CN114928288A - Parameter identification-based underwater propeller sensorless control method and system - Google Patents
Parameter identification-based underwater propeller sensorless control method and system Download PDFInfo
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- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
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- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/24—Vector control not involving the use of rotor position or rotor speed sensors
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- 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
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- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
- H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
- H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters
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Abstract
The invention discloses a parameter identification-based sensorless control system of an underwater propeller, which is used for operating the parameter identification-based sensorless control method of the underwater propeller and comprises the following steps: the device comprises a power circuit, a communication circuit, a main control circuit, a sampling circuit and a driving circuit; the communication circuit is connected with the main control circuit and transmits the received external communication message to the main control circuit; the sampling circuit is respectively connected with the external permanent magnet synchronous motor and the main control circuit, and transmits the acquired parameters of the synchronous motor to the main control circuit; the main control circuit is connected with the driving circuit and generates driving signals based on external communication messages and parameters; the driving circuit is connected with the external permanent magnet synchronous motor and drives the external permanent magnet synchronous motor according to the driving signal. The improved back-emf observer and the rotor position-speed identification algorithm are applied to the sensorless control of the underwater propeller, and the driving control of the motor is realized.
Description
Technical Field
The invention relates to the technical field of control of underwater propellers, in particular to a parameter identification-based sensorless control method and system for an underwater propeller.
Background
The ocean is an important strategic space for the sustainable development of human beings and is a strategic high point for gaining competitive advantages in all countries in the world at present. With the rapid development of science and technology, underwater robots are more and more widely applied. If the underwater robot is required to work safely and stably for a long time in a wide and complicated marine environment, higher performance requirements must be provided for an electric propulsion system of the underwater robot.
The permanent magnet synchronous motor has the advantages of good dynamic response, large torque, high electromechanical conversion efficiency and the like, and is widely applied to the field of high-power electric drive, so that the permanent magnet synchronous motor also becomes the preferred type of the underwater propeller motor. However, the permanent magnet synchronous motor is a strong coupling and complex nonlinear system, the driving control of the permanent magnet synchronous motor is complex, and the accuracy of the rotor position information is a necessary condition for realizing the precise control of the permanent magnet synchronous motor. Conventional permanent magnet synchronous motor drives employ position sensors to measure speed and angular position of the rotor, which sensors suffer from several disadvantages such as reduced reliability, susceptibility to noise, additional cost and weight, and increased complexity of the drive system. The sensorless control omits a mechanical sensor, and the rotation speed and the angle of the rotor are calculated according to the current equation and the mechanical equation of the motor by utilizing physical quantities such as voltage, current and the like which are easy to directly obtain, so that the defects can be effectively avoided, the size of the motor is reduced, the hardware cost is reduced, and the dependence of the system on the environment is reduced. In recent years, with the progress of digital processor technology, processors of ordinary motor drivers have been able to meet the calculation amount requirement for introducing sensorless control, and sensorless control methods have been greatly popularized.
With the application of the permanent magnet synchronous motor based on sensorless control in the robot becoming wider and wider, because the traditional square wave control permanent magnet synchronous motor has the problems of large torque pulsation, large noise and the like, the permanent magnet synchronous motor based on vector control is researched more and more deeply, and the vector control needs a sensorless algorithm to provide real-time and accurate position parameters and rotating speed parameters. Meanwhile, the sensorless algorithm depends on a motor model, while some motor parameters such as stator resistance, stator inductance and the like are set to be fixed values in the traditional sensorless algorithm, but the parameters can be changed under the influence of working conditions and the like in the motor operation process, and the change of the parameters can cause errors of position and speed identification of the position-free sensor, so that the operation efficiency of the permanent magnet synchronous motor is reduced, the output of the motor is reduced, the errors are too large, and even the motor is out of step, so that the whole electric propulsion system is unstable; the traditional calculation method has large errors on the speed and position information of the rotor, and the stability of the underwater robot electric propulsion system is also reduced.
Disclosure of Invention
The invention provides a parameter identification-based sensorless control method and system for an underwater propeller, and aims to solve the problems that in the prior art, a rotor position-speed identification algorithm in sensorless control is complex in design process and identification accuracy is sensitive to motor parameter change.
