Attitude control method and system for maritime embarkation gallery bridge
By analyzing hydraulic system and environmental data in real time and intelligently adjusting PID controller parameters, the problem of low attitude control accuracy of the boarding bridge at sea has been solved, thus improving the stability and safety of the boarding bridge at sea.
Patent Information
- Application Number
- CN202511145714.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-15
- Publication Date
- 2025-11-21
AI Technical Summary
In existing technologies, the attitude control methods for boarding bridges at sea rely on sensor fusion and dynamic modeling, neglecting the performance changes of the hydraulic system and the harsh marine environment, which leads to reduced attitude control accuracy and affects operational stability and safety.
By acquiring real-time hydraulic system data and marine environment data, analyzing hydraulic time-varying coefficients and environmental coefficients, intelligently adjusting the gain parameters of the PID controller, optimizing attitude control strategies, and improving the control accuracy and response speed of the hydraulic system.
It improves the operational stability and safety of the boarding bridge at sea under complex sea conditions, and achieves precise attitude control by identifying the effects of hydraulic system wear and environmental changes.
Smart Images
Figure CN120993948A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of marine platform control technology, specifically to an attitude control method and system for a boarding bridge at sea. Background Technology
[0002] With the rapid development of offshore wind power and marine engineering, the demand for efficient and safe transfer of personnel and materials at sea is increasing. Traditional transfer methods such as gondolas, helicopters, and cranes pose high operational risks. In recent years, offshore boarding bridges, as a new type of offshore transfer device, have gradually gained attention due to their high safety and adaptability. This device connects ships to fixed platforms, providing a stable passage for personnel and light equipment. However, in practical applications, affected by wind and waves, ships experience six degrees of freedom of motion: roll, pitch, heave, sway, yaw, and bow roll, which seriously affects the attitude stability and docking safety of the boarding bridge. Therefore, real-time and precise control of the attitude of the offshore boarding bridge is required to ensure the safety of offshore boarding bridge operations.
[0003] In existing technologies, attitude control of offshore boarding bridges largely relies on sensor fusion and dynamic modeling, adjusting the bridge's attitude by calculating compensation amounts. However, these methods generally focus on the design of attitude estimation and compensation algorithms, neglecting the cumulative errors in attitude control caused by changes in hydraulic system performance and harsh marine environmental conditions. This reduces the accuracy of attitude control, thereby lowering the stability and safety of offshore boarding bridge operation. Summary of the Invention
[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for attitude control of a sea boarding bridge, the specific technical solution of which is as follows:
[0005] In a first aspect, embodiments of this application provide a method for attitude control of a sea-boarding boarding bridge, the method comprising the following steps:
[0006] Real-time data on hydraulic oil temperature and impurities, vibration data of the hydraulic system, ocean temperature, and wave fluctuations within the boarding bridge at sea.
[0007] The attitude error of the boarding bridge at sea is acquired in real time within a preset time period before each moment. The preset time period is divided into multiple cycles. The hydraulic oil temperature and attitude error in each cycle are fitted. Based on the integral result of the fitted curve of each cycle and the previous cycle, the error baseline drift of each cycle is determined. Combined with the differences of all hydraulic oil impurity data and all vibration data between each cycle and the previous cycle within the preset time period before each moment, the hydraulic deviation at each moment is determined. By analyzing the average distribution of attitude error on the fitted curve of hydraulic oil temperature in all cycles within the corresponding preset time period at each moment, and combined with the hydraulic deviation, the hydraulic time-varying coefficient at each moment is determined.
[0008] The correlation between the hydraulic time-varying coefficients of ocean temperature and wave fluctuation data at all times within a preset time period before each time point and the seasonal trend of the hydraulic time-varying coefficients at all times points is analyzed to determine the temperature-hydraulic correlation and wave-hydraulic correlation at each time point. The dispersion of ocean temperature and wave fluctuation data at all times within a preset time period is analyzed to determine the temperature fluctuation and wave fluctuation at each time point. Combined with the temperature-hydraulic correlation and wave-hydraulic correlation, the hydraulic environment coefficient at each time point is determined.
[0009] Based on the hydraulic time-varying coefficient and hydraulic environment coefficient at the current moment, the hydraulic correction coefficient at the current moment is determined in order to adjust the gain parameter of the PID controller during the attitude control of the boarding bridge at sea.
[0010] Preferably, the expression for the error baseline drift of each cycle within the preset time period prior to each moment is: ΔF x,i f represents the baseline drift of the error in the i-th cycle within a preset time period prior to time x; x,i (t), f x,i―1 (t) represents the fitted curves of the i-th and (i-1)-th cycles within the preset time period before time x, respectively; t represents the hydraulic oil temperature on the horizontal axis of the fitted curve; a x,i b x,i These represent the minimum and maximum values of hydraulic oil temperature at all times within the i-th cycle and the adjacent preceding cycle in the preset time period before time x.
[0011] Preferably, the expression for the hydraulic deviation at each time point is: In the formula, Q x Indicates the hydraulic deviation at time x; v x z x ΔF represents the difference in the mean of all vibration data before time x and between the next adjacent period and the next adjacent period, respectively; ΔF represents the difference in the mean of all hydraulic oil impurity data. x,iΔv represents the baseline drift of the error in the i-th cycle within a preset time period prior to time x; x,i Δz x,i These represent the total difference in vibration data between the i-th cycle and its preceding cycle within a preset time period before time x, and the total difference in hydraulic oil impurities at all times, respectively; N x This indicates the number of all cycles within a preset time period prior to time x.
[0012] Preferably, the method for determining the hydraulic time-varying coefficient at each time point is as follows:
[0013] The mean value of the attitude error of the hydraulic oil temperature at each moment on the fitting curve of all cycles in the corresponding preset time period is calculated and recorded as the mean value of attitude error. The result of positively fusing the mean value of attitude error with the hydraulic deviation at the corresponding moment is used as the hydraulic time-varying coefficient at each moment.
