Intelligent landing leg automatic balance control system and control method

Abstract

The invention discloses an intelligent landing leg automatic balance control system and control method, and belongs to the technical field of landing leg control. The method is used for solving or improving the technical problems that the anti-shake capability is insufficient under the dynamic load and the sensor error affects the balance precision in the existing scheme. Through the technical advantage of independent operation of each module, the high-precision data of the sensing module, the intelligent strategy of the decision control module, the precise action of the execution module, the risk prevention and control of the security module, and the deep cooperation of the data-strategy-action closed loop, control-protection double tracks and scene adaptation optimization among the modules, the safety of the system is improved. The full-process efficient linkage of data acquisition, dynamic control and safety guarantee is realized, and the dynamic load adaptive capacity, the balance control precision and the system reliability are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of outrigger control, in particular to an intelligent outrigger automatic balance control system and control method. BACKGROUND

[0002] Outrigger automatic balance control is one of the core technologies for stable operation of engineering machinery, robots and other equipment. In the prior art, outrigger balance control mainly relies on traditional PID control or sliding mode control, but has the following defects: weak dynamic load adaptability: the traditional system mostly uses fixed parameter PID control, when the vehicle is impacted by start-stop, wind load or load mutation, the outrigger is prone to high-frequency jitter, causing the vehicle body inclination angle to fluctuate, and there is a risk of overturning; sensor error has a great influence: the outrigger inclination angle and pressure sensor are easily disturbed by temperature, vibration and other environmental factors, and the measurement deviation caused by zero drift and sensitivity error affects the balance control precision; single control strategy: the existing system is mostly in the "one-time leveling" mode, and lacks a multi-stage collaborative mechanism from rapid coarse adjustment, dynamic compensation to steady-state optimization, and cannot balance response speed and control accuracy. SUMMARY

[0003] The purpose of the present application is to provide an intelligent outrigger automatic balance control system and control method, which can dynamically adapt to load changes, compensate for sensor errors and have multi-stage collaborative control capability, to at least solve or improve one of the technical problems that the existing scheme has insufficient anti-shake ability under dynamic load and sensor error affects balance accuracy.

[0004] The purpose of the present application can be achieved by the following technical solutions: In a first aspect of the present application, an intelligent outrigger automatic balance control system is provided, comprising: a perception module, a decision control module, an execution module and a safety protection module; The perception module is used to collect the attitude, load, displacement and dynamic disturbance data of the outrigger and the vehicle body; The decision control module is used to process and analyze the collected data through a multi-stage collaborative strategy, generate control instructions, and adjust the control parameters using an adaptive PID algorithm combined with a sensor error compensation model; The execution module is used to drive the outrigger action according to the control instructions to achieve balance and anti-shake.

[0005] Preferably, it further comprises a safety protection module for monitoring system abnormalities and triggering a protection mechanism.

[0006] Preferably, the perception module comprises: a multi-axis inclination sensor for measuring the overall inclination angle of the vehicle body and the inclination angle of a single outrigger relative to the horizontal plane ; A strain pressure sensor is used to collect the real-time support force of each outrigger ; Laser displacement sensor for measuring outrigger extension length ; Triaxial acceleration sensor for capturing dynamic disturbance ; The sensor communicates with the decision control module through the CAN FD bus.

[0007] Preferably, the perception module further comprises a sensor error compensation unit, which performs a composite compensation method of Kalman filtering and neural network correction: Kalman filtering is performed on the original inclination and pressure data to suppress high-frequency noise. A BP neural network is trained based on historical calibration data, with sensor temperature, usage time and environmental humidity as inputs, and compensation values for sensor zero drift and sensitivity error as outputs. The compensation values are used for correction.

[0008] Preferably, the multi-stage collaborative strategy of the decision control module includes an initial leveling stage, a dynamic compensation stage and a steady-state optimization stage. Initial leveling stage: taking the body inclination as the main control target, using a PD control algorithm to quickly adjust the outrigger extension amount, so that it converges to within ±0.3° within ≤5s; Dynamic compensation stage: when a dynamic disturbance greater than a standard threshold is detected, switch to adaptive PID control, adjust PID parameters online according to real-time load changes, and suppress body shaking caused by sudden load changes. Steady-state optimization stage: when ≤±0.1° and real-time load change ΔFi≤5%, use the LQR optimal control algorithm to fine-tune the outrigger support force.

[0009] Preferably, the adaptive PID control involves the following parameter adjustment rules when implemented: Proportional coefficient , where is the initial proportional coefficient, and α is the load sensitivity coefficient, 0.1≤α≤0.3. Integral coefficient , where is the initial integral coefficient, and β is the inclination rate sensitivity coefficient, 0.5≤β≤1.0. Derivative coefficient ; where is the sign function of the inclination rate.

