A fuzzy compensation control method and system based on electric propulsion

Abstract

The present invention provides a fuzzy compensation control method and system based on electric propulsion, which relates to the field of digital processing technology. The method includes: building a propulsion system simulation model based on the Simulink visual simulation tool; obtaining the real-time water immersion depth and propeller speed of the propeller, using the experimental spectrum interpolation method to obtain the torque loss coefficient, and determining the target torque and target speed according to the propeller speed requirements of the air entrainment effect, using the Mamdani fuzzy reasoning method to calculate the fuzzy output, obtaining the torque compensation value of the propulsion motor, importing it into the propulsion system simulation model for simulation verification, and determining the control strategy. The technical problem of low speed control accuracy of the electric propulsion system and difficulty in ensuring stable operation of the propeller is solved, and a fuzzy compensation control strategy with torque loss compensation is achieved to improve the speed control accuracy, thereby achieving the technical effect of improving the compensation control accuracy while ensuring the stable operation of the propeller.

Description

Technical Field

[0001] The present invention relates to the field of digital processing technology, and in particular to a fuzzy compensation control method and system based on electric propulsion. Background Art

[0002] In severe sea conditions, insufficient ballast and strong waves will not ensure that the propeller always maintains sufficient immersion depth, which will lead to a decrease in propeller thrust and torque. The water discharge of the propeller will reduce its effective working disc area, which will lead to a loss of thrust and torque; when the propeller is close to the water surface, the negative pressure on its back sucks in the air on the water surface, causing the water surface to deform and form a vortex, which will eventually develop into an air funnel, causing the propeller blades to be covered by air and lose their water absorption power. This phenomenon is called the ventilation effect, which also leads to a loss of thrust and torque.

[0003] The integrated electric propulsion system with electric propulsion compensation has good maneuverability, fuel economy, flexibility and high redundancy, and is widely used in ships. In severe sea conditions, due to the influence of wind, waves, currents and operating conditions, the propeller may frequently enter and exit the sea surface, causing disk area loss and air entrainment effect, resulting in a step change in propeller load, causing drastic fluctuations in the speed of the propulsion motor and shaft system, accelerating shaft wear, causing resonance in the ship, and seriously affecting the stable operation of the propulsion system. In response to the above problems, an anti-over-rotation control strategy with an air suction effect identifier is proposed. By using the optimal speed target value under the air entrainment effect, the speed fluctuation of the propeller is reduced. However, the adjustment accuracy of torque loss compensation and fuzzy compensation control is low, and it is difficult to ensure the stable operation of the propulsion system.

[0004] The existing technology has the technical problem that the speed control accuracy of the electric propulsion system is low, making it difficult to ensure the stable operation of the propeller. Summary of the invention

[0005] The present application provides a fuzzy compensation control method and system based on electric propulsion, which solves the technical problem that the speed control accuracy of the electric propulsion system is low and it is difficult to ensure the stable operation of the propeller. It achieves a fuzzy compensation control strategy that adds torque loss compensation, improves the speed control accuracy, and thus achieves the technical effect of improving the compensation control accuracy while ensuring the stable operation of the propeller.

[0006] In view of the above problems, the present application provides a fuzzy compensation control method and system based on electric propulsion.

[0007] The first aspect of the present application provides a fuzzy compensation control method based on electric propulsion, wherein the method includes: building a propulsion system simulation model based on the Simulink visual simulation tool; obtaining the real-time propeller immersion depth and propeller speed; obtaining the torque loss coefficient based on the real-time propeller immersion depth and propeller speed by using the experimental spectrum interpolation method; determining the target torque according to the torque loss coefficient and the propeller speed requirement of the air entrainment effect; determining the target speed according to the target torque, and calculating the fuzzy output by using the Mamdani fuzzy reasoning method to obtain the torque compensation value of the propulsion motor; importing the torque compensation value of the motor into the propulsion system simulation model for simulation verification to determine the control strategy.