[0014] Preferably, determining the temperature-hydraulic correlation and wave-hydraulic correlation at each time point includes:
[0015] The hydraulic time-varying coefficients of all times within a preset time period before each time point are used as input to the time series decomposition algorithm, and the seasonal term sequence is output.
[0016] Ocean temperature and wave fluctuation data within a preset time period prior to each time point are used to form temperature and wave sequences, respectively. The temperature, wave, and seasonal sequences are used as inputs to a grey relational algorithm, which outputs the correlation between the temperature and seasonal sequences, as well as the correlation between the wave and seasonal sequences, as the temperature-hydraulic correlation and wave-hydraulic correlation at each time point, respectively.
[0017] Preferably, determining the temperature fluctuation and wave fluctuation at each time point includes:
[0018] The coefficients of variation of ocean temperature and the coefficients of variation of wave fluctuation data at all times within the preset time period before each time point are respectively used as the temperature fluctuation degree and wave fluctuation degree at each time point.
[0019] Preferably, the method for determining the hydraulic environment coefficient at each time point is as follows:
[0020] Calculate the sum of the temperature-hydraulic correlation and the wave-hydraulic correlation at each time point, and calculate the ratio of the temperature-hydraulic correlation to the sum of the sums at each time point, as well as the ratio of the wave-hydraulic correlation to the sum of the sums at each time point, and record them as the first ratio and the second ratio at each time point, respectively.
[0021] Calculate the product of the first ratio and the temperature fluctuation at each time point, and the product of the second ratio and the wave fluctuation at each time point, and record them as the first product and the second product at each time point, respectively. The result of positively fusing the first product and the second product at each time point is used as the hydraulic environment coefficient at each time point.
[0022] Preferably, the expression for the hydraulic correction coefficient at the current moment is: In the formula, W represents the hydraulic correction coefficient at the current moment; B and A represent the hydraulic environment coefficient and hydraulic time-varying coefficient at the current moment, respectively; B max A max These represent the maximum value of the hydraulic environment coefficient and the maximum value of the hydraulic time-varying coefficient at all times within the preset time period before the current time, respectively.
[0023] Preferably, adjusting the gain parameter of the PID controller during the attitude control of the boarding bridge at sea includes:
[0024] In the formula, K p K i K d These represent the proportional gain, integral gain, and derivative gain of the PID controller at the current moment, respectively. These represent the preset initial proportional gain, preset initial integral gain, and preset initial derivative gain in the PID controller, respectively; W represents the hydraulic correction coefficient at the current moment; and exp() represents an exponential function with the natural constant as its base. Secondly, embodiments of this application also provide an attitude control system for a sea-boarding boarding bridge, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the attitude control method for a sea-boarding boarding bridge described above.
[0025] This application has at least the following beneficial effects:
[0026] This application constructs a hydraulic time-varying coefficient by analyzing the relationship between hydraulic oil temperature, vibration, impurities, and attitude error, quantifying the impact of hydraulic system performance on the attitude control of the boarding bridge. The hydraulic time-varying coefficient decouples and quantifies the variable error and irreversible baseline drift error of the hydraulic system, identifying deterioration factors such as wear and contamination, which helps improve the accuracy of subsequent hydraulic system control. This provides a basis for real-time optimization of control strategies and enhancing the stability and safety of the boarding bridge in complex sea conditions. Furthermore, this application quantifies environmental changes by analyzing the correlation and inherent volatility of ocean temperature, wave fluctuations, and the hydraulic system state. The influence of the boarding bridge's attitude control generates a hydraulic environment coefficient, which helps the control system adjust its strategy according to the real-time environment, accelerates the response under adverse conditions, and improves the safety and stability of boarding bridge operations in complex sea conditions. This application evaluates system performance and environmental disturbances through hydraulic time-varying coefficients and hydraulic environment coefficients respectively, and intelligently adjusts PID parameters: when system performance is poor, the focus is on improving control accuracy, i.e., increasing the integral gain; when the environment is severe, the focus is on improving response speed, i.e., increasing the proportional-Euclidean-derivative gain. This effectively balances response speed and control accuracy, improving the stability and safety of the boarding bridge operation in complex sea conditions. Attached Figure Description
[0027] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0028] Figure 1 A flowchart illustrating the steps of an attitude control method for a sea boarding bridge according to an embodiment of this application;
[0029] Figure 2 This is a schematic diagram of the hydraulic correction coefficient extraction process provided in one embodiment of this application. Detailed Implementation
[0030] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a posture control method and system for a sea boarding bridge proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0032] The following description, in conjunction with the accompanying drawings, details a specific scheme for the attitude control method and system of a sea boarding bridge provided in this application.
[0033] Please see Figure 1 The diagram illustrates a flowchart of the attitude control method for a sea boarding bridge according to an embodiment of this application. The method includes the following steps:
[0034] Step S1: Real-time acquisition of hydraulic oil temperature and impurity data, hydraulic system vibration data, ocean temperature, and wave fluctuation data in the hydraulic system within the boarding bridge at sea.
[0035] The hydraulic system of the boarding bridge at sea is collected in real time and synchronously using an RTD temperature sensor, a laser particle counter, and a vibration sensor. Ocean temperature and wave fluctuation data are also collected in real time using a temperature sensor and a wave sensor. The attitude of the boarding bridge is detected using an IMU module. All the above data are collected in real time and synchronously at a frequency of f. All collected data are normalized. In this embodiment, the maximum-minimum normalization method is used to normalize various types of data. In practical applications, as other implementation methods, the implementer may also use other normalization methods such as z-score normalization, depending on the specific circumstances. This embodiment does not impose any special limitations.
[0036] Among them, the maximum-minimum normalization method is a well-known technique, and the specific process of using it to normalize data will not be elaborated here.