The invention provides a parameter identification-based sensorless control method for an underwater propeller, which comprises the following steps:
step 1: the method comprises the steps of collecting three-phase voltage and three-phase current of a permanent magnet synchronous point in real time;
step 2: calculating stator voltage and stator current of a two-phase stationary coordinate system according to the three-phase voltage and the three-phase current, and constructing a self-adaptive Luenberger observer;
and 3, step 3: designing a self-adaptive law of stator resistance and stator inductance of a two-phase static coordinate system, and constructing a self-adaptive Luenberger observer combining parameter identification;
and 4, step 4: obtaining a stator resistance estimation value and a stator inductance estimation value through a self-adaptation law of stator resistance and stator inductance of the two-phase static coordinate system, replacing the stator resistance and the stator inductance in the parameters of the self-adaptation Luenberger observer constructed in the step 3 to obtain the self-adaptation Luenberger observer after the motor parameters are corrected, and outputting to obtain a motor reverse potential estimation value under the two-phase static coordinate system;
and 5: converting the stator voltage and the stator current of the two-phase static coordinate system into the stator voltage and the stator current of the two-phase rotating coordinate system, substituting the stator resistance estimated value and the stator inductance estimated value into the back electromotive force observer parameter according to the voltage equation of the permanent magnet synchronous motor under the two-phase rotating coordinate system to obtain a back electromotive force observer after the motor parameter is corrected, and outputting to obtain the motor back electromotive force estimated value under the two-phase rotating coordinate system;
and 6: taking the motor counter potential estimated value under the two-phase static coordinate system and the motor counter potential estimated value under the two-phase rotating coordinate system as the input of a double phase-locked loop, and respectively estimating to obtain two motor rotor speed estimated values and two position information estimated values; substituting two groups of rotor speed-position identification results output by the double phase-locked loops into a T-S fuzzy weighting algorithm to obtain a motor rotor position estimated value and a rotor rotating speed estimated value within a full speed range;
and 7: constructing a rotating speed closed loop through a motor rotor position estimated value and a rotor rotating speed estimated value, and constructing a current closed loop according to stator currents under a two-phase rotating coordinate system; the stator current under the two-phase rotating coordinate system is output through a current closed loop to obtain optimized stator voltage under the two-phase rotating coordinate system, and the optimized stator voltage under the two-phase rotating coordinate system is converted into optimized stator voltage of the two-phase static coordinate system;
and 8: and performing SVPWM modulation on the optimized stator voltage of the two-phase static coordinate system to obtain a PWM control signal, and completing sensorless control of the permanent magnet synchronous motor in the underwater propeller through an inverter.
Further, the specific steps of constructing the adaptive lunberger observer in the step 2 are as follows:
step 21: constructing a voltage equation of the permanent magnet synchronous motor under a two-phase static coordinate system:
wherein u is α 、u β 、i α 、i β For stator voltages and currents in a two-phase stationary frame, R s Is stator resistance, L s Is the stator inductance, e α 、e β The back electromotive force is under a two-phase static coordinate system;
step 22: a mathematical model of the permanent magnet synchronous motor under a two-phase rotating coordinate system is constructed as follows:
the vector form of the mathematical model is:
wherein u is dq ＝[u d u q ] T ，i dq ＝[i d i q ] T ，E dq ＝[e d e q ] T ＝[0 φ m ω e ] T ，u d 、u q 、i d 、i q The stator voltage and current under a two-phase rotating coordinate system are shown, and J is the rotational inertia of the motor;
step 23: according to a voltage equation of the permanent magnet synchronous motor under a two-phase static coordinate system, a space state expression of a constructed system is as follows:
wherein x is [ i ═ i α i β e α e β ] T ，y＝[i α i β ] T ，
Step 24: according to the state space equation of the system, constructing an adaptive Luenberger observer as follows:
wherein,
g is a feedback gain coefficient matrix of a Luenberger observer,
step 25: and calculating a characteristic equation of the state matrix, solving a feedback gain coefficient, and completing the construction of the self-adaptive Luenberger observer.
Further, in the step 3, the stator resistance R is designed according to the Lyapunov stability principle s And stator inductance L s The method comprises the following specific steps:
step 31: estimating motor parameters according to a Lyapunov stability principle, and constructing a positive definite function:
V＝e T e+h(R s ，L s )
wherein
h(R s ，L s ) Is about stator resistance R s And stator inductance L s Positive definite function, i.e. h (R) s ，L s )＞0；
Step 32: derivation of positive definite function:
when in use
Then, the stator resistance R is obtained s And stator inductance L s The adaptive law of (2).
