Safety control methods, systems, equipment, and storage media for flight simulator boarding bridges

Abstract

This application relates to the field of memoelectric control of flight simulation training equipment, and provides a safety control method, system, equipment, and storage medium for a flight simulator boarding bridge in order to solve the problems of reduced operational stability and poor control accuracy of the boarding bridge during load changes. [Solution] The present invention includes: collecting altitude sensing signals and stress distribution signals during the lifting and lowering process of the passenger bridge; acquiring altitude data and stress distribution data through signal conditioning and analog-to-digital conversion processing; analyzing the real-time load distribution state based on this data and generating electric cylinder adjustment commands and preload adjustment commands to achieve dynamic compensation for center of gravity offset; generating lifting motion control signals using a fuzzy PID control model in combination; then processing these signals with an attitude estimation algorithm to acquire passenger bridge attitude estimation data; and finally correcting the motion control signals based on this data to achieve highly accurate and stable control of the lifting and lowering process of the passenger bridge. The present invention improves the operational stability and control accuracy of the passenger bridge during load changes.

Description

[Technical Field]

[0001] This application relates to the technical field of memoelectric control of flight simulation training equipment, and more particularly to safety control methods, systems, equipment, and storage media for flight simulator boarding bridges. [Background technology]

[0002] In the operation of ancillary facilities for flight simulators, safety and stability during the ascent and descent of connecting walkways are critical technical requirements. During the ascent and descent of these walkways, the distribution and movement of internal loads can cause an offset of the structure's center of gravity, resulting in tilting and vibration. If control is inadequate, this can not only affect the structural safety of the equipment itself but also potentially cause interference with the flight simulator. Therefore, there is a need for a method that can dynamically suppress attitude anomalies and ensure the stability and reliability of the ascent and descent process by detecting the state in real time, making intelligent decisions, and performing high-precision closed-loop control over motion.

[0003] Currently, one conventional approach to the above-mentioned technical requirements employs an active stabilization system based on fixed-parameter PID control. This system collects attitude and angle information of the passageway via sensors and transmits the angle deviation as an input to the controller. The controller calculates a control variable based on preset proportional, integral, and differential parameters, and then drives actuators to adjust position or force, thereby eliminating the angle deviation and maintaining the passageway's horizontal position.

[0004] In this conventional embodiment, the setting of control parameters in actual applications depends on the linearized model of the system and typical operating conditions. When the load on the passage changes significantly or rapidly, fixed control parameters make it difficult to maintain optimal control effects under various operating conditions, potentially causing overshoot in the system response or prolonging the adjustment time, which can affect the smoothness of the stabilization process. At the same time, in this embodiment, fluctuations in internal structural forces directly caused by load changes, and the impact of these changes on center of gravity compensation, are not adequately considered in the control decision-making. [Overview of the project] [Problems that the invention aims to solve]

[0005] This invention provides a safety control method, system, equipment, and storage medium for a flight simulator boarding bridge in order to solve the problems of reduced operational stability and poor control accuracy of boarding bridges during load changes in the prior art. [Means for solving the problem]

[0006] In order to solve the technical problems described above, in the first aspect, the present invention relates to a safety control method for a flight simulator boarding bridge, To collect altitude sensing signals and stress distribution signals from the flight simulator boarding bridge during the ascent and descent process, Signal conditioning and analog-to-digital conversion processing are performed on the aforementioned high-level sensing signal and stress distribution signal, respectively, to acquire high-level data and stress distribution data. Based on the altitude data and stress distribution data, the real-time load distribution state of the boarding bridge is acquired, and according to the real-time load distribution state, electric cylinder adjustment commands and preload adjustment commands are generated. Based on the aforementioned electric cylinder adjustment command and the aforementioned preload adjustment command, dynamic compensation is performed for the center of gravity offset of the passenger bridge, and a control signal for the passenger bridge's raising and lowering motion is generated using a fuzzy PID control model in combination. The safety control of the passenger bridge is achieved by processing the passenger bridge elevation / depression control signal using an attitude estimation algorithm to generate passenger bridge attitude estimation data, and modifying the passenger bridge elevation / depression control signal based on the passenger bridge attitude estimation data. including, This provides a safety control method for the boarding bridge of a flight simulator.

[0007] Preferably, dynamic compensation is performed for the center of gravity offset of the passenger bridge based on the electric cylinder adjustment command and the preload adjustment command described above, and a control signal for the passenger bridge's raising and lowering motion is generated in combination with a fuzzy PID control model. Based on the electric cylinder adjustment command, the electric cylinder driver is controlled to adjust the displacement or thrust of the electric cylinder according to the specified displacement adjustment amount and adjustment direction. Based on the aforementioned preload adjustment command, the corresponding preload is applied or maintained via a spring mechanism, and the adjusted preload is obtained. Based on the adjusted electric cylinder pressure and adjusted preload, dynamic compensation is performed for the center of gravity offset of the passenger bridge, and passenger bridge condition data after dynamic compensation is obtained. Based on the passenger bridge state data after the dynamic compensation, a control signal for the passenger bridge's elevation and lowering motion is generated using a fuzzy PID control model. Includes.

[0008] Preferably, based on the passenger bridge state data after the dynamic compensation described above, a passenger bridge elevation / depression control signal is generated using a fuzzy PID control model. The dynamic compensation-based passenger bridge state data is input to a fuzzy PID control model, and the fuzzy inference module within the fuzzy PID control model dynamically adjusts the control parameters, including proportional, integral, and differential parameters. Depending on the adjusted control parameters, the PID control module within the fuzzy PID control model generates target lifting speed control signals and target acceleration control signals. The signal synthesis module within the fuzzy PID control model performs signal synthesis processing on the target elevation speed control signal and the target acceleration control signal to generate a control signal for the elevation motion of the passenger bridge. Includes.

[0009] Preferably, the PID control module in the fuzzy PID control model generates the target lifting speed control signal and the target acceleration control signal according to the adjusted control parameters described above. Depending on the adjusted control parameters, the PID control module performs error calculation processing on the dynamically compensated passenger bridge state data to obtain altitude control error and stress control error. Based on the aforementioned high-level control error and stress control error, proportional calculation, integral calculation, and differential calculation are performed, respectively. The results of proportional calculations, integral calculations, and differential calculations are added together to generate provisional lifting speed control signals and provisional acceleration control signals. Limiter processing is performed on the provisional lifting speed control signal and the provisional acceleration control signal to generate a target lifting speed control signal and a target acceleration control signal. Includes.

[0010] Preferably, based on the altitude data and stress distribution data described above, the real-time load distribution state of the boarding bridge is acquired, and according to the real-time load distribution state, electric cylinder adjustment commands and preload adjustment commands are generated. The process involves performing time-domain synchronization on the aforementioned altitude data and stress distribution data to generate a time-aligned sensing dataset. Based on the pre-configured mapping relationship between sensing data and load distribution, load distribution calculation processing is performed on the time-aligned sensing dataset to generate a real-time load distribution state including load value parameters, load position parameters, and load change trend parameters. The process involves performing electric cylinder control strategy processing on the aforementioned load position parameters to generate electric cylinder adjustment commands, performing preload control strategy processing on the load value parameter and the load change trend parameter to generate a preload adjustment command; comprising.

[0011] Preferably, said processing the boarding bridge lifting motion control signal by the posture estimation algorithm to generate boarding bridge posture estimation data, and correcting the boarding bridge lifting motion control signal based on the boarding bridge posture estimation data comprises: collecting real-time motion sensing data during the lifting process of the boarding bridge; using a posture estimation algorithm to calculate expected posture data of the boarding bridge based on the boarding bridge lifting motion control signal, and taking the expected posture data as a temporary estimated value; performing data fusion processing on the temporary estimated value and the real-time motion sensing data to generate boarding bridge posture estimation data; calculating a control signal adjustment amount based on the inclination angle parameter and the vibration amplitude parameter in the boarding bridge posture estimation data; performing real-time correction on the boarding bridge lifting motion control signal according to the control signal adjustment amount; comprising.

[0012] Preferably, said performing signal conditioning and analog-to-digital conversion processing on the altitude sensing signal and the stress distribution signal respectively to obtain altitude data and stress distribution data comprises: performing signal amplification and filtering processing on the altitude sensing signal to obtain a conditioned altitude analog signal, and performing signal amplification and filtering processing on the stress distribution signal to obtain a conditioned stress analog signal; converting the conditioned altitude analog signal into altitude data via an analog-to-digital converter, and converting the conditioned stress analog signal into stress distribution data via an analog-to-digital converter; comprising.

[0013] In the second aspect, the present invention relates to a safety control system for a flight simulator boarding bridge, A collection module for collecting altitude sensing signals and stress distribution signals from the flight simulator boarding bridge during the lifting and lowering process, A conversion module for performing signal conditioning and analog-to-digital conversion processing on the aforementioned high-level sensing signal and stress distribution signal, respectively, to acquire high-level data and stress distribution data, A generation module for acquiring the real-time load distribution state of the boarding bridge based on the altitude data and the stress distribution data, and for generating electric cylinder adjustment commands and preload adjustment commands according to the real-time load distribution state, A compensation module for generating a control signal for the lifting and lowering motion of the passenger bridge, which performs dynamic compensation for the center of gravity offset of the passenger bridge based on the electric cylinder adjustment command and the preload adjustment command, and uses a fuzzy PID control model in combination with the above. A modification module for achieving safe control of a passenger bridge by processing the passenger bridge lifting and lowering motion control signal using a posture estimation algorithm to generate passenger bridge posture estimation data, and modifying the passenger bridge lifting and lowering motion control signal based on the passenger bridge posture estimation data, Equipped with, We provide safety control systems for flight simulator boarding bridges.