[0037] It should be noted that the data acquisition frequency f is set manually. In this embodiment, data is acquired once per minute, that is, the value of f is 1 / 60Hz. In actual applications, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.
[0038] Step S2: Analyze the bridge attitude error, hydraulic oil temperature and impurity vibration data to determine the hydraulic time-varying coefficient; determine the hydraulic environment coefficient by comprehensively considering the correlation and fluctuation degree between ocean temperature, wave fluctuation and hydraulic state.
[0039] Maritime boarding bridges are used for the safe transfer of personnel and cargo at sea. However, due to the complexity of the marine environment, surge conditions pose a serious threat to the stability and safety of these bridges, necessitating compensation to ensure stability and safety during the transfer process. Existing technologies largely focus on predicting wave conditions and calculating compensation amounts. However, the hydraulic system is responsible for the specific adjustment and control tasks in maritime boarding bridges. The complexity of the marine environment significantly impacts the hydraulic system. Both long-term wear and tear, as well as disturbances caused by changes in the marine environment, can lead to errors in the attitude control of the boarding bridge. These errors can also create safety hazards during the transfer process. Therefore, further analysis of the hydraulic system is needed, and the attitude control of the boarding bridge should be optimized based on its operating conditions. The specific process is as follows:
[0040] S2.1 The attitude error of the boarding bridge at sea within a preset time period before each moment is acquired in real time. The preset time period is divided into multiple cycles. The hydraulic oil temperature and attitude error within each cycle are fitted. Based on the integral result of the region enclosed by the fitted curve between each cycle and the previous cycle, the error baseline drift of each cycle is determined. Combined with the differences of all hydraulic oil impurity data and all vibration data between each cycle and the previous cycle within the preset time period before each moment, the hydraulic deviation at each moment is determined. By analyzing the average distribution of attitude error on the fitted curve of the hydraulic oil temperature of each moment on all cycles within the corresponding preset time period, and combined with the hydraulic deviation, the hydraulic time-varying coefficient at each moment is determined.
[0041] In the attitude control of the boarding bridge at sea, the hydraulic system is used to adjust the bridge to achieve active wave compensation control. However, the performance of the hydraulic system significantly impacts the adjustment accuracy, thus greatly affecting the attitude control of the boarding bridge. The performance of the hydraulic system affects its response delay and sensitivity, leading to untimely attitude control, inaccurate control parameters, and unstable control processes, ultimately causing safety issues. The performance degradation of the hydraulic system manifests in the following two aspects:
[0042] (1) During the operation of the hydraulic system of the boarding bridge at sea, the marine operating environment causes significant damage to the structure of the hydraulic system, resulting in increasingly severe mechanical friction between the various structures within the hydraulic system over time. Secondly, the complex environment and friction during operation lead to the presence of a large number of impurities in the hydraulic oil, which further increase the mechanical friction between the various structures during the operation of the hydraulic system. This mechanical friction consumes some energy and slows down the response of the hydraulic system, making it difficult to adjust the attitude of the boarding bridge at sea to the accurate position in a timely manner. At the same time, this error caused by structural wear or impurities in the hydraulic oil is irreversible and will become increasingly severe over time, leading to baseline drift, i.e., the attitude error of the boarding bridge at sea shows an increasingly serious trend.
[0043] (2) In addition, the heat generated during the operation of the hydraulic system will also cause the hydraulic oil temperature to change. When the hydraulic oil temperature is high, the viscosity of the hydraulic oil will decrease, resulting in overly sensitive attitude control. This sensitivity will lead to increased oscillation during the attitude adjustment process of the bridge and relatively weakened stability. However, since the change in hydraulic oil temperature is related to the mechanical friction of the entire system, it will also be affected by the external environment, thus making the change in hydraulic oil temperature fluctuate to a certain extent.
[0044] Therefore, based on the above analysis, this embodiment first divides the monitoring of the hydraulic system of the boarding bridge at sea, specifically as follows:
[0045] In this embodiment, firstly, the attitude data of the boarding bridge at sea is acquired in real time. The acquisition frequency is set to f (1 minute). The attitude data of the boarding bridge at each moment and within a preset time period before that moment are used as input to the Autoregressive Integral Moving Average (ARIMA) model. The prediction step size is set to 1 minute. The predicted attitude data of the boarding bridge 1 minute after each moment is output. The deviation between the predicted attitude data and the ideal attitude data is used as a compensation input to the PID controller to adjust the attitude of the boarding bridge. The actual attitude data of the boarding bridge after adjustment is acquired. The difference between the actual attitude data and the predicted attitude data is used as the attitude error at the corresponding moment 1 minute after each moment. The attitude error of the boarding bridge at each moment is obtained according to the method described above. The prediction step size is set manually. In practical applications, as other implementation methods, the implementer can also set it according to specific circumstances. This embodiment does not impose special restrictions. The Autoregressive Integral Moving Average model is a known technology, and the specific process of using it to predict future data will not be elaborated further.
[0046] Furthermore, the preset time period before each moment is divided into multiple cycles, each cycle having a length of T. In this embodiment, T is 24 hours. In actual applications, as other implementation methods, implementers can also set their own values according to specific circumstances. In particular, cycles shorter than T are not considered.
[0047] It should be noted that the preset time period length in this embodiment is set manually. In this embodiment, the preset time period length is 10 days. In actual application, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.
[0048] Furthermore, in this embodiment, the hydraulic oil temperature and attitude error within each cycle are fitted. Specifically, the hydraulic oil temperature within each cycle is used as the independent variable in the fitting algorithm, and the attitude error is used as the dependent variable in the fitting algorithm. The fitting curve related to the hydraulic oil temperature and attitude error is output as the fitting curve for each cycle.