[0008] The second aspect of the present application provides a fuzzy compensation control system based on electric propulsion, wherein the system includes: a simulation model building unit, the simulation model building unit is used to build a propulsion system simulation model based on the Simulink visual simulation tool; a data acquisition unit, the data acquisition unit is used to obtain the real-time propeller immersion depth and propeller speed; a loss coefficient acquisition unit, the loss coefficient acquisition unit is used to obtain the torque loss coefficient based on the real-time propeller immersion depth and propeller speed by using the experimental spectrum interpolation method; a torque determination unit, the torque determination unit is used to determine the target torque according to the torque loss coefficient and the propeller speed requirement of the air entrainment effect; a compensation value acquisition unit, the compensation value acquisition unit is used to determine the target speed according to the target torque, and use the Mamdani fuzzy reasoning method to perform fuzzy output calculation to obtain the torque compensation value of the propulsion motor; a simulation verification unit, the simulation verification unit is used to import the torque compensation value of the motor into the propulsion system simulation model for simulation verification to determine the control strategy.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By using the Simulink visual simulation tool, a propulsion system simulation model is built; the real-time propeller immersion depth and propeller speed are obtained, and the torque loss coefficient is obtained by using the experimental graph interpolation method; the target torque is determined according to the torque loss coefficient and the propeller speed requirement of the air entrainment effect; the target speed is determined according to the target torque, and the fuzzy output is calculated using the Mamdani fuzzy reasoning method to obtain the torque compensation value of the propulsion motor; the torque compensation value of the motor is imported into the propulsion system simulation model for simulation verification to determine the control strategy. This application achieves the fuzzy compensation control strategy of adding torque loss compensation, improves the speed control accuracy, and thus achieves the technical effect of improving the compensation control accuracy while ensuring the stable operation of the propeller. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flowchart of a fuzzy compensation control method based on electric propulsion in this application;

[0012] Figure 2 A schematic diagram of a flow chart of obtaining a torque compensation value of a propulsion motor in a fuzzy compensation control method based on electric propulsion in the present application;

[0013] Figure 3 A flow chart of a control strategy for a fuzzy compensation control method based on electric propulsion in this application;

[0014] Figure 4 This is a structural schematic diagram of a fuzzy compensation control system based on electric propulsion in this application.

[0015] Description of reference numerals: data acquisition unit 11 , projection mapping unit 12 , collection and acquisition unit 13 , calculation and evaluation unit 14 , data calibration unit 15 , image display unit 16 . DETAILED DESCRIPTION

[0016] The present application provides a fuzzy compensation control method and system based on electric propulsion, which solves the technical problem that the speed control accuracy of the electric propulsion system is low and it is difficult to ensure the stable operation of the propeller. It achieves a fuzzy compensation control strategy that adds torque loss compensation, improves the speed control accuracy, and thus achieves the technical effect of improving the compensation control accuracy while ensuring the stable operation of the propeller.

[0017] Embodiment 1

[0018] like Figure 1 As shown, the present application provides a fuzzy compensation control method based on electric propulsion, wherein the method includes:

[0019] Step S100: Building a propulsion system simulation model based on the Simulink visual simulation tool;

[0020] Furthermore, based on the Simulink visual simulation tool, a propulsion system simulation model is built, and step S100 includes:

[0021] Step S110: setting the modeling mother ship and obtaining propeller parameter information;

[0022] Step S120: obtaining a modeling preset value coefficient and an optimal rotation speed of a propeller with an air entrainment effect;

[0023] Step S130: Based on the Simulink visual simulation tool, the propeller parameter information, the modeling preset value coefficient, and the optimal speed of the propeller with the air entrainment effect are used to build the propulsion system simulation model.

[0024] Specifically, under severe sea conditions, the electric propulsion system has problems such as speed control speed fluctuation and insufficient stabilization time. By analyzing the propeller's reasoning loss and torque loss during disk area loss and air entrainment effect, a fuzzy compensation control strategy with torque loss compensation is determined, and simulation verification is carried out to ensure the stability of the propeller speed while improving the accuracy of electric propulsion control.

[0025] Specifically, based on the Simulink (Simulink is a module diagram environment) visual simulation tool, a propulsion system simulation model is built with a certain electric propulsion ship as the mother ship, providing a model basis for subsequent data processing.

