Flight simulator bridge safety control method, system, device and storage medium

Abstract

The application relates to the technical field of electromechanical control of flight simulation training equipment, and provides a flight simulator corridor bridge safety control method, system, equipment and storage medium, which solves the problems of poor running stability and low control precision of the corridor bridge under load change. The method comprises the following steps: collecting height sensing signals and stress distribution signals in the lifting process of the corridor bridge, obtaining height data and stress distribution data through signal conditioning and analog-digital conversion processing, analyzing the real-time load distribution state according to the height data and the stress distribution data, and generating an electric cylinder adjustment instruction and a pre-tightening force adjustment instruction to realize dynamic compensation of the gravity center deviation; combining a fuzzy PID control model to generate a lifting motion control signal, processing the corridor bridge attitude estimation data through an attitude estimation algorithm, and correcting the motion control signal according to the corridor bridge attitude estimation data, so that high-precision smooth control of the lifting process of the corridor bridge is finally realized. The application improves the running stability and control precision of the corridor bridge under load change.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electromechanical control of flight simulation training equipment, and in particular to a flight simulator bridge safety control method, system, device and storage medium. BACKGROUND

[0002] In the operation process of the flight simulator supporting facilities, the safety and stability of the lifting process of the connecting channel are key technical requirements. When the channel is lifted, due to the distribution and movement of the internal load, the center of gravity of the structure will be offset, causing tilting and vibration. If not properly controlled, not only will the safety of the equipment structure be affected, but also the flight simulator may be disturbed. Therefore, a method capable of real-time state sensing, intelligent decision-making and high-precision closed-loop control of motion is needed to dynamically suppress attitude abnormalities and ensure stable and reliable lifting process.

[0003] At present, one of the existing solutions for the above technical requirements is to use a fixed parameter PID control based active stabilization system. The system collects the attitude angle information of the channel through sensors and transmits the angle deviation as an input to the controller. The controller calculates the control amount according to the preset proportional, integral and differential parameters, and then drives the actuator to adjust the position or force in order to try to eliminate the angle deviation and maintain the channel water.

[0004] In actual application, the setting of the control parameters of the existing solution depends on the linearization model of the system and the typical working conditions. When the load of the channel changes or changes rapidly, it is difficult for the fixed control parameters to maintain optimal control effect in various working conditions, which may cause the system response to overshoot or the regulation time to be prolonged, affecting the smoothness of the stable process. At the same time, the solution has insufficient consideration of the correlation in control decision-making for the structural internal force fluctuation caused directly by load changes and its impact on the center of gravity compensation. SUMMARY

[0005] The present application provides a flight simulator bridge safety control method, system, device and storage medium to solve the problems of poor running stability and low control precision of the bridge under load changes in the prior art.

[0006] To solve the above technical problems, in a first aspect, the present application provides a flight simulator bridge safety control method, comprising:

[0007] Collecting height sensing signals and stress distribution signals of the flight simulator bridge during the lifting process;

[0008] Respectively performing signal conditioning and analog-digital conversion processing on the height sensing signals and the stress distribution signals to obtain height data and stress distribution data;

[0009] Based on the height data and the stress distribution data, a real-time load distribution state of the gallery bridge is obtained, and an electric cylinder adjustment instruction and a pre-tightening force adjustment instruction are generated according to the real-time load distribution state;

[0010] Based on the electric cylinder adjustment instruction and the pre-tightening force adjustment instruction, the center of gravity of the gallery bridge is dynamically compensated, and a gallery bridge lifting motion control signal is generated in combination with a fuzzy PID control model;

[0011] The gallery bridge lifting motion control signal is processed by a posture estimation algorithm to generate gallery bridge posture estimation data, and the gallery bridge lifting motion control signal is corrected based on the gallery bridge posture estimation data to achieve safe control of the gallery bridge.

[0012] Optionally, the gallery bridge lifting motion control signal is generated in combination with a fuzzy PID control model based on the electric cylinder adjustment instruction and the pre-tightening force adjustment instruction, comprising:

[0013] Based on the electric cylinder adjustment instruction, 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;

[0014] Based on the pre-tightening force adjustment instruction, the corresponding pre-tightening force is applied or maintained through the spring mechanism to obtain an adjusted pre-tightening force;

[0015] Based on the adjusted electric cylinder pressure and the adjusted pre-tightening force, the center of gravity of the gallery bridge is dynamically compensated to obtain dynamically compensated gallery bridge state data;

[0016] Based on the dynamically compensated gallery bridge state data, a gallery bridge lifting motion control signal is generated through a fuzzy PID control model.

[0017] Optionally, the gallery bridge lifting motion control signal is generated through a fuzzy PID control model based on the dynamically compensated gallery bridge state data, comprising:

[0018] The dynamically compensated gallery bridge state data is input into the fuzzy PID control model, and the control parameters including the proportional parameter, the integral parameter and the differential parameter are dynamically adjusted through the fuzzy reasoning module in the fuzzy PID control model;

[0019] According to the adjusted control parameters, a target lifting speed control signal and a target acceleration control signal are generated through the PID control module in the fuzzy PID control model;

[0020] The target lifting speed control signal and the target acceleration control signal are signal-synthesized through the signal synthesis module in the fuzzy PID control model to generate a gallery bridge lifting motion control signal.

[0021] Optionally, the target lifting speed control signal and the target acceleration control signal are generated by a PID control module in the fuzzy PID control model according to the adjusted control parameters, including:

[0022] According to the adjusted control parameters, the error calculation and processing of the dynamically compensated gallery bridge state data are performed by the PID control module to obtain a height control error and a stress control error;

[0023] Based on the height control error and the stress control error, proportional operation processing, integral operation processing, and differential operation processing are respectively performed;

[0024] The proportional operation result, the integral operation result, and the differential operation result are superimposed to generate a preliminary lifting speed control signal and a preliminary acceleration control signal;

[0025] The preliminary lifting speed control signal and the preliminary acceleration control signal are subjected to amplitude limiting processing to generate a target lifting speed control signal and a target acceleration control signal.

[0026] Optionally, the real-time load distribution state of the gallery bridge is obtained based on the height data and the stress distribution data, and the electric cylinder adjustment instruction and the pre-tightening force adjustment instruction are generated according to the real-time load distribution state, including:

[0027] The height data and the stress distribution data are subjected to time domain synchronization processing to generate a time-aligned sensor data set;

[0028] Based on a preset mapping relationship between the sensor data and the load distribution, the time-aligned sensor data set is subjected to load distribution calculation processing to generate a real-time load distribution state, the real-time load distribution state including a load size parameter, a load position parameter, and a load change trend parameter;

[0029] The load position parameter is subjected to electric cylinder control strategy processing to generate an electric cylinder adjustment instruction;

[0030] The load size parameter and the load change trend parameter are subjected to pre-tightening force control strategy processing to generate a pre-tightening force adjustment instruction.

[0031] Optionally, the gallery bridge lifting motion control signal is processed by a posture estimation algorithm to generate gallery bridge posture estimation data, and the gallery bridge lifting motion control signal is corrected based on the gallery bridge posture estimation data, including:

[0032] Real-time motion sensor data during the gallery bridge lifting process are collected;

[0033] An attitude estimation algorithm is used to calculate the expected attitude data of the gallery bridge based on the gallery bridge lifting motion control signal, and the expected attitude data is used as a preliminary estimate;

[0034] The preliminary estimate is data fused with the real-time motion sensing data to generate gallery bridge attitude estimation data;

[0035] Based on the inclination angle parameter and the vibration amplitude parameter in the gallery bridge attitude estimation data, a control signal adjustment amount is calculated;

[0036] According to the control signal adjustment amount, the gallery bridge lifting motion control signal is real-time corrected.

[0037] Optionally, the height sensing signal and the stress distribution signal are respectively signal-conditioned and analog-digital converted to obtain height data and stress distribution data, including:

[0038] The height sensing signal is signal-amplified and filtered to obtain a conditioned height analog signal, and the stress distribution signal is signal-amplified and filtered to obtain a conditioned stress analog signal;

[0039] The conditioned height analog signal is converted into height data by an analog-digital converter, and the conditioned stress analog signal is converted into stress distribution data by an analog-digital converter.