[0049] It should be noted that there are many commonly used fitting algorithms. In this embodiment, the least squares fitting method is used to fit the hydraulic oil temperature and attitude error. In practical applications, as other implementation methods, implementers may also use other fitting methods such as polynomial function fitting according to specific circumstances. This embodiment does not impose any special restrictions on the selection of fitting algorithms.
[0050] The least squares fitting method is a well-known technique, and the specific process of using it to fit hydraulic oil temperature and attitude error will not be elaborated here.
[0051] Furthermore, in this embodiment, the error baseline drift for each period is determined based on the integral result of the region enclosed by the fitted curve between each period and the previous period, specifically as follows:
[0052] As a specific implementation, in this embodiment, the error baseline drift ΔF for each cycle within a preset time period before time i is... x,i The expression is: f x,i (t), f x,i―1 (t) represents the fitted curves of the i-th and (i-1)-th cycles within the preset time period before time x, respectively; t represents the hydraulic oil temperature on the horizontal axis of the fitted curve; a x,i b x,i These represent the minimum and maximum values of hydraulic oil temperature at all times within the i-th cycle and the adjacent preceding cycle in the preset time period before time x.
[0053] Based on the error baseline drift of each cycle within the preset time period before each moment, it can be understood that the error baseline drift reflects the change in the reference value of the attitude adjustment error of the boarding bridge between two consecutive cycles. The magnitude of the error baseline drift reflects the health of the hydraulic system. The larger the error baseline drift, the greater the adjustment error of the hydraulic system to attitude control in long-term operation, the lower the stability of the hydraulic system, and the less accurate the attitude adjustment of the boarding bridge. This indicates that the hydraulic system has suffered severe wear or serious hydraulic oil contamination due to long-term operation. Conversely, the smaller the error baseline drift, the more stable the adjustment error of the hydraulic system to attitude control in long-term operation, without significant increase. This indicates higher hydraulic system stability, and the boarding bridge can maintain high accuracy in attitude adjustment. This indicates that the wear or hydraulic oil contamination caused by long-term operation is less severe, and the hydraulic system is in good condition.
[0054] Furthermore, this embodiment determines the hydraulic deviation at each moment based on the error baseline drift of each cycle, combined with the differences in all hydraulic oil impurity data and all vibration data between each cycle and the previous cycle within a preset time period before each moment. Specifically:
[0055] As a specific implementation method, in this embodiment, the hydraulic deviation Q at time x x The expression is: In the formula, Q x Indicates the hydraulic deviation at time x; v x z x ΔF represents the difference in the mean of all vibration data before time x and between the next adjacent period and the next adjacent period, respectively; ΔF represents the difference in the mean of all hydraulic oil impurity data. x,i Δv represents the baseline drift of the error in the i-th cycle within a preset time period prior to time x; x,i Δz x,i These represent the total difference in vibration data between the i-th cycle and its preceding cycle within a preset time period before time x, and the total difference in hydraulic oil impurities at all times, respectively; N x This indicates the number of all cycles within a preset time period prior to time x.
[0056] It should be noted that there are many methods to measure the difference between data. In this embodiment, the absolute value of the difference is used to measure the difference between data. For example, in this embodiment, the absolute value of the difference between the mean of all vibration data before time x and the period adjacent to time x and the next adjacent period is taken as the difference between the mean of all vibration data before time x and the period adjacent to time x and the next adjacent period. In practical applications, as other implementation methods, implementers may also choose other methods to measure the difference between data, such as the square or ratio of the difference, depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of methods to measure the difference between data.
[0057] Furthermore, it should be noted that there are many methods for measuring the differences between data groups. In this embodiment, the DTW distance of all vibration data between the i-th cycle and its previous cycle within a preset time period before time x is taken as the overall difference between all vibration data between the i-th cycle and its previous cycle within a preset time period before time x. Similarly, the DTW distance of all hydraulic oil impurity data between the i-th cycle and its previous cycle within a preset time period before time x is taken as the overall difference between all hydraulic oil impurity data between the i-th cycle and its previous cycle within a preset time period before time x. In practical applications, as other implementation methods, implementers may also choose other methods such as Euclidean distance or Mahalanobis distance to measure the overall difference between data groups, depending on the specific circumstances. This embodiment does not impose any special restrictions.
[0058] The method for calculating DTW distance is a well-known technique, and its specific calculation formula and process will not be elaborated here.
[0059] Based on the hydraulic deviation at each time point, it can be understood that the hydraulic deviation reflects the degree of deviation of the vibration data and hydraulic oil impurity data of the hydraulic system from the historical state. If the difference between the mean values of all vibration data and the mean values of all hydraulic oil impurity data between time x and the next adjacent period is greater, it indicates that the vibration and hydraulic oil impurity levels of the hydraulic system at time x have deviated significantly from the historical state. Therefore, the greater the corresponding hydraulic deviation, the more likely the hydraulic system state is deteriorating, leading to a greater negative impact on the attitude control of the boarding bridge at sea. Meanwhile, if the hydraulic deviation is greater than the predicted value before time x... The larger the error baseline drift in the i-th cycle within a time period, the more severe the irreversible error accumulation caused by the deterioration of the hydraulic system performance is. This directly indicates that the performance of the hydraulic system is gradually declining, and the corresponding hydraulic deviation is also greater. In addition, if the overall difference of all vibration data and the overall difference of all hydraulic oil impurities between the i-th cycle and the previous cycle within a preset time period before time x is smaller, then any small deviation of the hydraulic system state at time x will be more prominent relative to the small changes in the past. Therefore, the larger the corresponding hydraulic deviation, the more significant the impact of small changes in the hydraulic system state on the hydraulic system performance.
