1. K.N.Toosi University of Technology 1

2. 2  The Interconnection of two subsystems

3. K.N.Toosi University of Technology 3  The Control Problems IdentificationInstabilityRHP-zero Time delay

4. K.N.Toosi University of Technology 4 MIMO Flow-Level Plant Identification ِDecentralized Controller Limitations in designing Design the Robust Controller Conclusion

5. K.N.Toosi University of Technology 5  The flow System The flow valve has integrator property Valve positionControl Signal

6. K.N.Toosi University of Technology 6  Static Identification and The Operating points of system

7. K.N.Toosi University of Technology 7  The Identification of a MIMO system should be achieved in MISO form If the RGA matrix become If change to The system will be singular

8. K.N.Toosi University of Technology 8  The most important problems for Identification The excitation signals  should be Uncorrelated  Should be distinguished from output noise  Should not have the frequencies much more than actuator bandwidths  Should have a big range of frequency  Are Chirp in our experiments The sample time is selected by these rules ; ; or to

9. K.N.Toosi University of Technology 9  Input and output : The transfer function  The effect of input flow on output level is small The Disturbance model  This model has no delay

10. K.N.Toosi University of Technology 10  Nominal model Nominal Model for MIMO system The system with parametric Uncertainty : Converting to unstructured uncertainty

11. K.N.Toosi University of Technology 11  Scaling The scaling transfer function of plant and disturbance The requirement for performance is |e(ω)| ≤1 for |u(ω)| ≤1, |r(ω)| ≤1 and |d(ω)| ≤1 The max value of control signal 20 in flow Ch, and 30 in level Ch.

12. K.N.Toosi University of Technology 12 MIMO Flow-Level Plant Identification ِDecentralized Controller Limitations in designing Design the Robust Controller Conclusion

13. K.N.Toosi University of Technology 13  Decentralized Control Based on Passive Approaches Pairing  Avoid the Pairing correspond to negative RGA elements  The pairing is selected with RGA closed to unity Design controller for each subsystem  There is no specific approach for this problem Good phase and gain margin for each subsystem of nominal model PI controller for each channel

14. K.N.Toosi University of Technology 14  Stability Of Decentralized Control A system with decentralized controller is stable if  Each subsystem is stable√  Following inequality holds Where and is the complementary sensitivity function of If The system is closed two triangular this condition is always hold √

15. K.N.Toosi University of Technology 15  Performance Of Decentralized Control Desired performance by disturbance input ×  is the element of CLDG Matrix :, Desired performance by reference input  Where is the element of PRGA matrix × Flow Level

16. K.N.Toosi University of Technology 16  Step Response Of Decentralized Controller Settling time after 300 (sec) in both channels × The control signal is in its range √ The inverse response of non-minimum phase zero √ Overshoot is about 50% in flow ch. × And less than 5% in level ch. √ step response Control Signal

17. K.N.Toosi University of Technology 17  Disturbance Rejection of Decentralized Controller Disturbance rejection after 170 (sec) in flow Ch. √ and 400 in level ch. × The control signal is in its range. √ The maximum peak of response about 25(lit/h) in flow ch. × and about 6.4(cm) in level ch. √ Disturbance responseControl Signal

18. K.N.Toosi University of Technology 18 MIMO Flow-Level Plant Identification ِDecentralized Controller Limitations in designing Design the Robust Controller Conclusion

19. K.N.Toosi University of Technology 19  Uncertainty and Robustness Uncertainty in Input or in output The uncertainty is much more in MIMO systems Uncertainty can be parametric or unstructured Input Uncertainty Output Uncertainty

20. K.N.Toosi University of Technology 20  Nominal Model and Multiplicative uncertainty Z=1.7363  Output Multiplicative Model: is the upper band of and

21. K.N.Toosi University of Technology 21  The model Validation In MIMO systems the gain of system varies between minimum and maximum singular value The gain of system also can vary because of uncertainty

22. K.N.Toosi University of Technology 22  Limitations on Performance In MIMO systems each element has its own direction such as RHP-zero, RHP-Pole, disturbance, uncertainty and etc. Effect of zero in MIMO system There is no effect with input

23. K.N.Toosi University of Technology 23  Waterbed Effect Theorem In SISO systems In MIMO systems No special conclusion for MIMO system

24. K.N.Toosi University of Technology 24  Limitation on S and T S+T=I  We can not decrease both S and T Simultaneously  S is large if and only if T is large

25. K.N.Toosi University of Technology 25  Interpolation Condition for internal stability for the plant with no RHP- Pole and one RHP-zero This condition restricts the performance weight