[0040] In a second aspect, the application provides a gallery bridge safety control system for a flight simulator, including:

[0041] The acquisition module is configured to acquire height sensing signals and stress distribution signals of the gallery bridge of the flight simulator during lifting;

[0042] The conversion module is configured to signal-condition and analog-digital convert the height sensing signals and the stress distribution signals to obtain height data and stress distribution data;

[0043] The generation module is configured to obtain a real-time load distribution state of the gallery bridge based on the height data and the stress distribution data, and generate an electric cylinder adjustment instruction and a pre-tightening force adjustment instruction according to the real-time load distribution state;

[0044] The compensation module is configured to dynamically compensate for the gravity center offset of the gallery bridge based on the electric cylinder adjustment instruction and the pre-tightening force adjustment instruction, and generate a gallery bridge lifting motion control signal in combination with a fuzzy PID control model;

[0045] The correction module is configured to process the gallery bridge lifting motion control signal by an attitude estimation algorithm to generate gallery bridge attitude estimation data, and correct the gallery bridge lifting motion control signal based on the gallery bridge attitude estimation data to achieve safety control of the gallery bridge.

[0046] In a third aspect, the present application provides an electronic device, comprising:

[0047] a memory for storing a computer program;

[0048] a processor for executing the computer program to implement the steps of the method for controlling the safety of a flight simulator bridge as described in the first aspect above.

[0049] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program, when executed by a processor, can implement the steps of the method for controlling the safety of a flight simulator bridge as described in the first aspect above.

[0050] The technical solutions provided by the present application have the following beneficial effects:

[0051] The present application realizes all-round and real-time sensing of key physical quantities (height position and structure stress) during the lifting process of the bridge, provides a comprehensive and accurate raw information basis for subsequent intelligent control, and overcomes the possible sensing blind area of a single signal source. The original analog signal which is susceptible to interference is converted into stable and accurate digital quantity, which improves the anti-interference ability of the signal and the measurement accuracy of the system, and provides reliable data quality guarantee for subsequent data analysis and control decision. The sensing data is fused and solved into an intuitive load distribution state, realizing the leap from "sensing" to "cognition", so that the generation of control instructions is directly based on the understanding of the real-time mechanical state of the bridge, and the forward-looking and targetedness of control decision is improved. The influence of the center of gravity deviation is actively offset through the coordinated action of the actuator, and combined with the intelligent control algorithm which can adaptively adjust the parameters, accurate motion instructions are generated, so as to suppress the tilting and vibration trend at the source and ensure the initial stability of the lifting process. A closed-loop control system including feedforward prediction and feedback correction is constructed, which can real-time estimate and fine-tune the control effect, effectively compensate the model error and external disturbance, and finally realize the safe control of the bridge with stability and precision.

[0052] Further, the present application also accurately adjusts the output force or displacement of the electric cylinder and the pre-tightening force of the spring mechanism according to the instructions respectively; then, the center of gravity deviation is real-time offset by the synergistic effect of the two; finally, the compensated system state is input into the fuzzy PID controller, so as to synthesize the final motion control signal.

[0053] And, through the coordination of the electric cylinder and the pre-tightening force, the active and resultant force compensation for the center of gravity deviation of the gallery bridge is realized, a more stable controlled object is provided for the advanced control algorithm, and then, in combination with the fuzzy PID control model, the generation of the motion control signal is based on the effective initial state compensation and has the parameter self-adaptive optimization capability, so that the response speed and the stable precision of the entire control system are improved.

[0054] These aspects or other aspects of the present application will be more apparent in the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0056] Figure 1 A flow chart of a gallery bridge safety control method of a flight simulator provided by the embodiments of the present application;

[0057] Figure 2 A specific implementation schematic diagram of a gallery bridge safety control method of a flight simulator provided by the embodiments of the present application;

[0058] Figure 3 A structure schematic diagram of a gallery bridge safety control system of a flight simulator provided by the embodiments of the present application. DETAILED DESCRIPTION

[0059] In the prior art, the control logic of the scheme based on the fixed parameter PID control depends on the preset typical working condition. When the load borne by the channel changes greatly or rapidly, the fixed control mode is difficult to make a precise response completely matched with it, which may cause fluctuations in the stable process and affect the smoothness of the operation. In addition, the control core of the scheme is mainly concentrated on the correction of the visible attitude angle, and for the load distribution change, which is an internal factor affecting the structural stability, the associated embodiment in the control decision still has room for improvement.

[0060] To solve the above limitations, the application provides a flight simulator bridge safety control method. The core of the method is to accurately perceive the load state changes inside the channel structure by real-time collection and analysis of stress distribution and height information. Based on this state, the system first adjusts the output of the driving mechanism and the pre-tightening force inside the structure to actively offset the possible center of gravity shift. Then, an intelligent algorithm that can automatically optimize control parameters is combined to generate motion instructions, and finally, the instructions are fine-tuned through attitude prediction and feedback. This method changes the control basis from passive attitude correction to active load management, enabling the system to predict and suppress instability from the internal mechanism, thereby effectively overcoming the problem of insufficient adaptability of fixed parameter control under variable load conditions, achieving high stability and high safety in the lifting process under all conditions.

[0061] To better understand the application scheme for those skilled in the art, the application will be further described in detail below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0062] The core of the application is to provide a flight simulator bridge safety control method, and a specific embodiment of the method is shown in the flowchart as Figure 1 The method comprises:

[0063] Step 101: Collecting height sensing signals and stress distribution signals of the flight simulator bridge during the lifting process.

[0064] In step 101, the flight simulator refers to the aircraft object that needs to be connected, and the bridge refers to the lifting channel equipment used to connect the airport terminal and the flight simulator, which constitutes a physical connection relationship between the service and the served. The height sensing signal refers to the original electric signal generated by the displacement sensor or angle sensor installed on the bridge lifting mechanism, which is used to represent the current absolute height or height change of the bridge relative to the initial position. The stress distribution signal refers to the original electric signal set generated by a group of strain gauges pre-pasted on the surface of the key load-bearing structure (such as the main beam and the support arm) of the bridge, which is used to represent the force borne by these points under the lifting and loading state of the bridge. This group of signals collectively constitutes the information basis for sensing the overall running state and internal mechanical state of the bridge.

[0065] In the embodiments of the present application, it is realized by a sensor network deployed on a physical structure. First, the height sensor monitors the vertical position change of the gallery bridge in real time, and converts it into a height sensing signal in the form of voltage or current. At the same time, the small deformation of the structural member is sensed by the multi-channel strain gauge distributed at different positions of the gallery bridge, and is converted into a corresponding stress sensing signal. All these raw signals are synchronously collected and transmitted to the central processing unit, providing raw materials for the subsequent signal processing stage.

[0066] For example, on the gallery bridge matched with the flight simulator in the A training center, four high-precision laser ranging sensors (for height sensing) are installed at the top of the four main lifting columns, and a strain gauge group is pasted at the bottom of the main beam of the gallery bridge. When the gallery bridge starts to rise from the first floor to the third floor, these sensors continue to work. The four laser ranging sensors output four voltage signals (height sensing signals) representing the height of each measuring point in real time, and the six strain gauges output six voltage signals (stress distribution signals) representing the force condition of each point in real time. All these signals are sent to the industrial computer in the control room through data cables.

[0067] Step 102: signal conditioning and analog-digital conversion processing are performed on the height sensing signal and the stress distribution signal respectively, to obtain height data and stress distribution data.

[0068] In step 102, signal conditioning refers to the process of amplifying and filtering the original weak electrical signal collected by the sensor to improve the signal strength and remove noise interference, and the result is a stable and clean analog signal. Analog-digital conversion processing refers to the process of discretizing the conditioned analog signal into a sequence of digital quantities that can be directly recognized and processed by the computer through an analog-digital converter at a fixed sampling frequency. Height data is a data set accurately representing the height of each monitoring point of the gallery bridge in digital form after the above processing. Stress distribution data is a data set accurately representing the stress of each monitoring point of the gallery bridge in digital form after the above processing.

[0069] In the embodiments of the present application, first, the collected height sensing signal and each stress distribution signal are sent into the signal conditioning circuit, amplified to improve the signal amplitude, and filtered by low-pass filter to remove high-frequency noise interference, to obtain the conditioned height analog signal and stress analog signal. Then, these pure analog signals are sent into the analog-digital converter, which samples and quantizes the continuous analog signals at a set sampling frequency (for example, 1000 times per second) to convert them into a series of discrete digital values. Finally, these digital values are packaged into height data and stress distribution data for subsequent algorithms.

