Comparison of Feedback Controller for Link Stabilizing Units of the Laser Based Synchronization System used at the European XFEL

Size: px
Start display at page:

Download "Comparison of Feedback Controller for Link Stabilizing Units of the Laser Based Synchronization System used at the European XFEL"

Transcription

1 Comparison of Feedback Controller for Link Stabilizing Units of the Laser Based Synchronization System used at the European XFEL M. Heuer 1 G. Lichtenberg 2 S. Pfeiffer 1 H. Schlarb 1 1 Deutsches Elektronen Synchrotron Hamburg, Germany MOCZB3 2 Hamburg University of Applied Sciences, Germany International Beam Instrumentation Conference /09/15 Logovarianten und Mindestabstand 01 HAW Logo

2 Contents 1 Introduction 2 Link Stabilizing Unit 3 Introduction to Control 4 Implementation and Experimental Results 5 Conclusion and Outlook M. Heuer et al. 2014/08/28 Page 2/30

3 Contents 1 Introduction 2 Link Stabilizing Unit 3 Introduction to Control 4 Implementation and Experimental Results 5 Conclusion and Outlook M. Heuer et al. 2014/08/28 Page 3/30

4 Introduction LSU Control Experiments Conclusion European X-ray Free Electron Laser (XFEL) Idea I Build a Camera to capture ultrafast processes in an atomic scale I E.g.: Make a movie of the folding process of biomolecules Some Numbers I Wavelength of 0.05 to 6 nm, Pulse duration of less than 100 fs (10 15 ) I Total facility length of 3.4 km with 101 accelerator modules Courtesy of M. Heuer et al. 2014/08/28 Page 4/30

5 Laser Based Synchronization System (LbSynch) Gun e I0 BAM BAM I39H A1.M{1,2,3,4} A25.M{1,2,3,4} BAM Undulator Experiment Injector Laser Pump Probe Laser System M. Heuer et al. 2014/08/28 Page 5/30

6 Laser Based Synchronization System (LbSynch) Gun e I0 BAM BAM I39H A1.M{1,2,3,4} A25.M{1,2,3,4} BAM Undulator Experiment Injector Laser L2RF L2RF L2RF L2RF Pump Probe Laser System MLO M. Heuer et al. 2014/08/28 Page 5/30

7 Laser Based Synchronization System (LbSynch) Gun e I0 BAM BAM I39H A1.M{1,2,3,4} A25.M{1,2,3,4} BAM Undulator Experiment Injector Laser L2RF L2RF L2RF L2RF Pump Probe Laser System MLO L L L I(t), I(l) k + 1 k k 1 k 2 x(k + 1) = 0 x(k) > 0 x(k 2) < 0 t, l M. Heuer et al. 2014/08/28 Page 5/30

8 Laser Based Synchronization System (LbSynch) Gun e I0 BAM BAM I39H A1.M{1,2,3,4} A25.M{1,2,3,4} BAM Undulator Experiment Injector Laser L2RF L2RF L2RF L2RF Pump Probe Laser System LSU LSU LSU LSU LSU LSU LSU MLO L L L I(t), I(l) k + 1 k k 1 k 2 OXC with Balanced Detector Piezo x(k + 1) = 0 x(k) > 0 x(k 2) < 0 t, l C LSU (s) LSU n x LSU-Control M. Heuer et al. 2014/08/28 Page 5/30

9 Laser Based Synchronization System (LbSynch) Gun e I0 BAM BAM I39H A1.M{1,2,3,4} A25.M{1,2,3,4} BAM Undulator Experiment Injector Laser L2RF L2RF L2RF L2RF Pump Probe Laser System LSU LSU LSU LSU LSU LSU LSU k 1 k 1 MLO L L L I(t), I(l) k + 1 k k 1 k 2 k 1 OXC with Balanced Detector Piezo x(k + 1) = 0 x(k) > 0 x(k 2) < 0 t, l C LSU (s) LSU n x LSU-Control M. Heuer et al. 2014/08/28 Page 5/30

10 Laser Based Synchronization System (LbSynch) Gun e I0 BAM BAM I39H A1.M{1,2,3,4} A25.M{1,2,3,4} BAM Undulator Experiment Injector Laser L2RF L2RF L2RF L2RF Pump Probe Laser System LSU LSU LSU LSU LSU LSU LSU k 1 k 1 MLO L L L I(t), I(l) k + 1 k k 1 k 2 k 1 OXC with Balanced Detector k n k n Piezo x(k + 1) = 0 x(k) > 0 x(k 2) < 0 t, l C LSU (s) LSU n x LSU-Control M. Heuer et al. 2014/08/28 Page 5/30

11 Laser Based Synchronization System (LbSynch) Gun e I0 BAM BAM I39H A1.M{1,2,3,4} A25.M{1,2,3,4} BAM Undulator Experiment Injector Laser L2RF L2RF L2RF L2RF Pump Probe Laser System LSU LSU LSU LSU LSU LSU LSU k 1 k 1 MLO L L L I(t), I(l) k + 1 k k 1 k 2 k 1 OXC with Balanced Detector k n k n Piezo x(k + 1) = 0 x(k) > 0 x(k 2) < 0 t, l C LSU (s) LSU n x LSU-Control M. Heuer et al. 2014/08/28 Page 5/30

12 Laser Based Synchronization System (LbSynch) Requirements The relative jitter between all link ends should be less as possible M. Heuer et al. 2014/08/28 Page 6/30

13 Laser Based Synchronization System (LbSynch) Requirements The relative jitter between all link ends should be less as possible Current State Heuristically tuned PI controller M. Heuer et al. 2014/08/28 Page 6/30

14 Laser Based Synchronization System (LbSynch) Requirements The relative jitter between all link ends should be less as possible Current State Heuristically tuned PI controller New Approach Model based control M. Heuer et al. 2014/08/28 Page 6/30

15 Laser Based Synchronization System (LbSynch) Requirements The relative jitter between all link ends should be less as possible Current State Heuristically tuned PI controller New Approach Model based control 1. Model the dynamics of the system M. Heuer et al. 2014/08/28 Page 6/30

16 Laser Based Synchronization System (LbSynch) Requirements The relative jitter between all link ends should be less as possible Current State Heuristically tuned PI controller New Approach Model based control 1. Model the dynamics of the system 2. Synthesis a suitable controller with this model M. Heuer et al. 2014/08/28 Page 6/30

17 Laser Based Synchronization System (LbSynch) Requirements The relative jitter between all link ends should be less as possible Current State Heuristically tuned PI controller New Approach Model based control 1. Model the dynamics of the system 2. Synthesis a suitable controller with this model 3. Verify the controller performance in an experiment M. Heuer et al. 2014/08/28 Page 6/30

18 Problem Statement M. Heuer et al. 2014/08/28 Page 7/30

19 Problem Statement How to synthesis a model based controller? Has a model based controller a better performance? M. Heuer et al. 2014/08/28 Page 7/30

20 Contents 1 Introduction 2 Link Stabilizing Unit 3 Introduction to Control 4 Implementation and Experimental Results 5 Conclusion and Outlook M. Heuer et al. 2014/08/28 Page 8/30

21 Introduction LSU Control Experiments Conclusion Setup of the Link Stabilizing Unit M. Heuer et al. 2014/08/28 Page 9/30

