CN109391202B - Model prediction-direct torque control method for permanent magnet synchronous motor - Google Patents

Model prediction-direct torque control method for permanent magnet synchronous motor Download PDF

Info

Publication number
CN109391202B
CN109391202B CN201811325725.2A CN201811325725A CN109391202B CN 109391202 B CN109391202 B CN 109391202B CN 201811325725 A CN201811325725 A CN 201811325725A CN 109391202 B CN109391202 B CN 109391202B
Authority
CN
China
Prior art keywords
control
torque
flux linkage
motor
controller
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811325725.2A
Other languages
Chinese (zh)
Other versions
CN109391202A (en
Inventor
赵海艳
陶冶
冯宇驰
赵津杨
马彦
陈虹
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jilin University
Original Assignee
Jilin University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jilin University filed Critical Jilin University
Priority to CN201811325725.2A priority Critical patent/CN109391202B/en
Publication of CN109391202A publication Critical patent/CN109391202A/en
Application granted granted Critical
Publication of CN109391202B publication Critical patent/CN109391202B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/0004—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/14—Estimation or adaptation of motor parameters, e.g. rotor time constant, flux, speed, current or voltage
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/30—Direct torque control [DTC] or field acceleration method [FAM]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • H02P25/022—Synchronous motors
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • H02P25/022—Synchronous motors
    • H02P25/024—Synchronous motors controlled by supply frequency
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
    • H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Ac Motors In General (AREA)

Abstract

A permanent magnet synchronous motor model prediction-direct torque control method belongs to the technical field of control. The invention aims to finish accurate estimation of torque and flux linkage in a system by using a dimensionality reduction observer, and then a permanent magnet synchronous motor model prediction-direct torque control method of a hub motor driving system controller is designed by adopting a model prediction control algorithm. The method comprises the following steps: and selecting the motor torque and flux linkage as state quantities, and selecting a target function which enables the error between the predicted value and the expected value of the torque and flux linkage to be minimum, so as to obtain the three-phase voltage for controlling the motor to complete the control of the PMSM. The model predictive control algorithm applied by the invention can effectively process the optimization control problem of multi-input multi-output and multi-constraint, replaces a hysteresis comparator and a switch selection module in the traditional direct torque control, avoids the upper and lower limit pulsation and unnecessary switch loss of the hysteresis, and effectively inhibits the torque pulsation of the driving motor.

