Vorlesung Bewegungssteuerung durch geregelte elektrische Antriebe. Predictive Control
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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 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics Quanzhou, China May 31th - June 2nd, 2019
3 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
4 Predictive Control Why? it is not better performance! more power more dynamics etc. we already operate our systems at the physical limits!!!
5 Predictive Control Why? the real reason is simpler handling! you do not need a Ph.D. to do the set-up
6 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
7 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
8 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
9 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
10 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
11 Typical Cascaded Structure of Drive Control position controller speed controller current controller power electronics motor windings I inertia gear etc.
12 Typical Cascaded Structure of Drive Control position controller speed controller current controller power electronics motor windings I inertia gear etc.
13 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
14 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
15 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
16 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!!!
17 Usual Structure of Drive Control why PWM? consequences? DC link PI controller
18 Usual Structure of Drive Control why PWM? linearization of the inverter consequences? very high switching frequency DC link PI controller
19 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???
20 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
21
22 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
23 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 off-line Optimum Control definite number of switching elements next switching state or switching time can be fixed inverter
24 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
25 Outline Introduction Predictive Control Methods Trajectory Based Predictive Control Hysteresis Based Predictive Control Long-Range Predictive Control
26 Example : Trajectory Based Predictive Control Direct Self Control (DSC) acc. to Depenbrock
27 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
28 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
29 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
30 Outline Introduction Predictive Control Methods Trajectory Based Predictive Control Hysteresis Based Predictive Control Long-Range Predictive Control
31 Example : Hysteresis Based Predictive Control Predictive Current Control acc. to Holtz
32 Example : Hysteresis Based Predictive Control Predictive Current Control acc. to Holtz
33 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
34 Outline Introduction Predictive Control Methods Trajectory Based Predictive Control Hysteresis Based Predictive Control Long-Range Predictive Control
35 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
36 Model Based Control Model Predictive Control in fact all control schemes are model based (reality is just too complex!!!) the models are used either for parameter optimization/selection or for the integration into the control structure reference model (adaptive) control (MRAC) real output is compared with model output controller parameters are adjusted observer control real output is compared with model output ( controller parameters are adjusted) internal (non measurable) quantity is used as feedback
37 Model Based Control Model Predictive Control not all predictive control schemes are model based for the prediction is used either extrapolation or reflection of the past or a model trajectory based predictive control hysteresis based predictive control * (* no model at all)
38 Model Predictive Control History Future Page 38
39 Model Predictive Control History source : T. Geyer MPC was developed in the process industry in the early 1970s In 1999, there were 4500 reported applications of linear MPC in industry Table taken from Qin and Badgwell, 2003 Since then, the attention has focused on fast processes with short sampling intervals in the sub-second range
40 Number of IEEE publications per year Model Predictive Control Milestones in Predictive Control in Power Electronics first industrial tutorial in Germany (ECPE) source : T. Geyer First product: MPC for LCI First pilot: OPP-based MPC for 3-level converter First symposium: PRECEDE 50 First publications: J. Holtz: Cost function and bounds R. Kennel: Generalized predictive control J. Rodriguez: 1-step predictive control Year
41 Different way of thinking!!!
42 Direct Model Predictive Control System Model / Cost Function Page 42
43 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
44 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
45 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
46 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
47 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
48 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 48
49 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 49
50 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 50
51 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 51
52 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 52
53 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 53
54 Cost Function commonly used structure Source : Zhenbin Zhang Techn. Univ. Muenchen
55 Cost Function commonly used structure Source : Zhenbin Zhang Techn. Univ. Muenchen
56 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
57 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
58 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
59 Agenda Introduction & Motivation Classical Control Model Predictive Control Physical Model Simulation & Experimental Results Conclusion 59
60 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 60
61 Impedance Source Inveretrs Z-source inverter (ZSI) Buck-boost & Single-stage No dead-time Higher reliability 61
62 Impedance Source Inveretrs Quasi-Z-source inverter (qzsi) Continous input current Common earthing between input source and dc-link bus Smaller passive components 62
63 Classical Control Control Objectives: AC (Output voltage) DC (Dc-link voltage) linear/ non-linear Capacitor voltage Inductor current 63
64 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!!!! 64
65 Model Predicitve Control Control Objectives: 1- Reference tracking Output voltage Capacitor voltage Inductor current 2- Switching effort 65
66 Physical Model Non-shoot-through state Shoot-through state Boost mode Buck mode 66
67 Physical Model Continuous-time domain model: 67
68 Physical Model Discrete-time model: 68
69 Cost Function Design Derived system model 69
70 Simulation Results 70
71 Simulation Results 71
72 Simulation Results 72
73 Simulation Results 73
74 Simulation Results 74
75 Experimental Results 75
76 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. 76
77 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
78 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
79 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
80 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
81 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
82 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
83 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
84 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
85 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
86 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
87 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
88 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
89 Encoderless Control with Arbitrary Injection Limitations of HF Injection Methods - HF injection voltage margin limitation to medium and low speed usually the current ripple caused by the inverter switchings are sufficient to exploit the rorot position - 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 if not any current ripple can eben be music!!! - Finding a way to exploit this provides additional degrees of freedom
90
91 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
92 Experimental Results (DMPC) current control comparison : PI control model predictive control
93 Experimental Results (DMPC) current control Low switching frequency high switching frequency dynamic of step response is identical!
94 Experimental Results (DMPC) current control a change of the cost function (nothing else!!!) results in different behaviour!
95 Cost Function commonly used structure Source : Zhenbin Zhang Techn. Univ. Muenchen
96 Cost Function commonly used structure Source : Zhenbin Zhang Techn. Univ. Muenchen
97 Strengths and Challenges Source : Zhenbin Zhang Techn. Univ. Muenchen
98 There is definitely a strong demand for reducing the calculation power necessary for predictive control
99 There is definitely a strong demand for reducing the calculation power necessary for predictive control
100 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
101 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
102 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
103 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
104 Thank you!
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