DEDUCED FROM EMR» Prof. A. Bouscayrol (University Lille1, L2EP, MEGEVH, France)

Transcription

1 Aalto University Finland May 2011 «Energy Management of EVs & HEVs using Energetic Macroscopic Representation» «INVERSION-BASED CONTROL DEDUCED FROM EMR» Prof. A. Bouscayrol (University Lille1, L2EP, MEGEVH, France) based on the course of Master Electrical Engineering & Sustainable Development University Lille1 Aalto University

2 Parallel HEV «Inversion-Based Control from EMR» - Objective: example of HEV control - BAT VSI Fuel EM ICE Trans. 2 fast subsystem controls EM control ICE control Trans control slow system supervision Energy management (supervision/strategy) driver request

3 Parallel HEV «Inversion-Based Control from EMR» - Objective: example of HEV control - BAT VSI1 Fuel EM1 ICE EMR Trans. 3 fast subsystem controls How to define control scheme of complex ICE systems? Trans control control (algorithm? sensors?) EM1 control slow system supervision Energy management (supervision/strategy) driver request

4 - Outline - 1. Principle of model-based control Open-loop and closed-loop controls Inversion-based control 4 2. Inversion of EMR elements Inversion of accumulation and conversion elements Inversion of coupling elements 3. Inversion-based control structures Inversion-based methodology Maximum and practical control schemes 4. Conclusion: towards energy management

5 Aalto University Finland May 2011 «Energy Management of EVs & HEVs using Energetic Macroscopic Representation» 1. «Principle of model-based control» Aalto University

6 - Cybernetic systemic approach - or Black box approach: no internal knowledge 6 in out identification test: observation of out(t) from selected in(t) Behavior model: out(t) = f(t) in(t) control out ref closed-loop control of out(t): for uncertainty compensations Limitations: limited validity range of the model risk of physical mistake non-optimal management of internal energy multi-physical complex energetic systems?

7 - Cognitive systemic approach - or White box approach: prior internal knowledge 7 Physical laws of system components Knowledge model: out(t) = f(t) in(t) in out in out control out ref control = inversion of model: (closed loop = an inversion way) Limitations: physical knowledge of each subsystem all functions are not invertible multi-physical complex energetic systems?

8 - Causality principle and control - 8 Principle of causality physical causality is integral input output CONTROL = real-time process management x? cause t effect xdt OK in real-time area knowledge of past evolution t 1 impossible in real-time slope knowledge of future evolution dx dt

9 - Open-loop control and inversion - 9 Controlling a system for output tracking can be interpreted as inverting the system control y ref (t) wished output System -1 (.) u(t) input System y(t) output if we can implement a good approximation of the system s inverse. [Sicard 09]

10 - Principle of closed-loop control - 10 control A closed-loop control is required when: the model is not invertible, the model is ill known or too complex, and for robustness purpose. y ref (t) wished output controller u(t) input System y(t) output [Sicard 09] Controller objectives: tracking of reference changes rejection of disturbances and uncertainties

11 - Principle of Inversion-based methodology - 11 cause input System effect output [Hautier 96] measurements? right cause Control desired effect control = inversion of the causal path 1. Which algorithm? (how many controllers) 2. Which variables to measure? 3. How to tune controllers? 4. How to implement the control? Inversion-based methodology automatic control industrial electronics

12 - EMR and Inversion-based methodology - 12 cause effect input SS 1 SS 2 SS n output measure? measure? measure? right cause C 1 C 2 C n desired effect EMR = system decomposition in basic energetic subsystems (SSs) Remember, divide and conquer! Inversion-based control: systematic inversion of each subsystems using open-loop or closed-loop control

13 - Mirror effect - 13 cause SS 1 SS 2 SS n effect right cause C 1 C 2 C n desired effect The control scheme is developed as a mirror of the model [Hautier 96] Bouscayrol 03]

14 - Mirror effect: example of cascaded control loops - 14 Well-known cascaded control loops can be described in the same may y ref (t) C 2 C 1 u(t) SS 1 SS 2 y(t) u(t) y(t) SS 1 SS 2 C 1 C 2 y ref (t)

15 Aalto University Finland May 2011 «Energy Management of EVs & HEVs using Energetic Macroscopic Representation» 2. «Inversion of EMR elements» Aalto University

16 - Inversion of I/O relationships - 16 in(t) System out(t) measurements? Control in(t) Algorithm? out ref (t) There are 3 basic inversions: other inversion can be deduced from these basic inversions [Barre 06]