Description

Model prediction-direct torque control method for permanent magnet synchronous motor
Technical Field
The invention belongs to the technical field of control.
Background
With the improvement of the quality of life of people and the rapid development of the automobile industry, the quantity of automobiles kept in China is continuously increased in recent years, the reserves of non-renewable energy resources are continuously consumed in the world, the energy safety is seriously threatened, and the environment is further aggravated by the use of a large number of traditional fuel automobiles. The electric automobile has the advantages of high energy efficiency, zero emission and low pollution, and becomes the development trend of future automobiles.
The electric automobile driven by the hub motor is a research hotspot in recent years, and the electric automobile is mainly characterized in that the motor is arranged on a wheel of the electric automobile, the driving torque generated by the motor directly acts on the wheel, a complicated mechanical transmission device of the traditional automobile is omitted, the space is saved, the comfort level of the automobile is improved, the light weight of the automobile can be realized, the independent and accurate control of each driving wheel of the automobile is realized by the hub driving system, and the control force of the driving system on the automobile is improved. The whole vehicle torque control strategy of the electric vehicle provides torque demand for a driver to step on an accelerator pedal, the electric vehicle power assembly controller receives the information, calculates optimal driving torque by combining the current running condition of the whole vehicle and the road and environment conditions, and issues a torque control command, the hub motor control system responds to the command and provides the demanded torque, and the output torque changes frequently due to frequent change of working conditions, frequent starting of braking, upward and downward slopes and the like. Frequent torque pulsation can cause vibration and noise of a driving motor, reliability and control precision of a motor driving system are reduced, extra energy consumption of the motor can be increased, and the endurance mileage of the electric automobile is shortened. Therefore, the rapid and accurate torque tracking is an important control target of the permanent magnet synchronous motor for the vehicle.
The basic methods currently used for motor control are mainly Field oriented control (Field oriented control) and Direct torque control (Direct torque control). Many control strategies that are currently emerging in the field of motor control research are derived based on these two approaches.
Aiming at the torque tracking control of the permanent magnet synchronous motor for the vehicle, the following problems mainly exist:
1. the mathematical model of the hub motor is complex, the problems of current magnitude coupling, large dependence on motor parameters and the like exist, the calculated amount of a control system is increased by the transformation of the current magnitudes under different coordinate systems, the dynamic response is slowed, the control effect of a hub motor driving system is influenced, and particularly, under the condition of traffic sudden change, if the driving system cannot respond timely, traffic accidents are easily caused.
2. The problem of inaccurate flux linkage estimation exists, when a control system needs to directly track and control the torque and the flux linkage, the actual torque and flux linkage values at the current moment cannot be directly obtained through measurement, and the control effect of the hub motor driving system is directly influenced by the estimation deviation of the torque and the flux linkage.
3. The effective control of the three-phase inverter plays a critical role in improving the control performance of the motor driving system, and due to the dead time existing in the actual inverter, errors of torque and flux linkage can be caused, loss is increased, and even the oscillation phenomenon of the system can be caused.
Disclosure of Invention
The invention aims to finish accurate estimation of torque and flux linkage in a system by using a dimensionality reduction observer, and then a permanent magnet synchronous motor model prediction-direct torque control method of a hub motor driving system controller is designed by adopting a model prediction control algorithm.
The method comprises the following steps:
selecting motor torque and flux linkage as state quantity, i.e. x ═ Te ψs]TThe switching signal of the three-phase inverter is selected as a control variable, i.e., u ═ Sa Sb Sc]TSelecting the time-varying parameter of the system as d ═ psidAnd λ is dead zone compensation, the system state equation is:
Figure BDA0001858700190000021
in the formula, the matrix B is:
Figure BDA0001858700190000022
selecting J which minimizes the error between the predicted value and the expected value of the torque and flux linkage as a target function:
Figure BDA0001858700190000023
substituting various values corresponding to different selectable switch combinations into the objective function J, and screening to obtain a corresponding switch combination as the control quantity output by the controller when the objective function value is minimum;
the specific control process can be expressed as follows:
firstly, obtaining a voltage feedback quantity u through a sensord、uqAnd a current feedback quantity id、iqThe data is transmitted to a flux linkage observer to obtain the flux linkage estimated value at the current moment
Figure BDA0001858700190000024
And torque estimation
Figure BDA0001858700190000025
The estimated value and the given value are compared
Figure BDA0001858700190000026
Transmitting to MPC controller, obtaining controller output control quantity S through optimal calculationa、Sb、ScActing on the three-phase inverter, and then obtaining the three-phase voltage of the control motor to complete the control of the PMSM;
the control target of the driving system controller mainly comprises the following parts:
1) the controller is designed through a model predictive control algorithm, upper and lower limit pulsation caused by traditional hysteresis control is avoided, interference of the controller on target tracking control is effectively inhibited, and control precision is improved;
2) the controller is designed to select the inverter control signal as the control quantity, so that a switch selection auxiliary module is omitted, the system structure is simplified, and unnecessary loss is avoided;
3) on the premise of ensuring that the system is simple in structure and low in loss, an optimized objective function is selected, an optimal control quantity combination is obtained through solving, tracking errors of torque and flux linkage are effectively restrained, and a main control target of a driving system is completed;
4) the influence brought by the dead zone effect is considered, the dead zone modeling is added into the model prediction controller, the inverter is controlled more accurately, and the error brought by the dead zone is reduced.
The invention relates to a flux linkage dimension reduction observer, which comprises the following components:
i for three-phase current of permanent magnet synchronous motora,ib,icPerforming Clarke transformation to obtain two-phase current iα,iβThe stator flux linkage of two phases is psiα,ψβVoltage u of two phasesα,uβ(ii) a The permanent magnet synchronous motor state equation considering the dead zone is as follows:
Figure BDA0001858700190000031
Figure BDA0001858700190000032
the state space equation can be simplified as:
Figure BDA0001858700190000033
