Fuzzy PID (Proportion Integration Differentiation) dynamic positioning control method and system based on motion forecast assistance

Abstract

The invention provides a fuzzy PID dynamic positioning control method and system based on motion forecast assistance, and relates to the technical field of ship dynamic positioning control, and the method comprises the steps: obtaining ship motion state error information; dynamically adjusting the forecast length based on system frequency response analysis by adopting a forecast length adjuster; the motion forecasting module predicts a future position according to the dynamically adjusted forecasting length and error information; the fuzzy PID controller fuses the error information and the position prediction value to calculate a control instruction, and the proportional gain of the fuzzy PID controller is adaptively adjusted; the thrust distribution module distributes a control instruction to the thruster. The technical problems that in the prior art, a traditional PID dynamic positioning system is insufficient in positioning precision, slow in dynamic response, poor in environmental adaptability, high in energy consumption and the like in a complex marine environment are effectively solved, the positioning precision, the dynamic response performance and the environmental adaptability of the dynamic positioning system are remarkably improved, and energy consumption is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of ship dynamic positioning control, and in particular to a fuzzy PID dynamic positioning control method and system based on motion prediction assistance. Background Art

[0002] Dynamic Positioning (DP) system is a key technology in modern marine engineering. Traditional DP system mainly adopts PID control method. However, in complex marine environment, traditional PID control exposes the following deficiencies:

[0003] Insufficient positioning accuracy under low-frequency environmental load disturbances. Under complex sea conditions, the dynamic positioning system is susceptible to low-frequency environmental load disturbances such as wind, waves, and currents. Traditional PID control methods are difficult to effectively suppress such disturbances, resulting in reduced positioning accuracy.

[0004] Phase lag between feedback force and environmental load: There is a phase lag between the traditional PID control feedback force and the low-frequency environmental load, which leads to slow dynamic response of the system, reduced anti-disturbance ability, and further reduction in positioning accuracy.

[0005] The controller parameters are fixed and the environmental adaptability is poor. The traditional PID controller parameters are fixed and it is difficult to adapt to the complex and changeable sea conditions. Fixed parameters make it difficult to balance fast response and stability under different working conditions, resulting in poor environmental adaptability and insufficient robustness of the system.

[0006] Serious energy consumption and thruster wear. To maintain stable positioning, traditional PID control needs to frequently and significantly adjust thruster output under complex sea conditions, resulting in excessive energy consumption of the propulsion system, increased thruster wear, and increased operating and maintenance costs.

[0007] In addition, the motion prediction control method is considered to be an effective way to improve the performance of DP systems. However, the selection of prediction length is crucial to system performance. The existing technology lacks an effective method to adaptively adjust the prediction length according to the dynamic characteristics of the system. The prediction length setting relies on experience or offline adjustment, which is difficult to adapt to complex sea conditions, limiting the application effect of the motion prediction control method.

[0008] Therefore, how to improve the control performance of the dynamic positioning system in complex marine environments, such as improving positioning accuracy, improving dynamic response, enhancing environmental adaptability, and reducing energy consumption and wear, remains a key technical issue that needs to be urgently resolved in this technical field.

[0009] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field.

[0010] Application Contents

[0011] The present invention aims to provide a fuzzy PID dynamic positioning control method and system assisted by motion prediction, so as to overcome the deficiencies of the traditional PID control method in the existing technology for traditional dynamic positioning systems in complex marine environments.

[0012] The embodiment of the present application provides a fuzzy PID dynamic positioning control method assisted by motion prediction, including the following steps:

[0013] Obtain the motion state error information of the ship, where the motion state error information includes the current position error and the speed error;

[0014] Adopt a prediction length regulator to obtain the dynamically adjusted prediction length based on the system frequency response analysis;

[0015] Adopt a motion prediction module, and based on the current position error and speed error, predict the future position prediction value of the ship according to the dynamically adjusted prediction length;

[0016] Adopt a fuzzy PID controller to calculate the control input command according to the current position error, speed error and position prediction value; among them, the proportional gain of the fuzzy PID controller is adaptively adjusted by the fuzzy adjustment module based on the absolute value of the position error;

[0017] Adopt a thrust allocation module to allocate the control input command to the thrusters of the ship, generate a thruster command signal, so as to drive the thrusters to generate thrust, offset the environmental load, and realize the dynamic positioning of the ship.

[0018] In some optional embodiments, the position error and speed error are obtained after being filtered by a state observer.

[0019] In some optional embodiments, the motion state error information further includes the heading error of the ship.

[0020] In some optional embodiments, the system frequency response analysis includes performing frequency response analysis on the closed-loop system of the dynamic positioning control system.

[0021] In some optional embodiments, the prediction parameters dynamically adjusted by the prediction length regulator further include the model order of the motion prediction model.

[0022] In some optional embodiments, the motion prediction module uses an autoregressive model to predict the future position of the ship.

[0023] In some optional embodiments, the fuzzy PID controller uses a Mamdani fuzzy system for adaptive adjustment of the proportional gain.

[0024] In some alternative embodiments, the fuzzy PID controller further includes an integral gain and / or a derivative gain that are adaptively adjusted by a fuzzy adjustment module. According to the dynamic positioning control method of claim 1, the fuzzy PID controller further includes an integral gain and / or a derivative gain that are adaptively adjusted by a fuzzy adjustment module.

[0025] In some alternative embodiments, the thrust allocation module uses the pseudo-inverse method for thrust allocation.

[0026] Another aspect of the embodiments of the present application provides a fuzzy PID dynamic positioning control system based on motion prediction assistance for implementing the above method. The system includes:

[0027] A state observer for obtaining motion state error information of the ship, where the motion state error information includes the current position error and the velocity error;

[0028] A motion prediction module connected to the state observer for predicting the predicted value of the future position of the ship based on the current position error and the velocity error of the ship;

[0029] A prediction length regulator connected to the motion prediction module for dynamically adjusting the prediction length based on system frequency response analysis;

[0030] A fuzzy PID controller connected to the state observer, the motion prediction module, and the prediction length regulator respectively for calculating a control input command according to the current position error, the velocity error, and the position predicted value; wherein, the proportional gain of the fuzzy PID controller is adaptively adjusted by the fuzzy adjustment module based on the absolute value of the position error;

[0031] A fuzzy adjustment module connected to the fuzzy PID controller and the state observer for adaptively adjusting the proportional gain of the fuzzy PID controller based on the absolute value of the position error;

[0032] A thrust allocation module connected to the fuzzy PID controller for allocating the control input command to the thrusters of the ship to generate thruster command signals;

[0033] Thrusters connected to the thrust allocation module for generating thrust according to the thruster command signals to achieve dynamic positioning of the ship.