Further, the double phase-locked loop includes: a middle-high speed phase-locked loop and a zero-low speed phase-locked loop.
Further, the specific steps of step 6 are as follows:
step 61: taking the motor counter potential estimated value under the two-phase static coordinate system as the input of a middle-high speed phase-locked loop, and outputting to obtain a first rotor position and a first rotor rotating speed estimated value;
step 62: taking the first rotor position, the first rotor rotating speed estimated value and the motor counter potential estimated value under the two-phase rotating coordinate system as the input of a zero low-speed phase-locked loop, and outputting to obtain a second rotor position and a second rotor rotating speed estimated value;
and step 63: and taking the first rotor position, the first rotor speed estimation value, the second rotor position and the second rotor speed estimation value as the input of a T-S fuzzy weighting algorithm, and estimating the motor rotor position estimation value and the rotor speed estimation value in the full speed range by the following estimation equation:
wherein
Respectively representing the rotor position estimate and the rotor speed estimate, U, of the motor over a full speed range PLL1 、U PLL2 Respectively the output of the zero low-speed phase-locked loop and the medium-high speed phase-locked loop, and lambda is [0,1 ]]The relationship between the constant and the lambda value and the motor speed is as follows:
wherein, ω is e And calculating the fuzzy weight of the T-S.
The invention also provides a parameter identification-based sensor-free control system of the underwater propeller, which is used for operating the parameter identification-based sensor-free control method of the underwater propeller, and comprises the following steps: the device comprises a power circuit, a communication circuit, a main control circuit, a sampling circuit and a driving circuit;
the power circuit is externally connected with a direct current power supply, is respectively connected with the communication circuit main control circuit, the sampling circuit and the driving circuit and supplies power to each circuit;
the communication circuit is connected with the main control circuit and transmits the received external communication message to the main control circuit;
the sampling circuit is respectively connected with the external permanent magnet synchronous motor and the main control circuit, and transmits the acquired parameters of the external permanent magnet synchronous motor to the main control circuit;
the main control circuit is connected with the driving circuit and generates driving signals based on external communication messages and parameters of the external permanent magnet synchronous motor;
the driving circuit is connected with the external permanent magnet synchronous motor and drives the external permanent magnet synchronous motor according to the driving signal.
Further, the main control circuit comprises: the sensor-free control method of the underwater propeller based on parameter identification is disclosed.
The invention has the beneficial effects that:
1. the improved back emf observer and the rotor position-speed identification algorithm are applied to the sensorless control of the underwater propeller, so that the driving control of the motor is realized;
2. in practical application, a group of appropriate feedback gain coefficients is mostly found through trial and error when a feedback gain matrix of the back-emf observer is designed, and a relevant theoretical basis is lacked. For motors with different parameters, the coefficients may be greatly different, and certain workload is required for parameter setting. Aiming at the problem, the design method of the feedback gain matrix of the back electromotive force observer is improved, the self-adaptive Luenberger observer for correcting the feedback gain coefficient matrix in real time along with the running state of the motor is formed, and the design process of the back electromotive force observer is simplified;
3. in practical application, motor parameters can change along with the environment, which can reduce the observation accuracy of the counter-potential observer; aiming at the problem that the traditional back emf observer is sensitive to the change of motor parameters, the Lyapunov self-adaptive algorithm is introduced to identify the motor parameters, so that the observation precision of the back emf observer is improved, and the precision of a sensorless control algorithm is improved;
4. according to the invention, phase-locked loops suitable for medium-high speed and zero-low speed ranges are combined, a T-S fuzzy weighting algorithm is introduced to form a double phase-locked loop algorithm based on T-S fuzzy weighting, and a back-emf observer is combined with the algorithm, so that compared with the back-emf observer combined with an arctangent function, the rotor position and speed information acquisition is smoother and more accurate; compared with a back electromotive force observer combined with a traditional phase-locked loop, the problem that the position-speed identification precision of a full-speed domain rotor cannot be guaranteed is solved;
5. the invention adopts STM32 series single-chip microcomputer as the main control chip, designs the hardware circuits of the main control circuit, the drive circuit, the feedback circuit and the like, realizes the double closed-loop control of the rotating speed and the current by the built-in program of the main control chip, greatly simplifies the complexity of the circuit, improves the stability of the system, and is easy to expand the function of the system.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
