From Dynamic Optimization

Main: Dynamic Control Tuning

Dynamic controller tuning is the process of adjusting certain objective function terms to give more desirable solutions. As an example, a dynamic control application may either exhibit too aggressive manipulated variable movement or be too sluggish during set-point changes. Tuning is the iterative process of finding acceptable values that work over a wide range of operating conditions.

MPC Tuning IPython Widget

Common Tuning Parameters for MPC

Tuning typically involves adjustment of objective function terms or constraints that limit the rate of change (DMAX), penalize the rate of change (DCOST), or set absolute bounds (LOWER and UPPER). Measurement availability is indicated by the parameter (FSTATUS). The optimizer can also include (1=on) or exclude (0=off) a certain manipulated variable (MV) or controlled variable with STATUS. Below are common application, MV, and CV tuning constants that are adjusted to achieve desired model predictive control performance.

There are several ways to change the tuning values. Tuning values can either be specified before an application is initialized or while an application is running. To change a tuning value before the application is loaded, use the apm_option() function such as the following example to change the lower bound in MATLAB or Python for the MV named u.

 apm_option(server,app,'u.LOWER',0)

The upper and lower deadband targets for a CV named y are set to values around a desired set point of 10.0. In this case, an acceptable range for this CV is between 9.5 and 10.5.

 sp = 10.0
 apm_option(server,app,'y.SPHI',sp+0.5)
 apm_option(server,app,'y.SPHI',sp-0.5)

Application constants are modified by indicating that the constant belongs to the group nlc. IMODE is adjusted to either solve the MPC problem with a simultaneous (6) or sequential (9) method. In the case below, the application IMODE is changed to simultaneous mode.

 apm_option(server,app,'nlc.IMODE',6)

Exercise

Objective: Design a model predictive controller with one manipulated variable and two controlled variables with competing objectives that cannot be simultaneously satisfied. Tune the controller to achieve best performance. Estimated time: 2 hours.

Use the following system of linear differential equations for this exercise by placing the model definition in the file myModel.apm.

 Parameters
  u
 Variables
  x
  y
 Equations
  5 * $x = -x + 1.5 * u
  15 * $y = -y + 3.0 * u

In this case, the parameter u is the manipulated variable and x and y are the controlled variables. It is desired to maximize x and maintain values between 9 and 10. It is desired to maintain values of y between 2 and 7. For the first 10 minutes, the priority is to maintain the range for y and following this time period, it is desired to track the range for x. Tune the controller to meet these objectives while minimizing MV movement.

Solution

MPC Tuning Solution
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Page last modified on August 16, 2017, at 04:14 PM