[0010] Preferably, the PID control amount is calculated as: ; where ; ; The above rules enable the PID parameters to adapt to dynamic loads and disturbances in real time.

[0011] Preferably, the execution module includes an electro-hydraulic proportional servo unit, an actuator closed-loop control logic unit, and a dynamic anti-jitter unit: The electro-hydraulic proportional servo unit, consisting of a proportional directional valve, a hydraulic cylinder, and a pressure compensator, is used to drive the extension and retraction of the outriggers. The actuator closed-loop control logic unit is used for feedback from a laser displacement sensor. The proportional valve opening is adjusted by the PID control output from the decision control module, which serves as the input. Dynamic image stabilization unit, used when detecting... When the pressure is >1.0g, switch to damping compensation mode. The proportional valve quickly opens and closes to generate dynamic back pressure in the cylinder chamber, suppressing high-frequency vibration of the outrigger.

[0012] Preferably, the safety protection module includes a hardware protection unit, a software protection unit, and a fault diagnosis unit; The hardware protection unit includes: an electromagnetic limit switch on the outrigger telescopic sleeve and redundant relief valves in the hydraulic system to prevent damage from overtravel or overpressure. The software protection unit is used to preset safety thresholds. If the control command exceeds the limit, it will automatically cut off and trigger an audible and visual alarm. The fault diagnosis unit is used to identify faults through sensor data cross-verification and actuator status monitoring, and immediately output a full valve closure command to stop all actions.

[0013] Compared to existing solutions, the beneficial effects achieved by this invention are: This invention leverages the technical advantages of independent operation of each module, including high-precision data from the sensing module, intelligent strategies from the decision-making and control module, precise actions from the execution module, risk prevention and control from the security module, and deep collaboration between modules through data-strategy-action closed loop, dual-track control-protection, and scenario adaptation optimization. This achieves efficient linkage across the entire process from data acquisition to dynamic control to security assurance, effectively improving dynamic load adaptability, balanced control accuracy, and system reliability. Attached Figure Description

[0014] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a structural block diagram of an intelligent outrigger automatic balance control system according to the present invention; Figure 2 This is a flowchart illustrating an intelligent outrigger automatic balancing control method according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1 like Figure 1 As shown, the present invention is an intelligent outrigger automatic balance control system, including a sensing module, a decision control module, an execution module and a safety protection module; The sensing module is used to collect data on the attitude, load, displacement, and dynamic disturbances of the outriggers and vehicle body, and compensates for sensor errors using a composite method of Kalman filtering and neural network correction; specifically, it includes: Multi-axis tilt sensor for measuring overall vehicle body tilt angle and the angle of inclination of a single leg relative to the horizontal plane i represents different outriggers, with a measurement accuracy of ±0.05° and a response frequency of ≥200Hz; it can be placed at the vehicle's center of gravity and at the base of each outrigger. Strain gauge pressure sensors are used to collect real-time support forces of each outrigger. Measuring range 0~80MPa, accuracy ±0.3%FS; can be integrated into the piston rod end of the outrigger hydraulic cylinder; Laser displacement sensor used to measure the outrigger extension length Resolution 0.05mm, measuring range 0~5m; can be installed on the outer surface of the outrigger telescopic sleeve; A three-axis accelerometer is used to capture dynamic disturbances from vehicle start-stop and wind loads. Measuring range ±10g, frequency response ≥1kHz; can be positioned at the connection between the turntable and the frame. The sensor communicates with the decision control module via the CAN FD bus, with a data sampling period of ≤5ms.

[0017] In this embodiment of the invention, a combination of multi-axis tilt, pressure, laser displacement, and acceleration sensors achieves full-dimensional coverage of the outrigger status and external disturbances, avoiding the blind spots of a single sensor in a single dimension. For example, relying solely on a tilt sensor cannot identify uneven load distribution, while the introduction of a pressure sensor can directly monitor the force differences on each outrigger, providing crucial information for load balancing control.