[0070] For example, after receiving the above-mentioned 10-channel original voltage signals, the data acquisition card in the industrial computer first processes these signals. For example, the microvolt-level strain gauge signal is amplified to the volt level, and the 50Hz power frequency interference and other noises are filtered out by using a hardware filter. Then, the analog-to-digital converter in the data acquisition card converts these clean analog signals at a sampling rate of 1000 Hz. Assuming that at a certain moment, the height data obtained by converting the 4-channel height signals are [1250, 1248, 1255, 1249] (unit: millimeter) respectively, and the stress distribution data obtained by converting the 6-channel stress signals are [102.3, 98.7, 205.6, 201.1, 99.5, 101.8] (unit: kilogram force) respectively. These digital arrays are the processed results.

[0071] Step 103: Based on the height data and the stress distribution data, the real-time load distribution state of the gallery bridge is obtained, and the electric cylinder adjustment instruction and the pre-tightening force adjustment instruction are generated according to the real-time load distribution state.

[0072] In step 103, the real-time load distribution state is a comprehensive state quantity, which is obtained by fusion calculation of the height data and the stress distribution data, and is used to describe the total weight, the center of gravity position and the weight distribution trend of the load (such as personnel and equipment) on the gallery bridge at the current moment. The electric cylinder adjustment instruction is a command for controlling the action of the electric cylinder, which includes the displacement amount or the thrust size and the adjustment direction that need to be adjusted. The pre-tightening force adjustment instruction is a command for instructing the spring mechanism to exert or maintain a pre-tightening force of a specific size.

[0073] In the embodiment of the present application, first, the height data and the stress distribution data synchronously collected are time-aligned to ensure that they are state snapshots at the same moment. Then, according to the mapping relationship model between the sensing data and the load distribution established through calibration in advance, the real-time load distribution state is calculated, including the total load size, the coordinates of the load center of gravity in the gallery bridge plane and the speed of load change. Next, the control algorithm queries the control strategy table according to the position coordinates of the load center of gravity to generate the corresponding electric cylinder adjustment instruction, which specifies which electric cylinder or electric cylinders need to act and the displacement and direction of the action. At the same time, the algorithm calculates the optimal pre-tightening force value required to balance this load according to the total load size and its change trend, and generates the pre-tightening force adjustment instruction.

[0074] For example, the industrial computer synchronizes the height data [1250, 1248, 1255, 1249] and the stress distribution data [102.3, 98.7, 205.6, 201.1, 99.5, 101.8] obtained at time T. Through the built-in algorithm model analysis, it is found that the stress values (205.6, 201.1) of the left front and left rear areas of the gallery bridge are higher than those of other areas, and the height (1255) of the left front corner is slightly higher, indicating that there is a load of about 400 kg biased to the left side of the gallery bridge. The algorithm generates a real-time load distribution state accordingly. Subsequently, in order to pull the center of gravity back to the center, the algorithm generates an electric cylinder adjustment instruction: command the two electric cylinders on the right to each extend 2 mm upward, and the left electric cylinder remains unchanged. At the same time, in order to offset the overturning moment caused by the load bias, a pre-tightening force adjustment instruction is generated, requiring the pre-tightening force of the right support structure to be increased by 50 kgf.

[0075] Step 104: dynamically compensating the center of gravity deviation of the gallery bridge based on the electric cylinder adjustment instruction and the pre-tightening force adjustment instruction, and generating a gallery bridge lifting motion control signal in combination with a fuzzy PID control model.

[0076] In step 104, dynamic compensation refers to the process of adjusting the center of gravity deviation based on real-time state. The fuzzy proportional-integral-derivative (PID) control model refers to an intelligent control method combining fuzzy logic and PID control. The gallery bridge lifting motion control signal is the command finally output to the gallery bridge driving motor (such as a servo motor), used to accurately control the lifting speed and acceleration of the gallery bridge.

[0077] In the embodiments of the present application, first, the electric cylinder driver receives the electric cylinder adjustment instruction, and controls the specified electric cylinder to accurately extend or retract by a specified displacement amount (or output a specified thrust). At the same time, the system sets or maintains the pre-tightening force of the spring mechanism through mechanical means according to the pre-tightening force adjustment instruction. The synergistic effect of the two produces a corrective moment, which preliminarily dynamically compensates the detected center of gravity deviation. After compensation, the system collects new gallery bridge state data (such as attitude angle change rate). Then, the state data is input into the fuzzy PID control model. The fuzzy reasoning module in the model automatically adjusts the three parameters (P, I, D) of the PID controller online according to the deviation of the state data from the ideal state and its change trend, so that the controller characteristics adapt to the current dynamics. Then, the PID control module calculates the speed and acceleration instructions required for accurate tracking of the target lifting curve using the optimized parameters. Finally, the signal synthesis module synthesizes these instructions into the final gallery bridge lifting motion control signal.

[0078] For example, the control system issues the instruction generated in step 103. The two right-side electric cylinders are precisely extended 2 mm upward, and the pre-tightening force of the right-side spring set is adjusted to increase by 50 kgf. This action pulls the originally left-tilted gallery bridge's center of gravity back to the right, and the tilting trend is preliminarily suppressed (dynamic compensation). The industrial PC immediately reads the tilt sensor data and finds that the gallery bridge still has a slight angular velocity deviation. Then, the deviation data is input into the fuzzy PID control model. The model determines that the current state is a slight overshoot, and automatically reduces the proportional parameter P and increases the differential parameter D. The adjusted PID controller calculates and outputs an acceleration instruction requiring the gallery bridge to rise at a speed of 0.5 m / min and with a slight braking trend. The final synthesized gallery bridge lifting motion control signal is sent to the servo driver.

[0079] Step 105: processing the gallery bridge lifting motion control signal by a pose estimation algorithm to generate gallery bridge pose estimation data, and correcting the gallery bridge lifting motion control signal based on the gallery bridge pose estimation data to achieve safe control of the gallery bridge.

[0080] In step 105, the pose estimation algorithm is a data fusion algorithm combining a kinematic model and sensor feedback, used to predict and estimate the future pose of the gallery bridge. The gallery bridge pose estimation data is the output result of the algorithm, containing the predicted tilt angle, vibration amplitude and other pose information of the gallery bridge in the future period of time. Correction refers to adjusting the previously generated control signal in the opposite direction according to the deviation between the predicted pose and the ideal pose, forming a closed-loop feedback.

[0081] In the embodiments of the present application, the lifting motion control signal is input into the pose estimation algorithm, the expected pose data is calculated through the kinematic model, the actual motion sensor data is collected, the state deviation is obtained by comparing the expected data, the real-time pose parameters are calculated based on the deviation data, the motion control signal is adjusted through the control signal correction algorithm, and closed-loop control is achieved.

[0082] For example, the servo driver starts to execute the lifting motion control signal sent in step 104. At the same time, the inertial measurement unit installed on the gallery bridge measures the three-axis angular velocity and acceleration of the gallery bridge in real time (real-time motion sensor data). The pose estimation algorithm in the industrial PC uses the received motion control signal and the mass distribution model of the gallery bridge to predict that the gallery bridge may have a slight 0.5-degree forward and backward swing 50 milliseconds later due to inertia. Then, the algorithm fuses this prediction value with the actual readings of the inertial measurement unit to generate more accurate gallery bridge pose estimation data and confirm the swing trend. Based on this, the algorithm calculates a slight adjustment amount to correct the lifting motion control signal being executed, and slightly reduces the lifting acceleration before the swing occurs. This corrected signal makes the lifting motion of the gallery bridge more stable, effectively suppressing the predicted vibration.

[0083] The method realizes real-time perception of the load distribution state of the gallery bridge through multi-source sensor data fusion processing, realizes dynamic compensation and closed-loop optimization control based on a fuzzy PID control model and an attitude estimation algorithm, and effectively improves the operation stability, control accuracy and safety of the gallery bridge in the lifting process, reduces mechanical vibration and impact, and prolongs the service life of the equipment.

[0084] To solve the problem of how to cooperatively control the electric cylinder and the pre-tightening force to realize accurate gravity compensation and intelligently generate a motion control signal, in some embodiments, step 104: based on the electric cylinder adjustment instruction and the pre-tightening force adjustment instruction, dynamically compensating for the gravity offset of the gallery bridge, and generating a gallery bridge lifting motion control signal in combination with a fuzzy PID control model, as shown in Figure 2 , which includes:

[0085] Step 201: based on the electric cylinder adjustment instruction, controlling the electric cylinder driver to adjust the displacement or thrust of the electric cylinder according to the specified displacement adjustment amount and adjustment direction.