[0026] To further explain, during the process of the propeller entering and exiting the water, only the controller is used to control the propeller speed, the propulsion motor torque fluctuation and propeller thrust fluctuation are large, the propeller torque loss coefficient is large, resulting in instability of the power grid and vibration of the ship, and the control effect is poor. Based on the Simulink visual simulation tool, a propulsion system simulation model is built, specifically including: The propeller parameter information includes but is not limited to the moment of inertia I (in the simulation, the input is 25000kg·m 2 ), propeller diameter D (input is 3.6m in the simulation), pitch ratio H / D (input is 0.943 in the simulation), propeller / motor rated speed n bp (In the simulation, the input is 200rpm), taking a certain electric propulsion ship as the mother ship, data collection is performed to obtain propeller parameter information; k T is the thrust coefficient, k Q is the torque coefficient. In the Simulink block diagram simulation environment, the following simplifications are performed in the model building to set the modeling preset value coefficients, which include but are not limited to k T , k Q , where k T and k Q All are fixed values ​​(k T =0.46, k Q =0.068), the propulsion motor is a zero-delay actuator, the propeller depth caused by wind and wave current is h / R=sin(0.07t-1.57)+1.8, and the optimal speed of the propeller with air entrainment effect is set to n as=60rpm, based on the Simulink visual simulation tool, the simulation is performed through the controller, and the propeller parameter information, the modeling preset value coefficient, and the optimal propeller speed of the air entrainment effect are used to build the propulsion system simulation model (the simulation verification shows that the maximum fluctuation of the propeller torque is reduced from 73% to 50%; the maximum fluctuation of the propeller thrust is reduced from 70% to 28%), which effectively reduces the changes in propeller load and thrust, stabilizes the propeller speed, reduces the fluctuation of the power grid and the vibration of the ship, determines the propulsion system simulation model, provides a model basis for subsequent data processing, and accelerates the efficiency of data calculation and processing.

[0027] Step S200: obtaining the real-time water immersion depth and propeller speed of the propeller;

[0028] Step S300: Based on the real-time water immersion depth of the propeller and the propeller speed, the torque loss coefficient is obtained by using the experimental spectrum interpolation method;

[0029] Step S400: determining a target torque according to the torque loss coefficient and the propeller speed requirement due to the air entrainment effect;

[0030] Specifically, the propeller immersion depth is collected in real time through the position sensor to obtain the real-time propeller immersion depth (the data accuracy can be accurate to the thousandth place), and the data displayed on the tachometer of the electric propulsion ship is synchronously output to obtain the propeller speed (the data accuracy can be accurate to the thousandth place). Based on the real-time propeller immersion depth and the propeller speed, when the torque loss is detected by using the experimental spectrum interpolation, if the output torque loss coefficient ζ=1 is detected and maintained for a certain period of time, an anti-over-rotation control strategy of actively reducing the speed target value is adopted; otherwise, the output torque loss coefficient ζ=0 is measured, and a conventional PID (proportional-integral-derivative control) speed control strategy is adopted, which specifically includes: in order to reduce the influence of the entrainment effect, the optimal propeller speed n under the influence of the entrainment effect is defined as , where n as / n bp ≤n1 (n1 is the point where the air entrainment effect occurs, n bp is the propeller rated speed), it can be deduced that n as ≤n1n bp In severe sea conditions, the anti-over-rotation control strategy keeps the propeller speed stable at n as Nearby, the air entrainment effect is minimized, while the thrust is obtained as much as possible. In the above case, the target torque Q cas The setting reference formula is: Among them, the controller gain k p >0,k i>0,k d >0, if ζ=1, then e=n r -n; if ζ=0, then e=n as -n(n is the propeller speed, n r The target torque is set so that the target speed is reduced to the speed n1n that causes the air entrainment effect. bp This provides a data basis for avoiding the occurrence of air entrainment effect.

[0031] Step S500: determining a target speed according to the target torque, and using the Mamdani fuzzy inference method to perform fuzzy output calculation to obtain a torque compensation value of the propulsion motor;

[0032] Step S600: importing the torque compensation value of the motor into a propulsion system simulation model for simulation verification to determine a control strategy.

