Vorlesung Bewegungssteuerung durch geregelte elektrische Antriebe. Predictive Control

Transcription

1 Vorlesung Bewegungssteuerung durch geregelte elektrische Antriebe Predictive Control A Simple and Powerful Method to Control Power Converters and Drives Ralph M. Kennel, Technische Universitaet Muenchen, Germany Marian Kazmierkowski, Technical University of Warsaw, Poland José Rodríguez, Universidad Nacional Andrés Bello, Santiago, Chile

2 Outline Introduction Predictive Control - Why Predictive Control Principle Predictive Control Methods Different Way of Thinking Review of classical PWM The principle of MPC in Power Electronics Review of converter topologies controlled using MPC Some applications of converters controlled using MPC Predictive Control where s the future? Conclusions/Discussion

3 State of the Art : Field Oriented Control r mains field coordinates stator coordinates flux controller i s e j u s PWM 6 speed controller current controllers e -j i s u s r model M 3~ encoder

4 Problems of Linear Algorithms Linear control characteristics Control unit and controlled unit are assumed to be linear Control unit are assumed to be time constant Linear circuits show identical reactions in each operation range under the same reference commands Drive systems characteristics Drive systems are non-linear Drive systems are time-variant The behavior of a drive system is depending on the operation range

5 Problems of Linear Algorithms Feedforward Control high dynamic behaviour no impact by sensor characteristics models are not absolutely accurate high accuracy requires knowledge of all quantuities temperature and drift behaviour often cannot be described/modeled Advantages Feedback Control high accuracy high reliability Disadvantages high longterm stability simple optimization/adjustment procedure controlled quantities can be monitored (re)action only, when there is a control difference already sensors cause measuring errors

6 Problems of Linear Algorithms any controller optimization is a compromise making the inverter unnecessarily slow in many operation points controllers with parameter adaptation and/or structure adaptation are very complex they often have bad effects during the adaptation process itself converters cause harmonics leading to offset effects in combination with fast control loops the elimination of harmonics by filtering causes a time delay in the feedback and therefore leads to a less dynamic control

7 Problems of Linear Algorithms any controller optimization is a compromise making the inverter unnecessarily slow in many operation points controllers with parameter adaptation and/or structure adaptation are very complex they often have bad effects during the adaptation process itself converters cause harmonics leading to offset effects in combination with fast control loops the elimination of harmonics by filtering causes a time delay in the feedback and therefore leads to a less dynamic control

8 Typical Cascaded Structure of Drive Control position controller speed controller current controller power electronics motor windings I inertia gear etc.

9 Typical Cascaded Structure of Drive Control position controller speed controller current controller power electronics motor windings I inertia gear etc.

10 Problems of Linear Algorithms in cascaded control structures speed control must be much faster than position control and current control must be much faster than speed control current control must be very fast to achieve position control with reasonable cycle times in the controlled system (drive, converter, ) however, there is no time constant justifying cycle times of 100 µs or less

11 Problems of Linear Algorithms using cascaded PI(D) control most problems (= differences between theory and practical results) occur with the (inner) current control a linear controller tries to control an extremely non-linear inverter whose behaviour is depending on the modultaion method most developments of converter control deal with current control or flux control because these elements are closest to the inverter itself the behaviour of any improved current control is expected to be more linear than the inverter itself speed and position controllers can be designed as PI(D) controllers as before

12 Outline Introduction Predictive Control - Why Predictive Control Principle Predictive Control Methods Different Way of Thinking Review of classical PWM The principle of MPC in Power Electronics Review of converter topologies controlled using MPC Some applications of converters controlled using MPC Predictive Control where s the future? Conclusions/Discussion

13 Predictive Control Why? it is not better performance! more power more dynamics etc. we already operate our systems at the physical limits!!!

14 Predictive Control Why? the real reason is simpler handling! you do not need a Ph.D. to do the set-up

15 General Structure of a Predictive Controller prediction and calculation switching state actual machine state power electronics machine and power electronics model motor windings I inertia gear etc. reminds slightly to state control state control, however, is basically a linear control predictive control is not!!!