[0014] In the third aspect, the present application relates to electronic equipment, Memory for storing computer programs, When executing the aforementioned computer program, a processor is used to implement the steps of the safety control method for the flight simulator boarding bridge described in the first aspect above, Equipped with, We provide electronic equipment.

[0015] In the fourth aspect, the present invention provides a computer-readable storage medium in which a computer program is stored, and which, when the computer program is executed by a processor, enables the implementation of the steps of the safety control method for a flight simulator boarding bridge described in the first aspect. [Effects of the Invention]

[0016] The technical embodiment relating to this application has the following beneficial effects.

[0017] This invention enables the detection of critical physical quantities (altitude, position, and structural forces) in all directions and in real time during the lifting and lowering process of a passenger bridge, providing a comprehensive and accurate raw information base for subsequent intelligent control, thereby overcoming the detection dead zone that can exist in a single signal source. By converting interference-prone raw analog signals into stable and accurate digital data, the interference resistance of the signal and the measurement accuracy of the system are improved, ensuring reliable data quality for subsequent data analysis and control decision-making. By fusing the detected data to calculate an intuitive load distribution state, a leap from "detection" to "recognition" is achieved, and control commands are generated directly based on a real-time understanding of the mechanical state of the passenger bridge, improving the foresight and accuracy of control decision-making. By actively offsetting the effects of center of gravity offset through the coordinated operation of actuators and using an intelligent control algorithm that allows for autonomous adaptive adjustment of parameters to generate accurate motion commands, tilt and vibration tendencies are suppressed from the outset, ensuring initial stability during the lifting and lowering process. By constructing a closed-loop control system that includes feedforward prediction and feedback correction, real-time evaluation and precise fine-tuning of control effects are possible, model errors and external disturbances are effectively compensated for, and ultimately stable, accurate, and safe control of the passenger bridge is achieved.

[0018] This invention further precisely adjusts the output or displacement of the electric cylinder and the preload of the spring mechanism in accordance with commands, then uses the coordinated action of these two to cancel out the center of gravity offset in real time, and finally synthesizes the final motion control signal by inputting the compensated system state to a fuzzy PID controller.

[0019] Furthermore, the coordinated arrangement of the electric cylinder and preload enables active and resultant compensation for the center of gravity offset of the passenger bridge, providing a more stable controlled object to the higher-level control algorithm. In addition, by using a fuzzy PID control model, a foundation for effective initial state compensation is established before generating motion control signals, and the ability to autonomously adapt and optimize parameters is provided, thereby improving both the response speed and stabilization accuracy of the entire control system.

[0020] These or other aspects of the present application will be more clearly and readily understood by the following description of the embodiments. [Brief explanation of the drawing]

[0021] To more clearly explain the embodiments of the present application or the technical aspects of the prior art, the following is a brief introduction of the drawings that may be used in the description of the embodiments or the prior art. Needless to say, the drawings described below are some embodiments of the present application, and those skilled in the art can obtain other drawings based on these without requiring any creative work.

[0022] [Figure 1] This is a flowchart of the safety control method for a flight simulator boarding bridge according to an embodiment of the present invention. [Figure 2] This is a schematic diagram illustrating a specific implementation of a safety control method for a flight simulator boarding bridge according to an embodiment of the present invention. [Figure 3] This is a schematic diagram of the structure of a safety control system for a flight simulator boarding bridge according to an embodiment of the present invention. [Modes for carrying out the invention]

[0023] In conventional forms of control based on fixed-parameter PID control, the control logic relies on pre-set typical operating conditions. When the load on the passage changes significantly or rapidly, such a fixed control mode makes it difficult to provide a precise response that perfectly matches the changes, potentially leading to fluctuations in the stabilization process and affecting the smoothness of operation. Furthermore, the core of the control in this form mainly focuses on correcting the visible attitude angle, and changes in load distribution, which are intrinsic factors affecting structural stability, are not adequately related to and reflected in the control decision-making, leaving room for improvement.

[0024] To address the above constraints, this invention proposes a safety control method for a flight simulator boarding bridge. The core of this method lies in accurately detecting changes in the internal load state by collecting and analyzing stress distribution and altitude information on the passageway structure in real time. Based on this state, the system first coordinately adjusts the output of the drive mechanism and the preload inside the structure to actively counteract any possible center of gravity offset. Subsequently, it generates motion commands using an intelligent algorithm capable of automatically optimizing control parameters, and finally fine-tunes them through attitude prediction and feedback. This method shifts the basis of control from passive attitude correction to active load management, enabling the system to predict and suppress instability from its intrinsic mechanisms. This effectively overcomes the challenge of insufficient adaptability of fixed-parameter control under variable load operating conditions, achieving high stability and safety during the lifting and lowering process under all operating conditions.

[0025] To enable those skilled in the art to better understand the aspects of this application, the application will be described in further detail below, in combination with the drawings and specific embodiments. Of course, the embodiments described are only a selection of the embodiments of this application, not all of them. All other embodiments that can be obtained by those skilled in the art without creative work based on the embodiments of this application are all within the scope of protection of this application.

[0026] The core of this application is to provide a safety control method for a flight simulator boarding bridge, a flowchart of one specific embodiment of which is shown in Figure 1, and the method includes the following:

[0027] Step 101: Collect altitude sensing signals and stress distribution signals from the flight simulator boarding bridge during the ascent and descent process.

[0028] In Step 101, the flight simulator refers to an aircraft requiring docking operations, and the passenger bridge refers to the elevator equipment connecting the airport terminal building and the flight simulator. The two constitute a physical docking relationship for service provision and service receipt. The altitude sensing signal refers to a raw electrical signal generated by displacement sensors or angle sensors attached to the passenger bridge lifting mechanism, representing the current absolute altitude of the passenger bridge or the change in altitude relative to its initial position. The stress distribution signal refers to a set of raw electrical signals generated by strain gauges pre-attached to the surface of critical load-bearing structures of the passenger bridge (e.g., main girders, support arms), representing the magnitude of the forces acting on these parts during the lifting and loading of the passenger bridge. Together, these signal sets constitute an information infrastructure for detecting the overall operational status and internal mechanical state of the passenger bridge.

[0029] The embodiment of this invention is realized by a sensor network placed on the physical structure. First, altitude sensors monitor changes in the vertical position of the boarding bridge in real time and convert this into altitude sensing signals in the form of voltage or current. Simultaneously, multiple channel strain gauges distributed at different locations on the boarding bridge sense minute deformations of structural members and convert this into corresponding stress sensing signals. All of these raw signals are synchronously collected and transmitted to a central processing unit to provide raw materials for subsequent signal processing stages.

[0030] For example, on the boarding bridge attached to the flight simulator at Training Center A, four high-precision laser distance sensors (for altitude sensing) are mounted at the top of the four main elevator columns, and strain gauges are attached to six critical points on the main girder at the bottom of the boarding bridge. These sensors continue to operate as the boarding bridge begins to rise from the altitude of the first floor to the altitude of the third floor. The four laser distance sensors output four-channel voltage signals (altitude sensing signals) representing the altitude at each distance measurement point in real time, and the six strain gauges output six-channel voltage signals (stress distribution signals) representing the stress conditions at each point in real time. All of these signals are transmitted via data cables to an industrial computer in the control room.

[0031] Step 102: Signal conditioning and analog-to-digital conversion processing are performed on the altitude sensing signal and the stress distribution signal, respectively, to acquire altitude data and stress distribution data.

[0032] In step 102, signal conditioning refers to the process of improving signal strength and removing noise interference by performing operations such as amplification and filtering on the weak raw electrical signals collected by the sensors, resulting in a stable and clean analog signal. Analog-to-digital conversion refers to the process of discretizing the conditioned analog signal into a sequence of digital data that can be directly identified and processed by a computer at a constant sampling frequency via an analog-to-digital converter. Altitude data is a dataset obtained through the above process that accurately represents the altitude of each monitoring point on the boarding bridge in digital format. Stress distribution data is a dataset obtained through the above process that accurately represents the magnitude of the force received by each monitoring point on the boarding bridge in digital format.

[0033] In the embodiment of the present invention, first, the collected high-level sensing signal and the stress distribution signal for each channel are sent to a signal conditioning circuit, respectively, where they are amplified to improve the signal amplitude, and then filtered through low-pass filtering to remove high-frequency noise interference, thereby obtaining a conditioned high-level analog signal and a stress analog signal. Next, these clean analog signals are sent to an analog-to-digital converter, which samples and quantizes the continuous analog signal at a set sampling frequency (e.g., 1000 times per second), converting it into a series of discrete digital values. Finally, these digital values ​​are packaged as high-level data and stress distribution data and made available for use in subsequent algorithms.