designing a dimension reduction observer, and selecting a variable z to replace a state quantity x2Carrying out variable substitution to obtain:
Figure BDA0001858700190000034
wherein the specific matrices D, F, G, H are:
Figure BDA0001858700190000035
Figure BDA0001858700190000036
Figure BDA0001858700190000037
wherein gamma is a pole configuration parameter;
③ arranging poles d
d＝γ|ωr|＜0 (18)
And fourthly, when the gamma is smaller than 0, the observer converges, and the gamma is equal to-0.2.
Compared with the prior art, the invention has the beneficial effects that:
1. the motor torque and the flux linkage are used as controlled targets, system design is completed under a stator coordinate system, control is completed without the aid of current magnitude, the problems of complex coordinate change and current magnitude coupling in a motor model are effectively solved, the complexity of the system is reduced, and the dynamic response speed of the system and the intuition and reliability of the control effect of the system are improved.
2. When the motor torque and flux linkage estimation is carried out, the problem of error accumulation in the traditional integral estimation is considered, the invention applies the principle of the dimensionality reduction observer, converts the open-loop integral estimation in the traditional method into a closed-loop estimation control form, eliminates integral accumulation errors, improves the motor torque and flux linkage estimation accuracy, and simultaneously reduces the influence of the change of the inherent parameters of the motor on the flux linkage estimation.
3. Aiming at the problems that the inverter has dead zone time in actual engineering, so that the fundamental wave of the output voltage and current of the inverter is reduced, higher harmonic components are increased, torque pulsation is caused, the additional loss of a motor is increased, and even the oscillation phenomenon of a system can be caused, the inverter and the motor are modeled again, dead zone compensation is carried out, the influence caused by the dead zone is reduced, and the torque control effect is improved.
4. The model predictive control algorithm applied by the invention can effectively process the optimization control problem of multi-input multi-output and multi-constraint, replaces a hysteresis comparator and a switch selection module in the traditional direct torque control, avoids the upper and lower limit pulsation and unnecessary switch loss of the hysteresis, and effectively inhibits the torque pulsation of the driving motor.
Drawings
FIG. 1 is a schematic diagram of a three-phase inverter and PMSM system;
FIG. 2 is a structural block diagram of a permanent magnet synchronous motor direct torque control system based on observer and model predictive control;
fig. 3 is a schematic structural diagram of a three-phase inverter;
FIG. 4 is a simulation of inverter dead band logic;
FIG. 5 is a schematic diagram of inverter a-phase leg currents;
FIG. 6 is a schematic of inverter dead band philosophy;
FIG. 7 is a basic schematic diagram of model predictive control;
FIG. 8 is a comparison graph of MPC-DTC sinusoidal torque tracking simulation without dead band considerations and with dead band considerations.
FIG. 9 is a comparison graph of MPC-DTC step torque tracking simulation without dead band considerations and MPC-DTC step torque tracking with dead band considerations.
Fig. 10 is a flux linkage simulation diagram of the dimension reduction observer.
Detailed Description
The invention relates to a model prediction-direct torque control method of a permanent magnet synchronous motor for a vehicle, in particular to a model prediction control method which can effectively improve flux linkage estimation precision and reduce torque pulsation of the permanent magnet synchronous motor, and meanwhile, the control effect of an inverter is improved by compensating a dead zone of the inverter, so that the rapid and accurate tracking control of given torque is completed, and the requirement of a driving control system of an electric automobile hub motor is met.
The permanent magnet synchronous motor drive control system based on the model predictive control of the dead zone compensation and the dimensionality reduction observer can well solve the three problems. Firstly, the design selects a direct torque control method as a basis, the direct torque control does not need to rely on the current magnitude, the torque and the flux linkage are directly controlled, a mathematical model of the motor is simplified, complex coordinate transformation is avoided, and the dynamic response speed of the system is improved. Aiming at the problem that flux linkage and torque estimation in the traditional direct torque control are inaccurate, the flux linkage is observed by adopting a dimension reduction observer method in the design, so that the deviation generated in the flux linkage estimation process is reduced, the accurate estimation of the torque is further realized, and the torque pulsation is reduced. Finally, the controller of the motor driving control system is designed by adopting a model prediction control algorithm, a three-phase inverter is selected as an actuator of the control system, wherein the model prediction controller outputs an optimal switching signal to directly act on the inverter.
The flux linkage observer converts open-loop estimation in the traditional direct torque control into a closed-loop control form, reduces flux linkage estimation errors, and can eliminate error accumulation generated in an integral link in the traditional estimation, thereby reducing pulsation. The model predictive control method can effectively process the multi-objective optimization control problem, selects the optimal switching signal which minimizes the torque and flux linkage errors aiming at the dead zone effect, directly controls the three-phase inverter with the dead zone, and can effectively reduce the torque pulsation and the switching loss of the motor.
The method comprises the steps of firstly, completing the design of an estimator of a motor driving system by utilizing the principle of a dimensionality reduction observer, and calculating the torque and flux linkage of a motor in the current state in real time by combining the voltage and the current magnitude of a permanent magnet synchronous motor; secondly, the torque and flux linkage values obtained by the observer are used as feedback values and transmitted to the controller together with a reference given value, then the controller constructs a motor control system with an inverter dead zone by utilizing a model predictive control algorithm, a system control target function mainly comprises two control quantities which are the torque and flux linkage of the motor, and the target function represents the tracking error of the motor torque and flux linkage of the control system; and finally, obtaining a switch combination which enables the torque and flux linkage tracking error to be minimum by solving the optimal control problem corresponding to the objective function, and acting on the three-phase inverter with the dead zone to complete the rapid and accurate tracking control of the system on the motor torque and flux linkage.
For the purpose of illustrating the technical contents, constructional features, objects and the like of the present invention in detail, the present invention will be fully explained with reference to the accompanying drawings.