16 Usual Structure of Drive Control why PWM? linearization of the inverter consequences? very high switching frequency DC link PI controller

17 Structure of a Direct Control when Predictive Control overcomes some serious disadvantages of PWM DC link direct controller is it really reasonable to add a PWM to Predictive Control???

18 Outline Introduction Predictive Control - Why Predictive Control Principle Predictive Control Methods Different Way of Thinking Review of classical PWM The principle of MPC in Power Electronics Review of converter topologies controlled using MPC Some applications of converters controlled using MPC Predictive Control where s the future? Conclusions/Discussion

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20 Principle of Predictive Control reference commands precalculation of the behaviour for each of the switching states definite number of equivalent circuits without switching elements definite number of switching states comparison between precalculation and reference commands when this precalculation is performed on-line Predictive Control definite number of switching elements next switching state or switching time can be fixed inverter

21 Principle of Predictive Optimum Control reference commands precalculation of the behaviour for each of the switching states definite number of equivalent circuits without switching elements definite number of switching states comparison between precalculation and reference commands when this precalculation is performed on-line off-line Predictive Optimum Control definite number of switching elements next switching state or switching time can be fixed inverter

22 Outline Introduction Predictive Control - Why Predictive Control Principle Predictive Control Methods Different Way of Thinking Review of classical PWM The principle of MPC in Power Electronics Review of converter topologies controlled using MPC Some applications of converters controlled using MPC Predictive Control where s the future? Conclusions/Discussion

23 Outline Introduction Predictive Control Methods Trajectory Based Predictive Control Hysteresis Based Predictive Control Long-Range Predictive Control

24 Example : Trajectory Based Predictive Control Direct Self Control (DSC) acc. to Depenbrock

25 Example : Trajectory Based Predictive Control Direct Speed Control acc. to Mutschler * e model and prediction u k i s = ~ u d u s e / a k+1 k+1 S k+1 S k Hy e / a k+3 k+3 a = +Hy M 3~ e / a k k S k+2 e / a k+2 k+2 e = ref

26 Trajectory Based Predictive Control Strategies system states are forced to follow (pre-)defined natural reference trajectories difference to sliding mode control there the trajectories are not natural

27 Characteristics of Trajectory Based Predictive Control system states are forced to follow (pre-)defined reference trajectories switching takes place at intersections between different system-trajectories or at (pre-)defined instants switching frequency of the inverter can be fixed to a constant value control behaviour comparable to feedforward control exact knowledge of system parameters is required appropriate for realisation by digital circuits or controllers

28 Outline Introduction Predictive Control Methods Trajectory Based Predictive Control Hysteresis Based Predictive Control Long-Range Predictive Control

29 Example : Hysteresis Based Predictive Control Predictive Current Control acc. to Holtz

30 Example : Hysteresis Based Predictive Control Predictive Current Control acc. to Holtz

31 Characteristics of Hysteresis Based Predictive Control switching takes place at borders of a hysteresis area a maximum error can be (pre-)defined switching frequency of the inverter is not constant control behaviour comparable to feedback control exact knowledge of system parameters is not required appropriate for realisation by analog circuits

32 Outline Introduction Predictive Control Methods Trajectory Based Predictive Control Hysteresis Based Predictive Control Long-Range Predictive Control

33 The Human Behaviour of DMPC DMPC is like playing chess the player calculates in advance all possible moves until a prediction horizon the player chooses the move with the best expectations of success after each opponent s move pre-calculation and optimization is repeated

34 Model Predictive Control History Future Page 34

35 Direct Model Predictive Control System Model / Cost Function Page 35

36 Characteristics of Model Based Predictive Control basic ideas are derived from state-space control the past is explicitely considered (mostly by the system state) future control values are pre-calculated and optimized the first of the precalculated control values only model parameters can be estimated on-line until a (pre-)defined horizon is transmitted to the controlled system extension to MIMO-control is possible with little additional effort use of non-linear model is possible for non-linear control systems a lot of calculation power is required