[0034] For example, after an industrial computer receives the 10 channels of raw voltage signals described above, its internal data acquisition card first conditions these signals. For instance, it amplifies microvolt-level strain gauge signals to volt levels and uses hardware filters to filter out noise such as 50Hz commercial power frequency interference. Then, an analog-to-digital converter within the data acquisition card converts these clean analog signals at a sampling rate of 1000Hz. Suppose that at a certain time, the altitude data obtained by converting the 4 channels of altitude signals is [1250, 1248, 1255, 1249] (units: millimeters), and the stress distribution data obtained by converting the 6 channels of stress signals is [102.3, 98.7, 205.6, 201.1, 99.5, 101.8] (units: kilograms-force). These digital sequences represent the processed results.

[0035] Step 103: Based on the altitude data and stress distribution data, the real-time load distribution state of the boarding bridge is obtained, and electric cylinder adjustment commands and preload adjustment commands are generated according to the real-time load distribution state.

[0036] In step 103, the real-time load distribution state is a comprehensive state variable obtained by performing a fusion calculation on altitude data and stress distribution data, and is used to explain the total weight, center of gravity position, and trend of change in weight distribution of loads (e.g., personnel, equipment) on the boarding bridge at the current time. The electric cylinder adjustment command is a command that controls the operation of the electric cylinder, and includes the magnitude of the displacement or thrust to be adjusted, and the direction of adjustment. The preload adjustment command is a command to instruct the spring mechanism to apply or maintain a preload of a specific magnitude.

[0037] In the embodiment of the present invention, first, time alignment is performed on synchronously collected altitude data and stress distribution data to ensure that the state snapshots are of the same time. Next, according to a mapping relationship model between sensing data and load distribution constructed in advance by orientation, the real-time load distribution state is calculated, including the total load value, the coordinates of the load centroid in the plane of the boarding bridge, and the rate of load change. Then, the control algorithm matches the position coordinates of the load centroid with a control strategy table and generates a corresponding electric cylinder adjustment command, which specifies which electric cylinder should be operated, as well as the displacement and direction of operation. At the same time, the algorithm calculates the optimal preload value necessary to balance the load according to the total load value and its change trend, and generates a preload adjustment command.

[0038] For example, an industrial computer synchronizes altitude data [1250, 1248, 1255, 1249] and stress distribution data [102.3, 98.7, 205.6, 201.1, 99.5, 101.8] obtained at time T. Analysis by the built-in algorithm model reveals that the stress values ​​(205.6, 201.1) in the left front and left rear regions of the passenger bridge are higher than in other regions, and the altitude (1255) at the left front corner is slightly higher, indicating that approximately 400 kilograms of load are concentrated on the left side of the passenger bridge. Based on this, the algorithm generates a real-time load distribution state. Subsequently, to pull the center of gravity back towards the center, the algorithm generates electric cylinder adjustment commands. The two electric cylinders on the right are instructed to extend 2 millimeters upward each, while the electric cylinder on the left is to remain stationary. Simultaneously, to counteract the overturning moment caused by the uneven distribution of load, a preload adjustment command is generated to increase the preload of the right-side support structure by 50 kilograms.

[0039] Step 104: Based on the electric cylinder adjustment command and the preload adjustment command, dynamic compensation is performed for the center of gravity offset of the passenger bridge, and a control signal for the passenger bridge's raising and lowering motion is generated using a fuzzy PID control model.

[0040] In step 104, dynamic compensation refers to the process of adjusting the center of gravity offset according to the real-time state. The fuzzy proportional-integral-derivative (PID) control model refers to an intelligent control method that uses fuzzy logic and PID control in combination. The passenger bridge lifting motion control signal is a command that is ultimately output to the passenger bridge drive motor (e.g., servo motor) and is used to precisely control the lifting speed and acceleration of the passenger bridge.

[0041] In the embodiment of this invention, first, the electric cylinder driver receives an electric cylinder adjustment command and controls the designated electric cylinder to extend or retract (or output a specified thrust) precisely by a specified displacement. Simultaneously, the system sets or maintains the preload of the spring mechanism mechanically in response to a preload adjustment command. Through the coordinated action of these two systems, a corrective moment is jointly generated, providing provisional dynamic compensation for the detected center of gravity offset. After compensation, the system collects new passenger bridge state data (e.g., attitude angle change rate). This state data is then input into a fuzzy PID control model. The fuzzy inference module within the model automatically adjusts the three parameters (P, I, D) of the PID controller online according to the deviation between the state data and the ideal state and its changing trend, adapting the controller characteristics to the current dynamic state. The PID control module then uses the optimized parameters to calculate the velocity and acceleration commands necessary to accurately follow the target elevation curve. Finally, the signal synthesis module synthesizes these commands to create the final passenger bridge elevation motion control signal.

[0042] For example, the control system transmits the command generated in step 103. The two electric cylinders on the right side are extended upward by exactly 2 millimeters, and at the same time, the preload of the spring group on the right side is adjusted to increase by 50 kilograms. This action pulls the center of gravity of the boarding bridge, which was originally tilted to the left, back to the right, temporarily suppressing (dynamically compensating) the tilt tendency. The industrial computer immediately reads the tilt angle sensor data and identifies that there is still a small angular velocity deviation in the boarding bridge. This deviation data is then input into the fuzzy PID control model. The model determines that there is currently a slight overshoot and automatically decreases the proportional parameter P and increases the differential parameter D. After calculation, the adjusted PID controller outputs an acceleration command that raises the boarding bridge at a constant speed of 0.5 meters per minute, with a slight braking tendency. The finally synthesized boarding bridge lifting motion control signal is transmitted to the servo driver.

[0043] Step 105: The boarding bridge's lifting and lowering motion control signal is processed by the attitude estimation algorithm to generate boarding bridge attitude estimation data, and the boarding bridge's lifting and lowering motion control signal is modified based on the boarding bridge attitude estimation data to achieve safe control of the boarding bridge.

[0044] In step 105, the attitude estimation algorithm is a data fusion algorithm that uses a kinematic model and sensor feedback in combination, and is used to predict and estimate the future attitude of the passenger bridge. The passenger bridge attitude estimation data is the result output from the algorithm and includes attitude information such as the predicted tilt angle and vibration amplitude of the passenger bridge within a certain period in the future. Correction refers to adjusting the pre-generated control signals in the reverse direction according to the deviation between the predicted attitude and the ideal attitude, thereby forming a closed-loop feedback loop.

[0045] In the embodiment of this invention, a vertical motion control signal is input to a posture estimation algorithm, expected posture data is calculated using a kinematic model, actual motion sensing data is collected and compared with the expected data to obtain a state deviation, real-time posture parameters are calculated based on the deviation data, and the motion control signal is adjusted using a control signal correction algorithm to realize closed-loop control.

[0046] For example, the servo driver begins executing the lifting motion control signal transmitted from step 104. Simultaneously, an inertial measurement device attached to the passenger bridge measures the three-axis angular velocity and acceleration (real-time motion sensing data) of the passenger bridge in real time. The attitude estimation algorithm in the industrial computer uses the received motion control signal and the mass distribution model of the passenger bridge to predict that a slight forward and backward sway of 0.5 degrees due to inertia may occur in the passenger bridge after 50 milliseconds. The algorithm then fuses this predicted value with the measured value from the inertial measurement device to generate more accurate passenger bridge attitude estimation data and confirm the sway trend. Based on this, the algorithm calculates a small adjustment amount and modifies the ongoing lifting motion control signal, slightly reducing the upward acceleration before the sway occurs. This modified signal stabilizes the lifting motion of the passenger bridge and effectively suppresses the predicted vibration.

[0047] This method uses fusion processing of multi-source sensing data to detect the load distribution state of the passenger bridge in real time, and implements dynamic compensation and closed-loop optimized control based on a fuzzy PID control model and attitude estimation algorithm. This effectively improves the operational stability, control accuracy, and safety of the passenger bridge during the lifting and lowering process, reduces mechanical vibration and shock, and extends the service life of the equipment.

[0048] To address the challenges of how to coordinate control the electric cylinder and preload to achieve precise center of gravity compensation, and how to intelligently generate motion control signals, in some embodiments, step 104, based on the electric cylinder adjustment command and the preload adjustment command, dynamically compensates for the center of gravity offset of the passenger bridge and generates a passenger bridge lifting motion control signal using a fuzzy PID control model, as shown in Figure 2, and includes the following:

[0049] Step 201: Based on the electric cylinder adjustment command, the electric cylinder driver is controlled to adjust the displacement or thrust of the electric cylinder according to the specified displacement adjustment amount and adjustment direction.

[0050] In step 201, the electric cylinder driver is a power device that receives commands and drives the motor to operate. The "specified displacement adjustment amount and adjustment direction" is derived from an analysis calculation of the real-time load distribution state of the passenger bridge. Specifically, the control algorithm determines which electric cylinder needs adjustment, the amount of displacement compensation (adjustment amount) required to balance the center of gravity, and whether to extend or shorten (adjustment direction) by matching it with a preset control strategy table or by performing calculations using a geometric relationship model, depending on the offset distance and offset direction of the center of gravity of the passenger bridge relative to the geometric center. The fundamental purpose is to generate a corrective moment to counteract the tilting tendency due to the center of gravity offset. Adjusting the displacement of the electric cylinder means changing the mechanical position of its pushrod, and adjusting the thrust means changing the magnitude of the force output by its pushrod.