The implementation platform of the invention is a permanent magnet synchronous motor for vehicles, the permanent magnet synchronous motor is used as an alternating current motor, and a two-stage three-phase voltage source inverter is connected with a direct current power supply to realize drive control. The inverter is an indispensable component of the PMSM system, and in order to realize accurate driving of the motor, the motor and the inverter need to be considered as a complete controlled system together. The whole structure is shown in FIG. 1, wherein Q1,Q2,...,Q6Each represents an electrically driven switching element of the inverter.
In consideration of the problems of cost and development time, the permanent magnet synchronous motor MPC-DTC control system based on the dead zone compensation and the dimensionality reduction observer adopts a Matlab/Simulink platform to build a simulation model, and the model comprises a dimensionality reduction observer module; an MPC controller module; the controlled modules are a permanent magnet synchronous motor, a three-phase inverter module and a dead zone module. The structure of which is shown in figure 2.
The controlled object of the invention is a permanent magnet synchronous motor, the mathematical model of the motor is the basis for realizing other advanced theories, and the mathematical model of the motor mainly comprises a voltage equation, a flux linkage equation and an electromagnetic torque equation of a stator. The relevant parameters of the motor are shown in table 1.
TABLE-PERMANENT MAGNET SYNCHRONOUS MOTOR RELATED PARAMETERS
Figure BDA0001858700190000051
Figure BDA0001858700190000061
The following describes each module in detail:
1. inverter-permanent magnet synchronous motor modeling
In practical applications, the inverter often has dead zone effect, and the generation mechanism of the inverter can be divided into switching dead zone effect and setting dead zone effect. Electronic switching devices in an inverter have certain on-time and off-time, the on-time and the off-time are uncontrollable, and the time difference between the on-time and the off-time is generally called as a switching dead time. Because the on-time of a general switching device is less than the off-time, if complementary control signals are applied to the control electrodes of two switching devices of the upper and lower bridge arms of the same phase, the two switching devices will be in 'through' connection, and the consequences are very serious. Generally, the inverter employs a time delay control technique to delay the rising edge of an ideal control signal for a period of time, which is generally referred to as a set dead time. During the dead time, the switching devices are all in an off state, and the inverter output voltage is not controllable. Many research results show that, in the dead zone effect, the influence of the switch dead zone effect is small, and the control dead zone effect can be partially counteracted, and the dead zone effect plays a decisive role. Therefore, ignoring the on and off times of the switching devices herein, the dead times mentioned are all set dead times.
In the dead time, the power devices of the upper bridge arm and the lower bridge arm of the inverter are in an off state, the output voltage is uncontrollable, although the dead time is in a microsecond level and is short, the dead time of a single pulse cannot cause great influence on the system performance, the continuous accumulation effect of the dead time is enough to cause the stator voltage and current of the motor to be greatly influenced, and the influence of the dead time on the output voltage and the output current of the inverter is more and more serious along with the increase of the switching frequency. Particularly, under the condition of low-speed light load, the output voltage of the inverter is smaller, the magnitude of an error voltage vector generated by dead time is not changed, the dead time effect is more remarkable, and the phenomena that the fundamental wave of the output voltage current of the inverter is reduced, the higher harmonic component is increased, torque pulsation is caused, the additional loss of a motor is increased, and even the system can generate an oscillation phenomenon are caused.
The three-phase inverter mainly comprises a direct-current power supply, 6 switches, 3 inductors, 3 resistors and 3 back electromotive forces, and a structural schematic diagram of the three-phase inverter is shown in fig. 3.
Since the switching states of the upper bridge arm and the lower bridge arm of the three-phase inverter are respectively opposite, 3 switching signals S can be useda、SbAnd ScRepresenting 6 switch states Sx(1,2, …,6) as shown in Table two.
Relation between two switch states and switch signal
Figure BDA0001858700190000062
Figure BDA0001858700190000071
Ideally the inverter three-phase voltage Ua，Ub，UcComprises the following steps:
Figure BDA0001858700190000072
suppose the dead time is set to TdLogic implementation of dead zone the original pulse is delayed backward by T as shown in fig. 4dThe pulse obtained by performing and logic operation on the pulse obtained by time and the original pulse is the pulse with the dead zone.
In order to facilitate analysis of the influence of the dead time on the output performance of the inverter, an a-phase bridge arm is taken as an example for analysis, assuming that a power switch device is triggered efficiently (neglecting the on-off time of an IGBT switch tube), and a current direction is defined: the bridge arm of the inverter is positive; the incoming inverter leg is negative. Fig. 5 is a schematic current flow diagram of the a-phase bridge arm.
In fig. 6, a and b are respectively the trigger signals of two switching devices of the a-phase bridge arm under ideal conditions; graph c is the output voltage of the a-phase bridge arm under ideal conditions; graphs d, e are respectively adding dead time TdThe trigger signal of the upper and lower switch devices; graphs f, g are adding dead time T respectivelydThen, the a-phase arm outputs a voltage waveform when the currents are positive and negative, respectively, where the hatched portion indicates the error voltage.
When i isaAt > 0, the effect of the dead zone is equivalent to the loss of a square wave voltage during a PWM cycle as shown by the shaded portion in FIG. 6(f), for the same reason, at a PDuring the period WM, it produces a superposition effect of error square wave voltage opposite to the original voltage direction. The error voltage generated by the dead time has the following characteristics: only one offset voltage pulse exists in each switching period; the amplitude of the error voltage pulse is UdcWidth of Td(ii) a The polarity of the error voltage pulse is opposite to the polarity of the corresponding output current. That is, the dead time produces an error voltage that is dependent only on the direction of current flow, and independent of the magnitude of the current, which is (T) in magnitude (absolute value)d/Ts)/Udc. Therefore, when the dead time is added, the a-phase output voltage UaCan be expressed as:
Figure BDA0001858700190000073
wherein, UaFor actual output voltage value, Ua' is the theoretical output voltage value. It can be seen from the analysis that the voltage distortion caused by the dead zone is directly related to the polarity of the current, and is not related to the magnitude of the current, and the bias voltage Δ U generated by the dead zone always has a polarity opposite to the load current, thus always making the actual current smaller.
The three-term voltage of the inverter with dead zone is then:
Figure BDA0001858700190000081
in the formula of UdcIs the voltage value of the DC power supply, UiRepresenting three-phase voltage (i is a, b or c), and S can be used because the closed states of the switches of the upper and lower bridge arms are just oppositea、Sb、ScIndicating a switch closed state S of a three-phase inverterx(x＝1，2，...，6)。
The formula (3) is obtained by Clarke:
Figure BDA0001858700190000082
after Park transformation:
Figure BDA0001858700190000083
obtained from (3), (4) and (5):
Figure BDA0001858700190000084
modeling the PMSM, wherein a state equation of voltage of the PMSM is as follows:
Figure BDA0001858700190000085
the state equation of the flux linkage is:
Figure BDA0001858700190000086
defining the motor torque and flux linkage as:
Figure BDA0001858700190000087
2. flux linkage dimensionality reduction observer
The permanent magnet synchronous motor drive control system designed by the invention adopts the estimator required by the principle design of the dimensionality reduction observer to observe the stator flux linkage of the permanent magnet synchronous motor.
For three-phase current i of permanent magnet synchronous motora,ib,icClarke conversion is carried out to obtain two-phase current iα,iβThe stator flux linkage of two phases is psiα,ψβVoltage u of two phasesα,uβ. The permanent magnet synchronous motor state equation considering the dead zone is as follows:
Figure BDA0001858700190000091
Figure BDA0001858700190000092
the state space equation can be simplified as:
Figure BDA0001858700190000093
designing a dimension reduction observer, and selecting a variable z to replace a state quantity x2Carrying out variable substitution to obtain:
Figure BDA0001858700190000094
wherein the specific matrices D, F, G, H are:
Figure BDA0001858700190000095
Figure BDA0001858700190000096
Figure BDA0001858700190000097
where γ is the pole placement parameter.
In order to achieve stability, a pole d needs to be configured on the left half plane of the virtual axis, so that the observer converges, and the initial error of flux linkage can be eliminated:
d＝γ|ωr|＜0 (17)
finally, when gamma is smaller than 0, the observer converges, and gamma is-0.2.
The optimal estimated value of the flux linkage is obtained by the above formula, and the feedback value of the torque can be obtained according to the coordinate transformation and the formula (9).
3. Model prediction control method based on dead zone
The model predictive control is a step predictive control method, and the basic idea can be described as solving an open-loop optimal control problem in a limited time domain on line under the conditions of satisfying an objective function, a state, input constraints and the like. Predictive control can be briefly summarized in three steps: predicting the future dynamics of the system according to the obtained current measurement information and a prediction model; ensuring that the optimization problem is solved on line under the target function and the constraint condition; the first element of the solution acts on the system. The model predictive control uses the measured value at each sampling time as the initial condition of prediction. The basic principle of model predictive control is shown in fig. 7. At the current time t, a measured value x is obtained from the controlled system0Predicting the time domain T by the prediction system according to the measurement information and the prediction modelpFuture dynamic behavior of the interior
Figure BDA0001858700190000101
Optimizing the open-loop performance index function and finding out the control time domain TcInternally optimized control input sequence
Figure BDA0001858700190000102
The closer the predicted system output is to the desired system output, the better, i.e., the area of the shaded portion in fig. 7 is minimized.
The method comprises the following specific steps:
integrated modeling of inverter and PMSM
The motor torque and flux linkage are selected as state variables according to equations 3-8, i.e., x ═ Te ψs]TThe switching signal of the three-phase inverter is selected as a control variable, i.e., u ═ Sa Sb Sc]TSelecting the time-varying parameter of the system as d ═ psidAnd λ is dead zone compensation, the system state equation is:
Figure BDA0001858700190000103
in the formula, the matrix B is:
Figure BDA0001858700190000104
the MPC controller can be realized by the steps, and J which minimizes the error between the predicted value and the expected value of the torque and flux linkage is selected as a target function:
Figure BDA0001858700190000105
and substituting various values corresponding to different selectable switch combinations into the objective function J, and screening to obtain the corresponding switch combination as the control quantity output by the controller when the objective function value is minimum.
The specific control process can be expressed as:
firstly, obtaining a voltage feedback quantity u through a sensord、uqAnd a current feedback quantity id、iqThe data is transmitted to a flux linkage observer to obtain the flux linkage estimated value at the current moment
Figure BDA0001858700190000111
And torque estimation
Figure BDA0001858700190000112
The estimated value and the given value are compared
Figure BDA0001858700190000113
Transmitting to MPC controller, obtaining controller output control quantity S through optimal calculationa、Sb、ScAnd acting on the three-phase inverter, and then obtaining the three-phase voltage of the control motor to complete the control of the PMSM.
The control targets of the drive system controller designed by the invention mainly comprise the following parts:
1) the controller is designed through a model predictive control algorithm, upper and lower limit pulsation caused by traditional hysteresis control is avoided, interference of the controller on target tracking control is effectively inhibited, and control precision is improved.
2) The design controller selects the inverter control signal as the control quantity, omits auxiliary modules such as switch selection and the like, simplifies the system structure and avoids unnecessary loss.
3) On the premise of ensuring that the system is simple in structure and low in loss, a proper optimization objective function is selected, the optimal control quantity combination is obtained through solving, tracking errors of torque and flux linkage are effectively restrained, and the main control target of the driving system is completed.
4) The influence brought by the dead zone effect is considered, the dead zone modeling is added into the model prediction controller, the inverter is controlled more accurately, and the error brought by the dead zone is reduced.
The invention designs a model predictive control hub motor driving system based on dead zone compensation and a dimensionality reduction observer, and firstly, a torque and a flux linkage value of a motor in the current state are estimated in real time through a flux linkage observer module to obtain a predicted future state; secondly, ensuring that the objective function solves the optimization problem on line to obtain the optimal solution of the switching signal for controlling the inverter; and finally combining the optimal signals for the inverter.
The motor driving system controller designed by the invention can complete accurate tracking control of motor torque and flux linkage, is one of key bases for realizing independent and accurate control of four wheels of an electric automobile, effectively inhibits motor torque pulsation caused by an inverter dead zone in tracking control while simplifying system structure and reducing unnecessary switching loss, improves control accuracy, and theoretically greatly improves the control effect of a hub motor driving system.
Analysis of simulation results
From the simulation results of fig. 8 and 9, it can be known that the feasibility of the control system designed by the invention has been preliminarily verified, and by comparison, the torque tracking effect of the dead zone-based MPC-DTC control method is better than that of the MPC-DTC method without considering the dead zone, and from fig. 10, the flux linkage observer can obtain an accurate estimation value.