37 Features of (Longe Range) Predictive Control Advantages possibility to use foreknowledge about drive system (system model) inverter limitations and dynamic behaviours are taken into account improved representation of non-linear systems no need for time challenging cascade structure improved dynamic behaviour Disadvantages high processing capability required for industrial use change in teaching engineers necessary stationary accuracy and dynamic behaviour depend on accurracy of model parameters

38 Features of (Longe Range) Predictive Control Advantages possibility to use foreknowledge about drive system (system model) inverter limitations and dynamic behaviours are taken into account improved representation of non-linear systems no need for time challenging cascade structure improved dynamic behaviour Disadvantages high processing capability required for industrial use change in teaching engineers necessary stationary accuracy and dynamic behaviour depend on accurracy of model parameters

39 Features of (Longe Range) Predictive Control Advantages possibility to use foreknowledge about drive system (system model) inverter limitations and dynamic behaviours are taken into account improved representation of non-linear systems no need for time challenging cascade structure improved dynamic behaviour Disadvantages high processing capability required for industrial use change in teaching engineers necessary stationary accuracy and dynamic behaviour depend on accurracy of model parameters

40 Outline Introduction Predictive Control - Why Predictive Control Principle Predictive Control Methods Different Way of Thinking Review of classical PWM The principle of MPC in Power Electronics Review of converter topologies controlled using MPC Some applications of converters controlled using MPC Predictive Control where s the future? Conclusions/Discussion

41 Different Way of Thinking in Model Based Predictive Control 1. model of the controlled system this is no difference to conventional control the better the model, the better the prediction 2. cost function the engineer has to learn to describe what he wants the controlled system really to do!!! 3. stability that s a really good question next question? Page 41

42 Different Way of Thinking in Model Based Predictive Control 1. model of the controlled system this is no difference to conventional control the better the model, the better the prediction 2. cost function the engineer has to learn to describe what he wants the controlled system really to do!!! 3. stability that s a really good question next question? Page 42

43 Different Way of Thinking in Model Based Predictive Control 1. model of the controlled system this is no difference to conventional control the better the model, the better the prediction 2. cost function the engineer has to learn to describe what he wants the controlled system really to do!!! 3. stability that s a really good question next question? Page 43

44 Different Way of Thinking in Model Based Predictive Control 1. model of the controlled system this is no difference to conventional control the better the model, the better the prediction 2. cost function the engineer has to learn to describe what he wants the controlled system really to do!!! 3. stability that s a really good question next question? Page 44

45 Different Way of Thinking in Model Based Predictive Control 1. model of the controlled system this is no difference to conventional control the better the model, the better the prediction 2. cost function the engineer has to learn to describe what he wants the controlled system really to do!!! 3. stability that s a really good question next question? Page 45

46 Cost Function commonly used structure Source : Zhenbin Zhang Techn. Univ. Muenchen

47 Cost Function commonly used structure Source : Zhenbin Zhang Techn. Univ. Muenchen

48 Outline Introduction Predictive Control - Why Predictive Control Principle Predictive Control Methods Different Way of Thinking Review of classical PWM The principle of MPC in Power Electronics Review of converter topologies controlled using MPC Some applications of converters controlled using MPC Predictive Control where s the future? Conclusions/Discussion

49 Outline Introduction Predictive Control - Why Predictive Control Principle Predictive Control Methods Different Way of Thinking Review of classical PWM The principle of MPC in Power Electronics Review of converter topologies controlled using MPC Some more applications Predictive Control where s the future? Conclusions/Discussion

50 Direct Model Predictive Voltage Control of Quasi-Z-Source Inverters with LC Filters Ayman Ayad, Petros Karamanakos, Ralph Kennel Institute Of Electrical Drive Systems and Power Electronics (Prof. Dr. Ing. Ralph Kennel) Technische Universität München

51 Agenda Introduction & Motivation Classical Control Model Predictive Control Physical Model Simulation & Experimental Results Conclusion 51

52 Impedance Source Inveretrs DG Configurations dc-dc boost converter ac O/P < dc I/P (buck inverter) Dead-time EMI problem Control complexity Cost Efficiency Dead-time EMI problem 52