22 Introduction LSU Control Experiments Conclusion Setup of the Link Stabilizing Unit M. Heuer et al. 2014/08/28 Page 9/30

23 Introduction LSU Control Experiments Conclusion Setup of the Link Stabilizing Unit M. Heuer et al. 2014/08/28 Page 9/30

24 Introduction LSU Control Experiments Conclusion Setup of the Link Stabilizing Unit M. Heuer et al. 2014/08/28 Page 9/30

25 Contents 1 Introduction 2 Link Stabilizing Unit 3 Introduction to Control 4 Implementation and Experimental Results 5 Conclusion and Outlook M. Heuer et al. 2014/08/28 Page 10/30

26 General Control Loop d i d o r e u y Controller System y m n u(t) output voltage applied to the piezo amplifier based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 11/30

27 General Control Loop d i d o r e u y Controller System y m n u(t) output voltage applied to the piezo amplifier y(t) the real timing difference based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 11/30

28 General Control Loop d i d o r e u y Controller System y m n u(t) output voltage applied to the piezo amplifier y(t) the real timing difference y m (t) = y(t) + n(t) timing difference measured by the OXC based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 11/30

29 General Control Loop d i d o r e u y Controller System y m n u(t) output voltage applied to the piezo amplifier y(t) the real timing difference y m (t) = y(t) + n(t) timing difference measured by the OXC n(t) noise of the balanced detector based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 11/30

30 General Control Loop d i d o r e u y Controller System y m n u(t) output voltage applied to the piezo amplifier y(t) the real timing difference y m (t) = y(t) + n(t) timing difference measured by the OXC n(t) noise of the balanced detector d i (t) input disturbances, e.g. ripple of the piezo amplifier supply based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 11/30

31 General Control Loop d i d o r e u y Controller System y m n u(t) output voltage applied to the piezo amplifier y(t) the real timing difference y m (t) = y(t) + n(t) timing difference measured by the OXC n(t) noise of the balanced detector d i (t) input disturbances, e.g. ripple of the piezo amplifier supply d o (t) output disturbances, e.g. vibrations of the setup based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 11/30

32 General Control Loop d i d o r e u y Controller System y m n based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 12/30

33 General Control Loop d i d o r e u y Controller System y m n T (s) = P (s)c(s) 1+P (s)c(s) based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 12/30

34 General Control Loop d i d o r e u y Controller System y m n T (s) = P (s)c(s) 1+P (s)c(s) high bandwidth controller Tracking of a reference T (s) 1 based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 12/30

35 General Control Loop d i d o r e u y Controller System y m n T (s) = P (s)c(s) 1+P (s)c(s) S(s) = 1 T (s) = 1 1+P (s)c(s) high bandwidth controller Tracking of a reference T (s) 1 based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 12/30

36 General Control Loop d i d o r e u y Controller System y m n T (s) = P (s)c(s) 1+P (s)c(s) S(s) = 1 T (s) = 1 1+P (s)c(s) high bandwidth controller Tracking of a reference T (s) 1 Output Disturbance rejection S(s) 0 T (s) 1 based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 12/30

37 General Control Loop d i d o r e u y Controller System y m n T (s) = P (s)c(s) 1+P (s)c(s) high bandwidth controller Tracking of a reference T (s) 1 Output Disturbance rejection S(s) 0 T (s) 1 S(s) = 1 T (s) = 1 1+P (s)c(s) high bandwidth controller System output due to noisy measurements T (s) 0 based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 12/30

38 General Control Loop d i d o r e u y Controller System y m n T (s) = P (s)c(s) 1+P (s)c(s) high bandwidth controller Tracking of a reference T (s) 1 Output Disturbance rejection S(s) 0 T (s) 1 S(s) = 1 T (s) = 1 1+P (s)c(s) high bandwidth controller System output due to noisy measurements T (s) 0 Very large controller outputs u(t) based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 12/30

39 State Space Model ẋ(t) =Ax(t) + Bu(t), y(t) =Cx(t) + Du(t), based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 13/30

40 State Space Model ẋ(t) =Ax(t) + Bu(t), y(t) =Cx(t) + Du(t), x(t) states of the system (energy storages) u(t) input to the system y(t) output of the system based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 13/30

41 State Space Model ẋ(t) =Ax(t) + Bu(t), y(t) =Cx(t) + Du(t), x(t) states of the system (energy storages) u(t) input to the system y(t) output of the system A describes the dynamic behavior of the system B describes how the input acts on the state C describes how the state are combined to the output D describes which inputs have a direct influence on the output based on Skogestad and Postlethwaite (2005) M. Heuer et al. 2014/08/28 Page 13/30

42 Model Identification Identification Signal e.g. White Noise, Step,... System Measurement P (s) = Measurement Identification Signal Matlab System Identification Toolbox based on Ljung (1987) M. Heuer et al. 2014/08/28 Page 14/30

43 State Feedback Controller ẋ(t) =Ax(t) + Bu(t), y(t) =Cx(t) + Du(t), based on Zhou et al. (1996) M. Heuer et al. 2014/08/28 Page 15/30

44 State Feedback Controller ẋ(t) =Ax(t) + Bu(t), y(t) =Cx(t) + Du(t), u(t) = F x(t), based on Zhou et al. (1996) M. Heuer et al. 2014/08/28 Page 15/30

45 State Feedback Controller ẋ(t) =Ax(t) + Bu(t), y(t) =Cx(t) + Du(t), min V = 0 u(t) = F x(t), x(t) T Qx(t) + u(t) T Ru(t) dt, based on Zhou et al. (1996) M. Heuer et al. 2014/08/28 Page 15/30

46 State Feedback Controller ẋ(t) =Ax(t) + Bu(t), y(t) =Cx(t) + Du(t), min V = 0 u(t) = F x(t), x(t) T Qx(t) + u(t) T Ru(t) dt, Q and R are tuning parameter. e.g. Q = C T C and tune the response speed with R based on Zhou et al. (1996) M. Heuer et al. 2014/08/28 Page 15/30

47 State Feedback Controller ẋ(t) =Ax(t) + Bu(t), y(t) =Cx(t) + Du(t), min V = 0 u(t) = F x(t), x(t) T Qx(t) + u(t) T Ru(t) dt, Q and R are tuning parameter. e.g. Q = C T C and tune the response speed with R F = -lqr(a,b,c *C,R); based on Zhou et al. (1996) M. Heuer et al. 2014/08/28 Page 15/30

48 State Feedback Controller ẋ(t) =Ax(t) + Bu(t), y(t) =Cx(t) + Du(t), min V = 0 u(t) = F x(t), x(t) T Qx(t) + u(t) T Ru(t) dt, Q and R are tuning parameter. e.g. Q = C T C and tune the response speed with R F = -lqr(a,b,c *C,R); x(t) is not measured in most cases. based on Zhou et al. (1996) M. Heuer et al. 2014/08/28 Page 15/30

49 State Estimation d i d o u System y B 1 s C ỹ A x based on Zhou et al. (1996) M. Heuer et al. 2014/08/28 Page 16/30

50 State Estimation d i d o u System y L B 1 s C ỹ A x based on Zhou et al. (1996) M. Heuer et al. 2014/08/28 Page 16/30

51 State Estimation d i d o u System y L B 1 s C ỹ A x The dual problem to state feedback based on Zhou et al. (1996) M. Heuer et al. 2014/08/28 Page 16/30

