CN114944801A - PMSM (permanent magnet synchronous motor) position sensorless control method based on innovation self-adaptive extended Kalman - Google Patents

PMSM (permanent magnet synchronous motor) position sensorless control method based on innovation self-adaptive extended Kalman Download PDF

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CN114944801A
CN114944801A CN202210479783.0A CN202210479783A CN114944801A CN 114944801 A CN114944801 A CN 114944801A CN 202210479783 A CN202210479783 A CN 202210479783A CN 114944801 A CN114944801 A CN 114944801A
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extended kalman
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兰志勇
李延昊
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Xiangtan University
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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
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, 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
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/13—Observer control, e.g. using Luenberger observers or Kalman filters
    • 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
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/18—Estimation of position or speed
    • 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
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/20—Estimation of torque
    • 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
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/22—Current control, e.g. using a current control loop
    • 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
    • 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
    • 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
    • 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
    • H02P27/08—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 with pulse width modulation
    • H02P27/12—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 with pulse width modulation pulsing by guiding the flux vector, current vector or voltage vector on a circle or a closed curve, e.g. for direct torque 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
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/14—Electronic commutators
    • H02P6/16—Circuit arrangements for detecting position
    • H02P6/18—Circuit arrangements for detecting position without separate position detecting elements
    • 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
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/34—Modelling or simulation for control purposes
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/72—Electric energy management in electromobility

Abstract

The invention provides a PMSM (permanent magnet synchronous motor) position sensorless control method based on innovation self-adaptive extended Kalman, which comprises the steps of firstly utilizing current and voltage signals under a static coordinate system as input of Extended Kalman (EKF), and then obtaining the rotor position and the rotating speed of a motor through iterative computation. In order to improve the interference rejection performance of the EKF, a weighting coefficient is set on the innovation sequence calculation of the EKF, and an innovation value obtained by weighting is introduced into the calculation of the Kalman gain. According to the invention, when the input parameters and the noise statistics have deviation, the influence of error signals on the whole system is reduced, the robustness of the observer under disturbance is ensured, the prediction precision of the motor rotating speed and the rotor position is improved, and the method has wide application value in a position-free sensor of a permanent magnet synchronous motor.