[0051] In the embodiment of the present invention, the control system reads an electric cylinder adjustment command and analyzes the number of the electric cylinder that needs to be operated, the specific amount of movement, and the direction. It then transmits a control signal to the corresponding electric cylinder driver. The driver operates the motor in response to the signal and accurately completes the displacement or thrust adjustment requested by the command by converting the rotational motion through its internal mechanical structure into linear motion of the pushrod.

[0052] Step 202: Based on the preload adjustment command, the corresponding preload is applied or maintained via the spring mechanism to obtain the adjusted preload.

[0053] In step 202, the spring mechanism is positioned within the mechanical support structure of the passenger bridge. Applying or maintaining the corresponding preload means causing the spring mechanism to generate or maintain an initial tension of the magnitude required by the command. The adjusted preload refers to the tension value actually provided after the spring mechanism has finally stabilized.

[0054] In the embodiments of this invention, the control system analyzes a preload adjustment command and obtains a target preload value. In the case of a passive spring mechanism, the command is primarily used to record and verify the preload value that should be maintained within the system. If the mechanism has basic adjustment capabilities, the system triggers an adjustment mechanism (e.g., a ratchet mechanism or an electromagnetic lock drive) to compress or release the spring to the state required by the command, thereby outputting the corresponding preload. The system then updates the current preload to the adjusted preload.

[0055] Step 203: Based on the adjusted electric cylinder pressure and adjusted preload, dynamic compensation is performed for the center of gravity offset of the passenger bridge, and passenger bridge condition data after dynamic compensation is obtained.

[0056] In step 203, the passenger bridge state data after dynamic compensation refers to a dataset that reflects the latest attitude and load conditions of the passenger bridge, which is re-collected via sensors after the compensation operation is completed.

[0057] In the embodiment of this invention, changes in the position or thrust of the electric cylinder's push rod apply a direct force to the support points of the passenger bridge. Simultaneously, a preload from the spring mechanism also acts at a specific position. These two forces work together to form a corrective moment, counteracting the tilt tendency of the passenger bridge and thus achieving dynamic compensation. After the compensation operation is complete, the system immediately recollects real-time data such as the angle and pressure of the passenger bridge via tilt angle sensors, stress sensors, etc., and this latest data becomes the passenger bridge state data after dynamic compensation.

[0058] Step 204: Based on the passenger bridge state data after dynamic compensation, a passenger bridge elevation / depression control signal is generated using a fuzzy PID control model.

[0059] In the embodiment of this invention, the passenger bridge state data after dynamic compensation is input to a fuzzy PID control model. First, a fuzzy inference module dynamically adjusts the control parameters according to the state data. Then, a PID control module generates a control signal for the passenger bridge's raising and lowering motion based on the adjusted parameters.

[0060] The following is a specific example.

[0061] Following the above embodiment, after the control system transmits the generated electric cylinder adjustment command and preload adjustment command, the electric cylinder driver first receives the command and drives the push rods of the two electric cylinders on the right side to extend precisely 2 millimeters upward. At the same time, the preload of the spring group on the right side is mechanically adjusted to increase by 50 kilograms. This coordinated action increases the support force acting on the right side of the boarding bridge, canceling out the counterclockwise overturning moment caused by the uneven distribution of approximately 400 kilograms of load on the left side. This temporarily pulls the center of gravity of the boarding bridge back to the right, completing the initial dynamic compensation. After the compensation operation is performed, the industrial computer immediately collects new data from the tilt angle sensor and identifies that although the entire boarding bridge is nearly horizontal, there is still a small return angular velocity to the right, which is 0.1 degrees per second. In this case, the system inputs the dynamically compensated boarding bridge state data, including this angular velocity deviation, into a fuzzy PID control model. The fuzzy inference module within the model then determines the proportional control parameter K based on the characteristic that the current error is small but there is a persistent trend of change. p By automatically decreasing the initial value of 120 to 100, the response hypersensitivity is reduced, and at the same time, the differential control parameter K d By increasing the initial value from 20 to 30, the hunting suppression capability is enhanced, and the integral control parameter K i The value is kept at 10 and not changed. Next, the PID control module uses the adjusted parameters to perform calculations for the altitude control error and stress control error, where the altitude control error e his the difference between the set target altitude and the actual average altitude of 1247.5 mm. Since the set target altitude is 1250 mm, e h is equal to 2.5 mm, and the stress control error e s is the difference between the ideal equilibrium stress value of 150 kilogram-force and the actual average stress on the left side of 203.35 kilogram-force, that is, e s is equal to minus 53.35 kilogram-force. The output calculation formula of the PID controller is shown in Equation 1 below, where U(t) is the output value of the PID controller, K p is the PID proportional control parameter, e h is the altitude control error, K i is the PID integral control parameter, K d is the PID differential control parameter. Substituting the numerical values gives U(t)=100×2.5+10×5+30×0.5=250+50+15=315. After limiter processing, this result is converted into a target lifting speed control signal for causing the boarding bridge to move upward at a constant speed of 0.5 meters per minute. At the same time, a slight braking tendency value of minus 0.01 meters per second squared is added to the acceleration control signal. Finally, the signal synthesis module synthesizes these two signals as a boarding bridge lifting motion control signal and outputs it to the servo driver, thereby realizing precise control over the motion of the boarding bridge.

[0062] [Equation]

[0063] In the embodiment of the present application, the complete step mode described above realizes active and resultant force compensation for center of gravity offset through the cooperative operation of the electric cylinder and the spring mechanism, and provides a more stable control foundation for the subsequent upper-level control algorithm. Furthermore, by combining the online automatic parameter adjustment capability of the fuzzy PID control model, a motion control signal is generated after effective physical compensation is constructed, and the characteristic of intelligently adapting to dynamic changes of the system is provided, thereby improving the stability, response speed and control accuracy of the lifting process of the boarding bridge all together.

[0064] To address the challenges of accuracy and autonomous adaptation in generating control signals for the boarding bridge's lifting motion, in some embodiments, generating control signals for the boarding bridge's lifting motion using a fuzzy PID control model based on the boarding bridge state data after the dynamic compensation, as described in step 204, includes the following:

[0065] Step 301: The dynamically compensated boarding bridge state data is input into the fuzzy PID control model, and the fuzzy inference module within the fuzzy PID control model dynamically adjusts the control parameters, including proportional, integral, and differential parameters.

[0066] In step 301, the dynamically compensated boarding bridge state data refers to real-time data including altitude information and stress information acquired after center of gravity offset compensation; the proportional parameter refers to the adjustment coefficient that is directly proportional to the current error in the control system; the integral parameter refers to the adjustment coefficient that is directly proportional to the cumulative amount of past errors; and the differential parameter refers to the adjustment coefficient that is directly proportional to the rate of change of the error.

[0067] In the embodiment of the present invention, the passenger bridge state data after dynamic compensation is input to a fuzzy inference module, and the module dynamically calculates and outputs proportional, integral, and differential parameter values ​​that are best suited to the current state, based on the magnitude of the current altitude error and stress error, via a preset fuzzy rule base.

[0068] Step 302: Based on the adjusted control parameters, the PID control module in the fuzzy PID control model generates target lifting speed control signals and target acceleration control signals.

[0069] In step 302, the target elevation speed control signal refers to the desired elevation speed value of the passenger bridge, and the target acceleration control signal refers to the desired acceleration value of the passenger bridge.

[0070] In the embodiment of the present invention, the PID control module receives control parameters output from the fuzzy inference module and uses the current altitude error and stress error in combination to calculate and generate a target elevation speed control signal and a target acceleration control signal, respectively, using a proportional-integral-derivative algorithm.

[0071] Step 303: The signal synthesis module in the fuzzy PID control model performs signal synthesis processing on the target lifting speed control signal and the target acceleration control signal to generate a control signal for the lifting motion of the passenger bridge.

[0072] In step 303, the signal synthesis process refers to the process of merging multiple control signals into a unified control command.

[0073] In the embodiment of the present invention, the signal synthesis module receives a target lifting speed control signal and a target acceleration control signal, synthesizes the two signals into a single complete passenger bridge lifting motion control signal using a weighted fusion algorithm, and outputs it to the passenger bridge drive mechanism.

[0074] The following is a specific example.