[0086] In step 201, the electric cylinder driver is a power device that receives instructions and drives the motor to execute. The "specified displacement adjustment amount and adjustment direction" is derived from the analysis and calculation of the real-time load distribution state of the gallery bridge. Specifically, the control algorithm determines the electric cylinder that needs to be adjusted, the displacement compensation amount (adjustment amount) required to balance the gravity center, and whether to extend or retract (adjustment direction) by querying a pre-set control strategy table or using a geometric relationship model according to the offset distance and orientation of the load gravity center relative to the geometric center of the gallery bridge, the fundamental purpose of which is to generate a corrective torque to offset the tilting tendency caused by the gravity center offset. Adjusting the displacement of the electric cylinder means changing the mechanical position of its push rod, and adjusting the thrust means changing the size of the force output by its push rod.

[0087] In the embodiments of the present application, the control system reads the electric cylinder adjustment instruction, parses the electric cylinder number, specific movement amount and direction that need to be actuated, and then sends a control signal to the corresponding electric cylinder driver. The driver drives the motor to operate according to the signal, and converts the rotary motion into linear motion of the push rod through the internal mechanical structure, thereby accurately completing the displacement or thrust adjustment required by the instruction.

[0088] Step 202: based on the pre-tightening force adjustment instruction, applying or maintaining the corresponding pre-tightening force through the spring mechanism to obtain the adjusted pre-tightening force.

[0089] In step 202, the spring mechanism is deployed in the mechanical support structure of the gallery bridge. Applying or maintaining the corresponding pre-tightening force means causing or maintaining the spring mechanism to generate or maintain an initial tension of the required size. The adjusted pre-tightening force refers to the actual tension value provided by the spring mechanism after it is finally stabilized.

[0090] In the embodiments of the present application, the control system parses the pre-tightening force adjustment instruction to obtain a target pre-tightening force value. For a passive spring mechanism, the instruction is mainly used for system internal recording and confirmation of the force value state that should be maintained at present. If the mechanism has basic adjustment capability, the system will trigger the adjustment mechanism (such as driving a ratchet or electromagnetic lock) to make the spring be compressed or released to the state required by the instruction, so as to output the corresponding pre-tightening force. The system will then update the current pre-tightening force to the adjusted pre-tightening force.

[0091] Step 203: Based on the adjusted electric cylinder pressure and the adjusted pre-tightening force, dynamically compensate the center of gravity deviation of the gallery bridge to obtain dynamically compensated gallery bridge state data.

[0092] In step 203, the dynamically compensated gallery bridge state data refers to the data set reflecting the latest posture and stress condition of the gallery bridge after the compensation action is completed, which is re-acquired by the sensor.

[0093] In the embodiments of the present application, the change of the electric cylinder push rod position or the push force directly exerts a force on the gallery bridge support point. At the same time, the pre-tightening force of the spring mechanism also acts on a specific position. The two forces together form a correction torque to resist the tilting tendency of the gallery bridge and achieve dynamic compensation. After the compensation action is completed, the system immediately re-acquires the real-time angle, pressure and other data of the gallery bridge through the inclination sensor, stress sensor and the like. These latest data are the dynamically compensated gallery bridge state data.

[0094] Step 204: Based on the dynamically compensated gallery bridge state data, generate a gallery bridge lifting motion control signal through a fuzzy PID control model.

[0095] In the embodiments of the present application, the dynamically compensated gallery bridge state data is input into the fuzzy PID control model. First, the fuzzy reasoning module dynamically adjusts the control parameters according to the state data, and then the PID control module generates a gallery bridge lifting motion control signal based on the adjusted parameters.

[0096] The following is a specific example:

[0097] With the above embodiment, the control system will generate the electric cylinder adjustment instructions and pre-tightening force adjustment instructions, and then the electric cylinder driver first receives the instructions and drives the push rod of the right two electric cylinders to accurately extend 2 mm upward, while the pre-tightening force of the right spring group is mechanically adjusted to increase by 50 kgf. This coordinated action increases the support force on the right side of the gallery bridge, thereby offsetting the counterclockwise overturning moment caused by the left side of about 400 kg load bias, and preliminarily pulling the gallery bridge center of gravity back to the right side, completing a dynamic compensation. After the compensation action is executed, the industrial computer immediately collects new data of the inclination sensor, finds that the gallery bridge as a whole has approached the horizontal but still has a small right return angular velocity, and the value is 0.1 degrees per second. At this time, the system inputs the gallery bridge state data after dynamic compensation containing the angular velocity deviation into the fuzzy PID control model. According to the characteristics that the current error is small but there is a continuous change trend, the fuzzy reasoning module in the model automatically increases the proportional control parameter from the initial value 120 to 100 to reduce the response aggressiveness, while the differential control parameter is increased from the initial value 20 to 30 to enhance the ability to suppress jitter, and the integral control parameter remains unchanged at 10; then the PID control module uses the adjusted parameters to operate the height control error and the stress control error. The height control error is the difference between the set target height and the actual average height 1247.5 mm, and the set target height is 1250 mm, so equals 2.5 mm, and the stress control error is the difference between the ideal balanced stress value 150 kgf and the actual left average stress 203.35 kgf, i.e. equals negative 53.35 kgf, and the PID controller output operation formula is , wherein is the PID controller output value, is the PID proportional control parameter, is the height control error, is the PID integral control parameter, is the PID differential control parameter, and the value is . After the result is processed by amplitude limiting, it is converted into a target lifting speed control signal, which requires the gallery bridge to rise at a speed of 0.5 meters per minute, and at the same time an acceleration control signal is given a slight braking trend value of negative 0.01 meters per second. The final signal synthesis module combines these two signals into a gallery bridge lifting motion control signal and outputs it to the servo driver, thereby realizing fine control of the gallery bridge motion.

[0098] In the embodiment of the present application, the above complete step scheme realizes active and resultant force compensation for the center of gravity deviation through the coordinated action of the electric cylinder and the spring mechanism, thereby providing a more stable control basis for the subsequent advanced control algorithm; and then, in combination with the online parameter self-adjusting capability of the fuzzy PID control model, the generation of the motion control signal is not only based on effective physical compensation, but also has the characteristics of intelligent adaptation to system dynamic changes, thereby improving the stability, response speed and control accuracy of the gallery bridge lifting process.

[0099] To solve the precision and adaptability of the gallery bridge lifting motion control signal generation, in some embodiments, step 204: generating a gallery bridge lifting motion control signal through a fuzzy PID control model based on the dynamically compensated gallery bridge state data, comprises:

[0100] Step 301: inputting the dynamically compensated gallery bridge state data into a fuzzy PID control model, dynamically adjusting control parameters through a fuzzy reasoning module in the fuzzy PID control model, wherein the control parameters include proportional parameters, integral parameters and differential parameters.

[0101] In step 301, the dynamically compensated gallery bridge state data refers to real-time data containing height and stress information obtained after the center of gravity deviation compensation, the proportional parameter refers to a regulating coefficient proportional to the current error in the control system, the integral parameter refers to a regulating coefficient proportional to the historical error accumulation, and the differential parameter refers to a regulating coefficient proportional to the error change rate.

[0102] In the embodiment of the present application, the dynamically compensated gallery bridge state data is input into the fuzzy reasoning module, which dynamically calculates and outputs the proportional parameter, integral parameter and differential parameter values most suitable for the current state according to the size of the current height error and stress error through a pre-set fuzzy rule base.

[0103] Step 302: generating target lifting speed control signals and target acceleration control signals through a PID control module in the fuzzy PID control model according to the adjusted control parameters.

[0104] In step 302, the target lifting speed control signal refers to the expected gallery bridge lifting speed value, and the target acceleration control signal refers to the expected gallery bridge acceleration value.

[0105] In the embodiment of the present application, the PID control module receives the control parameters output by the fuzzy reasoning module, and combines the current height error and stress error to respectively calculate and generate the target lifting speed control signal and the target acceleration control signal through proportional integral differential algorithm.

[0106] Step 303: The signal synthesis module in the fuzzy PID control model is used to synthesize the target lifting speed control signal and the target acceleration control signal to generate a gallery bridge lifting motion control signal.

[0107] In step 303, signal synthesis processing refers to the process of fusing multiple control signals into a unified control instruction.

[0108] In the embodiments of the present application, the signal synthesis module receives the target lifting speed control signal and the target acceleration control signal, and uses a weighted fusion algorithm to synthesize the two signals into a complete gallery bridge lifting motion control signal, which is output to the gallery bridge driving mechanism.