Description

PMSM (permanent magnet synchronous motor) position sensorless control method based on innovation self-adaptive extended Kalman
Technical Field
The invention belongs to the field of control of permanent magnet synchronous motors, relates to a PMSM (permanent magnet synchronous motor) sensorless control method based on innovation self-adaptive extended Kalman, and particularly relates to a system model for realizing sensorless control in the full rotating speed range of a permanent magnet synchronous motor.
Background
Compared with a direct current motor, a Permanent Magnet Synchronous Motor (PMSM) has the advantages of high reliability, low cost, easiness in maintenance and the like, and is widely applied to high-performance speed regulation systems such as an electric vehicle main drive motor, an unmanned aerial vehicle, a robot, a crane and the like. In terms of a motor control algorithm, a traditional motor control system usually adopts a photoelectric encoder or a hall sensor to acquire the position and the rotating speed of a rotor of a motor, and the sensor is used to increase the cost and bring about the problems of low reliability, complex maintenance and the like. Therefore, the sensorless control system is an important research direction for the motor control system.
In order to realize the position-sensorless control of the permanent magnet synchronous motor, two methods, namely a sliding-mode observer and an extended Kalman filtering method, are commonly used. The sliding-mode observer mainly observes back electromotive force information in a dynamic model to extract rotor position information, and when a motor operates at medium and high speed, the back electromotive force is obvious and is beneficial to observation and extraction, so that a method based on high-frequency signal injection is often adopted at low speed. Extended Kalman Filtering (EKF) is an iterative algorithm based on minimum variance, and in recent years, the development of high-speed floating-point processors has solved the problem of large EKF calculation amount, and is widely applied to sensorless vector control systems. As an iterative discrete calculation method, EKF combines state prior estimation and measurement feedback of a system, and then a Kalman gain matrix is adjusted to obtain a posterior estimation value which infinitely approaches to a system state true value at the moment. Compared with other observation algorithms, the EKF has the advantages of wide applicable rotating speed range, strong interference resistance and the like, thereby becoming a hotspot for motor state estimation research.
Disclosure of Invention
The invention aims to solve the problem that the estimation error of the rotor position and the rotating speed is large due to the fact that the observation performance of a filter is reduced under the condition that an input signal is interfered or the statistical deviation of noise occurs in the extended Kalman filtering. The invention provides an adaptive extended Kalman filtering algorithm based on an innovation sequence, which finally influences the calculation of a Kalman gain matrix by setting a calculation rule for the innovation sequence and adjusting a calculation method of an innovation covariance, thereby improving the estimation performance of the extended Kalman filtering algorithm on the estimation of the position and the rotating speed of a rotor. The method has the advantages of strong robustness, higher precision and better stability.
In order to achieve the above object, the present invention provides a position-sensor-free control method based on innovation adaptive extended kalman filtering, which is characterized by comprising the following steps:
(1) establishing an extended Kalman filtering algorithm, inputting voltage and current signals of a static coordinate system, and outputting the voltage and current signals of the static coordinate system as the rotating speed and the rotor position of a motor, wherein the extended Kalman filtering algorithm comprises the following steps:
(A) under a static coordinate system, a permanent magnet synchronous motor model is established, which can be expressed as follows:
Figure BDA0003627105380000021
wherein R is s Is stator resistance, L s Is stator inductance, i α 、i β 、u α 、u β Respectively, current and voltage components, omega, in the estimated stationary frame e And θ is the motor speed and rotor position, respectively.
(B) The extended Kalman filtering is an application of Kalman filtering in a nonlinear system, in order to better control a permanent magnet synchronous motor, a motor model is firstly subjected to linearization and discretization, and a state equation of the extended Kalman filtering is established, wherein the state equation is expressed as follows:
Figure BDA0003627105380000022
where x is the state variable, y is the output quantity, B is the system input matrix, H is the output matrix, v is the white Gaussian noise, T s For the sampling period, the superscript "^" represents the estimate, k | k-1 represents from time k-1 to kThe state of the moment transitions.
(C) Establishing an implementation model of an extended Kalman algorithm, which mainly comprises the following steps: state prediction estimation, covariance estimation, Kalman gain calculation, state estimation value correction and error covariance matrix updating, which mainly comprise the following steps:
Figure BDA0003627105380000023
Figure BDA0003627105380000024
K k|k-1 ＝P k|k-1 H T [HP k|k-1 H T +R] -1
Figure BDA0003627105380000025
P k|k ＝(I-K k|k-1 H)P k|k-1
(2) analyzing the influence of interference on the extended Kalman filtering algorithm, wherein when the interference occurs, an innovation covariance calculation formula of the extended Kalman filtering algorithm is as follows:
Figure BDA0003627105380000026
introducing the disturbed innovation covariance into the calculation of a Kalman gain matrix, updating in real time in state estimation, and taking the updated innovation covariance as the weight of a deviation measurement quantity and an observed quantity of system state estimation, wherein the calculation formula is as follows:
Figure BDA0003627105380000027
(3) establishing an adaptive extended Kalman algorithm based on an innovation sequence, establishing an adaptive rule of an exponential weighting coefficient mainly in the selection of the innovation sequence, and when the interference and the statistical information deviation condition occur, the adaptive extended Kalman algorithm can select a nearby innovation covariance value as a calculation basis of a Kalman matrix to improve the robustness of the system, wherein the rule is that
Figure BDA0003627105380000028
Figure BDA0003627105380000029
Wherein the weight coefficient at the moment k is gamma i 。
(4) Importing the covariance matrix calculation after the weighting coefficient into the Kalman matrix calculation to obtain a new adaptive extended Kalman algorithm rule as follows
Figure BDA0003627105380000031
Drawings
The invention is further illustrated by the following figures and examples.
FIG. 1 is a schematic block diagram of PMSM sensorless vector control based on adaptive extended Kalman filter according to the present invention.
Fig. 2 is a control block diagram of the adaptive extended kalman filter according to the embodiment of the present invention.
Fig. 3 is a rotor position simulink simulation diagram of a permanent magnet synchronous motor sensorless vector control load mutation condition based on adaptive extended kalman filtering according to an embodiment of the present invention.
Fig. 4 is a simulation diagram of rotor position error simulink under the condition of sensorless vector control load mutation of the permanent magnet synchronous motor based on adaptive extended kalman filtering according to the embodiment of the present invention.
Fig. 5 is a simulink simulation diagram of the observed rotation speed and the actual rotation speed of the permanent magnet synchronous motor based on the adaptive extended kalman filter under the condition of the sensorless vector control load mutation provided by the embodiment of the present invention.
Detailed Description
In order to make the purpose and technical solution of the present invention more clearly understood, the following detailed description is made with reference to the accompanying drawings and examples, and the application principle of the present invention is described in detail. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the respective embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the present invention provides a PMSM position sensorless control method based on innovation adaptive extended kalman, which combines a vector control algorithm with an extended kalman observer. The method comprises the following specific implementation steps:
(A) establishing a motor current detection circuit, and obtaining a three-phase current value of the motor as an input of Clark conversion;
(B) obtaining a current signal i under a static coordinate system through Clark change α 、i β As input to an extended kalman filter;
(C) the extended Kalman filter is combined with state prior estimation and measurement feedback of the system to adjust a Kalman gain matrix to obtain a posterior estimation value infinitely approaching to a system state true value at the moment, namely the rotating speed and rotor position information of the motor;
(D) the error value of the estimated rotating speed and the actual rotating speed output by the extended Kalman filter is used as the input of a speed loop PI, and the output of the speed loop is a torque current component
Figure BDA0003627105380000032
Given torque current component
Figure BDA0003627105380000033
With the actual torque current i q Difference value of (d) and excitation current set value
Figure BDA0003627105380000034
With the actual value i d The difference of (c) is input as a current loop. According to the error information of the rotating speed and the current, combining the SVPWM vectorThe modulation technique completes the double closed-loop control of the PMSM.
As shown in FIG. 2, a structural block diagram of an adaptive extended Kalman control algorithm is provided, and a system selects a result close to an actual true value as an output of an observer by selecting a value of a Kalman gain matrix. When the system is interfered, the algorithm of the invention adjusts the proportion of the new information covariance matrix at the moment of approach and takes the proportion as the basis of Kalman gain calculation, thereby reducing the influence of interference information on Kalman gain and improving the robustness of the system, and the implementation steps of the anti-interference process are as follows:
c1, setting weighting factors in the calculation process of the innovation covariance matrix, and when disturbance occurs, adjusting the proportion of adjacent innovation sequences by the calculation of the innovation covariance matrix to be used as the calculation basis of the innovation covariance matrix;
c2, introducing the value of the new information covariance matrix obtained by C1 into the calculation of the Kalman gain matrix, and reducing the influence of interference signals on the Kalman gain matrix
C3 readjusting the weighting coefficients when the disturbance recovers
Fig. 3 to fig. 5 are diagrams showing the verification of the kalman filter performance under the interference condition. FIG. 3 is a diagram of an AEKF estimated rotor position under a disturbed condition, FIG. 4 is a curve of an AEKF rotor position error under the disturbed condition, and FIG. 5 is a graph of an actual rotating speed and an observed rotating speed under the disturbed condition. As can be seen from the simulation graphs, the PMSM position-free sensor control method based on the innovation self-adaptive extended Kalman has good robustness, small error between the estimated rotor position and the actual rotor position and good rotating speed tracking effect under the condition of suffering interference, so that the accuracy and the effectiveness of the method are proved.