Claims (2)

1. A permanent magnet synchronous motor model prediction-direct torque control method is characterized by comprising the following steps: the method comprises the following steps:
selecting motor torque and flux linkage as state quantity, i.e. x ═ Te ψs]TThe switching signal of the three-phase inverter is selected as a control variable, i.e., u ═ Sa Sb Sc]TSelecting the time-varying parameter of the system as d ═ psidAnd λ is dead zone compensation, the system state equation is:
Figure FDA0001858700180000011
in the formula, the matrix B is:
Figure FDA0001858700180000012
selecting J which minimizes the error between the predicted value and the expected value of the torque and flux linkage as a target function:
Figure FDA0001858700180000013
substituting various values corresponding to different selectable switch combinations into the objective function J, and screening to obtain a corresponding switch combination as the control quantity output by the controller when the objective function value is minimum;
the specific control process can be expressed as follows:
firstly, obtaining a voltage feedback quantity u through a sensord、uqAnd a current feedback quantity id、iqThe data is transmitted to a flux linkage observer to obtain the flux linkage estimated value at the current moment
Figure FDA0001858700180000014
And torque estimation
Figure FDA0001858700180000016
The estimated value and the given value are compared
Figure FDA0001858700180000017
Transmitting to MPC controller, obtaining controller output control quantity S through optimal calculationa、Sb、ScActing on the three-phase inverter, and then obtaining the three-phase voltage of the control motor to complete the control of the PMSM;
the control target of the driving system controller mainly comprises the following parts:
1) the controller is designed through a model predictive control algorithm, upper and lower limit pulsation caused by traditional hysteresis control is avoided, interference of the controller on target tracking control is effectively inhibited, and control precision is improved;
2) the controller is designed to select the inverter control signal as the control quantity, so that a switch selection auxiliary module is omitted, the system structure is simplified, and unnecessary loss is avoided;
3) on the premise of ensuring that the system is simple in structure and low in loss, an optimized objective function is selected, an optimal control quantity combination is obtained through solving, tracking errors of torque and flux linkage are effectively restrained, and a main control target of a driving system is completed;
4) the influence brought by the dead zone effect is considered, the dead zone modeling is added into the model prediction controller, the inverter is controlled more accurately, and the error brought by the dead zone is reduced.
2. The pm-electric synchronous machine model prediction-direct torque control method according to claim 1, characterized in that: the flux linkage dimension reduction observer is as follows:
i for three-phase current of permanent magnet synchronous motora,ib,icPerforming Clarke transformation to obtain two-phase current iα,iβThe stator flux linkage of two phases is psiα,ψβVoltage u of two phasesα,uβ(ii) a The permanent magnet synchronous motor state equation considering the dead zone is as follows:
Figure FDA0001858700180000021
Figure FDA0001858700180000022
the state space equation can be simplified as:
Figure FDA0001858700180000023
designing a dimension reduction observer, and selecting a variable z to replace a state quantity x2Carrying out variable substitution to obtain:
Figure FDA0001858700180000024
wherein the specific matrices D, F, G, H are:
Figure FDA0001858700180000025
Figure FDA0001858700180000026
Figure FDA0001858700180000027
wherein gamma is a pole configuration parameter;
③ arranging poles d
d＝γ|ωr|＜0 (18)
And fourthly, when the gamma is smaller than 0, the observer converges, and the gamma is equal to-0.2.
CN201811325725.2A 2018-11-08 2018-11-08 Model prediction-direct torque control method for permanent magnet synchronous motor Active CN109391202B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811325725.2A CN109391202B (en) 2018-11-08 2018-11-08 Model prediction-direct torque control method for permanent magnet synchronous motor