52 State Estimation d i d o u System y L B 1 s C ỹ A x The dual problem to state feedback Q obsv and R obsv are again tuning parameter. e.g. Q obsv = B B T and tune the filtering of the noise with R obsv based on Zhou et al. (1996) M. Heuer et al. 2014/08/28 Page 16/30

53 State Estimation d i d o u System y L B 1 s C ỹ A x The dual problem to state feedback Q obsv and R obsv are again tuning parameter. e.g. Q obsv = B B T and tune the filtering of the noise with R obsv L = -lqr(a,c,b*b,robsv); based on Zhou et al. (1996) M. Heuer et al. 2014/08/28 Page 16/30

54 Contents 1 Introduction 2 Link Stabilizing Unit 3 Introduction to Control 4 Implementation and Experimental Results 5 Conclusion and Outlook M. Heuer et al. 2014/08/28 Page 17/30

55 Matlab VHDL Toolbox Extends the Xilinx System Generator Toolbox Automatic code generation from a Simulink model (no VHDL knowledge required) Simulation of the real behavior (saturation, overflow, fixed point precision, etc.) M. Heuer et al. 2014/08/28 Page 18/30

56 Model Identification 0.1 y(t) u(t) y Model (t) Voltage [V] Time [ms] M. Heuer et al. 2014/08/28 Page 19/30

57 Model Identification 0.1 y(t) u(t) y Model (t) Voltage [V] Time [ms] The model fits well to the dynamic behavior of the real plant. M. Heuer et al. 2014/08/28 Page 19/30

58 Identification A = [ ] B = , [ ] C = , M. Heuer et al. 2014/08/28 Page 20/30

59 Effect of State Feedback Voltage [V] y pid (t) u pid (t) y lqg (t) u lqg (t) Time [ms] M. Heuer et al. 2014/08/28 Page 21/30

60 Effect of State Feedback Voltage [V] y pid (t) u pid (t) y lqg (t) u lqg (t) Time [ms] Its possible to change the dynamic behavior e.g. increase the damping. M. Heuer et al. 2014/08/28 Page 21/30

61 Control Startup 0.05 Voltage [V] Time [ms] y pid (t) u pid (t) y lqg (t) u lqg (t) M. Heuer et al. 2014/08/28 Page 22/30

62 Control Startup 0.05 Voltage [V] Time [ms] y pid (t) u pid (t) y lqg (t) u lqg (t) The model based controller reaches the steady state faster... M. Heuer et al. 2014/08/28 Page 22/30

63 Dynamic behavior of an input disturbances 10 2 Voltage [V] 5 0 y pid (t) u pid (t) y lqg (t) u lqg (t) Time [ms] M. Heuer et al. 2014/08/28 Page 23/30

64 Dynamic behavior of an input disturbances 10 2 Voltage [V] 5 0 y pid (t) u pid (t) y lqg (t) u lqg (t) Time [ms]... and rejects disturbances much better than the PID controller. M. Heuer et al. 2014/08/28 Page 23/30

65 Dynamic behavior of a coarse tuning step 10 2 Voltage [V] 5 0 y pid (t) u pid (t) y lqg (t) u lqg (t) Time [ms] M. Heuer et al. 2014/08/28 Page 24/30

66 Dynamic behavior of a coarse tuning step 10 2 Voltage [V] 5 0 y pid (t) u pid (t) y lqg (t) u lqg (t) Time [ms] Effects measurable with PID controller but not with LQG. M. Heuer et al. 2014/08/28 Page 24/30

67 Contents 1 Introduction 2 Link Stabilizing Unit 3 Introduction to Control 4 Implementation and Experimental Results 5 Conclusion and Outlook M. Heuer et al. 2014/08/28 Page 25/30

68 Statements M. Heuer et al. 2014/08/28 Page 26/30

69 Statements Use model based control approaches to a better performance M. Heuer et al. 2014/08/28 Page 26/30

70 Statements Use model based control approaches to a better performance It is possible to achieve good control results for the LSU with a LQG controller M. Heuer et al. 2014/08/28 Page 26/30

71 Conclusion Conclusion M. Heuer et al. 2014/08/28 Page 27/30

72 Conclusion Conclusion An overview of the LbSynch System was given M. Heuer et al. 2014/08/28 Page 27/30

73 Conclusion Conclusion An overview of the LbSynch System was given It was shown how to synthesis a LQG controller M. Heuer et al. 2014/08/28 Page 27/30

74 Conclusion Conclusion An overview of the LbSynch System was given It was shown how to synthesis a LQG controller The design controller was tested in an experimental setup M. Heuer et al. 2014/08/28 Page 27/30

75 Conclusion Conclusion An overview of the LbSynch System was given It was shown how to synthesis a LQG controller The design controller was tested in an experimental setup Outlook M. Heuer et al. 2014/08/28 Page 27/30

76 Conclusion Conclusion An overview of the LbSynch System was given It was shown how to synthesis a LQG controller The design controller was tested in an experimental setup Outlook Test other model based controller types M. Heuer et al. 2014/08/28 Page 27/30

77 Conclusion Conclusion An overview of the LbSynch System was given It was shown how to synthesis a LQG controller The design controller was tested in an experimental setup Outlook Test other model based controller types Include new MicroTCA boards and the final configuration M. Heuer et al. 2014/08/28 Page 27/30

78 Final Slide References Additional Slides The End Thank you very much for your attention M. Heuer et al. 2014/08/28 Page 28/30

79 Final Slide References Additional Slides Further Reading L. Ljung. System identification: theory for the user. Prentice-Hall information and system sciences series. Prentice-Hall, ISBN URL S. Skogestad and I. Postlethwaite. Multivariable Feedback Control - Analysis and Design. John Wiley & Sons, Ltd, 2nd edition, ISBN K. Zhou, J.C. Doyle, and K. Glover. Robust and Optimal Control. Feher/Prentice Hall Digital and. Prentice Hall, ISBN URL M. Heuer et al. 2014/08/28 Page 29/30

80 Final Slide References Additional Slides LQR via algebraic riccati equation min V = ẋ(t) =Ax(t) + Bu(t), y(t) =Cx(t) + Du(t), 0 u(t) = F x(t), x(t) T Qx(t) + u(t) T Ru(t) dt, F = R 1 B T P A T P + P A P BR 1 B T P + Q = 0 M. Heuer et al. 2014/08/28 Page 30/30

MATH4406 (Control Theory) Unit 1: Introduction Prepared by Yoni Nazarathy, July 21, 2012

MATH4406 (Control Theory) Unit 1: Introduction Prepared by Yoni Nazarathy, July 21, 2012 MATH4406 (Control Theory) Unit 1: Introduction Prepared by Yoni Nazarathy, July 21, 2012 Unit Outline Introduction to the course: Course goals, assessment, etc... What is Control Theory A bit of jargon,

More information

Pole placement control: state space and polynomial approaches Lecture 2

Pole placement control: state space and polynomial approaches Lecture 2 : state space and polynomial approaches Lecture 2 : a state O. Sename 1 1 Gipsa-lab, CNRS-INPG, FRANCE Olivier.Sename@gipsa-lab.fr www.gipsa-lab.fr/ o.sename -based November 21, 2017 Outline : a state