[0034] It should be understood that the above general description and the following detailed description are only exemplary and explanatory and should not limit the present disclosure.

[0035] The fuzzy PID dynamic positioning control method and system of the present application have the following beneficial effects:

[0036] Significantly improved the positioning accuracy of the dynamic positioning system. By introducing a motion prediction module and using the dynamically adjusted prediction length to predict the future position of the ship, feedforward compensation for low-frequency environmental loads was achieved, effectively suppressing the influence of low-frequency disturbances on the positioning accuracy and significantly improving the position-holding accuracy of the dynamic positioning system.

[0037] Effectively improved the dynamic response performance of the dynamic positioning system. Using the feedforward control signal provided by the motion prediction module, the burden of feedback control was reduced, the lag effect of feedback control was decreased, effectively improving the dynamic response speed and disturbance rejection ability of the system, enabling the system to track the desired position more quickly and accurately and suppressing external disturbances.

[0038] Significantly enhanced the adaptive ability of the dynamic positioning system to complex marine environments. By adopting a fuzzy PID controller, whose proportional gain Kp was adaptively adjusted by a fuzzy adjustment module based on the absolute value of the position error, enabling the controller parameters to be online self-tuned according to the actual error range and sea conditions changes, significantly enhancing the system's adaptive ability and robustness to complex marine environments and ensuring good control performance of the system under different working conditions.

[0039] Effectively reduced the energy consumption of the dynamic positioning system and was beneficial to reducing thruster wear. Through the synergistic effect of motion prediction feedforward control and fuzzy PID adaptive control, the control efficiency and accuracy of the control system were improved, reducing unnecessary frequent actions and large-scale adjustments of the thrusters, thus effectively reducing the energy consumption of the dynamic positioning system and being beneficial to reducing thruster wear and extending the service life of the thrusters. Description of the Drawings

[0040] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objectives, and advantages of the present application will become more apparent.

[0041] Figure 1 is a schematic flow chart of a fuzzy PID dynamic positioning control method assisted by motion prediction according to an embodiment of the present application;

[0042] Figure 2 is a schematic structural diagram of a fuzzy PID dynamic positioning control system assisted by motion prediction according to an embodiment of the present application;

[0043] Figure 3 is a schematic operating structural diagram of a fuzzy PID dynamic positioning control system assisted by motion prediction according to an embodiment of the present application. Detailed Embodiments

[0044] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments.

[0045] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0046] The flowcharts shown in the drawings are only exemplary illustrations and do not necessarily include all steps. For example, some steps may be decomposed, while some steps may be combined or partially combined. Therefore, the actual execution order may be changed according to the actual situation.

[0047] The dynamic positioning control method adopted by the present invention lies in the organic combination of motion prediction assisted control and adaptive fuzzy PID control. Motion prediction assisted control predicts the future motion state of the ship, anticipates in advance the impact of environmental disturbances on the ship, so that before the disturbances actually occur, the control system can make corresponding control decisions in advance, actively offset or weaken the impact of the disturbances. This feedforward control method can effectively overcome the lag of traditional feedback control. Especially when facing low-frequency and slowly varying ocean environmental loads, motion prediction assisted control can suppress disturbances more timely and effectively, and improve the response speed and control accuracy of the system. Adaptive fuzzy PID control utilizes the flexible reasoning ability of fuzzy logic, simulates expert control experience, constructs fuzzy rules, and based on real-time system error information, adjusts the parameters of the PID controller online. This adaptive control strategy can automatically adjust the controller parameters to the optimal or near-optimal state as the ocean environment and system working conditions change, overcome the disadvantage of poor environmental adaptability of traditional fixed-parameter PID controllers, and improve the robustness and control performance of the system under various complex working conditions. System frequency response analysis quantifies and describes the dynamic characteristics of the system, especially the phase lag of the system output relative to the input, by analyzing the output response characteristics of the dynamic positioning control system under the action of input signals with different frequencies. Based on the results of system frequency response analysis, parameters such as the prediction length of the motion prediction module can be dynamically adjusted to optimize the accuracy and effectiveness of motion prediction, and further improve the performance of the overall control system. Thrust allocation rationally distributes the magnitude and direction of the thrust of each thruster according to the total control torque command output by the control system and in combination with the layout and performance constraints of the ship thrusters, and while meeting the control torque requirements, optimizes the utilization efficiency of the thrusters as much as possible, reduces energy consumption and thruster wear. State observation estimates and extracts the motion state information of the ship, such as position, speed, etc., using sensor measurement information, in combination with the system model and filtering algorithm, provides accurate and reliable feedback signals for the control system, and suppresses the influence of sensor noise and environmental disturbances.

[0048] The fuzzy PID dynamic positioning control method based on motion prediction assistance provided by the present invention can effectively overcome the deficiencies of traditional PID control methods, significantly improve the positioning accuracy of the dynamic positioning system, improve the dynamic response performance of the system, and enhance the adaptive ability of the system to complex ocean environments under complex ocean environments, and at the same time is beneficial to reducing energy consumption and thruster wear.

[0049] As Figure 1 shown, the invention provides a fuzzy PID dynamic positioning control method based on motion prediction assistance, which aims to improve the control performance of the ship dynamic positioning system under complex ocean environments and overcome the deficiencies of traditional PID control methods in the prior art. The method includes the following steps:

[0050] S100. Obtain the motion state error information of the ship, where the motion state error information includes the current position error and the speed error, so as to achieve precise perception and feedback control of the ship's motion state. Among them, the motion state error information in the embodiments of the present invention refers to the physical quantity that can characterize the motion state deviation of the ship in the horizontal plane, which is specifically obtained by measurement or estimation and at least includes the current position error and the speed error. The "current position error" refers to the deviation between the actual position and the desired position of the ship, reflecting the current position control accuracy of the ship. The speed error refers to the deviation between the actual speed and the desired speed of the ship, reflecting the current motion trend and damping characteristics of the ship. By obtaining the motion state error information including the current position error and the speed error in real time, it provides an accurate feedback signal for the subsequent control link, which is the basis and prerequisite for realizing closed-loop control.