53 Impedance Source Inveretrs Z-source inverter (ZSI) Buck-boost & Single-stage No dead-time Higher reliability 53

54 Impedance Source Inveretrs Quasi-Z-source inverter (qzsi) Continous input current Common earthing between input source and dc-link bus Smaller passive components 54

55 Classical Control Control Objectives: AC (Output voltage) DC (Dc-link voltage) linear/ non-linear Capacitor voltage Inductor current 55

56 Model Predictive Control Classical PI control: MPC: What we need: Seperate multi-loop for each side Interaction between dc & ac side during transients Requires PLL & transformations abc/dq & dq/abc Needs modulation stage Can handle multiple objectives, i.e. single control loop Very fast dynamic response Manpulates the inveretr switches (No need for a modulator) Variable switching frequency An accurate mathematical model A good cost function... Determine the weighting factors!!!! 56

57 Model Predicitve Control Control Objectives: 1- Reference tracking Output voltage Capacitor voltage Inductor current 2- Switching effort 57

58 Physical Model Non-shoot-through state Shoot-through state Boost mode Buck mode 58

59 Physical Model Continuous-time domain model: 59

60 Physical Model Discrete-time model: 60

61 Cost Function Design Derived system model 61

62 Simulation Results 62

63 Simulation Results 63

64 Simulation Results 64

65 Simulation Results 65

66 Simulation Results 66

67 Experimental Results 67

68 Conclusion Quasi-Z-source inverter (qzsi): One-stage buck-boost converter with higher efficiency and less components. It is connected with an LC filter to be used as a UPS system. Both sides of the qzsi are to be simultaneously controlled. DMPC of qzsi: The full physical model of the system under review is derived and discretized. A cost fucntion is formulated that takes into account all variables of concern. Both sides of the qzsi are simultaneously controlled in one computational stage without requiring any subsequent control loops. The average switching frequency is kept at 10kHz. The proposed method is effective with both modes of operation. MPC manages to minimize the steady-state error and features favorable behavior during transients. 68

69 Saliency based Encoderless Predictive Torque Control without Signal Injection Overview Predictive Torque Control Saliency Tracking P. Landsmann, D. Paulus, P. Stolze and R. Kennel Technische Universitaet Muenchen Munich Germany Simulation Results Measurements Conclusion Institute for Electrical Drive Systems & Power Electronics Technische Universität München Arcisstr. 21, D Munich - peter.landsmann@tum.de

70 Basic Idea: A Predictive Torque Controller neglecting the saliency in the model causes a prediction error which contains the angle information Overview Predictive Torque Control Saliency Tracking Simulation Results Measurements Conclusion Institute for Electrical Drive Systems & Power Electronics Technische Universität München Arcisstr. 21, D Munich - peter.landsmann@tum.de

71 Predictive Torque Control Overview Predictive Torque Control Saliency Tracking Simulation Results Measurements Conclusion Institute for Electrical Drive Systems & Power Electronics Technische Universität München Arcisstr. 21, D Munich - peter.landsmann@tum.de

72 Simulation Results for PMSM Simulation parameter of PMSM Overview Predictive Torque Control Saliency Tracking Speed controlled encoderless predictive torque control Simulation Results Measurements Conclusion Institute for Electrical Drive Systems & Power Electronics Technische Universität München Arcisstr. 21, D Munich - peter.landsmann@tum.de

73 Simulation Results for PMSM Speed controlled step response to rated speed very good dynamics in simulation Overview Predictive Torque Control dependency on torque gradients Saliency Tracking Simulation Results Measurements Conclusion Institute for Electrical Drive Systems & Power Electronics Technische Universität München Arcisstr. 21, D Munich - peter.landsmann@tum.de

74 Measurements with Reluctance Machine Data of transverse laminated RM Overview Predictive Torque Control Saliency Tracking Simulation Results Measurements Conclusion Institute for Electrical Drive Systems & Power Electronics Technische Universität München Arcisstr. 21, D Munich - peter.landsmann@tum.de

75 Measurements with Reluctance Machine Speed controlled step response to 160% rated speed Overview Predictive Torque Control Saliency Tracking Simulation Results Measurements Conclusion Institute for Electrical Drive Systems & Power Electronics Technische Universität München Arcisstr. 21, D Munich - peter.landsmann@tum.de

