Apparatus and methods to build a reliable deep learning controller by imposing model constraints
Deep learning models and other complex models provide accurate representations of complex industrial processes. However, these models often fail to satisfy properties needed for their use in closed loop systems such as Advanced Process Control. In particular, models need to satisfy gain-constraints. Methods and systems embodying the present invention create complex closed-loop compatible models. In one embodiment, a method creates a controller for an industrial process. The method includes accessing a model of an industrial process and receiving indication of at least one constraint. The method further includes constructing and solving an objective function based on at least one constraint and the model of the industrial process. The solution of the objective function defines a modified model of the industrial process that satisfies the received constraint and can be used to create a closed-loop controller to control the industrial process.
1. A method of creating a controller for an industrial process, the method comprising:
accessing a model of an industrial process, the model having been created using operation data of the industrial process;
receiving at least one constraint;
constructing an objective function based on the received at least one constraint and the accessed model of the industrial process, said constructing being performed by a computer processor;
solving, by the computer processor, the objective function at an operating point of the industrial process, results of said solving defining a modified model of the industrial process;
based on the modified model, generating a controller that controls the industrial process;
implementing, using the generated controller, a first move plan for the industrial process from the operating point to an updated operating point;
determining if the updated operating point is outside a threshold;
resolving, based on the determination, the objective function at the updated operating point to create an updated modified model of the industrial process; and
based on the updated modified model updating the controller.
2. The method of claim 1 further comprising;
collecting measurement data of the industrial process at the updated operating point;
comparing the measurement data of the industrial process at the updated operating point to a prediction of the industrial process at the updated operating point generated by the modified model; and
revising, based on the comparison, the updated modified model.
3. The method of claim 1 further comprising:
generating a linear dynamic model of the industrial process localized at the operating point and derived from the modified model; and
wherein the controller utilizes the linear dynamic model to control the industrial process.
4. The method of claim 1 wherein the controller is configured for closed-loop control of the industrial process.
5. The method of claim 4 wherein the controller is further configured for Model Predictive Control of the industrial process.
6. The method of claim 1 wherein the received at least one constraint includes at least one gain constraint.
7. The method of claim 6 wherein the at least one gain constraint includes at least one of steady-state gain monotonicity, steady-state gain ratios, and equality or nonequality linear combination of steady-state gains.
8. The method of claim 1 wherein the accessed model of the industrial process is a deep learning model.
9. The method of claim 8 wherein the deep learning model is a recurrent neural network.
10. The method of claim 1 wherein the industrial process is a nonlinear system.
11. The method of claim 1 wherein the objective function is a quadradic programming problem.
12. The method of claim 11 wherein the quadradic programming problem is:
min
G
(
∂
y
∂
u
)
(
U
c
,
Y
s
)
-
G
where Ys is an output value, Uc is an input value, in the reference point [Uc, Ys] to calculate the derivatives
(
∂
y
∂
u
)
,
and G is gains.
13. The method of claim 11 wherein the quadradic programming problem finds a solution that minimizes deviation from the accessed model of the industrial process while the received at least one constraint is satisfied.
14. A system for controlling an industrial process, the system comprising:
a model of an industrial process, the model having been created using operation data of the industrial process and stored in computer memory;
an input module configured to receive at least one constraint;
a computer processor communicatively coupled to the input module and the computer memory, the computer processor configured to:
construct an objective function based on the received at least one constraint and the model of the industrial process;
solve the objective function at an operating point of the industrial process, results of said solving defining a modified model of the industrial process;
a controller responsive to the computer processor and generated based on the modified model configured to control the industrial process and to implement a first move plan for the industrial process from the operating point to an updated operating point; and
the computer processor is further configured to:
determine if the updated operating point is outside a threshold;
resolve, based on the determination, the objective function at the updated operating point to create an updated modified model of the industrial process; and
update, based on the updated modified model, the controller.
15. The system of claim 14 wherein the computer processor is further configured to compare measurement data of the industrial process at the updated operating point to a prediction of the industrial process at the updated operating point generated by the modified model, and based, on the comparison, revise the updated modified model.
16. The system of claim 14 wherein the computer processor is further configured generate a linear dynamic model of the industrial process localized at the operating point and derived from the modified model and the controller is further configured to utilize the linear dynamic model to control the industrial process.
17. The system of claim 14 wherein the controller is a closed-loop controller.
18. The system of claim 17 wherein the controller is a Model Predictive Control controller.
19. The system of claim 14 the received at least one constraint includes at least one gain constraint.
20. The system of claim 19 wherein the at least one gain constraint includes at least one of steady-state gain monotonicity, steady-state gain ratios, and equality or nonequality linear combination of steady-state gains.
21. The system of claim 19 the model of the industrial process is a deep learning model.
22. The system of claim 21 the deep learning model is a recurrent neural network.
23. The system of claim 14 wherein the industrial process is a nonlinear system.
24. The system of claim 14 wherein the objective function is a quadradic programming problem.
25. The system of claim 24 wherein the quadradic programming problem is:
min
G
(
∂
y
∂
u
)
(
U
c
,
Y
s
)
-
G
where Ys is an output value, Uc is an input value, in the reference point [Uc, Ys] to calculate the derivatives
(
∂
y
∂
u
)
,
and G is gains.
26. The system of claim 25 wherein the quadradic programming problem finds a solution that minimizes deviation from the model of the industrial process while the received at least one constraint is satisfied.
27. A non-transitory computer-readable data storage medium comprising instructions to cause a computer to:
access a model of an industrial process, the model having been created using operation data of the industrial process;
receive at least one constraint;
construct an objective function based on the received at least one constraint and the accessed model of the industrial process, said constructing being performed by a computer processor;
solve the objective function at an operating point of the industrial process, results of said solving defining a modified model of the industrial process;
based on the modified model, generate a controller that controls the industrial process;
implement, using the generated controller, a first move plan for the industrial process from the operating point to an updated operating point;
determine if the updated operating point is outside a threshold;
resolve, based on the determination, the objective function at the updated operating point to create an updated modified model of the industrial process; and
based on the updated modified model update the controller.