17 - Inversion 1: single-input atemporal relationship - output depends on a single input without delay 17 Example: u(t) u(t) K? 1/K y(t) y ref (t) 1. no measurement 2. no controller (open-loop control) y( t) K u( t) direct inversion 1 u( t) yref ( t) K Example: Resistance vt) v(t) 1/R Assumption: K well-know and constant R i(t) i ref (t) 1 i( t) v( t) R direct inversion v( t) R i ( t) ref

18 - Inversion 2: multiple-input atemporal relationship - Output depends on several inputs without delay 18 Example: u 1 (t) + + u 2 (t) y(t) y ( 2 t) u1( t) u ( t) u 1 is chosen to act on the output y u 2 becomes a disturbance input u(t) -? + y ref (t) direct inversion u1( t) yref ( t) u2meas ( t) 1. measurement of the disturbance input 2. no controller (open-loop control) Assumption: u 2 well-know and can be measured

19 - Inversion 2: single-input causal relationship - output depends on a single input and the time (delay) causality principle 19 Example: y( t) u( t)dt u(t) dt y(t) - direct inversion indirect inversion + C(t)? not possible u(t) y ref (t) in real-time d u( t) yref ( t) dt 1. measurement of output u( t) C( t) yref ( t) ymeas ( t) 2. a controller is required (closed-loop control) controller

20 - Example: PM-DC machine - 20 i L m r m multi-input causal relationship u u i e L m di dt u e r m i decomposition u u e U(s) + - E(s) U(s) K 1+ p I(s) L m di dt u r m i U(s) + + U ref (s) C(s) - + I ref (s) direct inversion closed-loop

21 - Inversion of a conversion element (1) - 21 Objective: to control y 2 u 1 y 2 y 1 u 21 y 2 = f(u 1, u 21 ) u 2 Ex : H-bridge chopper u Hb = m Hb V DC i Hb = m Hb i dcm m Hb = u Hb_ref / V DC_meas u 1-meas y 2-ref V DC_meas m Hb 1. Manipulate u 21 u 1 is a disturbance Basic rule: as a first step, compensate all disturbances assuming measurement is available. u Hb_ref

22 - Inversion of a conversion element (2) - 22 Objective: to control y 2 Ex : speed transmission u 1 y 2 y 2 = f(u 1, u 21 ) trans = k trans 1 T trans = k trans T load y 1 u 21 u 2 1_ref = trans_ref / k trans_meas u 1-reg y 2-ref k trans_meas 2. Manipulate u 1 u 21 is a disturbance 1_ref trans_ref

23 - Inversion of a conversion element (3) - 23 Objective: to control y 2 Ex : pulley or roller u 1 y 1 y 2 u 2 y 2 = f(u 1 ) V trans = r pull 1 T trans = r tpull F load 1_ref = trans_ref / r tpull u 1-reg y 2-ref 3. Manipulate u 1 1_ref 1 r tpull V trans_ref

24 - Inversion of an accumulation element - 24 u 1 y 1 Objective: to control y 2 y 2 u 2 y 2 =f(u 1, u 2 ) f is in integral form d J dt Ex : rotating shaft f T T em load u 2-meas u 1-reg Manipulate u 1 y 2-ref y 2-meas Direct inversion is in derivative form Approximate inversion by closed loop control u 2 is a disturbance Basic rule: compensate disturbances or linearize dynamics, then design a controller to obtain the desired dynamics. T em_ref T C( t)( em ref ref meas) _ T load _ meas + T load_meas + C(t) meas - + ref

25 - Example: armature winding of a DC machine - 25 i L m r m u di dt u i e L u e r i m m U(s) + - E(s) U(s) K 1+ p I(s) u i 1 e meas i e U(s) + + U ref (s) C(s) - + I ref (s) u i ref i meas

26 - Inversion of a coupling element (example) - 26 Objective: to control y 2 y2 =f(u11, u12 ) 2 inputs to impose 1 output S 11 u 11 y 2 y 11 S 2 u 12 y12 S 12 u 2 non-bijective inversion (different solutions) e 11-ref =k s 2-ref Inversion criteria distribute the reference signals e 12-ref =(1-k) s 2-ref s 2-ref k=1/2 equi-distribution k Other solutions (see next lecture)

27 Legend - Inversion of EMR elements - conversion element direct inversion + disturbance rejection 27 Control = light blue parallelograms with dark blue contour direct inversion accumulation element controller + disturbance rejection indirect inversion sensor mandatory link facultative link coupling element distribution criteria

28 Aalto University Finland May 2011 «Energy Management of EVs & HEVs using Energetic Macroscopic Representation» 3. «Inversion-based methodology for control structures» Prof. A. Bouscayrol (University Lille1, L2EP, MEGEVH, France) Prof. P. Sicard (Université Québec à Trois Rivières, Canada) based on the Keynote of EMR 09, Trois-Rivières (Canada) Aalto University