[0109] The following is a specific example:

[0110] According to the foregoing embodiment, after the industrial computer inputs the gallery bridge state data after dynamic compensation containing the information of a small right return angular velocity of 0.1 degrees per second to the fuzzy PID control model, the fuzzy reasoning module in the model first analyzes the input data. The module has multiple rules preset inside for automatically adjusting control parameters according to error size and error change rate. It judges that the height control error between the actual height of the gallery bridge 1247.5 mm and the target height 1250 mm is 2.5 mm, which belongs to a small error range, and the error is decreasing at a rate of 0.5 mm per second, which belongs to a medium change rate. According to the internal mapping relationship, the adjustment instruction is output to adjust the proportional parameter from 120 to 100, the differential parameter from 20 to 30, and the integral parameter remains 10 unchanged, so that the controller characteristics are more focused on stability and suppression of overshoot. From 120 to 100, the differential parameter From 20 to 30, and the integral parameter Remains 10 unchanged, so that the controller characteristics are more focused on stability and suppression of overshoot; then, the PID control module immediately uses the new control parameters, i.e., the proportional parameter 100, the integral parameter 10, and the differential parameter 30, for operation. The module processes the height control error and the stress control error, respectively. For the height control error Equal to 2.5 mm, proportional operation is performed, i.e., the proportional parameter multiplied by the height control error is 100 multiplied by 2.5, which is equal to 250. Integral operation is performed, i.e., the integral parameter multiplied by the integral value of the height control error. Assuming that the recent error accumulation value is 5, the integral operation result is 10 multiplied by 5, which is equal to 50. Differential operation is performed, i.e., the differential parameter multiplied by the change rate of the height control error. It is known that the error change rate is -0.5 mm per second, so the differential operation is 30 multiplied by -0.5, which is equal to -15. The sum of the three results is 250 plus 50 plus -15, which is equal to 285. After amplitude limiting processing to ensure that the output is within a safe range, the value is converted into a target lifting speed control signal instruction, and the gallery bridge rises at a speed of 0.5 meters per minute. Equal to negative 53.35 kgf, similar PID operation and synthesis of target acceleration control signal, give it a slight braking acceleration of negative 0.01 meters per second; finally, the signal synthesis module synthesizes the target lifting speed control signal and the target acceleration control signal, generates a complete gallery bridge lifting motion control signal containing speed and acceleration information and outputs it to the servo driver.

[0111] In the embodiments of the present application, by adjusting the control parameters adaptively and combining multi-signal fusion processing, the accurate generation of the gallery bridge lifting motion control signal is realized, the response speed and adaptability of the control system are improved, and the gallery bridge can run smoothly under various load conditions.

[0112] In order to solve the precision and stability problem of the PID control module when generating the control signal, in some embodiments, step 302: generating target lifting speed control signal and target acceleration control signal through PID control module in fuzzy PID control model according to adjusted control parameters, comprising:

[0113] Step 401: error calculation and processing of the dynamic compensation gallery state data through the PID control module according to the adjusted control parameters, obtaining height control error and stress control error.

[0114] In step 401, the height control error refers to the difference between the current height of the gallery bridge and the target height, and the stress control error refers to the difference between the current stress of the gallery bridge and the target stress.

[0115] In the embodiments of the present application, the PID control module receives the dynamic compensation gallery state data, compares and calculates the current height data with the preset target height to obtain the height control error, and compares and calculates the current stress data with the preset target stress to obtain the stress control error.

[0116] Step 402: based on the height control error and the stress control error, respectively, proportional operation processing, integral operation processing and differential operation processing.

[0117] In step 402, proportional operation processing refers to the calculation process of multiplying error by proportional parameter, integral operation processing refers to the calculation process of multiplying error accumulation value by integral parameter, and differential operation processing refers to the calculation process of multiplying error change rate by differential parameter.

[0118] In the embodiments of the present application, based on the height control error, proportional operation, integral operation and differential operation are performed to obtain proportional operation result, integral operation result and differential operation result corresponding to the height; at the same time, based on the stress control error, the same operation processing is performed to obtain proportional operation result, integral operation result and differential operation result corresponding to the stress.

[0119] Step 403: superimposing the proportional operation result, the integral operation result and the differential operation result to generate a preliminary lifting speed control signal and a preliminary acceleration control signal.

[0120] In step 403, the preliminary lifting speed control signal refers to a speed control quantity without amplitude limiting processing, and the preliminary acceleration control signal refers to an acceleration control quantity without amplitude limiting processing.

[0121] In the embodiment of the present application, the height-related proportional operation result, integral operation result and differential operation result are superimposed to generate a preliminary lifting speed control signal; and the stress-related proportional operation result, integral operation result and differential operation result are superimposed to generate a preliminary acceleration control signal.

[0122] Step 404: amplitude limiting processing is performed on the preliminary lifting speed control signal and the preliminary acceleration control signal to generate a target lifting speed control signal and a target acceleration control signal.

[0123] In step 404, amplitude limiting processing refers to a processing process of limiting a signal within an allowed range.

[0124] In the embodiment of the present application, the preliminary lifting speed control signal is subjected to upper and lower amplitude limiting processing to ensure that its value is within the allowed speed range, and the target lifting speed control signal is generated; and the preliminary acceleration control signal is subjected to upper and lower amplitude limiting processing to ensure that its value is within the allowed acceleration range, and the target acceleration control signal is generated.

[0125] The following is a specific example:

[0126] According to the foregoing embodiment, the PID control module starts to work according to the adjusted control parameters, i.e., the proportional parameter 100, the integral parameter 10 and the differential parameter 30. First, the error calculation processing is performed on the dynamically compensated gallery bridge state data, wherein the height control error The calculation result is 2.5 mm by subtracting the actually measured average height 1247.5 mm from the target height 1250 mm. The stress control error is 203.35 kgf by subtracting the actual left average stress 203.35 kgf from the ideal balanced stress value 150 kgf. The calculation result is 2.5 mm by subtracting the actually measured average height 1247.5 mm from the target height 1250 mm. The negative 53.35 kgf is subjected to proportional operation processing, integral operation processing and differential operation processing based on the height control error of 2.5 mm respectively. The proportional operation processing is that a proportional parameter is multiplied by the height control error, and the proportional operation result is 250 obtained by multiplying 100 by 2.5. The integral operation processing is that an integral parameter is multiplied by the integral value of the height control error, and the integral operation result is 50 obtained by multiplying 10 by 5, assuming that the integral value of the height control error is 5 mm·s calculated according to historical error data. The differential operation processing is that a differential parameter is multiplied by the rate of change of the height control error, and the differential operation result is negative 15 obtained by multiplying 30 by negative 0.5, knowing that the rate of change of the height control error is negative 0.5 mm / s. Then, the proportional operation result 250, the integral operation result 50 and the differential operation result negative 15 are subjected to superposition processing, that is, 250 plus 50 plus negative 15 equals 285, which is taken as the preliminary lifting speed control signal. Meanwhile, the same three operation processes are performed based on the stress control error negative 53.35 kgf to generate a preliminary acceleration control signal, assuming that the preliminary acceleration control signal is negative 0.012 m / s2. Finally, the preliminary lifting speed control signal 285 and the preliminary acceleration control signal negative 0.012 m / s2 are subjected to limiting processing. The limiting processing follows a preset safety rule, that is, the upper limit of the speed instruction corresponds to 300, and the absolute value upper limit of the acceleration instruction is 0.02 m / s2. Since 285 is less than 300 and the absolute value 0.012 of negative 0.012 is less than 0.02, the preliminary signal does not need to be clipped, and the target lifting speed control signal 285 corresponding to the gallery bridge lifting speed of 0.5 m / min and the target acceleration control signal negative 0.012 m / s2 corresponding to the slight braking acceleration are directly generated.

[0127] In the embodiments of the present application, through independent operation processing and superposition synthesis of multiple error sources, combined with limiting protection mechanism, the accuracy and safety of the control signal generation are ensured, the equipment abnormity caused by signal over-limiting is effectively prevented, and the reliability of system operation is improved.

[0128] In order to solve the problem of accuracy of gallery bridge load state sensing and control instruction generation, in some embodiments, step 103: based on the height data and the stress distribution data, obtaining the real-time load distribution state of the gallery bridge, and generating an electric cylinder adjusting instruction and a pre-tightening force adjusting instruction according to the real-time load distribution state, comprises:

[0129] Step 501: time domain synchronization processing is performed on the height data and the stress distribution data to generate a time-aligned sensing data set.