More information

Lecture 9. Introduction to Kalman Filtering. Linear Quadratic Gaussian Control (LQG) G. Hovland 2004

Lecture 9. Introduction to Kalman Filtering. Linear Quadratic Gaussian Control (LQG) G. Hovland 2004 MER42 Advanced Control Lecture 9 Introduction to Kalman Filtering Linear Quadratic Gaussian Control (LQG) G. Hovland 24 Announcement No tutorials on hursday mornings 8-9am I will be present in all practical

More information

Control Systems Lab - SC4070 Control techniques

Control Systems Lab - SC4070 Control techniques Control Systems Lab - SC4070 Control techniques Dr. Manuel Mazo Jr. Delft Center for Systems and Control (TU Delft) m.mazo@tudelft.nl Tel.:015-2788131 TU Delft, February 16, 2015 (slides modified from

More information

EXPERIMENTAL MODAL ANALYSIS OF AN ACTIVELY CONTROLLED SCALED METRO VEHICLE CAR BODY

EXPERIMENTAL MODAL ANALYSIS OF AN ACTIVELY CONTROLLED SCALED METRO VEHICLE CAR BODY ICSV14 Cairns Australia 9-12 July, 2007 EXPERIMENTAL MODAL ANALYSIS OF AN ACTIVELY CONTROLLED SCALED METRO VEHICLE CAR BODY S. Popprath 1, A. Schirrer 2 *, C. Benatzky 2, M. Kozek 2, J. Wassermann 1 1

More information

Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process

Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process D.Angeline Vijula #, Dr.N.Devarajan * # Electronics and Instrumentation Engineering Sri Ramakrishna

More information

Control Systems Design

Control Systems Design ELEC4410 Control Systems Design Lecture 18: State Feedback Tracking and State Estimation Julio H. Braslavsky julio@ee.newcastle.edu.au School of Electrical Engineering and Computer Science Lecture 18:

More information

MS-E2133 Systems Analysis Laboratory II Assignment 2 Control of thermal power plant

MS-E2133 Systems Analysis Laboratory II Assignment 2 Control of thermal power plant MS-E2133 Systems Analysis Laboratory II Assignment 2 Control of thermal power plant How to control the thermal power plant in order to ensure the stable operation of the plant? In the assignment Production

More information

Linear System Theory. Wonhee Kim Lecture 1. March 7, 2018

Linear System Theory. Wonhee Kim Lecture 1. March 7, 2018 Linear System Theory Wonhee Kim Lecture 1 March 7, 2018 1 / 22 Overview Course Information Prerequisites Course Outline What is Control Engineering? Examples of Control Systems Structure of Control Systems

More information

Structural System Identification (KAIST, Summer 2017) Lecture Coverage:

Structural System Identification (KAIST, Summer 2017) Lecture Coverage: Structural System Identification (KAIST, Summer 2017) Lecture Coverage: Lecture 1: System Theory-Based Structural Identification Lecture 2: System Elements and Identification Process Lecture 3: Time and

More information

Jitter measurement by electro-optical sampling

Jitter measurement by electro-optical sampling Jitter measurement by electro-optical sampling VUV-FEL at DESY - Armin Azima S. Duesterer, J. Feldhaus, H. Schlarb, H. Redlin, B. Steffen, DESY Hamburg K. Sengstock, Uni Hamburg Adrian Cavalieri, David

More information

Lecture 7 : Generalized Plant and LFT form Dr.-Ing. Sudchai Boonto Assistant Professor

Lecture 7 : Generalized Plant and LFT form Dr.-Ing. Sudchai Boonto Assistant Professor Dr.-Ing. Sudchai Boonto Assistant Professor Department of Control System and Instrumentation Engineering King Mongkuts Unniversity of Technology Thonburi Thailand Linear Quadratic Gaussian The state space

More information

Course Outline. Higher Order Poles: Example. Higher Order Poles. Amme 3500 : System Dynamics & Control. State Space Design. 1 G(s) = s(s + 2)(s +10)

Course Outline. Higher Order Poles: Example. Higher Order Poles. Amme 3500 : System Dynamics & Control. State Space Design. 1 G(s) = s(s + 2)(s +10) Amme 35 : System Dynamics Control State Space Design Course Outline Week Date Content Assignment Notes 1 1 Mar Introduction 2 8 Mar Frequency Domain Modelling 3 15 Mar Transient Performance and the s-plane

More information

Topic # Feedback Control Systems

Topic # Feedback Control Systems Topic #20 16.31 Feedback Control Systems Closed-loop system analysis Bounded Gain Theorem Robust Stability Fall 2007 16.31 20 1 SISO Performance Objectives Basic setup: d i d o r u y G c (s) G(s) n control

More information

Process Modelling, Identification, and Control

Process Modelling, Identification, and Control Jan Mikles Miroslav Fikar 2008 AGI-Information Management Consultants May be used for personal purporses only or by libraries associated to dandelon.com network. Process Modelling, Identification, and

More information

EE C128 / ME C134 Feedback Control Systems

EE C128 / ME C134 Feedback Control Systems EE C128 / ME C134 Feedback Control Systems Lecture Additional Material Introduction to Model Predictive Control Maximilian Balandat Department of Electrical Engineering & Computer Science University of

More information

Research Article. World Journal of Engineering Research and Technology WJERT.

Research Article. World Journal of Engineering Research and Technology WJERT. wjert, 2015, Vol. 1, Issue 1, 27-36 Research Article ISSN 2454-695X WJERT www.wjert.org COMPENSATOR TUNING FOR DISTURBANCE REJECTION ASSOCIATED WITH DELAYED DOUBLE INTEGRATING PROCESSES, PART I: FEEDBACK

More information

EL2520 Control Theory and Practice

EL2520 Control Theory and Practice EL2520 Control Theory and Practice Lecture 8: Linear quadratic control Mikael Johansson School of Electrical Engineering KTH, Stockholm, Sweden Linear quadratic control Allows to compute the controller

More information

Update on and the Issue of Circularly-Polarized On-Axis Harmonics

Update on and the Issue of Circularly-Polarized On-Axis Harmonics Update on FERMI@Elettra and the Issue of Circularly-Polarized On-Axis Harmonics W. Fawley for the FERMI Team Slides courtesy of S. Milton & Collaborators The FERMI@Elettra Project FERMI@Elettra is a single-pass

More information

MODERN CONTROL DESIGN

MODERN CONTROL DESIGN CHAPTER 8 MODERN CONTROL DESIGN The classical design techniques of Chapters 6 and 7 are based on the root-locus and frequency response that utilize only the plant output for feedback with a dynamic controller

More information

Computer Problem 1: SIE Guidance, Navigation, and Control

Computer Problem 1: SIE Guidance, Navigation, and Control Computer Problem 1: SIE 39 - Guidance, Navigation, and Control Roger Skjetne March 12, 23 1 Problem 1 (DSRV) We have the model: m Zẇ Z q ẇ Mẇ I y M q q + ẋ U cos θ + w sin θ ż U sin θ + w cos θ θ q Zw

More information

Overview of the Seminar Topic

Overview of the Seminar Topic Overview of the Seminar Topic Simo Särkkä Laboratory of Computational Engineering Helsinki University of Technology September 17, 2007 Contents 1 What is Control Theory? 2 History