29 - Maximum control scheme EMR of the system 2. Tuning path 3. Inversion step-by-step Strong assumption: all variables can be measured! SE SM Maximal Control Scheme : - maximum of sensors - maximum of operations

30 30 1. EMR of the system 2. Tuning path 3. Inversion step-by-step 3b. Strategy ES MS 4. Simplification of the control scheme 5. Estimation of non-measured variable 6. Tuning of controllers fusion PID controller Calculation of k P k I k D

31 - Paper processing system as an example - 31 Paper processing using 2 induction machines IM1 IM2 IM1 IM2 [Djani 06] Technical requirements: - paper tension control for high quality of paper roll - winding velocity control for high quality of processing

32 - Maximum control structure - 32 dc bus VSI 1 induction machine 1 roll 1 band roll 2 induction machine 2 VSI 2 dc bus ES V dc u vsi1 i im1 T im1 im1 v roll1 T band T roll2 im2 e im2 i im2 i vsi2 ES i vsi1 i im1 e im1 im1 T roll1 T band v roll2 im2 T im2 i im2 u vsi2 V dc s vsi1 s vsi2 Step 1: Develop the EMR of the system Step 2a: identify all control variables (outputs) and control inputs Step 2b: identify tuning paths from inputs to outputs, avoiding crossing the paths

33 - Maximum control structure - 33 dc bus VSI 1 induction machine 1 roll 1 band roll 2 induction machine 2 VSI 2 dc bus ES V dc u vsi1 i im1 T im1 im1 v roll1 T band T roll2 im2 e im2 i im2 i vsi2 ES i vsi1 i im1 e im1 im1 T roll1 T band v roll2 im2 T im2 i im2 u vsi2 V dc s vsi1 im1-ref s vsi2 PWM FOC u vsi1-ref i im1-ref T im1-ref im1-ref v roll1-ref T band-ref FOC: Field Oriented Control PWM: Pulse Width Modulation im2-ref FOC PWM v roll2-ref im2-ref T im2-ref i im2-ref u vsi2-ref Step 3: invert each element of the control paths by applying inversion rules assume that all the signals are measurable; compensate for all disturbances.

34 - Maximum control structure - 34 dc bus VSI 1 induction machine 1 roll 1 band roll 2 induction machine 2 VSI 2 dc bus ES V dc u vsi1 i im1 T im1 im1 v roll1 T band T roll2 im2 e im2 i im2 i vsi2 ES i vsi1 i im1 e im1 im1 T roll1 T band v roll2 im2 T im2 i im2 u vsi2 V dc s vsi1 im1-ref s vsi2 PWM FOC u vsi1-ref i im1-ref T im1-ref im1-ref v roll1-ref T band-ref FOC: Field Oriented Control PWM: Pulse Width Modulation im2-ref FOC PWM v roll2-ref im2-ref T im2-ref i im2-ref u vsi2-ref Maximum control structure: one elementary feedback controller per accumulator simple tuning is possible by time coordination/separation of the control loops

35 - Practical control structures - 35 dc bus VSI 1 induction machine 1 roll 1 band roll 2 induction machine 2 VSI 2 dc bus ES V dc u vsi1 i im1 T im1 im1 v roll1 T band T roll2 im2 e im2 i im2 i vsi2 ES i vsi1 i im1 e im1 im1 T roll1 T band v roll2 im2 T im2 i im2 u vsi2 V dc s vsi1 im1-ref s vsi2 PWM FOC u vsi1-ref i im1-ref T im1-ref im1-ref v roll1-ref T band-ref im2-ref FOC PWM v roll2-ref im2-ref T im2-ref i im2-ref u vsi2-ref Step 4: Simplify the MCS: group operations, do not reject disturbances explicitly. Impact will be on cost, on processing time and on performance

36 - Practical control structures - 36 dc bus VSI 1 induction machine 1 roll 1 band roll 2 induction machine 2 VSI 2 dc bus ES V dc u vsi1 i im1 T im1 im1 v roll1 T band T roll2 im2 e im2 i im2 i vsi2 ES i vsi1 i im1 e im1 im1 T roll1 T band v roll2 im2 T im2 i im2 u vsi2 V dc s vsi1 im1-ref s vsi2 PWM FOC u vsi1-ref i im1-ref T im1-ref im1-ref v roll1-ref T T roll2_est band-ref im2 im2-ref FOC PWM T roll2_est im2_est im2_est T im2_est i im2 v roll2-ref im2-ref T im2-ref i im2-ref u vsi2-ref Step 5: Estimate non-measured variables, e.g. disturbances that cannot be neglected, and estimate unknown or time varying parameters