[0130] In step 501, the time domain synchronization processing refers to the processing process of aligning the data collected at different times to the same time point, and the time-aligned sensing data set refers to a set of height data and stress distribution data with the same time stamp.

[0131] In the embodiment of the present application, the height data and stress distribution data are processed by the timestamp alignment algorithm to ensure that the two sets of data correspond to the same sampling time, and eliminate the problem of data asynchronization caused by sampling time difference.

[0132] Step 502: Based on the preset mapping relationship between sensing data and load distribution, the time-aligned sensing data set is subjected to load distribution calculation processing to generate a real-time load distribution state, which includes a load size parameter, a load position parameter and a load change trend parameter.

[0133] In step 502, the mapping relationship between sensing data and load distribution refers to the corresponding relationship between sensor data and actual load established through experiments, the load size parameter refers to the total weight value borne by the gallery bridge, the load position parameter refers to the distribution position information of the load on the gallery bridge, and the load change trend parameter refers to the rate of change of the load with time.

[0134] In the embodiment of the present application, based on the preset mapping relationship model, the time-aligned sensing data set is input into the load distribution calculation algorithm, and the real-time load distribution state including the load size, position and change trend is obtained through multi-sensor data fusion calculation.

[0135] Step 503: The load position parameter is subjected to electric cylinder control strategy processing to generate an electric cylinder adjustment instruction.

[0136] In the embodiment of the present application, according to the load position parameter in the real-time load distribution state, the required pressure adjustment amount and adjustment direction are calculated to generate a specific electric cylinder adjustment instruction.

[0137] Step 504: The load size parameter and the load change trend parameter are subjected to pretightening force control strategy processing to generate a pretightening force adjustment instruction.

[0138] In step 504, the pretightening force control strategy processing refers to the calculation process of generating a pretightening force adjustment instruction according to the load size and change trend.

[0139] In the embodiment of the present application, according to the load size parameter and the load change trend parameter in the real-time load distribution state, the required tension adjustment amount and action position are calculated through the pretightening force control algorithm to generate a specific pretightening force adjustment instruction.

[0140] The following is a specific example:

[0141] With the foregoing embodiment, the industrial computer obtains height data of 1250 mm, 1248 mm, 1255 mm, 1249 mm at a certain time and stress distribution data of 102.3 kgf, 98.7 kgf, 205.6 kgf, 201.1 kgf, 99.5 kgf, 101.8 kgf, respectively. First, the data is processed in time domain synchronization, that is, each group of data is marked with the same timestamp to ensure that they represent the state of the gallery bridge at the same instant, and a time-aligned sensor data set is generated. Then, based on the preset mapping relationship between the sensor data and the load distribution, the time-aligned sensor data set is calculated and processed. The mapping relationship is a calculation model established through the preliminary calibration experiment. The load size parameter is obtained by summing all stress distribution data and subtracting the self-weight of the gallery bridge. Assuming that the self-weight of the gallery bridge is 600 kgf, the total stress value is 102.3+98.7+205.6+201.1+99.5+101.8=809 kgf, and the load size parameter is 809-600=209 kgf. The load position parameter, that is, the gravity center coordinates, is obtained by calculating the stress moment. Assuming that the coordinates of the six strain gauges in the gallery bridge plane are point 1 coordinates (0, 0), point 2 coordinates (2, 0), point 3 coordinates (0, 1), point 4 coordinates (2, 1), point 5 coordinates (1, 0), and point 6 coordinates (1, 1), the gravity center X coordinate is equal to the sum of the product of each point stress value and its X coordinate divided by the total stress value, that is, 102.3×0+98.7×2+205.6×0+201.1×2+99.5×1+101.8×1=802.7, and then 802.7÷809≈0.992 m. Similarly, the gravity center Y coordinate is equal to the sum of the product of each point stress value and its Y coordinate divided 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 then 506.5÷809≈0.626 m. Therefore, the load position parameter is coordinates 0.992 m 0.626 m. The load change trend parameter is calculated by comparing the load size parameter of the previous time sequence. Assuming that the load at the previous time is 205 kgf, the current change trend is an increase of 4 kgf per second. Thus, the real-time load distribution state is generated. Then, the load position parameter, that is, the gravity center coordinates 0.992 m 0.626 m, is processed by the electric cylinder control strategy. The strategy stipulates that when the gravity center X coordinate is greater than the gallery bridge longitudinal center line coordinate 1 m, the right electric cylinder needs to be adjusted, and the adjustment amount is determined by the offset distance. The current X coordinate 0.992 m is less than 1 m but close, so a conservative electric cylinder adjustment instruction is generated to command the right electric cylinder to extend 1 mm upward and the left electric cylinder to be stationary. At the same time, the load size parameter 209 kgf and the load change trend parameter 4 kgf per second are processed by the pretightening force control strategy. The strategy determines the pretightening force increment based on the total load size and growth trend. After querying the preset strategy table, a pretightening force adjustment instruction is generated to increase the pretightening force of the right support structure by 25 kgf.

[0142] In the embodiment of the present application, the data consistency is ensured through time domain synchronization, the load state is accurately analyzed based on the mapping relationship, and the control instructions are generated according to the load characteristics, thereby improving the accuracy and timeliness of the electric cylinder and the pre-tightening force adjustment, and providing reliable guarantee for the stable operation of the gallery bridge.

[0143] In order to solve the precision problem of the gallery bridge posture estimation and control signal correction, in some embodiments, the step 105: processing the gallery bridge lifting motion control signal by the posture estimation algorithm to generate the gallery bridge posture estimation data, and correcting the gallery bridge lifting motion control signal based on the gallery bridge posture estimation data, comprises:

[0144] Step 601: collecting real-time motion sensing data in the gallery bridge lifting process.

[0145] In step 601, the real-time motion sensing data refers to the motion state data collected by the sensor in real time in the gallery bridge lifting process, including height change data and stress change data.

[0146] In the embodiment of the present application, the height change rate and stress change rate data in the lifting process are collected in real time by the sensor group installed on the gallery bridge, to provide real-time measurement data for posture estimation.

[0147] Step 602: using a posture estimation algorithm to calculate the expected posture data of the gallery bridge based on the gallery bridge lifting motion control signal, and taking the expected posture data as a preliminary estimate.

[0148] In step 602, the expected posture data refers to the future posture data of the gallery bridge predicted according to the motion control signal, and the preliminary estimate refers to the initial prediction result calculated based on the model.

[0149] In the embodiment of the present application, the expected posture data is calculated according to the gallery bridge lifting motion control signal by using the kinematic model in the posture estimation algorithm, as the preliminary result of the posture estimation.

[0150] Step 603: data fusion processing is performed on the preliminary estimate and the real-time motion sensing data to generate the gallery bridge posture estimation data.

[0151] In step 603, data fusion processing refers to the process of integrating the information of multiple data sources.

[0152] In the embodiment of the present application, the expected posture data and the real-time motion sensing data are subjected to data fusion processing, and a more accurate gallery bridge posture estimation data is generated by using a weighted fusion algorithm.

[0153] Step 604: Based on the inclination angle parameter and the vibration amplitude parameter in the gallery bridge attitude estimation data, calculate the control signal adjustment amount.

[0154] In step 604, the inclination angle parameter is obtained by differentiating the height change data of each part of the gallery bridge, which represents the inclination of the gallery bridge platform relative to the horizontal reference surface; the vibration amplitude parameter is obtained by frequency spectrum analysis of the stress change rate data of the gallery bridge, which represents the mechanical vibration intensity generated by the gallery bridge during lifting; the control signal adjustment amount refers to the control signal value that needs to be corrected.

[0155] In the embodiments of the present application, based on the inclination angle and vibration amplitude parameters in the gallery bridge attitude estimation data, the control signal adjustment amount is calculated through a control algorithm.

[0156] Step 605: According to the control signal adjustment amount, real-time correction is performed on the gallery bridge lifting motion control signal.

[0157] In the embodiments of the present application, according to the calculated control signal adjustment amount, the original gallery bridge lifting motion control signal is real-time corrected to generate more accurate control instructions.