[0051] S200. Adopt a prediction length regulator to obtain the dynamically adjusted prediction length based on the system frequency response analysis, so as to optimize the prediction accuracy of the motion prediction module. Among them, the prediction length regulator refers to the device or module used to dynamically adjust the prediction time span of the motion prediction module. The system frequency response analysis is a control system analysis method, which is used in the embodiments of the present invention to analyze the response characteristics of the dynamic positioning control system under different frequency environmental disturbances, especially the phase lag characteristics of the system output relative to the input. The dynamically adjusted prediction length refers to the prediction time span parameter calculated and adjusted online by the prediction length regulator based on the system frequency response analysis result and according to the system dynamic characteristics. By adopting the prediction length regulator and obtaining the dynamically adjusted prediction length based on the system frequency response analysis, it can enable the motion prediction module to adopt the optimal or better prediction length for motion state prediction according to the system's own characteristics and the external environment in the subsequent steps, thereby improving the accuracy and effectiveness of motion prediction.

[0052] S300. Adopt a motion prediction module, and based on the dynamically adjusted prediction length, predict the future position prediction value of the ship based on the current position error and speed error, so as to realize the prediction of the future motion state of the ship. Among them, the motion prediction module refers to a calculation module used to predict the future motion state of the ship. In the embodiment of the present invention, it adopts a data-driven prediction model, such as an autoregressive (AR) model; the position prediction value refers to the position information of the ship predicted by the motion prediction module at a certain future moment or time period; according to the dynamically adjusted prediction length means that when the motion prediction module performs prediction calculations, it uses the dynamically adjusted prediction length provided by the prediction length regulator as an important parameter of the prediction model; by adopting the motion prediction module and according to the dynamically adjusted prediction length, predicting the future position prediction value of the ship based on the current position error and speed error, it is possible to predict in advance the position information of the ship at a future moment, provide pre-judgment information for the subsequent feedforward control link, and effectively compensate for the lag of traditional feedback control.

[0053] S400. Adopt a fuzzy PID controller to calculate the control input command according to the current position error, speed error and position prediction value; among them, the proportional gain Kp of the fuzzy PID controller is adaptively adjusted by the fuzzy adjustment module based on the absolute value of the position error. To achieve precise control of the ship's motion state. In the embodiment of the present invention, the fuzzy PID controller refers to an intelligent controller that combines fuzzy logic control and traditional PID control. Its basic structure is a PID controller, but its control parameters (such as the proportional gain Kp) can be adaptively adjusted according to fuzzy logic rules; the fuzzy adjustment module refers to a module used to realize the adaptive adjustment of the parameters of the fuzzy PID controller; the proportional gain Kp is adaptively adjusted by the fuzzy adjustment module based on the absolute value of the position error, which means that the fuzzy adjustment module takes the absolute value of the current position error as the input, and according to the preset fuzzy control rules, online adjusts the proportional gain Kp parameter of the fuzzy PID controller to realize the adaptive tuning of the controller parameters; by adopting the fuzzy PID controller, integrating the position prediction value provided by the motion prediction module, and the mechanism of fuzzy adaptive adjustment of the proportional gain Kp, it is possible to comprehensively utilize the advantages of feedback control and feedforward control, and improve the adaptability of the controller to complex marine environments, thereby realizing precise control of the ship's motion state. Specifically, the fuzzy PID controller combines the feedforward value of motion prediction and the feedback signal of the traditional PID controller, and calculates the final control input command based on the position prediction value and speed prediction value:

[0054]

[0055] Among them is the ship position prediction value, η is the generalized low-frequency position vector of the ship (expressed in the geodetic coordinate system), R T$(ψ)$ is the transformation matrix between the earth coordinate system and the ship-fixed coordinate system, and $K$ i is the integral gain of the PID, is the low-frequency velocity vector of the ship (expressed in the ship-fixed coordinate system). The feedforward part uses the output of the motion prediction module to compensate for the low-frequency wave loads, achieving feedforward control of the wave loads.

[0056] S500. The thrust allocation module is adopted to allocate the control input command to the thrusters of the ship, generating thruster command signals to drive the thrusters to generate thrust to counteract the environmental loads and achieve dynamic positioning of the ship. The control input command is converted into the actual thruster control signal, and finally the dynamic positioning of the ship is realized. Among them, the thrust allocation module refers to the calculation module used to reasonably allocate the total control command output by the controller to each thruster; the thruster refers to the actuator in the ship's dynamic positioning system, which is used to generate the thrust required to control the ship's movement; the thruster command signal refers to the control signal output by the thrust allocation module and used to drive the thruster to work, such as the thruster rotation speed and thrust direction control signals; by adopting the thrust allocation module, allocating the control input command to the thrusters of the ship, generating thruster command signals, and driving the thrusters to generate thrust, the control decision of the controller can be converted into the actual ship maneuvering actions, and finally the purpose of counteracting the environmental loads and maintaining the ship at the desired position and heading is achieved, completing the dynamic positioning control process.

[0057] The fuzzy PID dynamic positioning control method based on motion prediction assistance provided by the present invention can effectively overcome the deficiencies of the traditional PID control method and improve the control performance of the dynamic positioning system in complex marine environments through the synergistic effects of technical means such as the feedforward control of the motion prediction module, the adaptive control of the fuzzy PID controller, and the dynamic adjustment of the prediction length.