76 Measurements with Reluctance Machine Response to 66% rated torque load step at speed controlled standstill Overview Predictive Torque Control Saliency Tracking Simulation Results Measurements Conclusion Institute for Electrical Drive Systems & Power Electronics Technische Universität München Arcisstr. 21, D Munich - peter.landsmann@tum.de

77 Summary Proposed Scheme: Neglect the saliency in PTC equations Prediction error contains angle information Reconstruct Prediction Error using PLL angle Vectorproduct of both is PLL input Benefits: Saliency based: permanent operation at standstill No signal injection: operation at high speed as well as at standstill Overview Predictive Torque Control Saliency Tracking Simulation Results Measurements Conclusion Institute for Electrical Drive Systems & Power Electronics Technische Universität München Arcisstr. 21, D Munich - peter.landsmann@tum.de

78 Summary Proposed Scheme: Neglect the saliency in PTC equations Prediction error contains angle information Reconstruct Prediction Error using PLL angle Vectorproduct of both is PLL input Benefits: Saliency based: permanent operation at standstill No signal injection: operation at high speed as well as at standstill Overview Predictive Torque Control Saliency Tracking Simulation Results Measurements Conclusion Institute for Electrical Drive Systems & Power Electronics Technische Universität München Arcisstr. 21, D Munich - peter.landsmann@tum.de

79 Summary Proposed Scheme: Neglect the saliency in PTC equations Prediction error contains angle information Reconstruct Prediction Error using PLL angle Vectorproduct of both is PLL input Benefits: Saliency based: permanent operation at standstill No signal injection: operation at high speed as well as at standstill Overview Predictive Torque Control Saliency Tracking Simulation Results Measurements Conclusion Institute for Electrical Drive Systems & Power Electronics Technische Universität München Arcisstr. 21, D Munich - peter.landsmann@tum.de

80 Encoderless Control with Arbitrary Injection Limitations of HF Injection Methods - HF injection voltage margin limitation to medium and low speed - Restriction to rotating or alternating shape due to algorithmic reasons Meaning of Arbitrary - No physical necessity for injection shape - Basically any current ripple contains the saliency angle information - Finding a way to exploit this provides additional degrees of freedom

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84 Outline Introduction Predictive Control - Why Predictive Control Principle Predictive Control Methods Different Way of Thinking Review of classical PWM The principle of MPC in Power Electronics Review of converter topologies controlled using MPC Some applications of converters controlled using MPC Predictive Control where s the future? Conclusions/Discussion

85 Experimental Results (DMPC) current control comparison : PI control model predictive control

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88 Strengths and Challenges Source : Zhenbin Zhang Techn. Univ. Muenchen

89 There is definitely a strong demand for reducing the calculation power necessary for predictive control

90 There is definitely a strong demand for reducing the calculation power necessary for predictive control

91 There is definitely a strong demand for reducing the calculation power necessary for predictive control Relying on Moore s Law is not sufficient! Heuristic Preselection Extrapolation instead of Exact calculation

92 Outline Introduction Predictive Control - Why Predictive Control Principle Predictive Control Methods Different Way of Thinking Review of classical PWM The principle of MPC in Power Electronics Review of converter topologies controlled using MPC Some applications of converters controlled using MPC Predictive Control where s the future? Conclusions/Discussion

93 Actual Situation in cascaded control structures speed control must be much faster than position control and current control must be much faster than speed control current control must be extremely fast to achieve position control with reasonable cycle times at the time most requirements in industrial applications are satisfied sufficiently there is no strong need for improvement in industry however at a certain time there will be a demand for improvement with respect to a future increase of requirements more investigations should be done Page 93

94 Discussion predictive control strategies offer the possibility to use foreknowledge about the drive system physical limitations and dynamic behaviour of power electronics are taken into account non-linear systems are represented better (by non-linear models) no need for time challenging cascaded structures the way of thinking is different model of the controlled system cost function with respect to a future increase of requirements more investigations should be done Page 94

95 Thank you!