[0060] Conversely, if the differences in the mean values of all vibration data and the mean values of all hydraulic oil impurity data between the periods preceding and adjacent to time x are smaller, it indicates that the vibration and hydraulic oil impurity levels of the hydraulic system at time x are relatively stable compared to historical conditions, without significant deviation. Therefore, the smaller the corresponding hydraulic deviation, the more likely the hydraulic system remains stable without a significant deterioration trend, resulting in a smaller negative impact on the attitude control of the boarding bridge at sea. Simultaneously, if the error baseline drift in the i-th period within the preset time frame before time x is smaller, it indicates that the hydraulic system performance is good. The accumulation of irreversible errors caused by degradation is not severe, which directly indicates that the performance of the hydraulic system remains stable or declines slowly, and the corresponding hydraulic deviation is also smaller. In addition, if the overall difference of all vibration data and the overall difference of all hydraulic oil impurities between the i-th cycle and the previous cycle within the preset time period before time x is greater, then any deviation of the hydraulic system state at time x will be less prominent compared to the large changes in the past. Therefore, the smaller the corresponding hydraulic deviation, the more it indicates that the fluctuation of the hydraulic system state is within the normal range of the past, and its change has a relatively small impact on the performance of the hydraulic system.
[0061] Furthermore, this embodiment analyzes the average distribution of attitude error on the fitting curves of hydraulic oil temperature at each moment across all cycles within the corresponding preset time period, and combines this with the hydraulic deviation to determine the hydraulic time-varying coefficient at each moment, specifically:
[0062] In this embodiment, the mean value of the attitude error of the hydraulic oil temperature at each moment on the fitting curve of all cycles in the corresponding preset time period is calculated and recorded as the mean value of attitude error. The result of positively fusing the mean value of attitude error with the hydraulic deviation at the corresponding moment is used as the hydraulic time-varying coefficient at each moment.
[0063] It should be understood that positive fusion refers to combining two or more indicators through addition or multiplication to obtain a comprehensive indicator, thereby more comprehensively and accurately assessing a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analytical methods. Implementers can choose according to specific circumstances, and this embodiment does not impose any special restrictions.
[0064] Preferably, in this embodiment, the sum of the mean value and the hydraulic deviation at the corresponding time is used as the hydraulic time-varying coefficient at each time.
[0065] Based on the hydraulic time-varying coefficient at each moment, it can be understood that the hydraulic time-varying coefficient reflects the degree of influence of the current and historical operating state of the hydraulic system on the attitude control accuracy of the boarding bridge. If the average distribution of attitude error on the fitted curve of all cycles within the corresponding preset time period is larger, the corresponding hydraulic time-varying coefficient is larger, indicating that the average value of attitude control error caused by hydraulic oil temperature change is larger at the current hydraulic oil temperature. This means that the impact of hydraulic oil temperature change on the attitude control accuracy of the boarding bridge is relatively significant. It may be that the hydraulic oil temperature is too high, resulting in a decrease in viscosity, or the hydraulic oil temperature is too low, resulting in a high viscosity of hydraulic oil, causing the hydraulic system to respond slowly, thus affecting the stability of attitude control. At the same time, if the hydraulic deviation is larger, the corresponding hydraulic time-varying coefficient is larger, indicating that the accumulation of impurities in the hydraulic oil and the vibration of the hydraulic system have a greater impact on the attitude control error of the boarding bridge. This suggests that the hydraulic system may be experiencing an accelerator of performance degradation, resulting in a decrease in the accuracy of attitude control.
[0066] Conversely, if the average distribution of attitude error on the fitted curve of all cycles within the corresponding preset time period is smaller, and the corresponding hydraulic time-varying coefficient is smaller, it indicates that the average value of attitude control error caused by hydraulic oil temperature change is smaller at the current hydraulic oil temperature. This means that the impact of hydraulic oil temperature change on the attitude control accuracy of the boarding bridge is relatively insignificant, and the hydraulic system can maintain relatively stable response characteristics at the current oil temperature, which is beneficial to the stability of attitude control. At the same time, if the hydraulic deviation is smaller, and the corresponding hydraulic time-varying coefficient is smaller, it indicates that the accumulation of hydraulic oil impurities and the vibration of the hydraulic system have less impact on the attitude control error of the boarding bridge. This indicates that the hydraulic system performance is relatively stable and has not experienced significant performance degradation, thus maintaining high attitude control accuracy.
[0067] Thus, this embodiment quantifies the impact of the hydraulic system state on the attitude control of the boarding bridge by analyzing the relationship between hydraulic oil temperature, vibration, impurities and other data and attitude error, and identifies deterioration factors such as wear and contamination. This provides a basis for real-time optimization of control strategies and improving the stability and safety of the boarding bridge in complex sea conditions.
[0068] S2.2 Analyze the correlation between the hydraulic time-varying coefficients of ocean temperature and wave fluctuation data at all times within a preset time period before each time point and the seasonal trend of the hydraulic time-varying coefficients at all times points, and determine the temperature-hydraulic correlation and wave-hydraulic correlation at each time point respectively; analyze the dispersion of ocean temperature and wave fluctuation data at all times within a preset time period, and determine the temperature fluctuation and wave fluctuation at each time point respectively, and combine the temperature-hydraulic correlation and wave-hydraulic correlation to determine the hydraulic environment coefficient at each time point.
[0069] The hydraulic time-varying coefficient was analyzed for the performance changes of the hydraulic system. However, the response and adjustment of the hydraulic system are not only related to the performance of the hydraulic system. Due to the complexity of the marine operating environment, the adjustment of the hydraulic system is also affected. At the same time, the degree of influence of environmental changes on the attitude adjustment of the boarding bridge varies under different marine operating environments, which makes the requirements for adjustment response speed different under different environments.