[0058] In some embodiments, in order to further improve the accuracy and reliability of the motion state error information and suppress the influence of sensor noise and environmental interference. Among them, the state observer is a commonly used state estimation technique in the field of control engineering. In the embodiments of the present invention, the state observer is designed to estimate and extract the true motion state information of the ship and filter out sensor measurement noise and environmental interference; the filtering process refers to that the state observer uses a filtering algorithm to process the original position and velocity signals directly measured by the sensor, removes the noise components in the signals, improves the signal-to-noise ratio and accuracy of the signals, so as to obtain more accurate and reliable position error and velocity error signals for subsequent control calculations; after filtering the position error and velocity error by using the state observer, the quality of the motion state error information can be effectively improved, providing a more accurate and reliable feedback signal for the subsequent motion prediction and fuzzy PID control links, and enhancing the performance and robustness of the entire dynamic positioning control system. For example, the state observer can adopt various filtering algorithms suitable for ship motion state estimation, such as the Kalman filter, the extended Kalman filter, the unscented Kalman filter, etc. The embodiments of the present invention do not make specific limitations on this, and those skilled in the art can select a suitable filter type according to actual application requirements. By using the state observer to filter the position error and velocity error, the accuracy and reliability of the motion state error information can be effectively improved, thereby enhancing the performance of the subsequent motion prediction and fuzzy PID control, and ultimately improving the overall control accuracy and robustness of the dynamic positioning system.

[0059] In some embodiments, in order to further enhance the overall control ability of the dynamic positioning system over the motion state of the ship and meet the application scenarios with high requirements for the ship's heading control, the present invention further expands the type of motion state error information, so that the motion state error information further includes the heading error of the ship. The heading error refers to the deviation between the actual heading of the ship (i.e., the bow direction) and the desired heading, which reflects the current heading control accuracy of the ship; in actual offshore engineering operations, in addition to maintaining the position stability of the ship, precisely controlling the heading of the ship is also crucial. For example, during certain underwater operations, offshore platform docking, or approaching specific targets, precise control of the ship's heading is required to ensure operation safety and efficiency; by adding the heading error of the ship to the motion state error information, the control system can simultaneously sense and feedback the position deviation, speed deviation, and heading deviation of the ship, thus laying a foundation for realizing position-heading joint control and further enhancing the control function and application scope of the dynamic positioning system. For example, the heading error can be measured by various heading sensors such as gyrocompasses, magnetic compasses, and global navigation satellite system (GNSS) heading antennas. The embodiments of the present invention do not make specific limitations on this, and those skilled in the art can select the appropriate type of heading sensor according to actual application requirements. By adding the heading error of the ship to the motion state error information, the dynamic positioning control method provided by the present invention can realize the joint control of the ship's position and heading, further enhancing the control function and application scope of the dynamic positioning system and better meeting the application scenario requirements with high requirements for the ship's heading control.

[0060] In some embodiments, in order to more precisely obtain the dynamic characteristic information of the dynamic positioning control system, so as to provide a more reliable basis for the dynamic adjustment of the prediction length, the system frequency response analysis adopted by the present invention includes performing frequency response analysis on the closed-loop system of the dynamic positioning control system. Among them, the closed-loop system of the dynamic positioning control system refers to a complete control loop system composed of multiple links such as the ship body, sensors, controllers, thrust distribution modules, thrusters, and environmental disturbances; performing frequency response analysis on the closed-loop system of the dynamic positioning control system means taking the entire closed-loop system as the analysis object, rather than only analyzing a certain or some independent links in the system, comprehensively considering the dynamic characteristics and interactions of each link in the system, and more comprehensively and truly reflecting the overall dynamic behavior of the dynamic positioning control system; specifically, by performing frequency response analysis on the closed-loop system, the output response characteristics of the system under different frequency environmental disturbances can be obtained more accurately, such as amplitude-frequency characteristics and phase-frequency characteristics, especially the phase lag characteristic of the system output (such as position error) relative to the input (such as environmental load); this phase lag characteristic is an important basis for the dynamic adjustment of the prediction length by the prediction length regulator, directly affecting the accuracy and effectiveness of the prediction length adjustment; compared with performing frequency response analysis only on the open-loop system or local links of the system, performing frequency response analysis on the closed-loop system can more comprehensively and accurately reflect the dynamic characteristics of the actual dynamic positioning control system, so as to provide more accurate system dynamic information for the prediction length regulator, enabling the prediction length after dynamic adjustment to better match the system characteristics and improving the accuracy of motion prediction and control effect. For example, the frequency response analysis can be obtained through various methods such as theoretical modeling analysis, numerical simulation analysis, or on-site test and measurement. The embodiments of the present invention do not make specific limitations in this regard, and those skilled in the art can select a suitable frequency response analysis method according to actual application conditions and accuracy requirements. By performing frequency response analysis on the closed-loop system of the dynamic positioning control system, the dynamic characteristic information of the system can be obtained more comprehensively and accurately, providing a more reliable basis for the prediction length regulator, so that the prediction length after dynamic adjustment can better match the system characteristics, further improving the accuracy of motion prediction and the overall performance of the dynamic positioning control system.

[0061] To further enhance the function of the prediction length regulator and provide more comprehensive adjustable parameters for the motion prediction module to adapt to more complex and dynamically changing marine environments, in some embodiments, the prediction parameters dynamically adjusted by the prediction length regulator of the present invention further include the model order of the motion prediction model. Among them, the model order of the motion prediction model refers to the number of historical motion state information considered for future motion state prediction in the motion prediction model (such as the autoregressive (AR) model). The higher the model order, the more historical information the model can remember and utilize, and the higher the complexity of the model. In theory, it can capture more complex system dynamic characteristics. For example, for a motion prediction module using an autoregressive (AR) model, the model order is the order of the AR model. The higher the order, the more complex the system dynamic characteristics that the AR model can fit, but it also means more model parameters, larger computational complexity, and higher requirements for input data. In practical applications, it is not that the higher the model order, the better. An overly high model order may lead to overfitting of the model, resulting in a decrease in prediction accuracy and an increase in computational burden. Therefore, dynamically adjusting the model order of the motion prediction model, like dynamically adjusting the prediction length, is to enable the parameters of the motion prediction module to better match the current system dynamic characteristics and environmental conditions to obtain optimal or relatively optimal prediction performance. By enabling the prediction parameters dynamically adjusted by the prediction length regulator to further include the model order of the motion prediction model, a more flexible parameter adjustment means can be provided for the motion prediction module, enabling the motion prediction module to adaptively adjust the model complexity according to the system frequency response characteristics or actual working conditions, achieving a better balance between prediction accuracy and computational efficiency, and further enhancing the environmental adaptability and robustness of the motion prediction module. For example, the prediction length regulator can dynamically adjust the model order of the motion prediction model based on the system frequency response analysis results and in combination with a preset model order selection rule or an adaptive algorithm. The embodiments of the present invention do not make specific limitations in this regard, and those skilled in the art can select a suitable model order dynamic adjustment strategy according to actual application requirements. By enabling the prediction parameters dynamically adjusted by the prediction length regulator to further include the model order of the motion prediction model, a more flexible parameter adjustment means can be provided for the motion prediction module, enabling the motion prediction module to adaptively adjust the model complexity according to the system dynamic characteristics and environmental conditions, achieving a better balance between prediction accuracy and computational efficiency, further enhancing the environmental adaptability and robustness of the motion prediction module, and ultimately enhancing the overall performance of the dynamic positioning control system.