[0075] Following the above embodiment, the industrial computer inputs dynamically compensated passenger bridge state data, including information on a small return angular velocity to the right of 0.1 degrees per second, into a fuzzy PID control model. The fuzzy inference module within the model first analyzes the input data. The module has pre-set rules that automatically adjust control parameters according to the magnitude and rate of change of the error. The module determines that the altitude control error of 2.5 millimeters between the current actual altitude of the passenger bridge (1247.5 millimeters) and the target altitude (1250 millimeters) falls within the small error range, and that the rate of change of the error, which is decreasing at a rate of 0.5 millimeters per second, falls within the medium range. It then outputs an adjustment command according to its internal mapping relationship, and the proportional parameter K p The differential parameter K is dynamically adjusted from 120 to 100. d Adjust the integral parameter K from 20 to 30. iThe value is kept at 10 and not changed, so that the controller's characteristics are more focused on stability and suppressing overshoot. Subsequently, the PID control module immediately performs calculations using this new set of control parameters, namely proportional parameter 100, integral parameter 10, and differential parameter 30, and the module processes the altitude control error and stress control error respectively, where the altitude control error e h This is equal to 2.5 millimeters, and by performing a proportional operation by multiplying the proportional parameter by the altitude control error, 100 multiplied by 2.5 is obtained, resulting in 250. Performing an integral operation by multiplying the integral value of the altitude control error by the integral parameter, assuming that the most recent cumulative error is 5, the integral result is equal to 10 multiplied by 5, resulting in 50. Performing a differential operation by multiplying the differential parameter by the rate of change of the altitude control error, and since the known rate of change of the error is minus 0.5 millimeters per second, the differential operation is equal to 30 multiplied by minus 0.5, resulting in minus 15. Adding these three results together, 250 + 50 + minus 15 = 285. This value is then processed with a limiter to ensure that the output is within a safe range, and is converted into a target elevation speed control signal that instructs the boarding bridge to rise at a speed of 0.5 meters per minute, and simultaneously, the stress control error e s Based on the fact that it is -53.35 kilograms-force, a similar PID calculation is performed to synthesize the target acceleration control signal, to which a slight braking acceleration of -0.01 meters per second per second is added. Finally, the signal synthesis module synthesizes the target lifting speed control signal and the target acceleration control signal to generate a single complete passenger bridge lifting motion control signal containing speed and acceleration information, which is output to the servo driver.

[0076] In the embodiment of this invention, by combining control parameter adjustment using fuzzy autonomous adaptation and multi-signal fusion processing, accurate generation of control signals for the lifting and lowering motion of the passenger bridge is achieved, improving the response speed and adaptive capability of the control system and ensuring that the passenger bridge can operate stably even under various load conditions.

[0077] To address the accuracy and stability issues in generating control signals by a PID control module, in some embodiments, generating a target lifting speed control signal and a target acceleration control signal by a PID control module in a fuzzy PID control model according to the adjusted control parameters in step 302 includes the following:

[0078] Step 401: Depending on the adjusted control parameters, the PID control module performs error calculation processing on the dynamically compensated passenger bridge state data to obtain the altitude control error and stress control error.

[0079] In step 401, the altitude control error refers to the difference between the current altitude of the passenger bridge and the target altitude, and the stress control error refers to the difference between the current stress of the passenger bridge and the target stress.

[0080] In the embodiment of the present invention, the PID control module receives the passenger bridge state data after dynamic compensation, calculates the altitude control error by comparing the current altitude data with a preset target altitude, and calculates the stress control error by comparing the current stress data with a preset target stress.

[0081] Step 402: Based on the altitude control error and the stress control error, proportional calculation, integral calculation, and differential calculation are performed, respectively.

[0082] In step 402, proportional calculation refers to the calculation process of multiplying the error by the proportionality parameter, integral calculation refers to the calculation process of multiplying the cumulative value of the error by the integral parameter, and differential calculation refers to the calculation process of multiplying the rate of change of the error by the differential parameter.

[0083] In the embodiment of the present invention, proportional, integral, and differential calculations are performed based on the altitude control error to obtain proportional, integral, and differential calculation results corresponding to altitude, and simultaneously, proportional, integral, and differential calculation results corresponding to stress are obtained by performing similar calculations based on the stress control error.

[0084] Step 403: The results of the proportional calculation, integral calculation, and differential calculation are added together to generate a provisional lifting speed control signal and a provisional acceleration control signal.

[0085] In step 403, the provisional lifting speed control signal refers to the speed control amount that has not been limited, and the provisional acceleration control signal refers to the acceleration control amount that has not been limited.

[0086] In the embodiment of the present invention, a provisional elevation speed control signal is generated by adding the proportional calculation results, integral calculation results, and differential calculation results related to altitude, and a provisional acceleration control signal is generated by adding the proportional calculation results, integral calculation results, and differential calculation results related to stress.

[0087] Step 404: Limiter processing is performed on the provisional lifting speed control signal and the provisional acceleration control signal to generate a target lifting speed control signal and a target acceleration control signal.

[0088] In step 404, limiter processing refers to the process of limiting the signal to an acceptable range.

[0089] In the embodiment of the present invention, upper and lower limit processing is performed on the provisional lifting speed control signal to ensure that its value is within the allowable speed range, and a target lifting speed control signal is generated. Upper and lower limit processing is performed on the provisional acceleration control signal to ensure that its value is within the allowable acceleration range, and a target acceleration control signal is generated.

[0090] The following is a specific example.

[0091] Following the above embodiment, the PID control module starts operating according to the adjusted control parameters, namely the proportional parameter 100, the integral parameter 10, and the differential parameter 30. First, it performs error calculation processing on the passenger bridge state data after dynamic compensation, where the altitude control error e hThis is obtained by subtracting the actually measured average altitude of 1247.5 millimeters from the target altitude of 1250 millimeters, and the calculated result e h This becomes 2.5 millimeters, and the stress control error e s This is obtained by subtracting the actual average stress on the left side, 203.35 kilograms-force, from the ideal equilibrium stress value of 150 kilograms-force, and the calculated result e sThis results in -53.35 kilograms-force. Next, proportional, integral, and differential calculations are performed based on the altitude control error of 2.5 millimeters. In the proportional calculation, where the proportional parameter is multiplied by the altitude control error, 100 is multiplied by 2.5 to obtain 250 as the proportional calculation result. In the integral calculation, where the integral parameter is multiplied by the integral value of the altitude control error, assuming that the integral value of the altitude control error calculated according to past error data is 5 milliseconds, the integral calculation is performed by multiplying 10 by 5 to obtain 50 as the integral calculation result. In the differential calculation, where the differential parameter is multiplied by the rate of change of the altitude control error, since the known rate of change of the altitude control error is -0.5 millimeters per second, the differential calculation is performed by multiplying 30 by -0.5 to obtain -15 as the differential calculation result. Next, the proportional calculation result of 250, the integral calculation result of 50, and the differential calculation result of minus 15 are added together, i.e., 250 + 50 + minus 15 = 285. This value is treated as the provisional lifting speed control signal. At the same time, three similar calculations are performed based on the stress control error of minus 53.35 kilograms-force, and these are added together to generate a provisional acceleration control signal, which is tentatively set to minus 0.012 meters per second per second. Finally, limiter processing is applied to the provisional lifting speed control signal of 285 and the provisional acceleration control signal of -0.012 meters per second per second. The limiter processing follows a pre-set safety rule where the upper limit of the speed command is 300 and the upper limit of the absolute value of the acceleration command is 0.02 meters per second per second. Since 285 is less than 300 and the absolute value of -0.012 is less than 0.02, there is no need to clip the provisional signals. 285 is directly generated as the target lifting speed control signal, which corresponds to an ascent speed of 0.5 meters per minute for the boarding bridge, and -0.012 meters per second per second is directly generated as the target acceleration control signal, which corresponds to a slight braking acceleration.

[0092] In the embodiments of this invention, accuracy and safety in the generation of control signals are ensured by using independent arithmetic processing and summation of multiple error sources in combination with a limiter protection mechanism, thereby effectively preventing equipment malfunctions caused by signal overlimiting and improving the reliability of system operation.

[0093] To address the accuracy challenges in detecting the load state of the boarding bridge and generating control commands, in some embodiments, step 103, which involves acquiring the real-time load distribution state of the boarding bridge based on the altitude data and the stress distribution data, and generating electric cylinder adjustment commands and preload adjustment commands according to the real-time load distribution state, includes the following:

[0094] Step 501: Perform time-domain synchronization processing on the altitude data and the stress distribution data to generate a time-aligned sensing dataset.

[0095] In step 501, time-domain synchronization refers to the process of aligning data collected at different times to the same time point, and time-aligned sensing dataset refers to a collection of altitude data and stress distribution data that have the same timestamp.

[0096] In the embodiment of this invention, a timestamp alignment algorithm is used to process the altitude data and stress distribution data, ensuring that the two data sets correspond to the same sampling time, thereby eliminating the problem of data asynchronousness caused by differences in sampling time.

[0097] Step 502: Based on the pre-configured mapping relationship between sensing data and load distribution, load distribution calculation processing is performed on the time-aligned sensing dataset to generate a real-time load distribution state including load value parameters, load position parameters, and load change trend parameters.

[0098] In step 502, the mapping relationship between sensing data and load distribution refers to the correspondence between sensor data constructed through experimentation and the actual load; the load value parameter refers to the total weight value received by the boarding bridge; the load position parameter refers to the distribution position information of the load on the boarding bridge; and the load change trend parameter refers to the rate of change of the load over time.

[0099] In the embodiment of the present invention, a time-aligned sensing dataset is input to a load distribution calculation algorithm based on a pre-set mapping relationship model, and a multi-sensor data fusion calculation is performed to obtain a real-time load distribution state including load values, positions, and change trends.