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811325725.2A CN109391202B (en) 2018-11-08 2018-11-08 Model prediction-direct torque control method for permanent magnet synchronous motor

Publications (2)

Publication Number Publication Date
CN109391202A CN109391202A (en) 2019-02-26
CN109391202B true CN109391202B (en) 2021-09-28

Family

ID=65428551

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811325725.2A Active CN109391202B (en) 2018-11-08 2018-11-08 Model prediction-direct torque control method for permanent magnet synchronous motor

Country Status (1)

Country Link
CN (1) CN109391202B (en)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109842338B (en) * 2019-03-13 2021-07-09 河北工业大学 Permanent magnet synchronous motor prediction model parameter online correction method
CN110445441B (en) * 2019-06-29 2021-07-27 天津大学 Permanent magnet synchronous motor predicted torque control method
CN110350837B (en) * 2019-07-08 2021-03-30 长安大学 Method for controlling direct torque through model prediction of simplified candidate finite state set
CN110445444A (en) * 2019-07-12 2019-11-12 西安理工大学 A kind of improved model predictive control method of asynchronous machine
CN112383255B (en) * 2019-07-29 2022-07-01 联合汽车电子有限公司 Method for detecting rotor temperature of motor
CN110808701B (en) * 2019-12-02 2021-02-02 长安大学 Switching frequency reduction method based on fuzzy control self-adaptive variable weight coefficient
CN113093542B (en) * 2021-03-31 2022-08-12 吉林大学 Motor torque optimization finite set prediction control parallel computing method