[0158] The following is a specific example:

[0159] When the servo driver starts to execute the gallery bridge lifting motion control signal requiring the gallery bridge to rise at a constant speed of 0.5 meters per minute with a slight braking acceleration of -0.01 meters per second, the system synchronously collects the real-time motion sensing data measured by the inertial measurement unit installed on the gallery bridge, which contains the angular velocity of the gallery 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 at this moment; then the attitude estimation algorithm is used, which takes the gallery bridge lifting motion control signal currently being executed, i.e. the speed instruction and the acceleration instruction, as the system input, combines the known mass distribution model of the gallery bridge to perform kinematic calculation, and predicts the attitude change of the gallery bridge at the future time of 50 milliseconds, calculating the expected attitude data of the gallery bridge as an expected increase of 0.1 degrees in the tilt angle around the X-axis and an expected decrease of 0.05 degrees in the tilt angle around the Y-axis, which is the preliminary estimate; then the preliminary estimate is fused with the actual angular velocity data collected by the inertial measurement unit in real time, and the weighted average method is used, in which the weight of the preliminary estimate is 0.6 and the weight of the real-time measurement value is 0.4. After fusion calculation, more accurate gallery bridge attitude estimation data is generated, which is a change estimation value of 0.08 degrees in the tilt angle around the X-axis and a change estimation value of -0.038 degrees in the tilt angle around the Y-axis, and it is estimated that the gallery bridge has a periodic vibration with an amplitude of 0.02 degrees; then based on the tilt angle parameters in the gallery bridge attitude estimation data, i.e. 0.08 degrees around the X-axis and -0.038 degrees around the Y-axis, and the vibration amplitude parameter 0.02 degrees, the control signal adjustment amount is calculated through a preset proportional relationship, in which the tilt compensation amount calculation formula is adjustment amount ΔS equal to tilt angle θ multiplied by gain coefficient K, and the gain coefficient K is set to 0.1 meters per degree per second. Therefore, the compensation amount ΔS_x for the tilt around the X-axis is 0.08 multiplied by 0.1, which is 0.008 meters per second, the compensation amount ΔS_y for the tilt around the Y-axis is -0.038 multiplied by 0.1, which is -0.0038 meters per second, and the vibration suppression adjustment amount is set to -0.005 meters per second; finally, according to the calculated control signal adjustment amount, the original gallery bridge lifting motion control signal is corrected in real time, i.e. the original rising speed of 0.00833 meters per second corresponding to the original rising speed of 0.5 meters per minute is reduced by the vibration suppression adjustment amount of 0.005 meters per second, and then the tilt compensation amount of 0.008 meters per second around the X-axis is added and the tilt compensation amount of 0.0038 meters per second around the Y-axis is subtracted, so that the corrected speed instruction is about 0.00753 meters per second, i.e. about 0.452 meters per minute, and the acceleration instruction is adjusted to integrate these compensation effects, thereby generating the final corrected control signal and issuing it to the servo driver for execution.

[0160] In the embodiment of the present application, through the fusion processing of real-time sensing data and expected data, accurate estimation of the gallery bridge posture is realized, and the control signal is dynamically corrected based on the posture parameters, which improves the stability and control accuracy of the gallery bridge operation, and effectively suppresses the vibration and deviation phenomenon.

[0161] In order to solve the noise interference and precision problem in sensor signal processing, in some embodiments, step 102: the height sensing signal and the stress distribution signal are respectively subjected to signal conditioning and analog-to-digital conversion processing to obtain height data and stress distribution data, comprising:

[0162] Step 701: performing signal amplification and filtering processing on the height sensing signal to obtain a conditioned height analog signal, and performing signal amplification and filtering processing on the stress distribution signal to obtain a conditioned stress analog signal.

[0163] In step 701, signal amplification processing refers to the process of amplifying weak signals to a suitable range, filtering processing refers to the process of removing noise components in the signal, the conditioned height analog signal refers to the height signal after amplification and filtering processing, and the conditioned stress analog signal refers to the stress signal after amplification and filtering processing.

[0164] In the embodiment of the present application, the height sensing signal is first subjected to signal amplification processing by an operational amplifier, and then subjected to filtering processing by a low-pass filter to obtain a conditioned height analog signal; the stress distribution signal is also subjected to amplification and filtering processing to obtain a conditioned stress analog signal.

[0165] Step 702: converting the conditioned height analog signal into height data through an analog-to-digital converter, and converting the conditioned stress analog signal into stress distribution data through an analog-to-digital converter.

[0166] In step 702, the analog-to-digital converter refers to a device that converts analog signals into digital signals.

[0167] In the embodiment of the present application, the conditioned height analog signal is input into the analog-to-digital converter and converted into height data through sampling and quantization processing; the conditioned stress analog signal is also converted into stress distribution data through the analog-to-digital converter.

[0168] The following is a specific example:

[0169] When the 4-way height sensing signals and the 6-way stress distribution signals are transmitted to the data acquisition card inside the industrial computer through the data cable, the data acquisition card first performs signal amplification and filtering processing on the height sensing signals, wherein the signal amplitude is raised by 100 times using an operational amplifier circuit with a fixed gain for amplification processing, and high-frequency noise is filtered out using a low-pass filter with a cutoff frequency of 10 Hz for filtering processing, so that the conditioned height analog signals are 4-way smooth voltage signals with amplitudes in the range of 0 to 5 volts. At the same time, signal amplification and filtering processing are performed on the stress distribution signals, wherein the microvolt-level signals generated by the strain gauge are first amplified to millivolt-level by a preamplifier with a gain of 1000, and then to volt-level by a secondary amplifier with a gain of 10, and 50 Hz power frequency interference and other noise are also removed by a 10 Hz low-pass filter, so that the conditioned stress analog signals are 6-way pure voltage signals with amplitudes in the range of 0 to 5 volts. Subsequently, the conditioned height analog signals are converted by a 12-bit analog-to-digital converter at a sampling rate of 1000 times per second, the reference voltage of the analog-to-digital converter is 5 volts, and the conversion formula is digital value D equal to analog voltage value U divided by reference voltage 5 volts multiplied by full-scale digital 4095. When the voltage of one-way height analog signal is 3.2 volts, the height data D is calculated as 3.2 divided by 5 multiplied by 4095, which is approximately equal to 2620. According to the sensor calibration formula height value H equal to slope K multiplied by digital value D plus offset B, wherein the slope K is 0.5 millimeters per digital unit and the offset B is negative 60 millimeters, the actual height data H is 0.5 x 2620 + (-60) = 1250 millimeters. Similarly, the remaining 3-way height analog signals are converted to obtain height data of 1248 millimeters, 1255 millimeters, and 1249 millimeters, respectively. At the same time, the conditioned stress analog signals are converted by another 12-bit analog-to-digital converter at the same sampling rate. When the voltage of one-way stress analog signal is 2.1 volts, the original digital value D is calculated as 2.1 divided by 5 multiplied by 4095, which is approximately equal to 1719. According to the stress sensor calibration formula stress value F equal to slope K multiplied by digital value D plus offset B, wherein the slope K is 0.1 kilogram-force per digital unit and the offset B is negative 70 kilogram-force, the stress distribution data F is 0.1 x 1719 + (-70) ≈ 101.9 kilogram-force. Similarly, the remaining 5-way stress analog signals are converted to obtain stress distribution data of approximately 98.7 kilogram-force, 205.6 kilogram-force, 201.1 kilogram-force, 99.5 kilogram-force, and 101.8 kilogram-force, respectively. These digital arrays are the final height data and stress distribution data obtained. wherein F represents the actual stress distribution data, the slope is 0.1 kilogram-force per digital unit, and the offset is negative 70 kilogram-force.

[0170] In the embodiment of the present application, the signal quality is effectively improved through signal amplification and filtering processing, and the data accuracy is ensured through high-precision analog-to-digital conversion, thereby providing a reliable data basis for subsequent load state analysis and improving the measurement accuracy and anti-interference ability of the system.

[0171] Figure 3 The structural schematic diagram of the flight simulator terminal bridge safety control system provided in the embodiment of the present application is described in the specific implementation part.

[0172] The acquisition module 31 is configured to acquire height sensing signals and stress distribution signals of the flight simulator terminal bridge in the lifting process.

[0173] The conversion module 32 is configured to perform signal conditioning and analog-to-digital conversion processing on the height sensing signals and the stress distribution signals respectively, to obtain height data and stress distribution data.

[0174] The generation module 33 is configured to obtain a real-time load distribution state of the terminal bridge based on the height data and the stress distribution data, and generate an electric cylinder adjustment instruction and a pre-tightening force adjustment instruction according to the real-time load distribution state.

[0175] The compensation module 34 is configured to perform dynamic compensation on the gravity center offset of the terminal bridge based on the electric cylinder adjustment instruction and the pre-tightening force adjustment instruction, and generate a terminal bridge lifting motion control signal in combination with a fuzzy PID control model.