[0062] In order to achieve efficient and accurate prediction of the future position of a ship by the motion prediction module, and on the premise of ensuring the prediction accuracy, reduce the computational complexity as much as possible to meet the real-time control requirements of the dynamic positioning system. In some embodiments, the motion prediction module of the present invention uses an autoregressive model to predict the future position of the ship. Among them, the autoregressive model (Autoregressive Model, abbreviated as AR model) is a time series prediction model that uses the historical information of the time series data itself for prediction, that is, it is assumed that the current value of the time series can be linearly combined by the values at several past moments; in the embodiments of the present invention, the autoregressive model is applied to the prediction of the future position of the ship, that is, it is assumed that the future position information of the ship can be linearly represented by the position error and speed error information within a certain period of its past; compared with other more complex prediction models (such as neural network models, physical models, etc.), the autoregressive model has the advantages of simple structure, fewer parameters, high computational efficiency, easy implementation, etc., and is particularly suitable for dynamic positioning control systems with high requirements for computational real-time performance; at the same time, for the low-frequency motion of the ship in the marine environment, such as the swaying motion affected by waves, swells, etc., its motion state usually has strong autocorrelation in time, that is, the motion state information at past moments has a certain predictive indication significance for the motion state at future moments, which enables the autoregressive model to effectively capture and utilize this autocorrelation characteristic of the ship's motion and achieve relatively accurate short-term motion state prediction; by using the autoregressive model to predict the future position of the ship, it is possible to minimize the computational complexity of the motion prediction module on the premise of ensuring a certain prediction accuracy, meet the real-time control requirements of the dynamic positioning system, and provide timely and effective future position prediction information for the subsequent feedforward control link. For example, the order of the autoregressive model can be selected and adjusted according to the system frequency response characteristics or actual application requirements, and the parameters of the model can be identified and updated through various parameter estimation methods such as the Levinson-Durbin algorithm and the least squares method. The embodiments of the present invention do not make specific limitations in this regard, and those skilled in the art can select appropriate autoregressive model orders and parameter estimation methods according to actual application conditions and accuracy requirements. By using the autoregressive model to predict the future position of the ship, it is possible to minimize the computational complexity of the motion prediction module on the premise of ensuring a certain prediction accuracy, meet the real-time control requirements of the dynamic positioning system, and provide timely and effective future position prediction information for the subsequent feedforward control link, thereby improving the overall control performance and real-time performance of the dynamic positioning system.

[0063] In order to achieve effective adaptive adjustment of the proportional gain Kp of the fuzzy PID controller and ensure the rationality and interpretability of the fuzzy control strategy, in some embodiments, the present invention adopts a Mamdani fuzzy system in the fuzzy PID controller for adaptive adjustment of the proportional gain. Among them, the Mamdani fuzzy system is a classic fuzzy inference system, which is based on fuzzy rules in the form of natural language and uses fuzzy set theory and fuzzy inference methods to achieve a non-linear mapping from input to output; in the embodiments of the present invention, the fuzzy adjustment module is a Mamdani fuzzy system, whose input is the absolute value of the position error and the output is the adjustment amount of the proportional gain Kp or the adjusted Kp value, and a series of pre-set fuzzy control rules are included inside; using the Mamdani fuzzy system for adaptive adjustment of the proportional gain means using the fuzzy inference ability of the Mamdani fuzzy system to online adjust the proportional gain Kp parameter of the fuzzy PID controller according to the absolute value of the current position error, so that the Kp parameter can be adaptively adjusted with the change of the position error; compared with other fuzzy inference systems (such as the T-S fuzzy system) or traditional PID parameter tuning methods, the Mamdani fuzzy system has the advantages of intuitive fuzzy rules, easy to understand, flexible design, strong robustness, etc., and is especially suitable for dealing with control problems where control rules and control experience can be described in natural language; in the dynamic positioning control system, control experts can usually summarize some intuitive PID parameter tuning experiences according to the size of the position error, for example, when the error is large, the proportional gain Kp should be increased to speed up the response speed, and when the error is small, the proportional gain Kp should be decreased to avoid overshoot and oscillation, etc.; the Mamdani fuzzy system can well transform the experience knowledge of these control experts into fuzzy control rules and be used for online parameter adaptive adjustment of the fuzzy PID controller; by adopting the Mamdani fuzzy system for adaptive adjustment of the proportional gain, the parameter tuning of the fuzzy PID controller can be made more reasonable, effective, and easy to understand and implement, and the experience knowledge of control experts can be fully utilized to improve the control performance and environmental adaptability of the fuzzy PID controller. For example, the fuzzy rules of the Mamdani fuzzy system can be set and adjusted according to the performance indicators, control experience or experimental data of the control system, and the form of the fuzzy rules can adopt the language description in the form of "IF... THEN...", such as "IF the absolute value of the position error is large THEN the proportional gain Kp increases", the embodiments of the present invention do not make specific limitations in this regard, and those skilled in the art can design and optimize the fuzzy control rules according to actual application requirements.By adopting a Mamdani fuzzy system to adaptively adjust the proportional gain Kp of the fuzzy PID controller, the parameter tuning of the fuzzy PID controller can be made more reasonable, effective, and easy to understand and implement. Moreover, it can make full use of the experience and knowledge of control experts to improve the control performance and environmental adaptability of the fuzzy PID controller, thereby ultimately enhancing the overall control performance and robustness of the dynamic positioning system. Specifically, the Mamdani fuzzy system is used to dynamically adjust the proportional gain (K p ) in the PID controller. Taking the absolute value of the position error as the input, the triangular membership function (trimf) is used to fuzzify the error value within the critical range, and the Z-shaped membership function (zmf) is used to fuzzify the error values at both ends. The fuzzy rules are designed as follows:

[0064] (1) When the absolute value of the position error is greater than the critical value δ, it is judged as a large error, and the growth rate of K p is high;

[0065] (2) When the absolute value of the position error is less than the critical value ∈, it is judged as a small error, and the growth rate of K p is low (negative);

[0066] (3) When the absolute value of the position error is between the critical values ∈ and δ, it is judged as a medium error, and the growth rate of K p is zero;

[0067] The output variable of the fuzzy system is the time derivative of the proportional gain K p . The centroid method is used for defuzzification to generate an accurate control quantity, ensuring that the controller can quickly respond to changes in different error ranges. Through the above fuzzy system design, the controller can enhance the response ability when the error is large and adjust smoothly when the error is small, taking into account both dynamic performance and stability performance.

[0068] In order to further enhance the adaptive ability and control flexibility of the fuzzy PID controller, and to address different types of disturbances and control requirements that the dynamic positioning system may face in complex marine environments, in some embodiments, the present invention further expands the types of parameters for adaptive adjustment of the fuzzy PID controller. The fuzzy PID controller further includes an integral gain Ki and / or a derivative gain Kd that are adaptively adjusted by a fuzzy adjustment module. Among them, the integral gain Ki is the gain parameter of the integral link in the PID controller, mainly used to eliminate the steady-state error of the system and improve the accuracy of the system without error; the derivative gain Kd is the gain parameter of the derivative link in the PID controller, mainly used to improve the dynamic performance of the system, suppress system overshoot and oscillation, and improve the stability of the system. That the fuzzy PID controller further includes an integral gain Ki and / or a derivative gain Kd that are adaptively adjusted by a fuzzy adjustment module means that in addition to the proportional gain Kp, the fuzzy adjustment module can also, according to preset fuzzy control rules, online adjust the integral gain Ki and / or the derivative gain Kd parameters of the fuzzy PID controller, so that the three basic parameters Kp, Ki, and Kd of the PID controller can all be adaptively tuned according to the system operating state. Compared with the fuzzy PID controller that only adaptively adjusts the proportional gain Kp, simultaneously or separately adapting the integral gain Ki and / or the derivative gain Kd can provide greater flexibility and freedom for controller parameter tuning, enabling the controller to better adapt to different types and frequencies of disturbances and meet different control performance index requirements. For example, in operating conditions where it is necessary to quickly eliminate the steady-state error, the integral gain Ki can be adaptively increased; in operating conditions where it is necessary to suppress system oscillation and overshoot, the derivative gain Kd can be adaptively increased. By adaptively adjusting the integral gain Ki and / or the derivative gain Kd through the fuzzy adjustment module, the control performance and environmental adaptability of the fuzzy PID controller can be further enhanced, enabling the dynamic positioning system to obtain better control effects in various complex marine environments. For example, the fuzzy adjustment module can, based on various input information such as the absolute value of the position error, the change rate of the position error, and the integral error, and based on preset multi-input multi-output fuzzy control rules, simultaneously or separately adjust the proportional gain Kp, the integral gain Ki, and the derivative gain Kd parameters. The form of the fuzzy control rules can adopt the language description in the form of multi-input multi-output "IF... AND... THEN...". The embodiments of the present invention do not make specific limitations in this regard, and those skilled in the art can design and optimize the fuzzy control rules and parameter adaptive adjustment strategies according to actual application requirements. By enabling the fuzzy PID controller to further include an integral gain Ki and / or a derivative gain Kd that are adaptively adjusted by a fuzzy adjustment module, the adaptive ability and control flexibility of the fuzzy PID controller can be further enhanced, enabling the controller to better adapt to different types and frequencies of disturbances and meet different control performance index requirements, thereby ultimately improving the control effect and overall performance of the dynamic positioning system in various complex marine environments.Specifically, the three-degree-of-freedom ship motion differential equation is simplified into three single-input single-output linear time-invariant systems as follows:

[0069]

[0070] where m is the mass of the ship, d is the inherent damping of the ship, K d is the differential gain of the PID, K p is the proportional gain of the PID, e is the position error of the ship, τ osc represents the low-frequency environmental load oscillating at a certain frequency. After the value of K p becomes stable, frequency response analysis is performed on it. The lag time between the system output (position error) and the input (low-frequency environmental load) can be calculated, and half of the lag time is used as the reference prediction length. If the reference prediction length is less than the prediction limit length (8 seconds), the reference length is adopted; otherwise, the limit length is adopted. This module realizes the dynamic adjustment of the prediction length, taking into account both prediction accuracy and calculation efficiency.