More information

ROBUST CONTROL OF A FLEXIBLE MANIPULATOR ARM: A BENCHMARK PROBLEM. Stig Moberg Jonas Öhr

ROBUST CONTROL OF A FLEXIBLE MANIPULATOR ARM: A BENCHMARK PROBLEM. Stig Moberg Jonas Öhr ROBUST CONTROL OF A FLEXIBLE MANIPULATOR ARM: A BENCHMARK PROBLEM Stig Moberg Jonas Öhr ABB Automation Technologies AB - Robotics, S-721 68 Västerås, Sweden stig.moberg@se.abb.com ABB AB - Corporate Research,

More information

FEL3210 Multivariable Feedback Control

FEL3210 Multivariable Feedback Control FEL3210 Multivariable Feedback Control Lecture 8: Youla parametrization, LMIs, Model Reduction and Summary [Ch. 11-12] Elling W. Jacobsen, Automatic Control Lab, KTH Lecture 8: Youla, LMIs, Model Reduction

More information

Linear Control Systems

Linear Control Systems Linear Control Systems Project session 3: Design in state-space 6 th October 2017 Kathleen Coutisse kathleen.coutisse@student.ulg.ac.be 1 Content 1. Closed loop system 2. State feedback 3. Observer 4.

More information

Opportunities and Challenges for X

Opportunities and Challenges for X Opportunities and Challenges for X -ray Free Electron Lasers for X-ray Ultrafast Science J. Hastings Stanford Linear Accelerator Center June 22, 2004 European XFEL Laboratory How Short is short? defined

More information

FLASH/DESY, Hamburg. Jörg Rossbach University of Hamburg & DESY, Germany - For the FLASH Team -

FLASH/DESY, Hamburg. Jörg Rossbach University of Hamburg & DESY, Germany - For the FLASH Team - First Lasing below 7nm Wavelength at FLASH/DESY, Hamburg Jörg Rossbach University of Hamburg & DESY, Germany - For the FLASH Team - email: joerg.rossbach@desy.de FLASH: The first FEL user facility for

More information

Robust LQR Control Design of Gyroscope

Robust LQR Control Design of Gyroscope Robust LQR Control Design of Gyroscope Ashok S. Chandak 1, Anil J. Patil 2 Abstract The basic problem in designing control systems is the ability to achieve good performance in the presence of uncertainties

More information

Integral action in state feedback control

Integral action in state feedback control Automatic Control 1 in state feedback control Prof. Alberto Bemporad University of Trento Academic year 21-211 Prof. Alberto Bemporad (University of Trento) Automatic Control 1 Academic year 21-211 1 /

More information

Modeling and Model Predictive Control of Nonlinear Hydraulic System

Modeling and Model Predictive Control of Nonlinear Hydraulic System Modeling and Model Predictive Control of Nonlinear Hydraulic System Petr Chalupa, Jakub Novák Department of Process Control, Faculty of Applied Informatics, Tomas Bata University in Zlin, nám. T. G. Masaryka

More information

LINEAR QUADRATIC GAUSSIAN

LINEAR QUADRATIC GAUSSIAN ECE553: Multivariable Control Systems II. LINEAR QUADRATIC GAUSSIAN.: Deriving LQG via separation principle We will now start to look at the design of controllers for systems Px.t/ D A.t/x.t/ C B u.t/u.t/

More information

Harmonic Lasing Self-Seeded FEL

Harmonic Lasing Self-Seeded FEL Harmonic Lasing Self-Seeded FEL E. Schneidmiller and M. Yurkov FEL seminar, DESY Hamburg June 21, 2016 In a planar undulator (K ~ 1 or K >1) the odd harmonics can be radiated on-axis (widely used in SR

More information

Chapter 3. LQ, LQG and Control System Design. Dutch Institute of Systems and Control

Chapter 3. LQ, LQG and Control System Design. Dutch Institute of Systems and Control Chapter 3 LQ, LQG and Control System H 2 Design Overview LQ optimization state feedback LQG optimization output feedback H 2 optimization non-stochastic version of LQG Application to feedback system design

More information

Experimental Optimization of Electron Beams for Generating THz CTR and CDR with PITZ

Experimental Optimization of Electron Beams for Generating THz CTR and CDR with PITZ Experimental Optimization of Electron Beams for Generating THz CTR and CDR with PITZ Introduction Outline Optimization of Electron Beams Calculations of CTR/CDR Pulse Energy Summary & Outlook Prach Boonpornprasert

More information

Robust Control. 2nd class. Spring, 2018 Instructor: Prof. Masayuki Fujita (S5-303B) Tue., 17th April, 2018, 10:45~12:15, S423 Lecture Room

Robust Control. 2nd class. Spring, 2018 Instructor: Prof. Masayuki Fujita (S5-303B) Tue., 17th April, 2018, 10:45~12:15, S423 Lecture Room Robust Control Spring, 2018 Instructor: Prof. Masayuki Fujita (S5-303B) 2nd class Tue., 17th April, 2018, 10:45~12:15, S423 Lecture Room 2. Nominal Performance 2.1 Weighted Sensitivity [SP05, Sec. 2.8,

More information

Chapter 13 Digital Control

Chapter 13 Digital Control Chapter 13 Digital Control Chapter 12 was concerned with building models for systems acting under digital control. We next turn to the question of control itself. Topics to be covered include: why one

More information

Linear State Feedback Controller Design

Linear State Feedback Controller Design Assignment For EE5101 - Linear Systems Sem I AY2010/2011 Linear State Feedback Controller Design Phang Swee King A0033585A Email: king@nus.edu.sg NGS/ECE Dept. Faculty of Engineering National University

More information

DECENTRALIZED PI CONTROLLER DESIGN FOR NON LINEAR MULTIVARIABLE SYSTEMS BASED ON IDEAL DECOUPLER

DECENTRALIZED PI CONTROLLER DESIGN FOR NON LINEAR MULTIVARIABLE SYSTEMS BASED ON IDEAL DECOUPLER th June 4. Vol. 64 No. 5-4 JATIT & LLS. All rights reserved. ISSN: 99-8645 www.jatit.org E-ISSN: 87-395 DECENTRALIZED PI CONTROLLER DESIGN FOR NON LINEAR MULTIVARIABLE SYSTEMS BASED ON IDEAL DECOUPLER

More information

DC-motor PID control

DC-motor PID control DC-motor PID control This version: November 1, 2017 REGLERTEKNIK Name: P-number: AUTOMATIC LINKÖPING CONTROL Date: Passed: Chapter 1 Introduction The purpose of this lab is to give an introduction to

More information

Reduced Basis Method for Parametric

Reduced Basis Method for Parametric 1/24 Reduced Basis Method for Parametric H 2 -Optimal Control Problems NUMOC2017 Andreas Schmidt, Bernard Haasdonk Institute for Applied Analysis and Numerical Simulation, University of Stuttgart andreas.schmidt@mathematik.uni-stuttgart.de

More information

Contents. PART I METHODS AND CONCEPTS 2. Transfer Function Approach Frequency Domain Representations... 42

Contents. PART I METHODS AND CONCEPTS 2. Transfer Function Approach Frequency Domain Representations... 42 Contents Preface.............................................. xiii 1. Introduction......................................... 1 1.1 Continuous and Discrete Control Systems................. 4 1.2 Open-Loop

More information

SPPS: The SLAC Linac Bunch Compressor and Its Relevance to LCLS

SPPS: The SLAC Linac Bunch Compressor and Its Relevance to LCLS LCLS Technical Advisory Committee December 10-11, 2001. SPPS: The SLAC Linac Bunch Compressor and Its Relevance to LCLS Patrick Krejcik LCLS Technical Advisory Committee Report 1: July 14-15, 1999 The

More information

START-TO-END SIMULATIONS FOR IR/THZ UNDULATOR RADIATION AT PITZ

START-TO-END SIMULATIONS FOR IR/THZ UNDULATOR RADIATION AT PITZ Proceedings of FEL2014, Basel, Switzerland MOP055 START-TO-END SIMULATIONS FOR IR/THZ UNDULATOR RADIATION AT PITZ P. Boonpornprasert, M. Khojoyan, M. Krasilnikov, F. Stephan, DESY, Zeuthen, Germany B.