fig. 1 is a topological diagram corresponding to a control method according to an embodiment of the present invention;
FIG. 2 is a block diagram of an adaptive Luenberger observer in accordance with an embodiment of the present invention;
FIG. 3 is a structural diagram of a parameter identification algorithm based on Lyapunov theory according to an embodiment of the present invention;
FIG. 4 is a block diagram of a dual phase-locked loop speed identification algorithm based on T-S fuzzy weighting according to an embodiment of the present invention;
FIG. 5 is a system block diagram of an embodiment of the present invention;
FIG. 6 is a diagram of a software program structure according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
As shown in fig. 1, a topological diagram corresponding to the control method according to the embodiment of the present invention, an algorithm part of the control method includes: the system comprises a rotating speed ring PI controller, a current ring PI controller, an SVPWM module, a self-adaptive Luenberger observer, a parameter identification module based on a Lyapunov theory and a double-phase-locked loop speed identification module, wherein the algorithm is realized by depending on a hardware circuit and a motor. The whole process is as follows: when the motor is started, a rotating speed omega is given first 1 The method comprises the steps of acquiring three-phase voltage and three-phase current of the permanent magnet synchronous motor in real time, and converting the three-phase voltage and the three-phase current into stator voltage and current u in a two-phase static coordinate system through Clark conversion α 、u β 、i α 、i β (ii) a Constructing a Luenberger observer under a two-phase static coordinate system; u is to be α 、u β 、i α 、i β Obtaining stator voltage and current u under a two-phase rotating coordinate system through Park conversion d 、u q 、i d 、i q (ii) a Will u d 、u q 、i d 、i q As the input of the motor parameter identification process, the stator resistance of the motor is obtained through output
And stator inductance
And then the stator resistance obtained by a parameter identification module based on the Lyapunov theory
And stator inductance
Replaces the motor stator resistance R in the established adaptive Luenberger observer parameters s And motor stator inductance L s And outputting to obtain the estimated value of the motor counter electromotive force
Then estimating the motor back electromotive force
The two sets of motor rotor position estimated values theta are obtained by output as the input and output of the double phase-locked loop speed identification module e With rotor speed estimate omega e Thereby forming a rotating speed closed loop; current i in two-phase rotating coordinate system d 、i q Obtaining the stator voltage u under a two-phase rotating coordinate system through current closed-loop output d 、u q (ii) a Then u is put d 、u q Obtaining the stator voltage u of the two-phase static coordinate system through inverse Park conversion α 、u β (ii) a Final u α 、u β And PWM control signals are obtained through SVPWM modulation, and sensorless control of the permanent magnet synchronous motor is realized through an inverter.
As shown in fig. 2, a structure diagram of the adaptive lunberg observer according to an embodiment of the present invention is shown, where a feedback gain coefficient of a conventional lunberg observer is designed to be a correlation function following a rotation speed of a motor, a feedback gain coefficient calculation unit is added to an input end of the conventional lunberg observer, the rotation speed of the motor and a parameter of the motor are used as input of the feedback gain coefficient calculation unit, and a model of the lunberg observer is modified by using a calculated new feedback gain coefficient, so as to obtain the variable feedback gain adaptive lunberg observer.
The specific control method of the constructed adaptive Luenberger observer comprises the following steps:
step S21: constructing a voltage equation of the permanent magnet synchronous motor under a two-phase static coordinate system:
wherein u is α 、u β 、i α 、i β For stator voltages and currents in a two-phase stationary frame, R s Is stator resistance, L s As stator inductances ex, e β Is the back electromotive force of the two-phase static coordinate system.
Step S22: a mathematical model of the permanent magnet synchronous motor under a two-phase rotating coordinate system is constructed as follows:
the above equation is written in vector form:
wherein E dq ＝[e d e q ]＝[0 φ m ω e ] T 。
Step S23: according to a voltage equation of the permanent magnet synchronous motor under a two-phase static coordinate system, a space state expression of a construction system is as follows:
wherein x＝[i α i β e α e β ] T ，y＝[i α i β ] T ，
Step S24: according to the state space equation of the system, a Luenberger observer is constructed as follows:
wherein
G is a feedback gain coefficient matrix of the Luenberger observer.
Let the feedback gain coefficient matrix of the luneberg observer be:
step S25: and calculating a characteristic equation of the state matrix, solving a feedback gain coefficient, and completing the construction of the self-adaptive Luenberger observer.