[0070] Firstly, the hydraulic oil temperature of the hydraulic system is affected by the environment, and the temperature difference in the offshore operating environment is relatively large. Therefore, when using the hydraulic system for attitude control of the offshore boarding bridge under different environmental conditions, changes in ambient temperature have a significant impact on the control accuracy. Secondly, wave fluctuations cause vibrations in the hydraulic system, and the more violent the wave fluctuations, the greater the mechanical impact on the hydraulic system. This impact increases the instability of the hydraulic system and may also increase the contact friction between hydraulic components, thereby slowing down the response speed and reducing stability. Especially in harsher environmental conditions, it becomes increasingly difficult to control the offshore boarding bridge to achieve the ideal attitude position.
[0071] However, in harsh environments, rapid attitude adjustment is crucial for quickly reaching the accurate position. Therefore, this embodiment analyzes the correlation between the hydraulic time-varying coefficients of ocean temperature and wave fluctuation data at all times within a preset time period and the seasonal trend of the hydraulic time-varying coefficients at all times, determining the temperature-hydraulic correlation and wave-hydraulic correlation at each time point. It also analyzes the dispersion of ocean temperature and wave fluctuation data at all times within a preset time period, determining the temperature fluctuation and wave fluctuation at each time point. Combining these temperature-hydraulic correlation and wave-hydraulic correlation, the hydraulic environmental coefficient at each time point is determined to assess the impact of environmental changes on the attitude control stability of the boarding bridge. This allows for targeted adjustment of the boarding bridge's attitude according to different environmental changes, avoiding safety hazards caused by slow adjustments in harsh environments. Specifically:
[0072] In this embodiment, firstly, the correlation between the hydraulic time-varying coefficient of ocean temperature and wave fluctuation data at all times within a preset time period prior to each time point and the seasonal trend of the hydraulic time-varying coefficient at all times is analyzed, specifically:
[0073] In this embodiment, the hydraulic time-varying coefficients of all times within a preset time period before each time point are used as the input of the time series decomposition algorithm, and the output is a seasonal term sequence;
[0074] It should be noted that the preset duration is set manually. In this embodiment, the preset duration is 24 hours. In actual applications, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.
[0075] Furthermore, it should be noted that there are many commonly used time series decomposition algorithms. In this embodiment, the STL time series decomposition algorithm is used to obtain the seasonal term sequence of hydraulic time-varying coefficients. In practical applications, as other implementation methods, implementers may also use other time series decomposition algorithms according to specific circumstances. This embodiment does not impose any special restrictions.
[0076] The STL time series decomposition algorithm is a well-known technique, and the specific process of obtaining the seasonal term series using it will not be elaborated here.
[0077] Furthermore, ocean temperature and wave fluctuation data within a preset time period prior to each time point are used to form temperature and wave sequences, respectively. The temperature, wave, and seasonal sequence are used as inputs to a grey relational analysis algorithm, where the temperature and wave sequences are used as subsequences and the seasonal sequence is used as the parent sequence. The correlation between the temperature and seasonal sequence, and the correlation between the wave and seasonal sequence are output as the temperature-hydraulic correlation and wave-hydraulic correlation at each time point, respectively.
[0078] Among them, the grey relational algorithm is a well-known technology. The specific process of using it to obtain the correlation between temperature series and seasonal item series, as well as the correlation between wave series and seasonal item series, will not be elaborated here.
[0079] Furthermore, this embodiment analyzes the dispersion of ocean temperature and wave fluctuation data at all times within a preset time period to determine the temperature fluctuation and wave fluctuation at each time point. Combined with the temperature-hydraulic correlation and wave-hydraulic correlation, the hydraulic environment coefficient at each time point is determined, specifically:
[0080] In this embodiment, the coefficient of variation of ocean temperature and the coefficient of variation of wave fluctuation data at all times within a preset time period before each time are respectively used as the temperature fluctuation and wave fluctuation at each time.
[0081] The method for calculating the coefficient of variation is a well-known technique, and its specific calculation formula and process will not be elaborated here.
[0082] Furthermore, the sum of the temperature-hydraulic correlation and the wave-hydraulic correlation at each time point is calculated, and the ratio of the temperature-hydraulic correlation to the sum of the sums and the ratio of the wave-hydraulic correlation to the sums at each time point are calculated respectively, and recorded as the first ratio and the second ratio at each time point.
[0083] Calculate the product of the first ratio and the temperature fluctuation at each time point, and the product of the second ratio and the wave fluctuation at each time point, and record them as the first product and the second product at each time point, respectively. The result of positively fusing the first product and the second product at each time point is used as the hydraulic environment coefficient at each time point.
[0084] Preferably, as an implementation method, in this embodiment, the sum of the first product and the second product at each time point is used as the hydraulic environment coefficient at each time point.
[0085] Based on the hydraulic environment coefficient at various times, it can be understood that the hydraulic environment coefficient reflects the degree of influence of changes in the marine environment on the performance of the hydraulic system and the stability and accuracy of the attitude control of the boarding bridge. If the proportion of temperature-hydraulic correlation is larger and the temperature fluctuation is greater, the corresponding hydraulic environment coefficient will also be larger, indicating that the more unstable the ocean temperature, the greater the impact on the hydraulic state. Drastically changing environmental factors are more likely to interfere with the stable operation of the hydraulic system and the accuracy of the attitude control of the boarding bridge. Similarly, if the proportion of wave-hydraulic correlation is larger and the wave fluctuation is greater, the corresponding hydraulic environment coefficient will also be larger, indicating that the more violent the wave fluctuation, the greater the impact on the health of the hydraulic system. Drastically changing environmental factors are more likely to interfere with the stable operation of the hydraulic system and the accuracy of the attitude control of the boarding bridge.
[0086] Conversely, if the proportion of temperature-hydraulic correlation is smaller and the temperature fluctuation is smaller, the corresponding hydraulic environment coefficient is also smaller. This indicates that the more stable the ocean temperature, the less impact it has on the hydraulic state, and the less interference environmental factors cause to the stable operation of the hydraulic system and the accuracy of attitude control of the boarding bridge at sea. Similarly, if the proportion of wave-hydraulic correlation is smaller and the wave fluctuation is smaller, the corresponding hydraulic environment coefficient is also smaller. This indicates that the more gentle the wave fluctuation, the less impact it has on the health of the hydraulic system, and the less interference environmental factors cause to the stable operation of the hydraulic system and the accuracy of attitude control of the boarding bridge at sea. In this case, the working environment of the hydraulic system is relatively friendly, and the control system can focus more on maintaining the accuracy of attitude.