[0033] Specifically, after the propeller speed is stabilized at n as After the simulation verification, it can be known that there are still problems such as inaccurate sampling of propeller water immersion depth, inability to obtain propeller thrust loss coefficient model in advance, and delay in obtaining torque loss. There are still certain limitations in actual use. The target speed is determined according to the target torque, and the fuzzy output is calculated using the Mamdani fuzzy reasoning method (there is no need to identify the fuzzy compensation control of torque loss, and the propeller speed and its acceleration are directly used as input to formulate fuzzy rules) to obtain the torque compensation value of the propulsion motor; the torque compensation value of the motor is imported into the propulsion system simulation model for simulation verification, and the control strategy is determined to keep the actual speed of the propeller stable at n as Nearby, high-precision dynamic adjustment of the target torque is achieved.

[0034] Further, such as Figure 2 As shown, the embodiment of the present application also includes:

[0035] Step S510: obtaining a propeller speed and a propeller acceleration, and setting a fuzzy rule based on the propeller speed and the propeller acceleration;

[0036] Step S520: determining the relationship between the target torque and the load torque based on the propeller acceleration, and obtaining the target speed and speed change rate;

[0037] Step S530: using the difference between the propeller speed and the target speed and the speed change rate as input parameters of the target torque compensation value;

[0038] Step S540: obtaining an output according to the input parameter and the fuzzy rule, wherein the output is a torque compensation amount within a cycle;

[0039] Step S550: Integrate the torque compensation value of the propulsion motor according to the torque compensation amount in the cycle.

[0040] Further, according to the input parameter, the output is obtained by the fuzzy rule, and the output is the torque compensation amount within the cycle. Step S540 includes:

[0041] Step S541: Based on the fuzzy rules, the fuzzy output quantity is calculated by the Mamdani fuzzy reasoning method to determine the fuzzified output quantity;

[0042] Step S542: Defuzzifying the fuzzified output using the centroid method to obtain the central projection of the membership function region shape;

[0043] Step S543: Based on the central projection of the shape of the membership function region, a torque compensation value within a cycle is calculated using a fuzzy algorithm as the output.

[0044] Further, according to the torque compensation amount in the cycle, the torque compensation value of the propulsion motor is obtained by integration, and step S550 includes:

[0045] Step S551: through the formula Q=∫ΔQdt, where Q is the torque compensation amount within the cycle, and ΔQdt is the torque change rate.

[0046] Specifically, propeller acceleration can directly reflect the relationship between target torque and load torque. Data is collected through corresponding sensors to obtain propeller speed and propeller acceleration. Fuzzy rules of input and output parameters are set based on the propeller speed and propeller acceleration according to the control requirements of the propeller in harsh environments. The relationship between target torque and load torque is determined based on the propeller acceleration (the basic logic of the relationship between target torque and load torque is: when the propeller speed difference increases, the target torque is reduced; at the same time, when the speed acceleration is large, the torque compensation is strengthened) to obtain the target speed and speed change rate. The propeller speed n and the target speed setting value (the target speed setting value is the optimal propeller speed n) are used to calculate the target speed and speed change rate. as ) and the rate of change of the rotational speed are used as input parameters of the target torque compensation value; based on the input parameters, the output is obtained through the fuzzy rules, and the output is the torque compensation within the cycle; based on the torque compensation within the cycle, the torque compensation value of the propulsion motor is obtained by integration, which provides a basis for ensuring the accuracy of the parameter indicators in the control strategy.