As shown in fig. 3, which is a structural diagram of a parameter identification algorithm based on the Lyapunov theory, an adaptive algorithm based on the Lyapunov theory is introduced into an output end of a base of a traditional lunberg observer, actual stator resistance and inductance parameters are estimated by using the output of the lunberg observer, and a motor model and a lunberg observer model are corrected by using calculated stator resistance and inductance estimated values.
The constructed parameter identification algorithm based on the Lyapunov theory comprises the following steps:
step S31: estimating motor parameters according to a Lyapunov theory, and constructing a positive definite function:
V＝e T e+h(R s ，L s )
wherein
h(R s ，L s ) Is about stator resistance R s And stator inductance L s A positive definite function, i.e. h (R) s ，L s )＞0。
Step S32: the positive definite function constructed in step S31 is derived:
according to the Lyapunov theorem of stability, when
The system is progressively stabilized. Separately determining the stator resistance R s And stator inductance L s The adaptive law of (2).
Fig. 4 is a structural diagram of a dual phase-locked loop speed identification algorithm based on T-S fuzzy weighting, which includes three algorithms, i.e., a medium-high speed phase-locked loop, a zero-low speed phase-locked loop, and T-S fuzzy weighting, where the rotor speed estimation value and the rotor position estimation value calculated by the two phase-locked loops respectively output final rotor speed estimation value and final rotor position estimation value through T-S fuzzy weighting.
The constructed double phase-locked loop speed identification algorithm based on the T-S fuzzy weighting comprises the following steps:
step S61: the output of the counter-potential observer, i.e. the motor counter-potential estimate
As an input to a double phase-locked loop, wherein
The rotor position is obtained as the input and output of the middle-high speed phase-locked loop
And rotor speed estimate and
the rotor position is obtained as the input and output of the zero low-speed phase-locked loop
And rotor speed estimate
For a middle-high speed phase-locked loop, the identification precision of the rotating speed is high when the rotating speed of the motor is in a middle-high speed range, and the identification precision of the phase-locked loop is low because the change of counter electromotive force is small when the rotating speed of the motor is in a zero low speed range. The structure of the middle-high speed phase-locked loop is as follows:
wherein
In order to be a position error,
is the rotational speed error.
For the zero low-speed phase-locked loop, the rotating speed identification precision is high when the rotating speed of the motor is in a zero low-speed range, but the phase-locked loop depends on flux linkage parameters, and the position identification precision is reduced along with the increase of the rotating speed of the motor. The structure of the zero low-speed phase-locked loop is as follows:
v and k are two constant constants for adjusting the identification convergence speed of the zero-low-speed phase-locked loop, and are components of the counter electromotive force observed value on the d axis and the q axis respectively.
Step S62: output of double phase-locked loop
As input to the T-S fuzzy weighting algorithm. The definition process of the T-S fuzzy inference uses a weighted average method, and the ith rule is set to output a result u i M is a number of rules, weighted by w i The inference output is then represented as follows:
estimated speed of rotation of the motor less than omega e1 And is greater than omega e2 And then respectively applying a zero low-speed phase-locked loop and a medium-high speed phase-locked loop. In the switching interval, the motor speed is obtained by carrying out T-S fuzzy weighting on the rotating speeds estimated by different methods, and the estimation equation is as follows:
wherein
Respectively representing the estimated speed and position, U PLL1 、U PLL2 The outputs of the phase-locked loop at zero low speed and medium and high speed, respectively. λ is [0,1 ]]The value of the constant and the motor speed relation are as follows:
wherein omega e And calculating the fuzzy weight of the T-S.
Upper limit ω of switching section in switching weight coefficient e1 And a lower limit ω e2 Experiments are needed to confirm that the T-S fuzzy weighting switching method can reduce the system buffeting, and the gradual change of the position and the speed is completed in the switching process of different estimation methods, so that the system of the permanent magnet synchronous motor is more stable.
Fig. 5 is a schematic system diagram of a parameter identification-based sensorless control system for an underwater vehicle, which is provided in the embodiment of the present invention, and is configured to operate the parameter identification-based sensorless control method for an underwater vehicle, where the method includes: the device comprises a power circuit, a communication circuit, a main control circuit, a sampling circuit and a driving circuit;
the power circuit is externally connected with a direct current power supply, is respectively connected with the communication circuit main control circuit, the sampling circuit and the driving circuit and supplies power to each circuit;
the communication circuit is connected with the main control circuit and transmits the received external communication message to the main control circuit;
the sampling circuit is respectively connected with the external permanent magnet synchronous motor and the main control circuit, and transmits the acquired parameters of the external permanent magnet synchronous motor to the main control circuit;
the master control circuit is connected with the driving circuit and generates driving signals based on external communication messages and parameters of the external permanent magnet synchronous motor;
the driving circuit is connected with the external permanent magnet synchronous motor and drives the external permanent magnet synchronous motor according to the driving signal.