More information

Robust H Control of a Scanning Tunneling Microscope under Parametric Uncertainties

Robust H Control of a Scanning Tunneling Microscope under Parametric Uncertainties 2010 American Control Conference Marriott Waterfront, Baltimore, MD, USA June 30-July 02, 2010 FrC08.6 Robust H Control of a Scanning Tunneling Microscope under Parametric Uncertainties Irfan Ahmad, Alina

More information

Design Methods for Control Systems

Design Methods for Control Systems Design Methods for Control Systems Maarten Steinbuch TU/e Gjerrit Meinsma UT Dutch Institute of Systems and Control Winter term 2002-2003 Schedule November 25 MSt December 2 MSt Homework # 1 December 9

More information

Modeling of Hydraulic Control Valves

Modeling of Hydraulic Control Valves Modeling of Hydraulic Control Valves PETR CHALUPA, JAKUB NOVAK, VLADIMIR BOBAL Department of Process Control, Faculty of Applied Informatics Tomas Bata University in Zlin nam. T.G. Masaryka 5555, 76 1

More information

Automatic Control II Computer exercise 3. LQG Design

Automatic Control II Computer exercise 3. LQG Design Uppsala University Information Technology Systems and Control HN,FS,KN 2000-10 Last revised by HR August 16, 2017 Automatic Control II Computer exercise 3 LQG Design Preparations: Read Chapters 5 and 9

More information

CHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING. Professor Dae Ryook Yang

CHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING. Professor Dae Ryook Yang CHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING Professor Dae Ryook Yang Spring 2018 Dept. of Chemical and Biological Engineering 11-1 Road Map of the Lecture XI Controller Design and PID

More information

Department of Electronics and Instrumentation Engineering M. E- CONTROL AND INSTRUMENTATION ENGINEERING CL7101 CONTROL SYSTEM DESIGN Unit I- BASICS AND ROOT-LOCUS DESIGN PART-A (2 marks) 1. What are the

More information

5. Observer-based Controller Design

5. Observer-based Controller Design EE635 - Control System Theory 5. Observer-based Controller Design Jitkomut Songsiri state feedback pole-placement design regulation and tracking state observer feedback observer design LQR and LQG 5-1

More information

LINEAR-QUADRATIC CONTROL OF A TWO-WHEELED ROBOT

LINEAR-QUADRATIC CONTROL OF A TWO-WHEELED ROBOT Доклади на Българската академия на науките Comptes rendus de l Académie bulgare des Sciences Tome 67, No 8, 2014 SCIENCES ET INGENIERIE Automatique et informatique Dedicated to the 145th Anniversary of

More information

Converter System Modeling via MATLAB/Simulink

Converter System Modeling via MATLAB/Simulink Converter System Modeling via MATLAB/Simulink A powerful environment for system modeling and simulation MATLAB: programming and scripting environment Simulink: block diagram modeling environment that runs

More information

Control Systems II. ETH, MAVT, IDSC, Lecture 4 17/03/2017. G. Ducard

Control Systems II. ETH, MAVT, IDSC, Lecture 4 17/03/2017. G. Ducard Control Systems II ETH, MAVT, IDSC, Lecture 4 17/03/2017 Lecture plan: Control Systems II, IDSC, 2017 SISO Control Design 24.02 Lecture 1 Recalls, Introductory case study 03.03 Lecture 2 Cascaded Control

More information

Fundamentals: Frequency & Time Generation

Fundamentals: Frequency & Time Generation Fundamentals: Frequency & Time Generation Dominik Schneuwly Oscilloquartz SA slide 1 v.1.0 24/10/12 SCDO Content 1. Fundamentals 2. Frequency Generation Atomic Cesium Clock (Cs) Rubidium Oscillator (Rb)

More information

FLASH overview. Nikola Stojanovic. PIDID collaboration meeting, Hamburg,

FLASH overview. Nikola Stojanovic. PIDID collaboration meeting, Hamburg, FLASH overview Nikola Stojanovic PIDID collaboration meeting, Hamburg, 16.12.2011 Outline Overview of the FLASH facility Examples of research at FLASH Nikola Stojanovic PIDID: FLASH overview Hamburg, December

More information

Undulator radiation from electrons randomly distributed in a bunch

Undulator radiation from electrons randomly distributed in a bunch Undulator radiation from electrons randomly distributed in a bunch Normally z el >> N u 1 Chaotic light Spectral property is the same as that of a single electron /=1/N u Temporal phase space area z ~(/

More information

Rozwiązanie zagadnienia odwrotnego wyznaczania sił obciąŝających konstrukcje w czasie eksploatacji

Rozwiązanie zagadnienia odwrotnego wyznaczania sił obciąŝających konstrukcje w czasie eksploatacji Rozwiązanie zagadnienia odwrotnego wyznaczania sił obciąŝających konstrukcje w czasie eksploatacji Tadeusz Uhl Piotr Czop Krzysztof Mendrok Faculty of Mechanical Engineering and Robotics Department of

More information

LQR CONTROL OF LIQUID LEVEL AND TEMPERATURE CONTROL FOR COUPLED-TANK SYSTEM

LQR CONTROL OF LIQUID LEVEL AND TEMPERATURE CONTROL FOR COUPLED-TANK SYSTEM ISSN 454-83 Proceedings of 27 International Conference on Hydraulics and Pneumatics - HERVEX November 8-, Băile Govora, Romania LQR CONTROL OF LIQUID LEVEL AND TEMPERATURE CONTROL FOR COUPLED-TANK SYSTEM

More information

Computer Aided Control Design

Computer Aided Control Design Computer Aided Control Design Project-Lab 3 Automatic Control Basic Course, EL1000/EL1100/EL1120 Revised August 18, 2008 Modified version of laboration developed by Håkan Fortell and Svante Gunnarsson

More information

Matrix Equations in Multivariable Control

Matrix Equations in Multivariable Control Matrix Euations in Multivariable Control OMAN POKOP, JIŘÍ KOBEL Tomas Bata University in Zlín Nám. T.G.M. 5555, 76 Zlín CZECH EPUBLIC prokop@fai.utb.cz http://www.utb.cz/fai Abstract: - The contribution

More information

Simulation of Quadruple Tank Process for Liquid Level Control

Simulation of Quadruple Tank Process for Liquid Level Control Simulation of Quadruple Tank Process for Liquid Level Control Ritika Thusoo 1, Sakshi Bangia 2 1 M.Tech Student, Electronics Engg, Department, YMCA University of Science and Technology, Faridabad 2 Assistant