[0047] Further specific description, according to the input parameter, the output is obtained through the fuzzy rule, and the output is the torque compensation amount within the cycle, which specifically includes: based on the fuzzy rule, the fuzzy output is calculated through the Mamdani fuzzy reasoning method (the Mamdani fuzzy reasoning method defines and describes the speed change rate and the speed difference Δn and the corresponding output through fuzzy language such as "negative large", "negative small", "moderate", "positive small", "positive large", etc.), and the fuzzified output is determined. The fuzzy output is the fuzzy language expression of the output obtained by fuzzy reasoning, which needs to be defuzzified to obtain an accurate value; the fuzzy output is calculated by the centroid method. The output is defuzzified to obtain the central projection of the membership function area shape (the precise value obtained by the defuzzification is the projection of the center of the membership function area shape finally obtained on the x-axis); based on the central projection of the membership function area shape, the torque compensation value within a cycle is calculated using a fuzzy algorithm as the output, and according to the torque compensation amount within the cycle, the torque compensation value of the propulsion motor is obtained by integration, specifically including: through the formula Q=∫ΔQdt, where Q is the torque compensation amount within a cycle, ΔQdt is the torque change rate (the torque change rate is simply the infinitesimal element of the torque), and the fuzzy compensation control target torque formula is Q cas1 =Q cas0 +Q, provides a reference for subsequent substitution operations.

[0048] Further, such as Figure 3 As shown, the embodiment of the present application also includes:

[0049] Step S710: Obtaining ship type and water area characteristic information;

[0050] Step S720: performing propeller influence analysis according to the ship type and the water area characteristic information to determine the influence relationship;

[0051] Step S730: adding the influence relationship as a constraint condition to the propulsion system simulation model to determine the control strategy.

[0052] Specifically, the type of ship is obtained through the displacement, length (total length, vertical length, waterline length), width (breadth, total width), depth, thruster model and other related parameter indicators of the electric propulsion ship, and the water area characteristic information is determined through the flow direction, basin area, water level, flow, sediment content and other related parameter indicators; according to the ship type and the water area characteristic information, the propeller influence analysis is performed, and the propeller influence analysis can be combined with PSO (Particle Swarm Optimization). SwarmOptimization, particle swarm optimization algorithm) or other impact evaluation and analysis algorithms, determine the fitness value obtained by the impact evaluation and analysis as the impact relationship, specifically, normalize the ship type and the water area characteristic information, use the cosine method to find the optimal matching feature and the worst matching feature of the normalized processing results of the ship type and the water area characteristic information and the propeller operation related parameters, then calculate the distance between each evaluation object (each evaluation object is other matching features excluding the optimal matching feature and the worst matching feature) and the optimal matching feature and the worst matching feature, and obtain the relative proximity between each evaluation object and the optimal matching feature, which is used as the basis for evaluation, determines the data that is relevant to the propeller operation related parameters, and extracts the impact relationship; adds the impact relationship as a constraint condition to the propulsion system simulation model, determines the control strategy, performs preliminary data screening through impact evaluation and analysis, accelerates the simulation response rate of the propulsion system simulation model, and provides support for improving the efficiency of fuzzy compensation control.

[0053] Furthermore, the embodiment of the present application also includes:

[0054] Step S740: obtaining the gain requirement of the controller;

[0055] Step S750: Based on the gain requirement of the controller, a shaft speed error analysis is performed to determine a propeller speed target control value.

[0056] Specifically, the gain requirement of the controller is obtained, which is the transmission operation requirement of the controller using the motor for the compensation control process. Commonly, the gain requirement includes the rotor critical speed, starting torque (the moment of power-on, the process from a stationary state to the beginning of rotation, the force used is the starting torque), maximum torque (representing the maximum load capacity that the motor can apply, which can be expressed as a multiple of the rated torque) and other related parameter indicators. After obtaining the torque compensation value, the shaft speed error analysis is performed based on the gain requirement of the controller. The shaft speed error is the error between the running torque value corresponding to the actual shaft speed of the motor and the torque compensation value during the compensation operation, and the shaft speed error statistics are performed. , obtain the shaft speed error direction and the shaft speed error amount, and use the shaft speed error direction and the shaft speed error amount to correct the torque compensation value within the range limited by the gain requirement. The correction is to control and optimize the torque compensation value through the shaft speed error direction and the shaft speed error amount. Specifically, take the torque compensation value as the motor operation target, and correct the shaft speed error direction and the shaft speed error amount to determine the propeller speed target control value. The propeller speed target control value is the correction result (the correction result is the superposition of the torque compensation value, the reverse direction of the shaft speed error and the shaft speed error amount). Error analysis is performed on the operation characteristics of the motor to provide support for ensuring the accuracy of the propeller speed target control value.

[0057] In summary, the fuzzy compensation control method and system based on electric propulsion provided by the present application have the following technical effects:

[0058] By adopting the Simulink visual simulation tool, a propulsion system simulation model is built; the real-time propeller immersion depth and propeller speed are obtained, and the torque loss coefficient is obtained by using the experimental graph interpolation method, and the target torque and target speed are determined according to the propeller speed requirements of the air entrainment effect. The fuzzy output is calculated using the Mamdani fuzzy reasoning method to obtain the torque compensation value of the propulsion motor; the torque compensation value of the difference motor is imported into the propulsion system simulation model for simulation verification to determine the control strategy. This application provides a fuzzy compensation control method and system based on electric propulsion, which achieves the fuzzy compensation control strategy of adding torque loss compensation and improves the speed control accuracy, thereby achieving the technical effect of improving the compensation control accuracy while ensuring the stable operation of the propeller.

[0059] By adopting the setting modeling mother ship, the propeller parameter information is obtained; the preset modeling value coefficient and the optimal speed of the propeller with entrainment effect are obtained; based on the Simulink visual simulation tool, the differential propeller parameter information, the preset modeling value coefficient and the optimal speed of the propeller with entrainment effect are used to build a propulsion system simulation model, which effectively reduces the changes in propeller load and thrust, stabilizes the propeller speed, reduces the fluctuation of the power grid and the vibration of the ship, and determines the propulsion system simulation model, providing a model basis for subsequent data processing and accelerating the efficiency of data calculation and processing.

[0060] By obtaining information on ship type and water characteristics, propeller influence analysis is carried out to determine the influence relationship; the influence relationship is added as a constraint condition to the propulsion system simulation model, and the control strategy is determined. Through impact evaluation and analysis, data is initially screened to accelerate the simulation response rate of the propulsion system simulation model, providing support for improving the efficiency of fuzzy compensation control.

[0061] Embodiment 2

[0062] Based on the same inventive concept as the fuzzy compensation control method based on electric propulsion in the aforementioned embodiment, Figure 4 As shown, the present application provides a fuzzy compensation control system based on electric propulsion, wherein the system includes:

[0063] A simulation model building unit 11, wherein the simulation model building unit 11 is used to build a propulsion system simulation model based on a Simulink visual simulation tool;

[0064] A data acquisition unit 12, the data acquisition unit 12 is used to obtain the real-time water immersion depth and propeller speed of the propeller;

[0065] A loss coefficient acquisition unit 13, wherein the loss coefficient acquisition unit 13 is used to obtain a torque loss coefficient based on the real-time water immersion depth of the propeller and the propeller speed by using an experimental spectrum interpolation method;

[0066] A torque determination unit 14, the torque determination unit 14 is used to determine a target torque according to the torque loss coefficient and the air entrainment effect propeller speed requirement;

[0067] A compensation value acquisition unit 15, the compensation value acquisition unit 15 is used to determine the target speed according to the target torque, and use the Mamdani fuzzy inference method to perform fuzzy output calculation to obtain the torque compensation value of the propulsion motor;

[0068] The simulation verification unit 16 is used to import the torque compensation value of the motor into the propulsion system simulation model for simulation verification to determine the control strategy.

[0069] Furthermore, the system comprises:

[0070] A fuzzy rule setting unit, the fuzzy rule setting unit is used to obtain the propeller speed and the propeller acceleration, and set the fuzzy rules according to the propeller speed and the propeller acceleration;

[0071] A speed parameter obtaining unit, the speed parameter obtaining unit is used to determine the relationship between the target torque and the load torque based on the propeller acceleration, and obtain the target speed and the speed change rate;

[0072] An input parameter determination unit, the input parameter determination unit is used to use the difference between the propeller speed and the target speed, and the speed change rate as input parameters of the target torque compensation value;

[0073] A torque compensation acquisition unit, the torque compensation acquisition unit is used to obtain an output according to the input parameter through the fuzzy rule, and the output is the torque compensation within the cycle;

[0074] An integral operation and torque compensation value acquisition unit, wherein the integral operation and torque compensation value acquisition unit is used to integrate the torque compensation amount within the cycle to obtain the torque compensation value of the propulsion motor.