Fig. 6 is a schematic diagram of a software program structure of a parameter identification-based method for controlling a sensorless underwater propulsion device in a main control circuit chip of a parameter identification-based sensorless underwater propulsion device control system according to an embodiment of the present invention, where the whole software program includes two parts, namely a main function and an interrupt service function:
the master function includes the system initialization process and the detection of the protection signal and the reset signal. The interruption service function mainly comprises a vector control algorithm and a parameter identification algorithm, SVPWM output in the vector control is updated to 50us, the current loop calculation period is 100us, the speed loop calculation period is 500us, and the main execution contents are current sampling, speed regulator calculation, current coordinate conversion, current regulator calculation, SVPWM calculation, voltage reconstruction, rotating speed estimation and the like. The identification period of the stator inductance and the stator resistance in the parameter identification algorithm is 500 us. The interrupt program is set as overflow interrupt trigger in the PWM module, and sends out a current sampling signal when the interrupt trigger, so that ADC sampling of motor phase current and voltage is realized.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (7)
1. A sensor-free control method of an underwater propeller based on parameter identification is characterized by comprising the following steps:
step 1: acquiring three-phase voltage and three-phase current of a permanent magnet synchronous point in real time;
step 2: calculating the stator voltage and the stator current of a two-phase static coordinate system according to the three-phase voltage and the three-phase current, and constructing a self-adaptive Luenberger observer;
and 3, step 3: designing a self-adaptive law of stator resistance and stator inductance of a two-phase static coordinate system, and constructing a self-adaptive Luenberger observer combining parameter identification;
and 4, step 4: obtaining a stator resistance estimation value and a stator inductance estimation value through a self-adaptation law of stator resistance and stator inductance of the two-phase static coordinate system, replacing the stator resistance and the stator inductance in the parameters of the self-adaptation Luenberger observer constructed in the step 3 to obtain the self-adaptation Luenberger observer after the motor parameters are corrected, and outputting to obtain a motor reverse potential estimation value under the two-phase static coordinate system;
and 5: converting the stator voltage and the stator current of the two-phase static coordinate system into the stator voltage and the stator current of the two-phase rotating coordinate system, substituting the stator resistance estimated value and the stator inductance estimated value into the back electromotive force observer parameter according to the voltage equation of the permanent magnet synchronous motor under the two-phase rotating coordinate system to obtain a back electromotive force observer after the motor parameter is corrected, and outputting to obtain the motor back electromotive force estimated value under the two-phase rotating coordinate system;
and 6: taking the motor counter potential estimated value under the two-phase static coordinate system and the motor counter potential estimated value under the two-phase rotating coordinate system as the input of a double phase-locked loop, and respectively estimating to obtain two motor rotor speed estimated values and two position information estimated values; substituting two groups of rotor speed-position identification results output by the double phase-locked loops into a T-S fuzzy weighting algorithm to obtain a motor rotor position estimation value and a rotor rotation speed estimation value in a full speed range;
and 7: constructing a rotating speed closed loop through a motor rotor position estimated value and a rotor rotating speed estimated value, and constructing a current closed loop according to stator currents under a two-phase rotating coordinate system; the stator current under the two-phase rotating coordinate system is output through a current closed loop to obtain optimized stator voltage under the two-phase rotating coordinate system, and the optimized stator voltage under the two-phase rotating coordinate system is converted into optimized stator voltage of the two-phase static coordinate system;
and step 8: and performing SVPWM modulation on the optimized stator voltage of the two-phase static coordinate system to obtain a PWM control signal, and completing sensorless control of the permanent magnet synchronous motor in the underwater propeller through an inverter.
2. The method for sensorless control of an underwater thruster based on parameter identification according to claim 1, wherein the specific steps of constructing the adaptive lunberg observer in the step 2 are as follows:
step 21: constructing a voltage equation of the permanent magnet synchronous motor under a two-phase static coordinate system:
wherein u is α 、u β 、i α 、i β For stator voltages and currents in a two-phase stationary frame, R s Is stator resistance, L s Is stator inductance, e α 、e β The back electromotive force is under a two-phase static coordinate system;
step 22: a mathematical model of the permanent magnet synchronous motor under a two-phase rotating coordinate system is constructed as follows:
the vector form of the mathematical model is:
wherein u is dq ＝[u d u q ] T ，i dq ＝[i d i q ] T ，E dq ＝[e d e q ] T ＝[0 φ m ω e ] T ，u d 、u q 、i d 、i q The stator voltage and current under a two-phase rotating coordinate system are shown, and J is the rotational inertia of the motor;
step 23: according to a voltage equation of the permanent magnet synchronous motor under a two-phase static coordinate system, a space state expression of a constructed system is as follows:
wherein x is [ i ═ i α i β e α e β ] T ，y＝[i α i β ] T ，
And step 24: according to the state space equation of the system, constructing an adaptive Luenberger observer as follows:
wherein,
g is a feedback gain coefficient matrix of a Luenberger observer,
step 25: and calculating a characteristic equation of the state matrix, solving a feedback gain coefficient, and completing the construction of the self-adaptive Luenberger observer.
3. The parameter identification-based sensorless control method for the underwater propeller according to claim 1, wherein in the step 3, the stator resistance R is designed according to Lyapunov stability principle s And stator inductance L s The method comprises the following specific steps:
step 31: estimating motor parameters according to a Lyapunov stability principle, and constructing a positive definite function:
V＝e T e+h(R s ,L s )
wherein
h(R s ,L s ) Is about stator resistance R s And stator inductance L s Positive definite function, i.e. h (R) s ,L s )＞0；
Step 32: derivation of positive definite function:
when in use
Then, the stator resistance R is obtained s And stator inductance L s The adaptive law of (2).
4. The parameter identification-based sensorless control method of an underwater propeller of claim 1, wherein the double phase-locked loop includes: a middle-high speed phase-locked loop and a zero-low speed phase-locked loop.
5. The method for sensorless control of an underwater thruster based on parameter identification as claimed in claim 4, wherein the specific steps of the step 6 are as follows:
step 61: taking the motor counter potential estimated value under the two-phase static coordinate system as the input of a middle-high speed phase-locked loop, and outputting to obtain a first rotor position and a first rotor rotating speed estimated value;
step 62: taking the first rotor position, the first rotor rotating speed estimated value and the motor counter potential estimated value under the two-phase rotating coordinate system as the input of a zero low-speed phase-locked loop, and outputting to obtain a second rotor position and a second rotor rotating speed estimated value;
and step 63: and taking the first rotor position, the first rotor speed estimation value, the second rotor position and the second rotor speed estimation value as the input of a T-S fuzzy weighting algorithm, and estimating the motor rotor position estimation value and the rotor speed estimation value in the full speed range by the following estimation equation:
wherein
Respectively representing the estimated rotor position and the estimated rotor speed, U, of the motor over the full speed range PLL1 、U PLL2 Respectively the output of the zero low-speed phase-locked loop and the medium-high speed phase-locked loop, and lambda is [0,1 ]]The relationship between the lambda value and the motor speed is as follows:
wherein, ω is e And calculating the fuzzy weight of the T-S.
6. A parameter identification based sensorless control system for an underwater vehicle for operating the parameter identification based sensorless control method of any one of claims 1 to 5, wherein the parameter identification based sensorless control system comprises: the device comprises a power circuit, a communication circuit, a main control circuit, a sampling circuit and a driving circuit;
the power circuit is externally connected with a direct current power supply, is respectively connected with the communication circuit main control circuit, the sampling circuit and the driving circuit and supplies power to all the circuits;
the communication circuit is connected with the main control circuit and transmits the received external communication message to the main control circuit;
the sampling circuit is respectively connected with the external permanent magnet synchronous motor and the main control circuit, and transmits the acquired parameters of the external permanent magnet synchronous motor to the main control circuit;
the main control circuit is connected with the driving circuit and generates driving signals based on external communication messages and parameters of the external permanent magnet synchronous motor;
the driving circuit is connected with the external permanent magnet synchronous motor and drives the external permanent magnet synchronous motor according to the driving signal.
7. The parameter identification based sensorless control system of claim 6 wherein the master control circuit comprises: the sensor-free control method of the underwater propeller based on parameter identification is disclosed.
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