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103731084A (en) * 2014-01-10 2014-04-16 西北工业大学 Permanent magnet synchronous motor low inverter power consumption direct torque control method and device
DE102015011745B3 (en) * 2015-09-04 2016-09-22 Audi Ag Charge state determination in a motor vehicle battery
CN106026815A (en) * 2016-07-29 2016-10-12 东南大学 Field-weakening control method of axial magnetic field flux switching type hybrid permanent magnet memory motor
CN108134549A (en) * 2017-12-25 2018-06-08 西安理工大学 A kind of method for improving permanent magnet synchronous motor speed estimate stability
CN108306570A (en) * 2018-01-31 2018-07-20 武汉理工大学 Direct torque control method for permanent magnetic synchronous electric machine and system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9614461B2 (en) * 2014-12-02 2017-04-04 Princeton Power Systems, Inc. Bidirectional high frequency variable speed drive for CHP (combined heating and power) and flywheel applications

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103731084A (en) * 2014-01-10 2014-04-16 西北工业大学 Permanent magnet synchronous motor low inverter power consumption direct torque control method and device
DE102015011745B3 (en) * 2015-09-04 2016-09-22 Audi Ag Charge state determination in a motor vehicle battery
CN106026815A (en) * 2016-07-29 2016-10-12 东南大学 Field-weakening control method of axial magnetic field flux switching type hybrid permanent magnet memory motor
CN108134549A (en) * 2017-12-25 2018-06-08 西安理工大学 A kind of method for improving permanent magnet synchronous motor speed estimate stability
CN108306570A (en) * 2018-01-31 2018-07-20 武汉理工大学 Direct torque control method for permanent magnetic synchronous electric machine and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A Feedback Linearization Control Scheme Based on Direct Torque Control for Permanent Magnet Synchronous Motor;Yuchi Feng 等;《Proceedings of the 37th Chinese Control Conference》;20180727;87-92 *
ModelSim/Simulink co-simulation of a sensorless control for PMSM drives based on I-F startup and EKF;Ying-Shieh Kung;《2016 International Conference on Applied System Innovation (ICASI)》;20160530;全文 *

Also Published As

Publication number Publication date
CN109391202A (en) 2019-02-26

Similar Documents

Publication Publication Date Title
CN109391202B (en) Model prediction-direct torque control method for permanent magnet synchronous motor
CN108900128B (en) Direct torque control method of permanent magnet synchronous motor based on model predictive control
Sutikno et al. A review of direct torque control of induction motors for sustainable reliability and energy efficient drives
CN108900119B (en) Permanent magnet synchronous motor model prediction control method based on dead zone effect
CN112422004B (en) Disturbance suppression method for permanent magnet synchronous motor in weak magnetic control mode
CN108054972B (en) Method for improving dynamic control performance of permanent magnet synchronous linear motor
CN110429895B (en) Construction method of switched reluctance BSG (magnetic reluctance generator) optimized linear controller for hybrid electric vehicle
CN104467596A (en) Method for reducing torque ripples of switched reluctance motor
CN112217437A (en) Permanent magnet synchronous motor three-vector model prediction current control circuit and method
CN111262491B (en) Incremental direct prediction speed control method suitable for permanent magnet motor system
CN205051611U (en) Motor drive system that restraines switched reluctance motor torque ripple
Li et al. Research on IPMSM drive system control technology for electric vehicle energy consumption
CN112886893A (en) Switched reluctance motor torque control method and system based on turn-off angle optimization
CN113904598B (en) Predictive control method for alternating-current permanent magnet synchronous motor
CN113708688A (en) Permanent magnet motor vector reduction model prediction control method
CN111884554B (en) Method for prolonging service life of permanent magnet synchronous motor driving system and accurately controlling torque
Jie et al. Research on control strategy of the electric power steering system for all-terrain vehicles based on model predictive current control
CN111614279A (en) Space vector modulation method and system based on model predictive control and inverter
KR101841769B1 (en) System and method for controlling induction motor of EPS(electronic power steering) in vehicle using adaptive backstepping controller
Wang et al. Design of a wide speed range control strategy of switched reluctance motor for electric vehicles
CN108649852B (en) Permanent magnet synchronous motor control method for improving current loop
CN102684581A (en) Control method of driving duty ratio of permanent magnet motor
CN113285634B (en) Permanent magnet synchronous motor high-speed weak magnetic control method and system based on multi-step zero delay model prediction
Cao et al. Neural network self-adaptive PID control for driving and regenerative braking of electric vehicle
Gao et al. Servo press drive using model predictive control of motor current

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant