Using model predictive control to optimize variable trajectories and system control

• US 8,504,175 B2
• Filed: 06/02/2010
• Issued: 08/06/2013
• Est. Priority Date: 06/02/2010
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Pekar, Jaroslav, Stewart, Gregory E.
• Primary Examiner(s): Ali, Mohammad
• Assistant Examiner(s): Booker, Kelvin
• Application Number:
  US12/792,468
• Publication Number:
  US 20110301723A1
• Time in Patent Office: 1,161 Days
• Field of Search: 700/18, 700/19, 700/29, 700/28, 700/12, 700/14, 700/16, 706/12, 706/14, 706/16, 703/2
• US Class Current: 700/29
• CPC Class Codes: B60W 10/06 including control of combustion engines
• CPC Class Codes: F02D 2041/1412 using a predictive controller
• CPC Class Codes: F02D 2041/1433 using a model or simulation of the system
• CPC Class Codes: F02D 41/0002 Controlling intake air
• CPC Class Codes: F02D 41/0007 for control of turbo-charged or super-charged engines control of the pumps per se F02B37/12
• CPC Class Codes: F02D 41/0072 Estimating, calculating or determining the EGR rate, amount or flow sensors in EGR systems F02M26/45
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05B 19/042 using digital processors G05B19/05 takes precedence
• CPC Class Codes: G05B 19/05 Programmable logic controllers, e.g. simulating logic interconnections of signals according to ladder diagrams or function charts

Abstract

A method and system of predictive model control of a controlled system with one or more physical components using a model predictive control (MPC) model, determining an iterative, finite horizon optimization of a system model of the controlled system, in order to generate a manipulated value trajectory as part of a control process. At time t sampling a current state of the controlled system a cost function minimizing manipulated variables trajectories is computed with the MPC model for a relatively short time horizon in the future, wherein the MPC uses a quadratic programming (QP) algorithm to find the optimal solution, and wherein the QP algorithm is solved using an Active Sets solver (AS) class algorithm with simple constraints based on gradient projection and using Newton step projection. A move of the manipulated value trajectory is implemented and the control process is moved forward by continuing to shift the prediction horizon forward.

Claims

1. A method of model predictive control of a controlled system with one or more physical components, comprising:

at time t, sampling a current state of the controlled system and computing a cost function minimizing manipulated variables trajectories with a model predictive control (MPC) for a time horizon in the future, wherein the MPC uses a quadratic programming (QP) algorithm to find an optimal solution, and wherein the QP algorithm is solved using an Active Sets solver (AS) class algorithm with constraints based on gradient projection and using Newton step projection, wherein the Newton step projection facilitates adding of multiple constraints to a working set in each iteration of the algorithm, to facilitate identification of a set of active constraints in the optimal solution;

implementing a move of the manipulated variables trajectories including outputting a control signal for directly or indirectly controlling the controlled system; and

shifting the prediction horizon forward in time and repeating the sampling, computing and implementing steps.

2. A method according to claim 1, wherein the active set class algorithm uses constraints for optimization variables, wherein the constraints are defined as the lower and upper bounds.

3. A method according to claim 1, further wherein the gradient projection facilitates adding or removing of multiple constraints from a working set in each iteration of the algorithm, to facilitate identification of a set of active constraints in the optimal solution.

4. A controller used to control a controlled system with one or more physical components, comprising:

a computer system including one or more computer programs operative on the computer system to;

at time t, sample a current state of the controlled system and compute a cost function minimizing manipulated variables trajectories with a model predictive control (MPC) model for a time horizon in the future, wherein the MPC uses a quadratic programming (QP) algorithm to find an improved solution, and wherein the QP algorithm is solved using an Active Sets solver (AS) class algorithm with constraints based on gradient projection and using Newton step projection, wherein the Newton step projection facilitates adding of multiple constraints to a working set in each iteration of the algorithm, to facilitate identification of a set of active constraints in the improved solution; and

implement a move of the manipulated variables trajectories by outputting a control signal used directly or indirectly to control the controlled system.

5. A controller according to claim 4, wherein the active set class algorithm uses constraints for optimization variables wherein the constraints are defined as the lower and upper bounds.

6. A controller according to claim 4, further wherein the gradient projection facilitates adding or removing of multiple constraints from a working set in each iteration of the algorithm, to facilitate identification of a set of active constraints in the improved solution.

7. A controller used to control an automotive system with one or more physical components, wherein the automotive system comprises one or more automotive components requiring control, the controller comprising:

a computer system including one or more computer programs operative on the computer system to;

at time t, sampling a current state of the one or more automotive components and computing a cost function minimizing manipulated variables trajectories with a model predictive control (MPC) for a time horizon in the future, wherein the MPC uses a quadratic programming (QP) algorithm to find an improved solution, and wherein the QP algorithm is solved using an Active Sets solver (AS) class algorithm with constraints based on gradient projection and using Newton step projection, wherein the Newton step projection facilitates adding of multiple constraints to a working set in each iteration of the algorithm, to facilitate identification of a set of active constraints in the improved solution using a specified maximum number of iterations; and

implementing a move of the manipulated variables trajectories to facilitate control the one or more automotive components.

8. A controller according to claim 7, wherein the AS class algorithm uses constraints for optimization variables wherein the constraints are defined as the lower and upper bounds.

9. A controller according to claim 7, further wherein the gradient projection facilitates adding or removing of multiple constraints from a working set in each iteration of the algorithm, to facilitate identification of a set of active constraints in the improved solution.

10. A controller according to claim 7, further including moving the control horizon forward and repeating the determination of manipulated variable trajectories.

11. A controller for an automotive system, comprising:

processing unit;

an input for providing one or more controlled variables (CV) and/or disturbance variables (DV) to the processing unit from the automotive system;

an output for providing one or more manipulated variables (MV) from the processing unit to the automotive system, wherein each of the one or more manipulated variables (MV) have a trajectory;

the processing unit configured to sample one or more of the one or more controlled variables (CV) and/or disturbance variables (DV) at a time “

t”

, and to compute a cost function minimizing the trajectory of one or more of the manipulated variables (MV) using a model predictive control (MPC) over a time horizon, wherein the MPC uses a quadratic programming (QP) algorithm that has constraints and that uses Newton step projection, wherein the Newton step projection facilitates adding of multiple constraints to a working set in each iteration of the algorithm, to facilitate identification of a set of active constraints in an improved solution using a specified maximum number of iterations; and

implementing a move of the trajectory of one or more of the manipulated variables (MV), and outputting one or more of the manipulated variables (MV) to the automotive system.

12. A controller according to claim 11, wherein the quadratic programming (QP) algorithm is solved using an Active Sets solver (AS) class algorithm with constraints based on gradient projection and using Newton step projection.

13. A controller according to claim 11, wherein the processing unit shifts the time horizon forward, and then samples one or more of the one or more controlled variables (CV) and/or disturbance variables (DV) a second time.

14. A controller according to claim 11, wherein after the processing unit samples one or more of the one or more controlled variables (CV) and/or disturbance variables (DV) for the second time, the processing unit again compute a cost function minimizing the trajectory of one or more of the manipulated variables (MV) using a model predictive control (MPC) over a time horizon, and implements a move of the trajectory of one or more of the manipulated variables (MV), and further outputs one or more of the manipulated variables (MV) to the automotive system.

15. A controller according to claim 11, wherein the automotive system includes one or more actuators, and wherein the one or more manipulated variables (MV) are used to control one or more of the actuators.

16. A controller according to claim 15, wherein one or more of the actuators include an EGR valve.

17. A controller according to claim 15, wherein one or more of the actuators include an Waste Gate Valve.