More information

Control Systems I. Lecture 2: Modeling. Suggested Readings: Åström & Murray Ch. 2-3, Guzzella Ch Emilio Frazzoli

Control Systems I. Lecture 2: Modeling. Suggested Readings: Åström & Murray Ch. 2-3, Guzzella Ch Emilio Frazzoli Control Systems I Lecture 2: Modeling Suggested Readings: Åström & Murray Ch. 2-3, Guzzella Ch. 2-3 Emilio Frazzoli Institute for Dynamic Systems and Control D-MAVT ETH Zürich September 29, 2017 E. Frazzoli

More information

Simulations of the IR/THz Options at PITZ (High-gain FEL and CTR)

Simulations of the IR/THz Options at PITZ (High-gain FEL and CTR) Case Study of IR/THz source for Pump-Probe Experiment at the European XFEL Simulations of the IR/THz Options at PITZ (High-gain FEL and CTR) Introduction Outline Simulations of High-gain FEL (SASE) Simulation

More information

Automatic Control 2. Loop shaping. Prof. Alberto Bemporad. University of Trento. Academic year

Automatic Control 2. Loop shaping. Prof. Alberto Bemporad. University of Trento. Academic year Automatic Control 2 Loop shaping Prof. Alberto Bemporad University of Trento Academic year 21-211 Prof. Alberto Bemporad (University of Trento) Automatic Control 2 Academic year 21-211 1 / 39 Feedback

More information

Discussion on: Measurable signal decoupling with dynamic feedforward compensation and unknown-input observation for systems with direct feedthrough

Discussion on: Measurable signal decoupling with dynamic feedforward compensation and unknown-input observation for systems with direct feedthrough Discussion on: Measurable signal decoupling with dynamic feedforward compensation and unknown-input observation for systems with direct feedthrough H.L. Trentelman 1 The geometric approach In the last

More information

ELEC4631 s Lecture 2: Dynamic Control Systems 7 March Overview of dynamic control systems

ELEC4631 s Lecture 2: Dynamic Control Systems 7 March Overview of dynamic control systems ELEC4631 s Lecture 2: Dynamic Control Systems 7 March 2011 Overview of dynamic control systems Goals of Controller design Autonomous dynamic systems Linear Multi-input multi-output (MIMO) systems Bat flight

More information

Experimental Measurements of the ORION Photoinjector Drive Laser Oscillator Subsystem

Experimental Measurements of the ORION Photoinjector Drive Laser Oscillator Subsystem Experimental Measurements of the ORION Photoinjector Drive Laser Oscillator Subsystem D.T Palmer and R. Akre Laser Issues for Electron RF Photoinjectors October 23-25, 2002 Stanford Linear Accelerator

More information

Start-to-End Simulations

Start-to-End Simulations AKBP 9.3 Case Study for 100 µm SASE FEL Based on PITZ Accelerator for Pump-Probe Experiment at the European XFEL Start-to-End Simulations Outline Introduction Beam Optimization Beam Transport Simulation

More information

APPLICATION OF D-K ITERATION TECHNIQUE BASED ON H ROBUST CONTROL THEORY FOR POWER SYSTEM STABILIZER DESIGN

APPLICATION OF D-K ITERATION TECHNIQUE BASED ON H ROBUST CONTROL THEORY FOR POWER SYSTEM STABILIZER DESIGN APPLICATION OF D-K ITERATION TECHNIQUE BASED ON H ROBUST CONTROL THEORY FOR POWER SYSTEM STABILIZER DESIGN Amitava Sil 1 and S Paul 2 1 Department of Electrical & Electronics Engineering, Neotia Institute

More information

Tracking of Spread Spectrum Signals

Tracking of Spread Spectrum Signals Chapter 7 Tracking of Spread Spectrum Signals 7. Introduction As discussed in the last chapter, there are two parts to the synchronization process. The first stage is often termed acquisition and typically

More information

A brief introduction to robust H control

A brief introduction to robust H control A brief introduction to robust H control Jean-Marc Biannic System Control and Flight Dynamics Department ONERA, Toulouse. http://www.onera.fr/staff/jean-marc-biannic/ http://jm.biannic.free.fr/ European

More information

CHAPTER 6 FAST OUTPUT SAMPLING CONTROL TECHNIQUE

CHAPTER 6 FAST OUTPUT SAMPLING CONTROL TECHNIQUE 80 CHAPTER 6 FAST OUTPUT SAMPLING CONTROL TECHNIQUE 6.1 GENERAL In this chapter a control strategy for hyperthermia system is developed using fast output sampling feedback control law which is a type of

More information

YTÜ Mechanical Engineering Department

YTÜ Mechanical Engineering Department YTÜ Mechanical Engineering Department Lecture of Special Laboratory of Machine Theory, System Dynamics and Control Division Coupled Tank 1 Level Control with using Feedforward PI Controller Lab Date: Lab

More information

Pierre Bigot 2 and Luiz C. G. de Souza 3

Pierre Bigot 2 and Luiz C. G. de Souza 3 INTERNATIONAL JOURNAL OF SYSTEMS APPLICATIONS, ENGINEERING & DEVELOPMENT Volume 8, 2014 Investigation of the State Dependent Riccati Equation (SDRE) adaptive control advantages for controlling non-linear

More information

EECE Adaptive Control

EECE Adaptive Control EECE 574 - Adaptive Control Overview Guy Dumont Department of Electrical and Computer Engineering University of British Columbia Lectures: Thursday 09h00-12h00 Location: PPC 101 Guy Dumont (UBC) EECE 574

More information

IMC based automatic tuning method for PID controllers in a Smith predictor configuration

IMC based automatic tuning method for PID controllers in a Smith predictor configuration Computers and Chemical Engineering 28 (2004) 281 290 IMC based automatic tuning method for PID controllers in a Smith predictor configuration Ibrahim Kaya Department of Electrical and Electronics Engineering,

More information

AN ULTRA-HIGH RESOLUTION PULSED-WIRE MAGNET MEASUREMENT SYSTEM. Alex D Audney Thesis Defense Colorado State University

AN ULTRA-HIGH RESOLUTION PULSED-WIRE MAGNET MEASUREMENT SYSTEM. Alex D Audney Thesis Defense Colorado State University AN ULTRA-HIGH RESOLUTION PULSED-WIRE MAGNET MEASUREMENT SYSTEM Alex D Audney Thesis Defense Colorado State University 1 Overview Introduction and Background Pulsed-Wire Method Overview The CSU Undulator

More information

Modelling the Temperature Changes of a Hot Plate and Water in a Proportional, Integral, Derivative Control System

Modelling the Temperature Changes of a Hot Plate and Water in a Proportional, Integral, Derivative Control System Modelling the Temperature Changes of a Hot Plate and Water in a Proportional, Integral, Derivative Control System Grant Hutchins 1. Introduction Temperature changes in a hot plate heating system can be

More information

Linear Quadratic Gausssian Control Design with Loop Transfer Recovery

Linear Quadratic Gausssian Control Design with Loop Transfer Recovery Linear Quadratic Gausssian Control Design with Loop Transfer Recovery Leonid Freidovich Department of Mathematics Michigan State University MI 48824, USA e-mail:leonid@math.msu.edu http://www.math.msu.edu/

More information

Modeling and Control Overview

Modeling and Control Overview Modeling and Control Overview D R. T A R E K A. T U T U N J I A D V A N C E D C O N T R O L S Y S T E M S M E C H A T R O N I C S E N G I N E E R I N G D E P A R T M E N T P H I L A D E L P H I A U N I

More information

MRAGPC Control of MIMO Processes with Input Constraints and Disturbance

MRAGPC Control of MIMO Processes with Input Constraints and Disturbance Proceedings of the World Congress on Engineering and Computer Science 9 Vol II WCECS 9, October -, 9, San Francisco, USA MRAGPC Control of MIMO Processes with Input Constraints and Disturbance A. S. Osunleke,

More information

First operation of a Harmonic Lasing Self-Seeded FEL

First operation of a Harmonic Lasing Self-Seeded FEL First operation of a Harmonic Lasing Self-Seeded FEL E. Schneidmiller and M. Yurkov ICFA workshop, Arcidosso, Italy, 22.09.2017 Outline Harmonic lasing Harmonic lasing self-seeded (HLSS) FEL Experiments

More information

Extensions and applications of LQ

Extensions and applications of LQ Extensions and applications of LQ 1 Discrete time systems 2 Assigning closed loop pole location 3 Frequency shaping LQ Regulator for Discrete Time Systems Consider the discrete time system: x(k + 1) =

More information

Lecture 12. Upcoming labs: Final Exam on 12/21/2015 (Monday)10:30-12:30

Lecture 12. Upcoming labs: Final Exam on 12/21/2015 (Monday)10:30-12:30 289 Upcoming labs: Lecture 12 Lab 20: Internal model control (finish up) Lab 22: Force or Torque control experiments [Integrative] (2-3 sessions) Final Exam on 12/21/2015 (Monday)10:30-12:30 Today: Recap

More information

Lecture 4 Continuous time linear quadratic regulator

Lecture 4 Continuous time linear quadratic regulator EE363 Winter 2008-09 Lecture 4 Continuous time linear quadratic regulator continuous-time LQR problem dynamic programming solution Hamiltonian system and two point boundary value problem infinite horizon

More information

Fall 線性系統 Linear Systems. Chapter 08 State Feedback & State Estimators (SISO) Feng-Li Lian. NTU-EE Sep07 Jan08

Fall 線性系統 Linear Systems. Chapter 08 State Feedback & State Estimators (SISO) Feng-Li Lian. NTU-EE Sep07 Jan08 Fall 2007 線性系統 Linear Systems Chapter 08 State Feedback & State Estimators (SISO) Feng-Li Lian NTU-EE Sep07 Jan08 Materials used in these lecture notes are adopted from Linear System Theory & Design, 3rd.

More information

Simulation Study on Pressure Control using Nonlinear Input/Output Linearization Method and Classical PID Approach

Simulation Study on Pressure Control using Nonlinear Input/Output Linearization Method and Classical PID Approach Simulation Study on Pressure Control using Nonlinear Input/Output Linearization Method and Classical PID Approach Ufuk Bakirdogen*, Matthias Liermann** *Institute for Fluid Power Drives and Controls (IFAS),

More information

Developments for the FEL user facility

Developments for the FEL user facility Developments for the FEL user facility J. Feldhaus HASYLAB at DESY, Hamburg, Germany Design and construction has started for the FEL user facility including the radiation transport to the experimental

More information

The output voltage is given by,

The output voltage is given by, 71 The output voltage is given by, = (3.1) The inductor and capacitor values of the Boost converter are derived by having the same assumption as that of the Buck converter. Now the critical value of the

More information

D(s) G(s) A control system design definition

D(s) G(s) A control system design definition R E Compensation D(s) U Plant G(s) Y Figure 7. A control system design definition x x x 2 x 2 U 2 s s 7 2 Y Figure 7.2 A block diagram representing Eq. (7.) in control form z U 2 s z Y 4 z 2 s z 2 3 Figure

More information

Dr Ian R. Manchester

Dr Ian R. Manchester Week Content Notes 1 Introduction 2 Frequency Domain Modelling 3 Transient Performance and the s-plane 4 Block Diagrams 5 Feedback System Characteristics Assign 1 Due 6 Root Locus 7 Root Locus 2 Assign

More information

Identification and Control of Mechatronic Systems

Identification and Control of Mechatronic Systems Identification and Control of Mechatronic Systems Philadelphia University, Jordan NATO - ASI Advanced All-Terrain Autonomous Systems Workshop August 15 24, 2010 Cesme-Izmir, Turkey Overview Mechatronics

More information

Lecture 1: Feedback Control Loop

Lecture 1: Feedback Control Loop Lecture : Feedback Control Loop Loop Transfer function The standard feedback control system structure is depicted in Figure. This represend(t) n(t) r(t) e(t) u(t) v(t) η(t) y(t) F (s) C(s) P (s) Figure

More information

CDS 101/110a: Lecture 8-1 Frequency Domain Design

CDS 101/110a: Lecture 8-1 Frequency Domain Design CDS 11/11a: Lecture 8-1 Frequency Domain Design Richard M. Murray 17 November 28 Goals: Describe canonical control design problem and standard performance measures Show how to use loop shaping to achieve

More information

Control Systems Lab - SC4070 System Identification and Linearization

Control Systems Lab - SC4070 System Identification and Linearization Control Systems Lab - SC4070 System Identification and Linearization Dr. Manuel Mazo Jr. Delft Center for Systems and Control (TU Delft) m.mazo@tudelft.nl Tel.:015-2788131 TU Delft, February 13, 2015 (slides

More information

Richiami di Controlli Automatici

Richiami di Controlli Automatici Richiami di Controlli Automatici Gianmaria De Tommasi 1 1 Università degli Studi di Napoli Federico II detommas@unina.it Ottobre 2012 Corsi AnsaldoBreda G. De Tommasi (UNINA) Richiami di Controlli Automatici

More information

PID Control. Objectives

PID Control. Objectives PID Control Objectives The objective of this lab is to study basic design issues for proportional-integral-derivative control laws. Emphasis is placed on transient responses and steady-state errors. The

More information

LMI Based Model Order Reduction Considering the Minimum Phase Characteristic of the System

LMI Based Model Order Reduction Considering the Minimum Phase Characteristic of the System LMI Based Model Order Reduction Considering the Minimum Phase Characteristic of the System Gholamreza Khademi, Haniyeh Mohammadi, and Maryam Dehghani School of Electrical and Computer Engineering Shiraz

More information

MaRIE. MaRIE X-Ray Free-Electron Laser Pre-Conceptual Design

MaRIE. MaRIE X-Ray Free-Electron Laser Pre-Conceptual Design Operated by Los Alamos National Security, LLC, for the U.S. Department of Energy MaRIE (Matter-Radiation Interactions in Extremes) MaRIE X-Ray Free-Electron Laser Pre-Conceptual Design B. Carlsten, C.

More information

Department of Electrical and Computer Engineering. EE461: Digital Control - Lab Manual

Department of Electrical and Computer Engineering. EE461: Digital Control - Lab Manual Department of Electrical and Computer Engineering EE461: Digital Control - Lab Manual Winter 2011 EE 461 Experiment #1 Digital Control of DC Servomotor 1 Objectives The objective of this lab is to introduce

More information