37 - Practical control structures - 37 dc bus VSI 1 induction machine 1 roll 1 band roll 2 induction machine 2 VSI 2 dc bus ES V dc u vsi1 i im1 T im1 im1 v roll1 T band T roll2 im2 e im2 i im2 i vsi2 ES i vsi1 i im1 e im1 im1 T roll1 T band v roll2 im2 T im2 i im2 u vsi2 V dc s vsi1 im1-ref s vsi2 PWM FOC u vsi1-ref i im1-ref T im1-ref im1-ref v roll1-ref T T roll2_est band-ref im2-ref FOC PWM v roll2-ref im2-ref T im2-ref i im2-ref u vsi2-ref Step 6: choice and tune all controllers (dynamic decoupling PI controller OK except

38 - Practical control structures - 38 dc bus VSI 1 induction machine 1 roll 1 band roll 2 induction machine 2 VSI 2 dc bus ES V dc u vsi1 i im1 T im1 im1 v roll1 T band T roll2 im2 e im2 i im2 i vsi2 ES i vsi1 i im1 e im1 im1 T roll1 T band v roll2 im2 T im2 i im2 u vsi2 V dc s vsi1 im1-ref s vsi2 PWM FOC u vsi1-ref i im1-ref T im1-ref im1-ref v roll1-ref T T roll2_est band-ref im1-ref im2-ref strategy im1-ref v roll2-ref im2-ref FOC T im2-ref i im2-ref u vsi2-ref PWM 3b: Exploit degrees of freedom to implement advanced strategies (to be presented in the next lecture)

39 Aalto University Finland May 2011 «Energy Management of EVs & HEVs using Energetic Macroscopic Representation» 4. «Application to energy management of EVs and HEVs» Aalto University

40 Parallel HEV «Inversion-Based Control from EMR» BAT - Different control levels (2) - VSI1 Fuel EM1 ICE EMR Trans. 40 fast subsystem controls EM1 control ICE control Trans control Inversion-based control slow system supervision Energy management (supervision/strategy) Strategy (supervision) Next Step driver request

41 Aalto University Finland May 2011 «Energy Management of EVs & HEVs using Energetic Macroscopic Representation» «Conclusion» Inversion based control = inversion of EMR based on the cognitive systemic and the causality principle (energy) Inversion rules for control scheme closed-loop control to invert accumulation element, direct inversion for conversion element, degree of freedom to invert coupling element Different steps on the control scheme From Maximum Control Schemes. to Practical Control Schemes. to the strategy level Aalto University

42 - References - P. J. Barre, A. Bouscayrol, P. Delarue, E. Dumetz, F. Giraud, J. P. Hautier, X. Kestelyn, B. Lemaire-S , E. S , "Inversion-based control of electromechanical systems using causal graphical descriptions", IEEE- IECON'06, Paris, November A. Bouscayrol, B. Davat, B. de Fornel, B. François, J. P. Hautier, F. Meibody-Tabar, E. Monmasson, M. Pietrzak-David, H. Razik, E. S , M. F. Benkhoris, "Control Structures for Multi-machine Multi-converter Systems with upstream coupling", Mathematics and Computers in Simulation, vol. 63, no. 3-5, pp , November 2003, (common paper of GE 44, GREEN, L2EP, LEEI and LESiR, according to the MMS project), A. Bouscayrol, M. Pietrzak-David, P. Delarue, R. Peña-Eguiluz, P. E. Vidal, X. Kestelyn, Weighted control of traction drives with parallel-connected AC machines, IEEE Transactions on Industrial Electronics, December 2006, vol. 53, no. 6, p (common paper of L2EP Lille and LEEI Toulouse). P. Delarue, A. Bouscayrol, A. Tounzi, X. Guillaud, G. Lancigu, Modelling, control and simulation of an overall wind energy conversion system, Renewable Energy, July 2003, vol. 28, no. 8, p (common paper L2EP Lille and Jeumont SA). Y. Djani Wankam, P. Sicard, A. Bouscayrol, "Maximum control structure of a five-drive paper system using Energetic Macroscopic Representation", IEEE-IECON'06, Paris, November 2006, (common paper of GREI Université du Québec à Trois-Rivières and L2EP Lille). J. P. Hautier, J. Faucher, Le graphe informationnel causal, (Text in French), Bulletin de l'union des Physiciens, vol. 90, juin 1996, pp P. Sicard, A. Bouscayrol, B. Lemaire-S , "Inversion-based control of electromechanical deduced from EMR", EMR 09, Trois-Rivières (Canada), September