[0100] Step 503: Perform electric cylinder control strategy processing on the load position parameters and generate electric cylinder adjustment commands.

[0101] In the embodiment of this invention, the required pressure adjustment amount and adjustment direction are calculated according to the load position parameters in the real-time load distribution state, and a specific electric cylinder adjustment command is generated.

[0102] Step 504: Perform preload control strategy processing on the load value parameter and the load change trend parameter to generate a preload adjustment command.

[0103] In step 504, the preload control strategy processing refers to the calculation process that generates preload adjustment commands according to the load value and the trend of change.

[0104] In the embodiment of this invention, the preload control algorithm calculates the required tension adjustment amount and application position according to the load value parameter and load change trend parameter in the real-time load distribution state, and generates a specific preload adjustment command.

[0105] The following is a specific example.

[0106] Following the above-described embodiment, the industrial computer acquires altitude data of 1250 mm, 1248 mm, 1255 mm, and 1249 mm at a given time, and stress distribution data of 102.3 kg-force, 98.7 kg-force, 205.6 kg-force, 201.1 kg-force, 99.5 kg-force, and 101.8 kg-force. First, it performs time-domain synchronization on these data, that is, assigns the same timestamp to each data group to ensure that they represent the state of the boarding bridge at the same instant, thereby generating a time-aligned sensing dataset.Next, based on the pre-defined mapping relationship between sensing data and load distribution, load distribution calculation processing is performed on the time-aligned sensing dataset. This mapping relationship is a calculation model constructed through prior orientation experiments. Here, the load value parameter is obtained by subtracting the self-weight of the boarding bridge from the sum of all stress distribution data. Assuming the self-weight of the boarding bridge is 600 kilograms-force, the total stress value is 102.3 + 98.7 + 205.6 + 201 0.1 + 99.5 + 101.8 = 809 kilograms-force, and therefore the load value parameter is 809 - 600 = 209 kilograms-force. The load position parameter, i.e., the center of gravity coordinate, can be obtained by calculating the stress moment. Assuming that the coordinates of the six strain gauges in the plane of the boarding bridge are (0,0) for point 1, (2,0) for point 2, (0,1) for point 3, (2,1) for point 4, (1,0) for point 5, and (1,1) for point 6, the X-coordinate of the center of gravity is the stress value at each point. The sum of the results obtained by multiplying each x-coordinate by the total stress value is equal to 102.3×0 + 98.7×2 + 205.6×0 + 201.1×2 + 99.5×1 + 101.8×1 = 802.7, and further dividing 802.7 by 809 gives approximately 0.992 meters. Similarly, the Y-coordinate of the centroid is equal to the sum of the results obtained by multiplying the stress value of each point by its respective Y-coordinate and dividing by the total stress value, that is, 102.3×0 + 98.7×0 + 205.6×1 + 201.1×1 + 99.5× 0 + 101.8 × 1 = 506.5, and further 506.5 ÷ 809 gives approximately 0.626 meters. Therefore, the load position parameters are coordinates 0.992 meters and 0.626 meters. The load change trend parameter is calculated by comparing it with the load value parameter of the previous time series. Assuming the load at the previous time was 205 kilograms-force, the current change trend is an increase of 4 kilograms-force per second, which generates the real-time load distribution state.Subsequently, an electric cylinder control strategy is processed for the load position parameters, namely the center of gravity coordinates of 0.992 meters and 0.626 meters. This strategy stipulates that the right electric cylinder needs to be adjusted when the X coordinate of the center of gravity is greater than 1 meter, which is the coordinate of the longitudinal centerline of the boarding bridge. The amount of adjustment is determined by the offset distance, and since the current X coordinate of 0.992 meters is less than 1 meter but close to 1 meter, a conservative electric cylinder adjustment command is generated, instructing the right electric cylinder to be extended upward by 1 millimeter while keeping the left one stationary. At the same time, a preload control strategy is processed for the load value parameter of 209 kilograms-force and the load change trend parameter of an increase of 4 kilograms-force per second. In this strategy, the increment of the preload is determined according to the total load value and the increasing trend, and after being compared with a pre-configured strategy table, a preload adjustment command is generated to increase the preload of the right support structure by 25 kilograms-force.

[0107] In the embodiments of this invention, data consistency is ensured by time-domain synchronization, accurate analysis of load conditions is achieved based on mapping relationships, and appropriate control commands are generated according to load characteristics. This improves the accuracy and immediacy of electric cylinders and preload adjustments, providing reliable assurance for the stable operation of the passenger bridge.

[0108] To solve the accuracy issues in estimating the attitude of the passenger bridge and correcting the control signals, in some embodiments, step 105, which involves processing the passenger bridge lifting motion control signals using the attitude estimation algorithm described above to generate passenger bridge attitude estimation data and correcting the passenger bridge lifting motion control signals based on the passenger bridge attitude estimation data, includes the following:

[0109] Step 601: Collect real-time motion sensing data during the raising and lowering process of the boarding bridge.

[0110] In step 601, real-time motion sensing data refers to motion state data collected in real time via sensors during the raising and lowering process of the passenger bridge, and includes altitude change data and stress change data.

[0111] In this embodiment, real-time measurement data for attitude estimation is provided by collecting altitude change rate and stress change rate data in real time during the ascent and descent process via a group of sensors attached to the boarding bridge.

[0112] Step 602: Using the attitude estimation algorithm, the expected attitude data of the boarding bridge is calculated based on the boarding bridge elevation control signal, and the expected attitude data is set as a provisional estimate.

[0113] In step 602, the expected attitude data refers to the future attitude data of the passenger bridge predicted in response to the motion control signal, and the provisional estimate refers to the initial prediction result based on the model calculation.

[0114] In the embodiment of this invention, a kinematic model in the attitude estimation algorithm is used to calculate expected attitude data in response to the boarding bridge elevation control signal, and this is used as the provisional result of attitude estimation.

[0115] Step 603: Data fusion processing is performed on the provisional estimates and the real-time motion sensing data to generate boarding bridge attitude estimation data.

[0116] In step 603, data fusion processing refers to the process of integrating and processing information from multiple data sources.

[0117] In the embodiment of this invention, data fusion processing is performed on the expected posture data and the real-time motion sensing data, and a weighted fusion algorithm is used to generate more accurate passenger bridge posture estimation data.

[0118] Step 604: Based on the tilt angle parameter and vibration amplitude parameter in the boarding bridge attitude estimation data, the control signal adjustment amount is calculated.

[0119] In step 604, the inclination angle parameter is obtained by calculating the difference in altitude change data for each part of the boarding bridge, and represents the degree of inclination of the boarding bridge platform with respect to the horizontal reference plane. The vibration amplitude parameter is obtained by performing frequency spectral analysis on the stress change rate data of the boarding bridge, and represents the intensity of mechanical vibrations that occur during the raising and lowering process of the boarding bridge. The control signal adjustment amount refers to the numerical value of the control signal that needs to be corrected.

[0120] In the embodiment of this invention, the amount of adjustment of the control signal is calculated by a control algorithm based on the tilt angle and vibration amplitude parameters in the boarding bridge attitude estimation data.

[0121] Step 605: In accordance with the amount of control signal adjustment, the control signal for raising and lowering the passenger bridge is modified in real time.

[0122] In the embodiment of this invention, a more accurate control command is generated by making real-time corrections to the original boarding bridge elevation / lowering motion control signal according to the calculated control signal adjustment amount.

[0123] The following is a specific example.

[0124] Following the above embodiment, when the servo driver starts executing a control signal for the lifting and lowering motion of the boarding bridge, which raises the boarding bridge at a constant velocity of 0.5 meters per minute and accompanied by a slight braking acceleration of -0.01 meters per second, the system synchronously collects real-time motion sensing data measured by an inertial measuring device attached to the boarding bridge. This data includes the current angular velocity of the boarding bridge around the X-axis of 0.05 degrees per second and the angular velocity around the Y-axis of -0.02 degrees per second. Subsequently, using an attitude estimation algorithm, the algorithm takes the currently executing boarding bridge lifting and lowering motion control signal, i.e., the velocity command and acceleration command, as input to the system and performs kinematic estimation in conjunction with a known mass distribution model of the boarding bridge to predict the attitude change of the boarding bridge at a future time of 50 milliseconds. It calculates expected attitude data of the boarding bridge where the tilt angle around the X-axis is expected to increase by 0.1 degrees and the tilt angle around the Y-axis is expected to decrease by 0.05 degrees, and this prediction result is taken as a provisional estimate. Subsequently, this provisional estimate and the actual angular velocity data collected in real time by the inertial measurement device are subjected to data fusion processing. A weighted average method is employed for this processing, where the provisional estimate is weighted at 0.6 and the real-time measured values ​​are weighted at 0.4. After the fusion calculation, more accurate passenger bridge attitude estimation data is generated. Specifically, the estimated change in tilt angle around the X axis is +0.08 degrees, and the estimated change in tilt angle around the Y axis is -0.038 degrees. At the same time, it is estimated that there is a periodic vibration with an amplitude of 0.02 degrees on the passenger bridge. Next, based on the tilt angle parameters in the boarding bridge attitude estimation data, namely +0.08 degrees around the X axis and -0.038 degrees around the Y axis, and the vibration amplitude parameter of 0.02 degrees, the control signal adjustment amount is calculated according to a predetermined proportional relationship. Here, in the formula for calculating the tilt compensation amount, the adjustment amount ΔS is equal to the tilt angle θ multiplied by the gain coefficient K. If the gain coefficient K is set to 0.1 meters per second, the compensation amount ΔS_x for tilt around the X axis becomes 0.08 multiplied by 0.1, resulting in 0.008 meters per second, and the compensation amount ΔS_y for tilt around the Y axis becomes -0.038 multiplied by 0.1, resulting in -0.0038 meters per second. The vibration suppression adjustment amount is then set to -0.005 meters per second.Finally, based on the calculated control signal adjustment amount, the raw boarding bridge lifting motion control signal is corrected in real time. Specifically, starting from a base of 0.00833 meters per second, which corresponds to the original ascent speed of 0.5 meters per minute, a vibration suppression adjustment amount of 0.005 meters per second is subtracted, a tilt compensation amount of 0.008 meters per second around the X axis is added, and then the absolute value of the tilt compensation amount around the Y axis, 0.0038 meters per second, is subtracted. This results in a corrected speed command of approximately 0.00753 meters per second, or approximately 0.452 meters per minute. Simultaneously, the acceleration command is adjusted to match these compensation effects, thereby generating the final corrected control signal, which is then transmitted to the servo driver for execution.