[0075] Furthermore, the system comprises:

[0076] A parameter substitution unit is used to substitute a parameter through the formula Q=∫ΔQdt, wherein Q is the torque compensation amount within the cycle, and ΔQdt is the torque change rate.

[0077] Furthermore, the system comprises:

[0078] An output quantity calculation unit, the output quantity calculation unit is used to perform fuzzy output quantity calculation based on fuzzy rules and through Mamdani fuzzy reasoning method to determine the fuzzified output quantity;

[0079] A defuzzification processing unit, the defuzzification processing unit is used to perform defuzzification processing on the fuzzified output using a centroid method to obtain a central projection of a membership function area shape;

[0080] An output determination unit is used to calculate a torque compensation value within a cycle using a fuzzy algorithm based on a central projection of the shape of the membership function region as the output.

[0081] Furthermore, the system comprises:

[0082] A propeller parameter obtaining unit, wherein the propeller parameter obtaining unit is used to set a modeling mother ship and obtain propeller parameter information;

[0083] A preset value coefficient and optimal speed obtaining unit, wherein the preset value coefficient and optimal speed obtaining unit is used to obtain a preset value coefficient for modeling and an optimal speed of a propeller with an entrainment effect;

[0084] A simulation model building unit is used to build the propulsion system simulation model based on the Simulink visual simulation tool, using the propeller parameter information, modeling preset value coefficients, and the optimal speed of the air entrainment effect propeller.

[0085] Furthermore, the system comprises:

[0086] A characteristic information obtaining unit, which is used to obtain ship type and water area characteristic information;

[0087] An influence relationship determination unit, the influence relationship determination unit is used to perform propeller influence analysis according to the ship type and the water area characteristic information to determine the influence relationship;

[0088] A control strategy determination unit is used to add the influence relationship as a constraint condition to the propulsion system simulation model to determine the control strategy.

[0089] Furthermore, the system comprises:

[0090] A gain requirement obtaining unit, wherein the gain requirement obtaining unit is used to obtain a gain requirement of the controller;

[0091] A shaft speed error analysis unit is used to perform shaft speed error analysis based on the gain requirement of the controller to determine a propeller speed target control value.

[0092] This specification and drawings are merely exemplary illustrations of the present application, and various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. These modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, and the present application intends to include these modifications and variations.

Claims

1. A fuzzy compensation control method based on electric propulsion, characterized in that: The method comprises: Build a propulsion system simulation model based on the Simulink visual simulation tool; Obtain the real-time propeller immersion depth and propeller speed; Based on the real-time water immersion depth of the propeller and the propeller speed, a torque loss coefficient is obtained by using an experimental graph interpolation method; According to the torque loss coefficient and the air entrainment effect propeller speed requirement, the target torque is determined, including: Among them, the controller gain k p >0,k i >0,k d >0, if ζ=1, then e=n r -n; if ζ=0, then e=n as -n, where n is the propeller speed, n r is the propeller speed target value, n as is the optimum propeller speed, ζ is the torque loss coefficient; Determine the target speed according to the target torque, use the Mamdani fuzzy inference method to perform fuzzy output calculation, and obtain the torque compensation value of the propulsion motor; Importing the torque compensation value of the motor into a propulsion system simulation model for simulation verification to determine a control strategy; The target speed is determined according to the target torque, and the fuzzy output is calculated using the Mamdani fuzzy reasoning method to obtain the torque compensation value of the propulsion motor, including: Obtaining a propeller speed and a propeller acceleration, and setting a fuzzy rule based on the propeller speed and the propeller acceleration; Based on the propeller acceleration, determining the relationship between the target torque and the load torque, and obtaining the target speed and speed change rate; The difference between the propeller speed and the target speed and the speed change rate are used as input parameters of the target torque compensation value; According to the input parameter, an output is obtained through the fuzzy rule, and the output is a torque compensation amount within a cycle; Integrating the torque compensation amount within the cycle to obtain a torque compensation value of the propulsion motor; Wherein, the output quantity is obtained according to the input parameter through the fuzzy rule, and the output quantity is the torque compensation quantity within the cycle, including: Based on fuzzy rules, the fuzzy output quantity is calculated by Mamdani fuzzy reasoning method to determine the fuzzified output quantity; Defuzzification is performed on the fuzzified output using a centroid method to obtain a central projection of the membership function region shape; Based on the central projection of the membership function region shape, a torque compensation value within a cycle is calculated using a fuzzy algorithm as the output.