[0176] The correction module 35 is configured to process the terminal bridge lifting motion control signal through an attitude estimation algorithm, to generate terminal bridge attitude estimation data, and correct the terminal bridge lifting motion control signal based on the terminal bridge attitude estimation data, so as to realize safety control of the terminal bridge.

[0177] The flight simulator terminal bridge safety control system in the embodiment of the present application is used to realize the flight simulator terminal bridge safety control method described above, and therefore the specific implementation part of the flight simulator terminal bridge safety control system can refer to the embodiment part of the flight simulator terminal bridge safety control method in the foregoing description, and the specific implementation part can refer to the description of the corresponding embodiment part, which will not be described herein again.

[0178] The present application also provides an electronic device, comprising a memory for storing a computer program, and a processor for executing the computer program to realize the steps of the flight simulator terminal bridge safety control method described above.

[0179] The present application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the flight simulator terminal bridge safety control method described above.

[0180] In an example embodiment, the computer readable storage medium described above can include, but is not limited to, a U disk, a read-only memory, a random access memory, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store computer programs.

[0181] Embodiments of the present application also provide a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps in any of the flight simulator bridge safety control method embodiments described above.

[0182] Those skilled in the art can further realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in the above description in general terms. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0183] The flight simulator bridge safety control method, system, device and storage medium provided by the present application are described in detail above. The principles and implementation modes of the present application are described by applying specific examples in this paper. The above description of the examples is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, some improvements and modifications can be made to the present application, and these improvements and modifications also fall within the protection scope of the present application.

Claims

1. A method for controlling the safety of a flight simulator bridge, characterized in that, The method comprises the following steps: Collecting height sensing signals and stress distribution signals of a flight simulator corridor bridge during lifting; Respectively performing signal conditioning and analog-to-digital conversion processing on the height sensing signals and the stress distribution signals to obtain height data and stress distribution data; Based on the height data and the stress distribution data, obtaining a real-time load distribution state of the corridor bridge, and generating an electric cylinder adjustment instruction and a pre-tightening force adjustment instruction according to the real-time load distribution state; Based on the electric cylinder adjustment instruction and the pre-tightening force adjustment instruction, dynamically compensating the center of gravity deviation of the corridor bridge, and generating a corridor bridge lifting motion control signal in combination with a fuzzy PID control model; Processing the corridor bridge lifting motion control signal through a pose estimation algorithm to generate corridor bridge pose estimation data, and correcting the corridor bridge lifting motion control signal based on the corridor bridge pose estimation data to achieve safe control of the corridor bridge.

2. The flight simulator bridge safety control method of claim 1, wherein, The method of dynamically compensating the center of gravity deviation of the corridor bridge based on the electric cylinder adjustment instruction and the pre-tightening force adjustment instruction, and generating the corridor bridge lifting motion control signal in combination with the fuzzy PID control model comprises the following steps: Based on the electric cylinder adjustment instruction, controlling the electric cylinder driver to adjust the displacement or thrust of the electric cylinder according to the specified displacement adjustment amount and adjustment direction; Based on the pre-tightening force adjustment instruction, applying or maintaining the corresponding pre-tightening force through the spring mechanism to obtain the adjusted pre-tightening force; Based on the adjusted electric cylinder pressure and the adjusted pre-tightening force, dynamically compensating the center of gravity deviation of the corridor bridge to obtain dynamically compensated corridor bridge state data; Based on the dynamically compensated corridor bridge state data, generating the corridor bridge lifting motion control signal through the fuzzy PID control model.

3. The flight simulator bridge safety control method of claim 2, wherein, The method of generating the corridor bridge lifting motion control signal through the fuzzy PID control model based on the dynamically compensated corridor bridge state data comprises the following steps: Inputting the dynamically compensated corridor bridge state data into the fuzzy PID control model, dynamically adjusting the control parameters through the fuzzy reasoning module in the fuzzy PID control model, wherein the control parameters include proportional parameters, integral parameters and differential parameters; According to the adjusted control parameters, generating target lifting speed control signals and target acceleration control signals through the PID control module in the fuzzy PID control model; Through the signal synthesis module in the fuzzy PID control model, performing signal synthesis processing on the target lifting speed control signals and the target acceleration control signals to generate the corridor bridge lifting motion control signal.

4. The flight simulator bridge safety control method of claim 3, wherein, The method of generating the target lifting speed control signals and the target acceleration control signals through the PID control module in the fuzzy PID control model according to the adjusted control parameters comprises the following steps: According to the adjusted control parameters, performing error calculation processing on the dynamically compensated corridor bridge state data through the PID control module to obtain height control errors and stress control errors; Based on the height control errors and the stress control errors, respectively performing proportional operation processing, integral operation processing and differential operation processing; Superimposing the proportional operation result, the integral operation result and the differential operation result to generate preliminary lifting speed control signals and preliminary acceleration control signals; The preliminary lifting speed control signal and the preliminary acceleration control signal are subjected to amplitude limiting processing to generate a target lifting speed control signal and a target acceleration control signal.

5. The flight simulator bridge safety control method of claim 1, wherein, The real-time load distribution state of the gallery bridge is obtained based on the height data and the stress distribution data, and the gallery bridge lifting motion control signal is generated based on the real-time load distribution state. The height data and the stress distribution data are subjected to time domain synchronization processing to generate a time-aligned sensor data set. The time-aligned sensor data set is subjected to load distribution calculation processing based on a preset mapping relationship between sensor data and load distribution to generate a real-time load distribution state, which includes a load size parameter, a load position parameter, and a load change trend parameter. The load position parameter is subjected to electric cylinder control strategy processing to generate an electric cylinder adjustment instruction. The load size parameter and the load change trend parameter are subjected to pre-tightening force control strategy processing to generate a pre-tightening force adjustment instruction.

6. The flight simulator bridge safety control method of claim 1, wherein, The gallery bridge posture estimation data is generated by processing the gallery bridge lifting motion control signal through a posture estimation algorithm, and the gallery bridge lifting motion control signal is corrected based on the gallery bridge posture estimation data. Real-time motion sensor data during the gallery bridge lifting process is collected. The expected posture data of the gallery bridge is calculated based on the gallery bridge lifting motion control signal using a posture estimation algorithm, and the expected posture data is used as a preliminary estimation. The gallery bridge posture estimation data is generated by data fusion processing of the preliminary estimation and the real-time motion sensor data. The control signal adjustment amount is calculated based on the inclination angle parameter and the vibration amplitude parameter in the gallery bridge posture estimation data. The gallery bridge lifting motion control signal is corrected in real time according to the control signal adjustment amount.

7. The flight simulator bridge safety control method of claim 1, wherein, The height data and the stress distribution data are obtained by signal conditioning and analog-to-digital conversion processing of the height sensing signal and the stress distribution signal, respectively. The height sensing signal is subjected to signal amplification and filtering processing to obtain a conditioned height analog signal, and the stress distribution signal is subjected to signal amplification and filtering processing to obtain a conditioned stress analog signal. The conditioned height analog signal is converted into height data by an analog-to-digital converter, and the conditioned stress analog signal is converted into stress distribution data by an analog-to-digital converter.

8. A flight simulator jetty safety control system, characterized in that, The height sensing signal and the stress distribution signal during the gallery bridge lifting process are collected by the collection module. The height data and the stress distribution data are obtained by signal conditioning and analog-to-digital conversion processing of the height sensing signal and the stress distribution signal, respectively. The real-time load distribution state of the gallery bridge is obtained based on the height data and the stress distribution data, and the gallery bridge lifting motion control signal is generated based on the real-time load distribution state. The center of gravity of the gallery bridge is dynamically compensated based on the electric cylinder adjustment instruction and the pre-tightening force adjustment instruction, and the gallery bridge lifting motion control signal is generated in combination with a fuzzy PID control model. ​ The correction module is configured to process the gallery bridge lifting motion control signal by a pose estimation algorithm to generate gallery bridge pose estimation data, and correct the gallery bridge lifting motion control signal based on the gallery bridge pose estimation data to achieve safe control of the gallery bridge.

9. An electronic device, comprising: The method comprises the steps of: a memory for storing a computer program; a processor for executing the computer program to implement the steps of the method for safe control of a gallery bridge of a flight simulator according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer program is stored in the computer readable storage medium and is executable by the processor to implement the method for safe control of a gallery bridge of a flight simulator according to any one of claims 1 to 7.

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