≦x≦{fraction (b/a)}
f(x)=1, if x>{fraction (b/a)}
or an equivalent thereof, wherein a is an arbitrary constant and b=½;
h) each said controller being arranged to produce a control signal u(n) which is the sum of the output v(n) of said neural network and the outputs of all the compensators associated with the other said controllers, v(n) being of the form v ⁢ ( n ) = K c ⁢ e ⁢ ( n ) + 100 ⁢ [ a ⁢ ∑ j = 1 N ⁢ h j ⁢ ( n ) ⁢ q j ⁢ ( n ) + b ] ,
or an equivalent thereof, in which Kc is the controller gain;
e(n) is said error signal;
hj(n) is the weighting factor for the jth hidden neuron output;
and qj(n) is the jth hidden neuron output;
and
i) each of said compensators having an input-output relationship of the form C ⁢ ( S ) = K s ⁢ K c T c ⁢ ⁢ S + 1 ,
or an equivalent thereof, in which Ks is a sign factor, Kc is the compensator gain, Tc is the compensator time constant, and S is the Laplace transform operator.

16 more elements

A multivariable model-free adaptive predictive control system, comprising:

a) a plurality of processes, each process having a first process output responsive to a control signal and a sub-process having an output which is additively combined with said first process output of another of said plurality of processes to form a second process output of said other process;
b) a plurality of predetermined setpoints;
c) a plurality of controllers;
d) a plurality of predictors;
e) each of said controllers having an iteratively adjusted first control output which is a function of an error signal representing the difference between said second process output of one of said plurality of processes plus the outputs of said plurality of predictors, and a corresponding one of said setpoints;
f) said controllers each including a neural network with an input layer including a plurality of input neurons arranged to receive normalized, scaled and delayed forms of said error signal, a hidden layer including a plurality of hidden neurons each arranged to sum the signals received by each of said input neurons weighted by an individual first weighting factor, and an output neuron which sums the individually weighted outputs of hidden neurons, the weighting factors for said hidden neuron outputs being iteratively adjusted, and an activation function f(x) of the form
f(x)=0, if x<−{fraction (b/a)}
f(x)=ax+b, if −{fraction (b/a)}≦x≦{fraction (b/a)}
f(x)=1, if x>{fraction (b/a)}
or an equivalent thereof, wherein a is an arbitrary constant and b=½;
g) each said controller being arranged to produce a control signal u(n) of the form u ⁢ ( n ) = K c ⁢ e ⁢ ( n ) + 100 ⁢ [ a ⁢ ∑ j = 1 N ⁢ h j ⁢ ( n ) ⁢ q j ⁢ ( n ) + b ] ,
or an equivalent thereof, in which Kc is the controller gain;
e(n) is said error signal;
hj(n) is the weighting factor for the jth hidden neuron output;
and qj(n) is the jth hidden neuron output;
and
h) each of said predictors having as its input the output of a controller associated with another of said predictors, and having an input-output relationship of the form C ⁢ ( S ) = K s ⁢ K c ⁢ ( 1 - 1 T c ⁢ S + 1 ) ,
or an equivalent thereof, in which Ks is a sign factor, Kc is the predictor gain, Tc is the predictor time constant, and S is the Laplace transform operator.

15 more elements

Dependencies are inferred from the claim text.

Abstract

Claims18

Dependent claims

A controller for a process having a process output which is controlled by a control signal applied to an input of said process, said controller including a neural network comprising:

a) an error input representative of the difference between a predetermined setpoint and said process output;
b) a normalization unit for normalizing said error input to a predetermined range of values;
c) a scaling function for scaling said normalized error input to produce a value E1 of the form E 1 = K c T c ⁢ N ⁡ ( e ⁡ ( t ) ) ,
or an equivalent thereof, in which Kc is the controller gain;
Tc is the user-selected time constant of said process;
N(.) is the normalization function of said normalization unit;
and e(t) is the value of said error input at any given time;
d) a layer of input neurons having as their inputs successively time-delayed values of E1;
e) a layer of hidden neurons each having as its output the sum of individually weighted ones of said successively time-delayed values of E1;
f) an output neuron having as its output the sum of a first function of the individually weighted outputs of said hidden neurons;
and
g) a control output which is at least in part the denormalized value of a second function of the output of said output neuron.

9 more elements

A controller for a process having a process output which is controlled by a control signal applied to an input of said process, said controller comprising:

a) an error input connected to receive an error signal representative of the difference between a predetermined setpoint signal and said process output;
and
b) a dynamic neural network connected to said error input and arranged to produce a control signal for application to said process input, said control signal being such as to cause said process output to change and thereby reduce said error signal;
c) said neural network having a piecewise linear activation function.