2. The method according to claim 1, characterized in that The propulsion system simulation model is built based on the Simulink visual simulation tool, including: Set up the modeling mother ship and obtain propeller parameter information; Obtain the modeling preset value coefficient and the optimal speed of the propeller with air entrainment effect; Based on the Simulink visual simulation tool, the propulsion system simulation model is built using the propeller parameter information, modeling preset value coefficients, and the optimal speed of the propeller due to the air entrainment effect.

3. The method according to claim 1, characterized in that The method further comprises: Obtain information on ship types and water characteristics; Perform propeller influence analysis based on the ship type and water feature information to determine the influence relationship; The influence relationship is added as a constraint condition into the propulsion system simulation model to determine the control strategy.

4. The method according to claim 1, characterized in that The method further comprises: Obtain the gain requirement of the controller; Based on the gain requirement of the controller, a shaft speed error analysis is performed to determine the propeller speed target control value.

5. A fuzzy compensation control system based on electric propulsion, characterized in that: The system comprises: A simulation model building unit, wherein the simulation model building unit is used to build a propulsion system simulation model based on a Simulink visual simulation tool; A data acquisition unit, the data acquisition unit is used to obtain the real-time water immersion depth and propeller speed of the propeller; A loss coefficient acquisition unit, the loss coefficient acquisition unit is used to obtain a torque loss coefficient based on the real-time water immersion depth of the propeller and the propeller speed by using an experimental spectrum interpolation method; A torque determination unit, the torque determination unit is used to determine the target torque according to the torque loss coefficient and the air entrainment effect propeller speed requirement, including: Among them, the controller gain k p >0,k i >0,k d >0, if ζ=1, then e=n r -n; if ζ=0, then e=n as -n, where n is the propeller speed, n r is the propeller speed target value, n as is the optimum propeller speed, ζ is the torque loss coefficient; A compensation value acquisition unit, the compensation value acquisition unit is used to determine a target speed according to the target torque, perform fuzzy output quantity calculation using a Mamdani fuzzy inference method, and acquire a torque compensation value of the propulsion motor; A simulation verification unit, the simulation verification unit is used to import the torque compensation value of the motor into a propulsion system simulation model for simulation verification to determine a control strategy; The target speed is determined according to the target torque, and the fuzzy output is calculated using the Mamdani fuzzy reasoning method to obtain the torque compensation value of the propulsion motor, including: Obtaining a propeller speed and a propeller acceleration, and setting a fuzzy rule based on the propeller speed and the propeller acceleration; Based on the propeller acceleration, determining the relationship between the target torque and the load torque, and obtaining the target speed and speed change rate; The difference between the propeller speed and the target speed and the speed change rate are used as input parameters of the target torque compensation value; According to the input parameter, an output is obtained through the fuzzy rule, and the output is a torque compensation amount within a cycle; Integrating the torque compensation amount within the period to obtain a torque compensation value of the propulsion motor; Wherein, the output quantity is obtained according to the input parameter through the fuzzy rule, and the output quantity is the torque compensation quantity within the cycle, including: Based on fuzzy rules, the fuzzy output quantity is calculated by Mamdani fuzzy reasoning method to determine the fuzzified output quantity; Defuzzification is performed on the fuzzified output using a centroid method to obtain a central projection of the membership function region shape; Based on the central projection of the membership function region shape, a torque compensation value within a cycle is calculated using a fuzzy algorithm as the output.

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