[0087] Thus, this embodiment quantifies the impact of environmental changes on the attitude control of the boarding bridge by analyzing the correlation and inherent volatility of ocean temperature, wave fluctuations, and hydraulic system status, and generates a hydraulic environment coefficient. This helps the control system adjust its strategy according to the real-time environment, accelerate response under adverse conditions, and improve the safety and stability of boarding bridge operations at sea in complex sea conditions.
[0088] Step S3: Based on the hydraulic time-varying coefficient and hydraulic environment coefficient at the current moment, determine the hydraulic correction coefficient at the current moment in order to adjust the gain parameter of the PID controller during the attitude control of the boarding bridge at sea.
[0089] In controlling hydraulic systems, existing technologies commonly use PID control for adjustment. However, existing PID control schemes do not consider the impact of hydraulic system performance changes and environmental variations on control accuracy and response speed. The hydraulic time-varying coefficient and hydraulic environment coefficient are analyzed to examine the error in the adjustment and control of the boarding bridge at sea from the perspectives of hydraulic system performance changes and their environmental influence. Therefore, this embodiment determines the hydraulic correction coefficient based on the hydraulic time-varying coefficient and hydraulic environment coefficient at the current moment to adjust the gain parameter of the PID controller during the attitude control of the boarding bridge at sea. Specifically:
[0090] As a specific implementation method, in this embodiment, the expression for the hydraulic correction coefficient W at the current moment is: In the formula, B and A represent the hydraulic environment coefficient and hydraulic time-varying coefficient at the current moment, respectively; B max A max These represent the maximum value of the hydraulic environment coefficient and the maximum value of the hydraulic time-varying coefficient at all times within the preset time period before the current time, respectively.
[0091] Preferably, the schematic diagram of the hydraulic correction coefficient extraction process provided in this embodiment is as follows: Figure 2 As shown.
[0092] It should be noted that the preset time period length is set manually. In this embodiment, the preset time period length is 24 hours. In actual applications, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.
[0093] Based on the hydraulic correction coefficient at the current moment, it can be understood that if the ratio of the hydraulic environmental coefficient at the current moment to the maximum value among all hydraulic environmental coefficients within the preset time period before the current moment is larger, it indicates that the severity of the current marine environment is relatively high compared to the worst environmental state observed within the preset time period before the current moment. Therefore, it is necessary to control the hydraulic system to respond more actively and quickly to environmental changes. If the ratio of the hydraulic time-varying coefficient at the current moment to the maximum value among all hydraulic time-varying coefficients within the preset time period before the current moment is larger, it indicates that the hydraulic system itself is in poor condition. It is necessary to control the hydraulic system to be more robust and avoid over-adjustment.
[0094] Furthermore, based on the hydraulic correction coefficient, the gain parameter in the PID controller is adjusted as follows:
[0095] In the formula, K p K i K d These represent the proportional gain, integral gain, and derivative gain of the PID controller at the current moment, respectively. These represent the preset initial proportional gain, preset initial integral gain, and preset initial derivative gain in the PID controller, respectively; W represents the hydraulic correction coefficient at the current moment; exp() represents the exponential function with the natural constant as the base.
[0096] It is understandable that appropriately increasing the proportional gain and derivative gain can help enhance the response speed of PID control. However, increasing the integral gain can slow down the response of PID control and cause oscillations. Therefore, in order to improve the response speed, it is necessary to appropriately increase the proportional gain and derivative gain, while avoiding excessive expansion of the integral gain. Conversely, in order to improve the control accuracy, it is necessary to appropriately increase the integral gain and derivative gain, while avoiding excessive expansion of the proportional gain.
[0097] When using the hydraulic environment coefficient and hydraulic time-varying coefficient to measure the performance of a hydraulic system, a larger hydraulic time-varying coefficient indicates a worse performance of the hydraulic system. This can lead to a larger error in PID control of the hydraulic system, thus requiring improved PID control accuracy. Conversely, a larger hydraulic environment coefficient indicates a greater impact of environmental changes on the adjustment accuracy of the hydraulic system. Due to the variability of the environment, the system needs to reach the adjustment position more quickly to reduce the impact of environmental fluctuations, thus requiring a faster response speed of PID control.
[0098] Thus, this embodiment evaluates system performance and environmental interference by using hydraulic time-varying coefficients and hydraulic environmental coefficients respectively, and intelligently adjusts PID parameters: when system performance is poor, the focus is on improving control accuracy, i.e., increasing integral gain; when the environment is harsh, the focus is on improving response speed, i.e., increasing proportional-Euclidean-derivative gain, thereby effectively balancing response speed and control accuracy to adapt to complex working conditions.
[0099] Based on the same inventive concept as the above methods, this application also provides an attitude control system for a sea boarding bridge, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described attitude control methods for a sea boarding bridge.
[0100] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.
[0101] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0102] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.