[0125] In the embodiment of this invention, accurate estimation of the attitude of the passenger bridge is achieved by fusing real-time sensing data and expected data, and the stability and control accuracy of the passenger bridge operation are improved, and vibration and offset phenomena are effectively suppressed by dynamically modifying the control signal based on the attitude parameters.

[0126] In order to solve the problems of noise interference and accuracy in sensor signal processing, in some embodiments, step 102, which involves performing signal conditioning and analog-to-digital conversion processing on the high-level sensing signal and the stress distribution signal respectively to obtain high-level data and stress distribution data, includes the following:

[0127] Step 701: The advanced sensing signal is subjected to signal amplification and filtering to obtain the conditioned advanced analog signal, and the stress distribution signal is subjected to signal amplification and filtering to obtain the conditioned stress analog signal.

[0128] In step 701, signal amplification refers to the process of amplifying a weak signal to an appropriate range, filtering refers to the process of removing noise components from the signal, the conditioned high-resolution analog signal refers to the high-resolution signal after amplification and filtering, and the conditioned stressed analog signal refers to the stressed signal after amplification and filtering.

[0129] In the embodiment of the present invention, the advanced sensing signal is first amplified via an operational amplifier and then filtered via a low-pass filter to be acquired as a conditioned advanced analog signal. Similarly, the stress distribution signal is amplified and filtered to be acquired as a conditioned stress analog signal.

[0130] Step 702: The conditioned high-resolution analog signal is converted into high-resolution data via an analog-to-digital converter, and the conditioned stress analog signal is converted into stress distribution data via an analog-to-digital converter.

[0131] In step 702, an analog-to-digital converter refers to a device that converts an analog signal into a digital signal.

[0132] In the embodiment of the present invention, the conditioned high-resolution analog signal is input to an analog-to-digital converter and converted into high-resolution data through sampling and quantization processing. Similarly, the conditioned stress analog signal is converted into stress distribution data via an analog-to-digital converter.

[0133] The following is a specific example.

[0134] After four channels of advanced sensing signals and six channels of stress distribution signals are transmitted via data cables to a data acquisition card inside an industrial computer, the data acquisition card first performs signal amplification and filtering on the advanced sensing signals. Here, an operational amplifier circuit with a fixed gain of 100x is used for amplification to improve the signal amplitude, and a low-pass filter with a cutoff frequency of 10 Hz is used for filtering to filter out high-frequency noise. As a result, a smooth voltage signal with an amplitude in the range of 0 to 5 volts for all four channels is obtained as a conditioned advanced analog signal. Simultaneously, signal amplification and filtering are performed on the stress distribution signals. Here, the microvolt-level signal generated by the strain gauge is first amplified to millivolt-level via a pre-amplifier with a gain of 1000x, and then further amplified to volt level via a second-stage amplifier with a gain of 10x. Similarly, noise such as 50 Hz commercial power frequency interference is removed via a 10 Hz low-pass filter. As a result, a clean voltage signal with an amplitude in the range of 0 to 5 volts for all six channels is obtained as a conditioned stress analog signal.Next, the conditioned high-resolution analog signal is converted at a sampling rate of 1000 times per second through a single 12-bit precision analog-to-digital converter. The reference voltage of the analog-to-digital converter is 5 volts. The conversion formula is such that the digital value D is equal to the analog voltage value U divided by the reference voltage of 5 volts, multiplied by 4095, which is the full-scale digital value. If the voltage of the high-resolution analog signal of a certain channel is 3.2 volts, then its high-resolution data D is approximately 26, obtained by dividing 3.2 by 5 and multiplying by 4095. 20 is calculated, and further, according to the sensor orientation formula, the altitude value H is equal to the tilt K multiplied by the digital value D plus the offset amount B, where the tilt K is in 0.5 millimeters in digital units and the offset amount B is minus 60 millimeters. Based on this, the actual altitude data H is 0.5 × 2620 + (-60) = 1250 millimeters, and by similarly converting the altitude analog signals of the remaining 3 channels, altitude data of 1248 millimeters, 1255 millimeters, and 1249 millimeters are obtained, respectively. At the same time, the stress analog signals after conditioning are converted at the same sampling rate through another 12-bit precision analog-to-digital converter, and if the voltage of the stress analog signal of a certain channel is 2.1 volts, its raw digital value D is calculated to be approximately 1719 by dividing 2.1 by 5 and multiplying by 4095, and according to the stress sensor orientation formula, the stress value F = K. s ×D+B s Here, F represents the actual stress distribution data, and the slope K s This is a digital unit of 0.1 kilograms per unit, and offset amount B s This corresponds to minus 70 kilograms-force, and based on this, the stress distribution data F is approximately 101.9 kilograms-force, calculated as 0.1 × 1719 + (-70). Similarly, by converting the remaining 5 channels of stress analog signals, stress distribution data of approximately 98.7 kilograms-force, 205.6 kilograms-force, 201.1 kilograms-force, 99.5 kilograms-force, and 101.8 kilograms-force are obtained, and the digital sequence of these becomes the final acquired altitude data and stress distribution data.

[0135] In the embodiments of this invention, signal quality is effectively improved through signal amplification and filtering processes, and data accuracy is ensured through high-precision analog-to-digital conversion, thereby providing a reliable data base for subsequent load state analysis and enhancing the system's measurement accuracy and interference tolerance.

[0136] Figure 3 is a schematic diagram of the structure of the safety control system for a flight simulator boarding bridge according to an embodiment of the present invention, and the specific embodiment is described below.

[0137] The collection module 31 is for collecting altitude sensing signals and stress distribution signals from the flight simulator boarding bridge during the ascent and descent process. The conversion module 32 performs signal conditioning and analog-to-digital conversion processing on the altitude sensing signal and the stress distribution signal, respectively, in order to acquire altitude data and stress distribution data. The generation module 33 acquires the real-time load distribution state of the passenger bridge based on the altitude data and the stress distribution data, and generates electric cylinder adjustment commands and preload adjustment commands according to the real-time load distribution state. The compensation module 34 performs dynamic compensation for the center of gravity offset of the passenger bridge based on the electric cylinder adjustment command and the preload adjustment command, and generates a control signal for the passenger bridge's raising and lowering motion using a fuzzy PID control model in combination. The modification module 35 processes the passenger bridge lifting motion control signal using an attitude estimation algorithm to generate passenger bridge attitude estimation data, and modifies the passenger bridge lifting motion control signal based on the passenger bridge attitude estimation data, thereby achieving safe control of the passenger bridge.

[0138] The safety control system for a flight simulator boarding bridge according to the embodiment of the present application is used to realize the safety control method for a flight simulator boarding bridge described above. Therefore, for specific embodiments of the safety control system for a flight simulator boarding bridge, refer to the section on embodiments of the safety control method for a flight simulator boarding bridge in the preamble, and for specific embodiments, refer to the descriptions of the embodiments of the corresponding parts, and will not be described repeatedly here.

[0139] The present invention further provides an electronic device comprising: a memory for storing a computer program; and a processor for implementing the steps of the safety control method for a flight simulator boarding bridge described in any one of the above-mentioned documents when executing the computer program.

[0140] The present invention further provides a computer-readable storage medium in which a computer program is stored, wherein when the computer program is executed by a processor, steps of the safety control method for a flight simulator boarding bridge described in any one of the above descriptions are realized.

[0141] In one exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random-access memory, portable hard disks, magnetic disks, or optical disks.

[0142] Embodiments of the present invention further provide a computer program product including a computer program, wherein when the computer program is executed by a processor, the steps in any one of the above embodiments of the safety control method for a flight simulator boarding bridge are realized.

