Enhanced Single-Loop Control Strategies

1. Enhanced Single-Loop Control Strategies Chapter 16

2. Chapter 16

3. Chapter 16

4. Cascade Control (multi-loop) • Distinguishing features: • Two FB controllers but only a single control valve (or other -final control element). • 2. Output signal of the "master" controller is the set-point for “slave" controller. • 3. Two FB control loops are "nested" with the "slave" (or "secondary") control loop inside the "master" (or "primary") control loop. • Terminology • slave vs. master • secondary vs. primary • inner vs. outer Chapter 16

5. Chapter 16 (Eq.16-5)

6. Chapter 16

7. Chapter 16

8. Time Delay Compensation • Model-based feedback controller that improves closed-loop performance when time delays are present • Effect of added time delay on PI controller performance for a second order process (t1 = 3, t2 = 5) shown below Chapter 16

9. Chapter 16

10. Chapter 16 No model error: (sensitive to model errors > +/- 20%)

11. Chapter 16

12. Direct Synthesis Approach (Smith Predictor) Assume time delay between set-point change and controlled variable (same as process time delay,  ). If Chapter 16 then From Block Diagram, Equating...

13. Chapter 16

14. SELECTIVE CONTROL SYSTEMS (Overrides) For every controlled variable, there must be at least one manipulated variable. In some applications # of manipulated variables # of controlled variables Chapter 16 • Low selector: • High selector:

15. Chapter 16 Figure 16.13 Control of a reactor hot spot temperature by using a high selector. multiple measurements one controller one final control element

16. Chapter 16 Figure 16.15 A selective control system to handle a sand/water slurry. Override using PI controllers - “old way” (vs. digital logic) 2 measurements, 2 controllers, 1 F.C.E.

17. Split Range Control Chapter 16 2 Manipulated Vars.: V1 and V2 1 Controlled Var.: Reactor pressure While V1 opens, V2 should close

18. Chapter 16 3 6 9 15

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20. Inferential Control • Problem: Controlled variable cannot be measured or has large sampling period. • Possible solutions: • Control a related variable (e.g., temperature instead of composition). • Inferential control: Control is based on an estimate of the controlled variable. • The estimate is based on available measurements. • Examples: empirical relation, Kalman filter • Modern term: soft sensor Chapter 16

21. Chapter 16

22. Gain Scheduling • Objective:Make the closed-loop system as linear as possible. • Basic Idea: Adjust the controller gain based on current measurements of a “scheduling variable”, e.g., u, y, or some other variable. Chapter 16 • Note: Requires knowledge about how the process gain changes with this measured variable.

23. Examples of Gain Scheduling • Example 1. Once through boiler The open-loop step response are shown in Fig. 16.18 for two different feedwater flow rates. Chapter 16 Fig. 16.18 Open-loop responses. • Proposed control strategy: Vary controller setting with w, the fraction of full-scale (100%) flow. • Example 2. Titration curve for a strong acid-strong base neutralization.

26. Adaptive Control • A general control strategy for control problems where the process or operating conditions can change significantly and unpredictably. Example: Catalyst decay, equipment fouling • Many different types of adaptive control strategies have been proposed. • Self-Tuning Control (STC): • A very well-known strategy and probably the most widely used adaptive control strategy. • Basic idea: STC is a model-based approach. As process conditions change, update the model parameters by using least squares estimation and recent u & y data. • Note: For predictable or measurable changes, use gain scheduling instead of adaptive control Reason: Gain scheduling is much easier to implement and less trouble prone. Chapter 16

27. Chapter 16 16-26

28. Chapter 16 Figure 16.20 Membership functions for room temperature.

29. Chapter 16 Figure 16.23 Membership functions for the inputs of the PI fuzzy controller (N is negative, P is positive, and Z is zero).

30. Chapter 16

31. Chapter 16

32. Basic Design Information Chapter 16

33. Final Control System – Distillation Train Chapter 16

34. Chapter 16 Previous chapter Next chapter