Claims
1. A method for attitude control of a boarding bridge at sea, characterized in that, The method includes the following steps: Real-time data on hydraulic oil temperature and impurities, vibration data of the hydraulic system, ocean temperature, and wave fluctuations within the boarding bridge at sea. The attitude error of the boarding bridge at sea is acquired in real time within a preset time period before each moment. The preset time period is divided into multiple cycles. The hydraulic oil temperature and attitude error in each cycle are fitted. Based on the integral result of the fitted curve of each cycle and the previous cycle, the error baseline drift of each cycle is determined. Combined with the differences of all hydraulic oil impurity data and all vibration data between each cycle and the previous cycle within the preset time period before each moment, the hydraulic deviation at each moment is determined. By analyzing the average distribution of attitude error on the fitted curve of hydraulic oil temperature in all cycles within the corresponding preset time period at each moment, and combined with the hydraulic deviation, the hydraulic time-varying coefficient at each moment is determined. The correlation between the hydraulic time-varying coefficients of ocean temperature and wave fluctuation data at all times within a preset time period before each time point and the seasonal trend of the hydraulic time-varying coefficients at all times points is analyzed to determine the temperature-hydraulic correlation and wave-hydraulic correlation at each time point. The dispersion of ocean temperature and wave fluctuation data at all times within a preset time period is analyzed to determine the temperature fluctuation and wave fluctuation at each time point. Combined with the temperature-hydraulic correlation and wave-hydraulic correlation, the hydraulic environment coefficient at each time point is determined. Based on the hydraulic time-varying coefficient and hydraulic environment coefficient at the current moment, the hydraulic correction coefficient at the current moment is determined in order to adjust the gain parameter of the PID controller during the attitude control of the boarding bridge at sea.
2. The attitude control method for a sea boarding bridge as described in claim 1, characterized in that, The expression for the error baseline drift of each cycle within the preset time period prior to each moment is: ΔF x,i f represents the baseline drift of the error in the i-th period within a preset time period prior to time x; x,i (t), f x,i―1 (t) represents the fitted curves of the i-th and (i-1)-th cycles within the preset time period before time x, respectively; t represents the hydraulic oil temperature on the horizontal axis of the fitted curve; a x,i b x,i These represent the minimum and maximum values of hydraulic oil temperature at all times within the i-th cycle and the adjacent preceding cycle in the preset time period before time x.
3. The attitude control method for a sea boarding bridge as described in claim 1, characterized in that, The expression for the hydraulic deviation at each time point is: In the formula, Q x Indicates the hydraulic deviation at time x; v x z x ΔF represents the difference in the mean of all vibration data before time x and between the next adjacent period and the next adjacent period, respectively; ΔF represents the difference in the mean of all hydraulic oil impurity data. x,i Δv represents the baseline drift of the error in the i-th cycle within a preset time period prior to time x; x,i Δz x,i These represent the total difference in vibration data between the i-th cycle and its preceding cycle within a preset time period before time x, and the total difference in hydraulic oil impurities at all times, respectively; N x This indicates the number of all cycles within a preset time period prior to time x.
4. The attitude control method for a sea boarding bridge as described in claim 1, characterized in that, The method for determining the hydraulic time-varying coefficient at each time point is as follows: The mean value of the attitude error of the hydraulic oil temperature at each moment on the fitting curve of all cycles in the corresponding preset time period is calculated and recorded as the mean value of attitude error. The result of positively fusing the mean value of attitude error with the hydraulic deviation at the corresponding moment is used as the hydraulic time-varying coefficient at each moment.
5. The attitude control method for a sea boarding bridge as described in claim 1, characterized in that, The determination of the temperature-hydraulic correlation and wave-hydraulic correlation at each time point includes: The hydraulic time-varying coefficients of all times within a preset time period before each time point are used as input to the time series decomposition algorithm, and the seasonal term sequence is output. Ocean temperature and wave fluctuation data within a preset time period prior to each time point are used to form temperature and wave sequences, respectively. The temperature, wave, and seasonal sequences are used as inputs to a grey relational algorithm, which outputs the correlation between the temperature and seasonal sequences, as well as the correlation between the wave and seasonal sequences, as the temperature-hydraulic correlation and wave-hydraulic correlation at each time point, respectively.
6. The attitude control method for a sea boarding bridge as described in claim 1, characterized in that, The determination of temperature fluctuation and wave fluctuation at each time point includes: The coefficients of variation of ocean temperature and the coefficients of variation of wave fluctuation data at all times within the preset time period before each time point are respectively used as the temperature fluctuation degree and wave fluctuation degree at each time point.
7. The attitude control method for a sea boarding bridge as described in claim 1, characterized in that, The method for determining the hydraulic environment coefficient at each time point is as follows: Calculate the sum of the temperature-hydraulic correlation and the wave-hydraulic correlation at each time point, and calculate the ratio of the temperature-hydraulic correlation to the sum of the sums at each time point, as well as the ratio of the wave-hydraulic correlation to the sum of the sums at each time point, and record them as the first ratio and the second ratio at each time point, respectively. Calculate the product of the first ratio and the temperature fluctuation at each time point, and the product of the second ratio and the wave fluctuation at each time point, and record them as the first product and the second product at each time point, respectively. The result of positively fusing the first product and the second product at each time point is used as the hydraulic environment coefficient at each time point.
8. The attitude control method for a sea boarding bridge as described in claim 1, characterized in that, The expression for the hydraulic correction coefficient at the current moment is: In the formula, W represents the hydraulic correction coefficient at the current moment; B and A represent the hydraulic environment coefficient and hydraulic time-varying coefficient at the current moment, respectively; B max A max These represent the maximum value of the hydraulic environment coefficient and the maximum value of the hydraulic time-varying coefficient at all times within the preset time period before the current time, respectively.
9. The attitude control method for a sea boarding bridge as described in claim 1, characterized in that, The adjustment of the gain parameters of the PID controller during the attitude control of the boarding bridge at sea includes: In the formula, K p K i K d These represent the proportional gain, integral gain, and derivative gain of the PID controller at the current moment, respectively. These represent the preset initial proportional gain, preset initial integral gain, and preset initial derivative gain in the PID controller, respectively; W represents the hydraulic correction coefficient at the current moment; exp() represents the exponential function with the natural constant as the base.
10. A posture control system for a sea-boarding boarding bridge, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the attitude control method for a sea boarding bridge as described in any one of claims 1-9.