[0071] In order to achieve efficient and reasonable distribution of control input commands by the thrust allocation module, and while meeting the requirements of control performance, minimize the computational complexity and energy loss as much as possible. In some embodiments, the thrust allocation module uses the pseudoinverse method for thrust allocation. Among them, the thrust allocation module using the pseudoinverse method for thrust allocation means that an algorithm based on the theory of matrix pseudoinverse is adopted inside the thrust allocation module to decompose the total control torque command output by the fuzzy PID controller into the thrust magnitude and direction commands required by each thruster. The pseudoinverse method is a mathematical method for solving linear equations, especially suitable for solving underdetermined or overdetermined equations, as well as singular or ill-conditioned equations. In the thrust allocation problem of a dynamic positioning system, it is usually necessary to distribute the control torque commands of three degrees of freedom (or six degrees of freedom) to multiple thrusters, and the number and layout of thrusters are often redundant, that is, the number of thrusters is more than the number of degrees of freedom, which makes the thrust allocation problem a typical problem of solving an underdetermined equation system. The pseudoinverse method can provide an analytical solution for such an underdetermined equation system, that is, it can directly calculate a set of thrust allocation schemes for thrusters that satisfy the control torque command through matrix operations. Compared with other thrust allocation algorithms (such as optimization algorithms, rule algorithms, etc.), the pseudoinverse method has the advantages of high computational efficiency, simple algorithm, and easy implementation, and can meet the requirements of the dynamic positioning system for the real-time performance and computational efficiency of the thrust allocation algorithm. At the same time, the pseudoinverse method can ensure the existence of the thrust allocation solution, and even in the case of redundant thruster configuration or constraints, it can find a solution (or least squares solution) that satisfies the control torque command. By using the pseudoinverse method for thrust allocation, it is possible to achieve efficient and fast conversion of control input commands to thruster commands, meet the real-time control requirements of the dynamic positioning system, and provide reasonable and effective control commands for the subsequent thruster actuators, ensuring the control performance and response speed of the dynamic positioning system. For example, the pseudoinverse method can be implemented by various specific mathematical methods such as singular value decomposition (SVD), generalized inverse matrix, etc. When the thrust allocation module uses the pseudoinverse method for thrust allocation, it can also consider various actual constraint conditions such as power constraints, speed constraints, and thrust direction constraints of the thrusters to further optimize the thrust allocation scheme, improve the system energy efficiency and the service life of the thrusters. The embodiments of the present invention do not make specific limitations in this regard, and those skilled in the art can select appropriate implementation methods and constraint conditions of the pseudoinverse method according to actual application requirements. By using the pseudoinverse method for thrust allocation, it is possible to achieve efficient and fast conversion of control input commands to thruster commands, meet the real-time control requirements of the dynamic positioning system, and provide reasonable and effective control commands for the subsequent thruster actuators, ensuring the control performance and response speed of the dynamic positioning system, and facilitating the reduction of computational complexity and system energy loss.

[0072] Such as Figure 2As shown in the figure, another aspect of the present invention provides a fuzzy PID dynamic positioning control system assisted by motion prediction, which aims to implement the aforementioned dynamic positioning control method, thereby improving the dynamic positioning control performance of ships in complex marine environments. This system mainly consists of the following functional modules:

[0073] The state observer M100 is used to obtain the motion state error information of the ship, and the motion state error information includes the current position error and the speed error. Among them, the state observer M100, as the error information acquisition and preprocessing unit of the system, is configured in the embodiment of the present invention to monitor the actual motion state of the ship in real time, compare the measured or estimated ship motion state information with the desired motion state information, calculate the motion state error information, and filter the error information to improve the accuracy and reliability of the error information; the state observer M100 is the information source for the system to implement closed-loop feedback control, providing accurate and reliable feedback signals for the subsequent control modules.

[0074] The motion prediction module M200 is connected to the state observer M100 and is used to predict the future position prediction value of the ship based on the current position error and speed error of the ship. Among them, the motion prediction module M200, as the feedforward control information generation unit of the system, is connected to the state observer M100, receives the current position error and speed error information output by the state observer M100, and uses a motion prediction model (such as an autoregressive model) to predict the position information of the ship in the future for a period of time according to the dynamically adjusted prediction length, generating a position prediction value; the motion prediction module M200 is the core component of the system to implement motion prediction-assisted control, and the position prediction value output by it contains the prediction information of the future environmental disturbance trend, providing a feedforward control signal for the fuzzy PID controller.

[0075] The prediction length regulator M300 is connected to the motion prediction module M200 and is used to dynamically adjust the prediction length based on the system frequency response analysis. Among them, the prediction length regulator M300, as the prediction parameter self-optimization unit of the system, is connected to the motion prediction module M200 and is used to provide dynamic and optimal prediction length parameters for the motion prediction module M200; the prediction length regulator M300 evaluates the dynamic characteristics and phase lag characteristics of the dynamic positioning control system based on the system frequency response analysis, and calculates and adjusts the prediction length online according to the analysis results, so that the motion prediction module M200 can predict the motion state using the optimal or better prediction length according to the system's own characteristics and the external environment, thereby improving the accuracy and effectiveness of the motion prediction and optimizing the overall control performance of the system.

[0076] The fuzzy PID controller M400 is respectively connected to the state observer M100, the motion prediction module M200 and the prediction length regulator M300, and is used to calculate the control input command according to the current position error, speed error and position prediction value; among them, the proportional gain of the fuzzy PID controller M400 is adaptively adjusted by the fuzzy adjustment module based on the absolute value of the position error. Among them, the fuzzy PID controller M400 serves as the control decision-making unit of the system. It is respectively connected to the state observer M100, the motion prediction module M200 and the prediction length regulator M300, and receives the motion state error information output by the state observer M100, the position prediction value output by the motion prediction module M200, and the adaptive proportional gain Kp parameter output by the fuzzy adjustment module. It combines the advantages of feedback control and feedforward control, uses the current position error and speed error as the feedback control input, uses the aforementioned position prediction value as the feedforward control input, and comprehensively calculates the control input command according to the adaptive proportional gain Kp parameter provided by the fuzzy adjustment module to drive the ship propeller to generate a control force; the fuzzy PID controller M400 is the control core of the system. It combines motion prediction-assisted feedforward control and fuzzy adaptive PID feedback control, and can achieve precise, stable and adaptive control of the ship's motion state.

[0077] The fuzzy adjustment module M500 is connected to the fuzzy PID controller M400 and the state observer M100, and is used to adaptively adjust the proportional gain of the fuzzy PID controller M400 based on the absolute value of the position error. Among them, the fuzzy adjustment module M500 serves as the PID parameter adaptive tuning unit of the system. It is connected to the fuzzy PID controller M400 and the state observer M100, receives the position error information output by the state observer M100, and based on the preset fuzzy control rules, according to the magnitude of the absolute value of the position error, online adjusts the proportional gain Kp parameter of the fuzzy PID controller M400, and outputs the adjusted proportional gain Kp parameter to the fuzzy PID controller M400; the fuzzy adjustment module M500 is a key component for the system to implement fuzzy adaptive PID control. It can adjust the PID controller parameters in real time according to the system operation state, and improve the adaptability of the controller to complex marine environments.

