Constrained dynamic inversion control algorithm

• US 20040107013A1
• Filed: 12/02/2002
• Published: 06/03/2004
• Est. Priority Date: 12/02/2002
• Status: Active Grant

• Current Assignee: Rtx Corporation
• Original Assignee: United Technologies Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Fuller, James W., Das, Indraneel
• Granted Patent: US 6,882,889 B2
• Time in Patent Office: Days
• US Class Current: 700/53
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

A method of controlling a multivariable system includes the step of receiving a plurality of sensor signals indicating current conditions of the system and receiving a plurality of commands. The desired dynamic response of the system is then determined based upon the commands and the sensor signals. The problem of controlling the system to achieve the desired dynamic response without violating numerous actuator and physical constraints is then formulated as a quadratic programming problem. By solving the quadratic programming problem, the effector commands are determined and the physical constraints are enforced.

Claims

1. A method for controlling a multivariable system including the steps of:

a) Receiving a plurality of sensor signals indicating current conditions of the system which are periodically sampled;

b) Receiving a plurality of commands;

c) Determining a desired change in output of the system for one or more of the next controller samples based upon the commands and sensor signals;

d) Formulating the problem of determining actuator commands to achieve the change in output by the next, and possibly more future, controller sample(s) as a quadratic programming problem that includes physical limits or constraints of the system.

2. The method of claim 1 wherein said step c) further includes the steps of

i) determining how a nominal system output should change in the next controller sample(s),

ii) specifying the physical limits and constraints, and

iii) determining the actuator commands to achieve the change.

3. The method of claim 1 further including the step of formulating the problem into a part that determines how a nominal system output should change in the next controller sample(s), a part that determines the physical limits and constraints, and a part that determines the actuator commands to achieve this change.

4. The method of claim 3 wherein the change requested for the next controller sample(s) is specified in terms of the next output value, a rate of change of the output, acceleration of the output, or net generalized forces and moments on the system.

5. The method of claim 3 wherein the part determining the change in system output required in the next controller sample(s), in order to achieve a desired command response trajectory, is accomplished using feedback control, feedforward control, explicit use of a model of the desired response or neural networks.

6. The method of claim 1 where the physical constraints are directly included.

7. The method of claim 1 where the physical constraints are indirectly included in terms of the fastest approach to the limit allowed.

8. The method of claim 1 further including the step of:

e) Solving the quadratic programming problem to determine effector commands that will cause the desired change in output.

9. The method of claim 8 wherein the desired dynamic response includes a plurality of outputs.

10. The method of claim 9 wherein the multivariable system is cross-coupled, such that each of the effector commands affects each of the outputs.

11. The method of claim 10 further including the step of enforcing effector limits.

12. The method of claim 11 further including the step of enforcing a prioritization of the commands and physical limits, when not all commands can be accomplished.

13. The method of claim 12 wherein the step of enforcing a prioritization further including the step of turning off feedback control integrators associated with lower priority outputs, if all outputs can not be achieved.

14. The method of claim 13 wherein the step of enforcing a prioritization further includes setting higher performance index weights on the desired change of the higher priority outputs.

15. The method of claim 14 further including the step of changing a weighting on each of a plurality of the commands and physical limits.

16. A multivariable control system comprising:

A plurality of sensors indicating current conditions of the system;

A plurality of effectors for changing dynamics of the system in response to effector commands;

A dynamic feed forward module determining how a system output should change in a subsequent sample period;

A dynamic model follower comparing desired and actual system responses to adjust an output change command as needed; and

A dynamic inversion module determining the effector commands in order to change the system output based upon the determination by the dynamic feed forward and dynamic model follower modules by formulating a quadratic programming problem to include physical limits of the system.

17. The multivariable control system of claim 16 wherein the dynamic inversion module includes a constraint formation module for transforming the physical limits of the system into a form of linear inequality equations with the effector commands as independent variables.

18. The method of claim 17 where the linear inequality equations express the fastest dynamic trajectory with which the limits can be approached.

19. The multivariable system of claim 16 wherein the dynamic inversion module further includes a performance index formation module formulating the effector commands as a solution of the quadratic programming problem.

20. The multivariable system of claim 19 wherein the dynamic inversion block further includes a quadratic programming block for solving the quadratic optimization problem and linear equality equations.

21. A computer readable medium storing a computer program, which when executed by a computer performs the steps of:

a) Receiving a plurality of sensor signals indicating current conditions of the system which are periodically sampled;

b) Receiving a plurality of commands;

c) Determining a desired change in output of the system for a next controller sample based upon the commands and sensor signals;

d) Formulating the problem of determining actuator commands to achieve the change in output by the next controller sample as a quadratic programming problem that includes physical limits or constraints of the system.

22. The computer readable medium of claim 21 which when executed by a computer further performs the steps of solving the quadratic programming problem to determine effector commands.

23. The computer readable medium of claim 22 wherein the desired dynamic response includes a plurality of outputs.

24. The computer readable medium of claim 23 wherein the multivariable system is cross-coupled, such that each of the effector commands affects each of the outputs.

25. The computer readable medium of claim 24 further performing the step of enforcing effector limits.

26. The computer readable medium of claim 25 further performing the step of prioritizing the commands and physical limits.

27. The computer readable medium of claim 26 further performing the step of changing a weighting on each of a plurality of the commands and physical limits.