26. K.N.Toosi University of Technology 26  Limitations By Time Delay In SISO systems delay will limit the bandwidth In MIMO systems, every output has at least the minimum delay of the elements in its own row Delay may improve performance of MIMO systems

27. K.N.Toosi University of Technology 27 By using Maximum Modules Theorem For tight control at low frequencies For tight control at high frequencies  Limitation By RHP zero

28. K.N.Toosi University of Technology 28  Maximum Peak Criterion The growing on maximum peak of sensitivity function cause to big overshoot M more than 2 is not suitable The phase margin of system will decrease

29. K.N.Toosi University of Technology 29  Limitations Caused By Uncertainty In each channel for robust performance it is required

30. K.N.Toosi University of Technology 30  Limitations Caused By Disturbance Condition number of plant and disturbance A single disturbance with one RHP-zero  is the output direction of RHP-zero Another condition Flow Level

31. K.N.Toosi University of Technology 31  The Selection of Performance Weight Performance weight in each channel  The selection of bandwidth  Restricting the overshoot

32. K.N.Toosi University of Technology 32  The selection of Control Signal Weight Performance Weight

33. K.N.Toosi University of Technology 33 MIMO Flow-Level Plant Identification ِDecentralized Controller Limitations in designing Design the Robust Controller Conclusion

34. K.N.Toosi University of Technology 34  Robust Control Design Integrator in each element of controller The order of controller is equal to the states of generalized plant order The controller is obtained by iterative algorithm

35. K.N.Toosi University of Technology 35  The Generalized Plant in Our Problem The infinity norm by considering disturbance in SISO system Without consideration of disturbance

36. K.N.Toosi University of Technology 36  The Step Responses Settling time 150 (sec) in both channels without considering disturbance √ and 200 in second one. √ The control signal is in its range √ The inverse response of non-minimum phase zero √ The Overshoot is about 15% in flow ch. √ and 5% in level ch. √ without considering disturbance and more than 40% for the controller by considering disturbance × Step response Control signal

37. K.N.Toosi University of Technology 37  Disturbance Responses Disturbance rejection after 170 (sec) for both controllers in flow ch.× Disturbance rejection after 200 (sec) in controller with disturbance model and 300(sec) in another controller in level ch.× The control signal is in its range. √ The maximum peak of response 20(lit/h) in flow ch. with disturbance model× and 25(lit/h) for another controller √ The maximum peak of response 5.7(cm) √ and about 7(cm) for another controller in level ch.. √

38. K.N.Toosi University of Technology 38  Robust Stability Robust Design Robust Stability Nominal Performance Robust Performance  Sufficient Condition  Necessary and Sufficient Condition

39. K.N.Toosi University of Technology 39  Nominal Performance Robust Design Robust Stability Nominal Performanc e Robust Performanc e

40. K.N.Toosi University of Technology 40  Robust Performance Robust Design Robust Stability Nominal Performanc e Robust Performanc e

41. K.N.Toosi University of Technology 41  µ-Analysis for Robust Performance Max Peak of µ:  1.2544 without disturbance model  1.7257 in second design  Too large for Decentralized controller

42. K.N.Toosi University of Technology 42  μ-Synthesis and DK-Iteration Step K: Design a controller such that Step D: Find D such that following equation become minimum

43. K.N.Toosi University of Technology 43  Step Response for DK-Iteration Controller Settling time after 150 (sec) in both channels The control signal is in its range√ The inverse response of non-minimum phase zero × The overshoot less than 15% in both outputs √ The order of controller is 16

44. K.N.Toosi University of Technology 44  Disturbance Response for DK-Iteration Controller Disturbance rejection after 200 in flow Ch. √ and 300 in level channel × The control signal is in its range √ The maximum peak of response 13(lit/h) in flow ch. √ and 5.7(cm) in level ch. √ Step ResponseControl Signal

45. K.N.Toosi University of Technology 45  Controller Order Reduction Coprime factorization Balanced Residualization Balanced Truncation Level Flow

46. K.N.Toosi University of Technology 46 MIMO Flow-Level Plant Identification ِDecentralized Controller Limitations in designing Design the Robust Controller Conclusion

47. K.N.Toosi University of Technology 47  Conclusions: Decentralized controller for the systems with small interactions and performance is not important problem Obtaining uncertainty for MIMO systems is hard Useful tool for analyzing the robustness of the system The induced norm can represent many properties of system High order of H∞ controller and order reduction

48. K.N.Toosi University of Technology 48  Suggestions: The position of valve(3) can be rearranged to have more interaction Designing controller by considering input uncertainty Designed Controller by considering parametric uncertainty The structure of controller can be determined by designer The QFT robust controller combined with decentralized control approaches. Fetching control signal in saturation

49. K.N.Toosi University of Technology 49 Acknowledgment