[0078] The thrust allocation module M600 is connected to the fuzzy PID controller M400 and is used to allocate the control input command to the thrusters of the ship to generate thruster command signals. Among them, the thrust allocation module M600 serves as the control command execution and conversion unit of the system. It is connected to the fuzzy PID controller M400, receives the control input command output by the fuzzy PID controller M400, and according to the layout and performance characteristics of the ship's thrusters, adopts a thrust allocation algorithm (such as the pseudo-inverse method) to decompose the total control torque command into the thrust magnitude and direction commands required by each thruster, generating thruster command signals; the thrust allocation module M600 is the bridge connecting the control decision-making layer and the actuator layer of the system, which can ensure the effective execution of control commands and the efficient collaborative work of thrusters.

[0079] The thruster M700 is connected to the thrust allocation module M600 and is used to generate thrust according to the thruster M700 command signal to achieve the dynamic positioning of the ship. Among them, the thruster M700 serves as the control force execution unit of the system. It is connected to the thrust allocation module M600, receives the thruster M700 command signal output by the thrust allocation module M600, and generates corresponding thrust according to the command signal to drive the ship to move, offset the environmental load, and finally achieve the dynamic positioning of the ship at the desired position and heading; the thruster M700 is the final actuator for the system to achieve dynamic positioning control, and its performance directly affects the control effect and energy consumption level of the system.

[0080] Through the collaborative work of the above-mentioned various functional modules, the embodiment of the present invention provides a fuzzy PID dynamic positioning control system based on motion prediction assistance, which can effectively implement the above-mentioned dynamic positioning control method and significantly improve the dynamic positioning control performance of the ship in a complex marine environment.

[0081] As Figure 3 shown, it is a schematic diagram of the operating structure of a fuzzy PID dynamic positioning control system based on motion prediction assistance provided by the embodiment of the present invention, and the operating process is as follows:

[0082] Input signal: In the state observer M100, the target position and speed of the ship (η d and v d ) and the actual position and speed (η and v) are subjected to a difference operation to obtain the position error and the speed error

[0083] Motion prediction module M200: According to the real-time state parameters, predict the future motion state of the ship and output the position prediction value

[0084] Prediction Length Regulator M300: The prediction length regulator M300 determines the optimal prediction length based on the system frequency response analysis, improving the accuracy of motion prediction and the response speed of the system.

[0085] Fuzzy Adjustment Module M500: Dynamically adjusts the proportional gain (K p ) of the PID controller so that the control parameters can adapt to the dynamic changes of the error.

[0086] Motion Prediction-Assisted PID Controller: Combines feedback and feedforward signals to calculate the control force τ c .

[0087] Thrust Allocation Module M600 and Environmental Load: The thrust allocation module M600 distributes the control signal to each thruster M700, and the actual acting force cancels out the external environmental loads (such as waves, wind, and currents).

[0088] The above content is a further detailed description of the present application in combination with specific preferred implementation manners. It cannot be determined that the specific implementation of the present application is only limited to these descriptions. For those of ordinary skill in the technical field to which the present application belongs, without departing from the concept of the present application, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present application.

Claims

1. A fuzzy PID dynamic positioning control method based on motion prediction assistance, characterized in that Including the following steps: Obtain the motion state error information of the ship, where the motion state error information includes the current position error and the speed error; Adopt a prediction length regulator to obtain the dynamically adjusted prediction length based on the system frequency response analysis; Adopt a motion prediction module, and based on the dynamically adjusted prediction length, predict the future position prediction value of the ship according to the current position error and the speed error; Adopt a fuzzy PID controller to calculate the control input command according to the current position error, the speed error and the position prediction value; wherein, the proportional gain of the fuzzy PID controller is adaptively adjusted by a fuzzy adjustment module based on the absolute value of the position error; Adopt a thrust allocation module to allocate the control input command to the thrusters of the ship to generate thruster command signals to drive the thrusters to generate thrust to offset the environmental load and achieve the dynamic positioning of the ship.

2. The dynamic positioning control method according to claim 1, characterized in that The position error and the speed error are obtained after being filtered by a state observer.

3. The dynamic positioning control method according to claim 1, characterized in that The motion state error information further includes the heading error of the ship.

4. The dynamic positioning control method according to claim 1, wherein The system frequency response analysis includes performing frequency response analysis on the closed-loop system of the dynamic positioning control system.

5. The dynamic positioning control method according to claim 1, characterized in that, The prediction parameters dynamically adjusted by the prediction length regulator further include the model order of the motion prediction model.

6. The dynamic positioning control method according to claim 1, wherein, The motion prediction module uses an autoregressive model to predict the future position of the ship.

7. The dynamic positioning control method according to claim 1, characterized in that The fuzzy PID controller uses a Mamdani fuzzy system to perform adaptive adjustment of the proportional gain.

8. The dynamic positioning control method according to claim 1, characterized in that The fuzzy PID controller further includes that the integral gain and / or the derivative gain are adaptively adjusted by the fuzzy adjustment module.

9. The dynamic positioning control method according to claim 1, characterized in that, The thrust allocation module uses the pseudo-inverse method for thrust allocation.

10. A fuzzy PID dynamic positioning control system assisted by motion prediction, which is used to implement the method described in any one of claims 1 to 9, and is characterized in that The system includes: A state observer for obtaining the motion state error information of the ship, where the motion state error information includes the current position error and the speed error; A motion prediction module connected to the state observer for predicting the future position prediction value of the ship based on the current position error and the speed error of the ship; A prediction length regulator connected to the motion prediction module for dynamically adjusting the prediction length based on the system frequency response analysis; A fuzzy PID controller connected to the state observer, the motion prediction module and the prediction length regulator respectively for calculating the control input command according to the current position error, the speed error and the position prediction value; wherein, the proportional gain of the fuzzy PID controller is adaptively adjusted by a fuzzy adjustment module based on the absolute value of the position error; A fuzzy adjustment module connected to the fuzzy PID controller and the state observer for adaptively adjusting the proportional gain of the fuzzy PID controller based on the absolute value of the position error; A thrust allocation module connected to the fuzzy PID controller for allocating the control input command to the thrusters of the ship to generate thruster command signals; Thrusters connected to the thrust allocation module for generating thrust according to the thruster command signals to achieve the dynamic positioning of the ship.

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