1 more element

A multivariable model-free adaptive process control system, comprising:

a) a plurality of processes, each process having a first process output responsive to a control signal and a sub-process having an output which is additively combined with said first process output of another of said plurality of processes to form a second process output of said other process;
b) a plurality of predetermined setpoints;
c) a plurality of controllers;
d) a plurality of compensators;
e) each of said controllers having an iteratively adjusted first control output which is a function of an error signal representing the difference between said second process output of one of said plurality of processes and a corresponding one of said setpoints;
f) each said compensator having said first control output of a corresponding controller as its input, the output of said compensator being additively combined with said first control output of another of said controllers to form a second control output which is the input to one each of said processes and sub-processes;
g) said controllers each including a neural network with an input layer including a plurality of input neurons arranged to receive normalized, scaled and delayed forms of said error signal, a hidden layer including a plurality of hidden neurons each arranged to sum the signals received by each of said input neurons weighted by an individual first weighting factor, and an output neuron which sums the individually weighted outputs of hidden neurons, the weighting factors for said hidden neuron outputs being iteratively adjusted, and an activation function f(x) of the form
f(x)=0, if x<−{fraction (b/a)}
f(x)=ax+b, if −{fraction (b/a)}≦x≦{fraction (b/a)}
f(x)=1, if x>{fraction (b/a)}
or an equivalent thereof, wherein a is an arbitrary constant and b=½;
h) each said controller being arranged to produce a control signal u(n) which is the sum of the output v(n) of said neural network and the outputs of all the compensators associated with the other said controllers, v(n) being of the form v ⁢ ( n ) = K c ⁢ e ⁢ ( n ) + 100 ⁢ [ a ⁢ ∑ j = 1 N ⁢ h j ⁢ ( n ) ⁢ q j ⁢ ( n ) + b ] ,
or an equivalent thereof, in which Kc is the controller gain;
e(n) is said error signal;
hj(n) is the weighting factor for the jth hidden neuron output;
and qj(n) is the jth hidden neuron output;
and
i) each of said compensators having an input-output relationship of the form C ⁢ ( S ) = K s ⁢ K c T c ⁢ ⁢ S + 1 ,
or an equivalent thereof, in which Ks is a sign factor, Kc is the compensator gain, Tc is the compensator time constant, and S is the Laplace transform operator.

16 more elements

A multivariable model-free adaptive predictive control system, comprising:

a) a plurality of processes, each process having a first process output responsive to a control signal and a sub-process having an output which is additively combined with said first process output of another of said plurality of processes to form a second process output of said other process;
b) a plurality of predetermined setpoints;
c) a plurality of controllers;
d) a plurality of predictors;
e) each of said controllers having an iteratively adjusted first control output which is a function of an error signal representing the difference between said second process output of one of said plurality of processes plus the outputs of said plurality of predictors, and a corresponding one of said setpoints;
f) said controllers each including a neural network with an input layer including a plurality of input neurons arranged to receive normalized, scaled and delayed forms of said error signal, a hidden layer including a plurality of hidden neurons each arranged to sum the signals received by each of said input neurons weighted by an individual first weighting factor, and an output neuron which sums the individually weighted outputs of hidden neurons, the weighting factors for said hidden neuron outputs being iteratively adjusted, and an activation function f(x) of the form
f(x)=0, if x<−{fraction (b/a)}
f(x)=ax+b, if −{fraction (b/a)}≦x≦{fraction (b/a)}
f(x)=1, if x>{fraction (b/a)}
or an equivalent thereof, wherein a is an arbitrary constant and b=½;
g) each said controller being arranged to produce a control signal u(n) of the form u ⁢ ( n ) = K c ⁢ e ⁢ ( n ) + 100 ⁢ [ a ⁢ ∑ j = 1 N ⁢ h j ⁢ ( n ) ⁢ q j ⁢ ( n ) + b ] ,
or an equivalent thereof, in which Kc is the controller gain;
e(n) is said error signal;
hj(n) is the weighting factor for the jth hidden neuron output;
and qj(n) is the jth hidden neuron output;
and
h) each of said predictors having as its input the output of a controller associated with another of said predictors, and having an input-output relationship of the form C ⁢ ( S ) = K s ⁢ K c ⁢ ( 1 - 1 T c ⁢ S + 1 ) ,
or an equivalent thereof, in which Ks is a sign factor, Kc is the predictor gain, Tc is the predictor time constant, and S is the Laplace transform operator.

15 more elements

Dependencies are inferred from the claim text.

Model-free adaptive control for industrial processes

Life of this patent

Abstract

Claims18

Model-free adaptive control for industrial processes

Life of this patent

Abstract

Claims18