Claims (5)

1. A PMSM (permanent magnet synchronous motor) position sensor-free control method based on innovation self-adaptive extended Kalman realizes a control method of a position sensor-free control method in a full-speed domain of a motor, and comprises the following steps:
(1) collecting three-phase current signals as inputs to a clark transform, with its output i α 、i β Is the input signal of the extended kalman. In the mathematics of PMSMAnd after the model is subjected to discretization and linearization, establishing a state equation of the extended Kalman filter by combining the extended Kalman equation.
(2) Adopting an iterative calculation method to carry out rotation speed signal omega of PMSM e And rotor position theta is estimated. And (4) calculating the estimated value of the current moment by considering the influence of measurement and system noise on the state equation. On the basis, the jacobian matrix is synthesized to complete the calculation of the error covariance matrix, and then the measurement matrix is combined to complete the calculation of the Kalman gain matrix.
(3) After the Kalman gain matrix is obtained, the ratio of the estimated value to the true value is selected by adjusting the Kalman gain, so that the estimated value in the step (2) is corrected. And updating the value of the error covariance matrix by combining the Kalman gain and the measurement matrix.
(4) On the basis of the traditional extended Kalman, a weighting coefficient is set for the calculation of an innovation sequence in the extended Kalman, the weighting coefficient is introduced into the calculation of the extended Kalman gain, and when interference occurs, the proportion of an adjacent sequence is adjusted, so that the influence of interference on state estimation is reduced.
2. The method according to claim 1, wherein the extended kalman filter state equation is established by:
a1: building a mathematical model of the permanent magnet synchronous motor based on a static coordinate system to obtain i α 、i β As state variables of the system.
Figure FDA0003627105370000011
In the formula, R s Is a stator resistor; omega e Is the rotor electrical angular velocity; psi f Is a permanent magnet flux linkage; i.e. i α And i β The stator current alpha and beta axis components are respectively; u. of α And u β Stator voltage α, β axis components, respectively, and θ is the rotor position angle.
A2: on the basis of A1, a state equation and an observation equation are established
Figure FDA0003627105370000012
Where f (x) is a system state matrix, and x ═ i α ,i β ,ω e ,θ] T Is a state variable, B is a system input matrix; h is an output matrix; w and v are respectively a system error and a measurement error, and the system error and the measurement error are mutually uncorrelated white gaussian noise.
A3: combining the state of A2 with the observation equation, the state equation for establishing an extended Kalman is as follows:
Figure FDA0003627105370000013
where x is the state variable, y is the output quantity, B is the system input matrix, H is the output matrix, v is Gaussian white noise, T s For the sampling period, superscript "^" represents the estimate, k | k-1 represents the state transition from time k-1 to time k.
3. The PMSM position sensorless control method based on the innovation adaptive extended Kalman, according to claim 1, characterized in that: the extended kalman filter of claim 1, wherein the input is i in a stationary coordinate system obtained by Park transformation in sensorless vector control of the pmsm α 、i β Performing state estimation by iterative computation in combination with the extended Kalman state equation of claim 2, the output of which is a rotation speed signal ω e And rotor position theta. The whole prediction process comprises 5 steps of pre-estimation value calculation, error covariance matrix calculation, Kalman gain matrix calculation, pre-estimation value correction and error covariance matrix updating, and the specific steps are as follows:
Figure FDA0003627105370000021
Figure FDA0003627105370000022
K k|k-1 ＝P k|k-1 H T [HP k|k-1 H T +R] -1
Figure FDA0003627105370000023
P k|k ＝(I-K k|k-1 H)P k|k-1 。
4. the PMSM position sensorless control method based on the innovation adaptive extended Kalman, according to claim 1, characterized in that: and setting a weighting coefficient in the calculation of the innovation sequence of the extended Kalman, adjusting the occupation ratio of the innovation sequence at the moment of approach when interference occurs, taking the interference component into account in the innovation calculation, and finally introducing the interference component into the Kalman gain matrix calculation. The calculation of the innovation sequence and the calculation of the Kalman gain matrix are as follows:
Figure FDA0003627105370000024
Figure FDA0003627105370000025
。
5. the PMSM position sensorless control method based on the innovation adaptive extended Kalman, according to claim 1, characterized in that: and importing the weighted innovation sequence into Kalman gain calculation, and improving the traditional extended Kalman algorithm on the basis, wherein the calculation formula of the self-adaptive extended Kalman is as follows:
Figure FDA0003627105370000026
the system output is the rotation speed and rotor position of the PMSM, the values are used as the input of a speed PI regulator and a current PI regulator, and the system output, an inverter, a power supply and other modules form a sensorless vector control system of the PMSM.
CN202210479783.0A 2022-05-05 2022-05-05 PMSM (permanent magnet synchronous motor) position sensorless control method based on innovation self-adaptive extended Kalman Pending CN114944801A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116488514A (en) * 2023-04-26 2023-07-25 江南大学 Sensorless control method and system for permanent magnet synchronous motor based on reduced order EKF
CN116502478A (en) * 2023-06-29 2023-07-28 中国电建集团华东勘测设计研究院有限公司 Submarine topography monitoring-based pile-off auxiliary decision-making method for self-lifting platform

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116488514A (en) * 2023-04-26 2023-07-25 江南大学 Sensorless control method and system for permanent magnet synchronous motor based on reduced order EKF
CN116488514B (en) * 2023-04-26 2023-11-10 江南大学 Sensorless control method and system for permanent magnet synchronous motor based on reduced order EKF
CN116502478A (en) * 2023-06-29 2023-07-28 中国电建集团华东勘测设计研究院有限公司 Submarine topography monitoring-based pile-off auxiliary decision-making method for self-lifting platform
CN116502478B (en) * 2023-06-29 2023-09-01 中国电建集团华东勘测设计研究院有限公司 Submarine topography monitoring-based pile-off auxiliary decision-making method for self-lifting platform

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