[0143] A person skilled in the art will also be aware that each exemplary unit and algorithmic step described in accordance with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the compatibility between hardware and software, the above description generalizes the configuration and steps of each example by function. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical embodiment. A person skilled in the art may implement the described functions using different methods for each specific application, but such implementations should not be considered to be departures from the scope of this application.

[0144] The safety control method, system, equipment, and storage medium for a flight simulator boarding bridge according to the present application have been described in detail above. In this specification, the principles and embodiments of the present application are discussed using specific examples, and the above-described examples are merely for the purpose of facilitating the understanding of the method and its core concept. It should be noted that those skilled in the art can make various improvements and modifications to the present application without departing from the principles of the present application, and these improvements and modifications are also included within the scope of protection of the present application.

Claims

1. A safety control method for a flight simulator boarding bridge, To collect altitude sensing signals and stress distribution signals from the flight simulator boarding bridge during the ascent and descent process, Signal conditioning and analog-to-digital conversion processing are performed on the aforementioned high-level sensing signal and stress distribution signal, respectively, to acquire high-level data and stress distribution data. Based on the altitude data and stress distribution data, the real-time load distribution state of the passenger bridge is acquired, and according to the real-time load distribution state, an electric cylinder adjustment command is generated to adjust the displacement or thrust of the electric cylinder that applies force to the support points of the passenger bridge, and a preload adjustment command is generated to apply or maintain preload to the spring mechanism located within the mechanical support structure of the passenger bridge. Based on the aforementioned electric cylinder adjustment command and preload adjustment command, dynamic compensation is performed for the center of gravity offset of the boarding bridge, and a boarding bridge lifting motion control signal is generated, which is output to the boarding bridge drive motor and controls the lifting speed and acceleration of the boarding bridge, using a fuzzy PID control model in combination. The safety control of the passenger bridge is achieved by processing the passenger bridge elevation / depression control signal using an attitude estimation algorithm to generate passenger bridge attitude estimation data, and modifying the passenger bridge elevation / depression control signal based on the passenger bridge attitude estimation data. including, A safety control method for a flight simulator boarding bridge, characterized by the following features.

2. Based on the aforementioned electric cylinder adjustment command and preload adjustment command, dynamic compensation is performed for the center of gravity offset of the boarding bridge, and a fuzzy PID control model is used in conjunction to generate a boarding bridge lifting motion control signal that is output to the boarding bridge drive motor and controls the lifting speed and acceleration of the boarding bridge. Based on the electric cylinder adjustment command, the electric cylinder driver is controlled to adjust the displacement or thrust of the electric cylinder according to the specified displacement adjustment amount and adjustment direction. Based on the aforementioned preload adjustment command, the corresponding preload is applied or maintained via a spring mechanism, and the adjusted preload is obtained. Based on the adjusted displacement or thrust of the electric cylinder and the adjusted preload, dynamic compensation is performed for the center of gravity offset of the passenger bridge, and passenger bridge state data after dynamic compensation is obtained. Based on the passenger bridge state data after the dynamic compensation, a control signal for the passenger bridge's raising and lowering motion is generated using a fuzzy PID control model. including, The safety control method for a flight simulator boarding bridge according to feature 1.

3. Based on the aforementioned passenger bridge state data after dynamic compensation, generating a control signal for the passenger bridge's elevation and lowering motion using a fuzzy PID control model is: The passenger bridge state data after the dynamic compensation is input to a fuzzy PID control model, and the control parameters, including proportional, integral, and differential parameters, are dynamically adjusted by a fuzzy inference module within the fuzzy PID control model. Based on the adjusted control parameters, the PID control module within the fuzzy PID control model generates target lifting speed control signals and target acceleration control signals. The signal synthesis module within the fuzzy PID control model performs signal synthesis processing on the target elevation speed control signal and the target acceleration control signal to generate a control signal for the elevation movement of the passenger bridge. including, The safety control method for a flight simulator boarding bridge according to feature 2.

4. As described above, generating target lifting speed control signals and target acceleration control signals using the PID control module within the fuzzy PID control model, according to the adjusted control parameters, Depending on the adjusted control parameters, the PID control module performs error calculation processing on the dynamically compensated passenger bridge state data to obtain altitude control error and stress control error. Based on the aforementioned high-level control error and stress control error, proportional calculation, integral calculation, and differential calculation are performed, respectively. The results of proportional calculations, integral calculations, and differential calculations are added together to generate provisional lifting speed control signals and provisional acceleration control signals. Limiter processing is performed on the provisional lifting speed control signal and the provisional acceleration control signal to generate a target lifting speed control signal and a target acceleration control signal. including, The safety control method for a flight simulator boarding bridge according to feature 3.

5. Based on the aforementioned altitude data and stress distribution data, the real-time load distribution state of the passenger bridge is acquired, and according to the real-time load distribution state, an electric cylinder adjustment command is generated to adjust the displacement or thrust of the electric cylinder that applies force to the support points of the passenger bridge, and a preload adjustment command is generated to apply or maintain preload to the spring mechanism located within the mechanical support structure of the passenger bridge. The process involves performing time-domain synchronization on the aforementioned altitude data and stress distribution data to generate a time-aligned sensing dataset. Based on the pre-configured mapping relationship between sensing data and load distribution, load distribution calculation processing is performed on the time-aligned sensing dataset to generate a real-time load distribution state including load value parameters, load position parameters, and load change trend parameters. The process involves performing electric cylinder control strategy processing on the aforementioned load position parameters to generate electric cylinder adjustment commands, and The process involves performing a preload control strategy processing on the load value parameter and the load change trend parameter to generate a preload adjustment command. including, The safety control method for a flight simulator boarding bridge according to feature 1.

6. The above-mentioned attitude estimation algorithm processes the passenger bridge elevation / depression control signal to generate passenger bridge attitude estimation data, and modifies the passenger bridge elevation / depression control signal based on the passenger bridge attitude estimation data. To collect real-time motion sensing data during the raising and lowering process of the boarding bridge, Using an attitude estimation algorithm, the expected attitude data of the passenger bridge is calculated based on the passenger bridge elevation control signal, and the expected attitude data is used as a provisional estimate. Data fusion processing is performed on the aforementioned provisional estimates and the aforementioned real-time motion sensing data to generate boarding bridge attitude estimation data. Based on the tilt angle parameter and vibration amplitude parameter in the aforementioned boarding bridge attitude estimation data, the control signal adjustment amount is calculated. In accordance with the amount of adjustment of the control signal, the control signal for raising and lowering the passenger bridge will be modified in real time. including, The safety control method for a flight simulator boarding bridge according to feature 1.

7. Performing signal conditioning and analog-to-digital conversion processing on the aforementioned high-altitude sensing signal and stress distribution signal, respectively, to acquire high-altitude data and stress distribution data is: The process involves performing signal amplification and filtering on the aforementioned high-level sensing signal to obtain a conditioned high-level analog signal, and performing signal amplification and filtering on the aforementioned stress distribution signal to obtain a conditioned stress analog signal. The high-level analog signal after conditioning is converted into high-level data via an analog-to-digital converter, and the stress analog signal after conditioning is converted into stress distribution data via an analog-to-digital converter. including, The safety control method for a flight simulator boarding bridge according to feature 1.

8. This is a safety control system for a flight simulator boarding bridge. A collection module for collecting altitude sensing signals and stress distribution signals from the flight simulator boarding bridge during the lifting and lowering process, A conversion module for performing signal conditioning and analog-to-digital conversion processing on the aforementioned high-level sensing signal and stress distribution signal, respectively, to acquire high-level data and stress distribution data, A generation module for acquiring the real-time load distribution state of the passenger bridge based on the altitude data and the stress distribution data, and generating electric cylinder adjustment commands for adjusting the displacement or thrust of electric cylinders that apply force to the support points of the passenger bridge according to the real-time load distribution state, and for generating preload adjustment commands for applying or maintaining preload to spring mechanisms located within the mechanical support structure of the passenger bridge, A compensation module for generating a control signal for the lifting and lowering motion of the passenger bridge, which is output to the passenger bridge drive motor and controls the lifting speed and acceleration of the passenger bridge, by dynamically compensating for the center of gravity offset of the passenger bridge based on the electric cylinder adjustment command and the preload adjustment command, and by using a fuzzy PID control model in combination with the electric cylinder adjustment command and the preload adjustment command, A modification module for achieving safe control of a passenger bridge by processing the passenger bridge lifting and lowering motion control signal using a posture estimation algorithm to generate passenger bridge posture estimation data, and modifying the passenger bridge lifting and lowering motion control signal based on the passenger bridge posture estimation data, Equipped with, A safety control system for a flight simulator boarding bridge, characterized by the following features.

9. It is an electronic device, Memory for storing computer programs, When executing the computer program, a processor for realizing the steps of the safety control method for a flight simulator boarding bridge described in any one of claims 1 to 7, Equipped with, An electronic device characterized by the following features.

10. A computer-readable storage medium in which a computer program is stored, wherein when the computer program is executed by a processor, the safety control method for a flight simulator boarding bridge described in any one of claims 1 to 7 can be realized. A computer-readable storage medium characterized by the following features.

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