[0018] The sensing module also includes a sensor error compensation unit, used to implement a composite compensation method of Kalman filter and neural network correction; the specific steps include: Kalman filtering is performed on the original inclination and pressure data to suppress high-frequency noise, such as signal fluctuations caused by hydraulic system vibration; The original inclination data is the unfiltered body inclination angle measurement value directly output by the three-axis acceleration sensor, which is used to reflect the real-time attitude of the vehicle body relative to the horizontal plane. The pressure data is the unfiltered hydraulic pressure measurement value directly output by the strain pressure sensor, which is used to reflect the hydraulic oil pressure in the outrigger cylinder and indirectly represent the load borne by the outrigger, with the pressure being proportional to the load. A BP neural network is trained based on historical calibration data, with the input being sensor temperature, usage time, and environmental humidity, and the output being compensation values for sensor zero drift and sensitivity error. The compensation values are used for correction, so that the inclination error after correction is ≤±0.02° and the pressure error is ≤±0.2%FS. It should be noted that training the BP neural network based on historical calibration data is a conventional technical means, and the specific implementation steps are not described here. In addition, a federated learning framework can also be introduced based on Kalman filtering and BP neural network, which specifically includes system architecture building, federated learning initialization, distributed model training, global model aggregation, model iteration optimization, local compensation deployment, and dynamic maintenance and update. When building the system architecture, a central server and multiple client devices are set up, and a communication protocol is defined to realize encrypted parameter transmission. The central server is a parameter aggregation node, and the client device is a sensor terminal. The communication protocol can be MQTT or HTTPs. The client stores sensor data such as temperature, humidity, usage time, and error labels locally, and the original data is not transmitted across devices. When initializing the federated learning, the server initializes the BP neural network parameters such as weights and biases and distributes them to all clients. The client divides the training set / verification set in a ratio of 8:2 and uses Z-score standardization to process the input features. When performing distributed model training, the client uses Kalman filtering to preprocess real-time inclination and pressure data to generate noise-suppressed intermediate results. The BP neural network is trained based on local data, such as a fixed learning rate of 0.01 and 50 iterations, to calculate the parameter gradient. The client homomorphically encrypts the gradient parameters and uploads them to the central server. When performing global model aggregation, the server aggregates the gradients weighted by the amount of data from the clients, such as weight = local sample size / total sample size. The Shapley value is used to detect outlier gradients and eliminate malicious client parameters. When the model is iteratively optimized, the server updates the aggregated parameters to a new global model and issues it to the client; Stop when the validation set compensation error is less than 0.5% or 100 rounds of global iterations are reached. When the local compensation deployment is performed, the client saves the final global model parameters for real-time inference, and the Kalman filter processes the original sensor data. The BP neural network inputs temperature, humidity and time length, and outputs zero drift and sensitivity compensation values. The fusion filter result and the compensation value are fused to output the corrected measurement data. When the dynamic maintenance and update is performed, when the client detects that the compensation error exceeds the threshold, for example, exceeds 2%, it actively requests to participate in a new round of federated training; the server regularly eliminates long-term offline devices and includes new network devices to maintain the model generalization.

[0019] It should be noted that the sensor data of multiple devices is used for distributed training to optimize the BP neural network model, so that the model can better adapt to the sensor error characteristics under different environments and use conditions, and the accuracy of the compensation value is improved. In the embodiment of the application, the composite compensation method of implementing Kalman filtering and neural network correction is independent of the subsequent control process, which significantly improves the quality of the original data. For example, in a low temperature environment of-20℃, the error of a traditional tilt sensor caused by temperature drift reaches ±0.15°, while the error of the module after correction is only ±0.02°, which provides more reliable input for the decision control module and avoids misjudgment caused by distorted data, such as misjudging normal vibration as vehicle body tilt.

[0020] The decision control module is used for data processing and analysis of the collected data through a multi-stage cooperative strategy, generating control instructions, and adjusting control parameters by using an adaptive PID algorithm combined with a sensor error compensation model. The multi-stage cooperative strategy includes an initial leveling stage, a dynamic compensation stage and a steady-state optimization stage. The initial leveling stage takes the vehicle body tilt as the main control target, and uses a PD control algorithm to quickly adjust the leg extension amount, so that it converges to the range of ±0.3° within ≤5s; the specific steps include: Step 1: Perform system state check and target parameter setting. When the system state is checked, no fault is confirmed, for example, the sensor data mutual check is normal, the actuator has no card jam, the perception module completes sensor initialization, for example, the tilt sensor is zeroed and calibrated, the pressure sensor is zero pressure calibrated, the communication module confirms that the CAN FD bus connection is stable, for example, the ACK response rate is ≥99%; When the target parameter setting is performed, the decision control module presets the control target of the initial leveling stage: the body inclination angle =±0.3°, the maximum allowable leveling time Tmax=5s, the outrigger extension speed upper limit vmax=40mm / s, and the corresponding proportional valve maximum flow rate is 50L / min; Step 2: Real-time data acquisition and error calculation are performed; Among them, the multi-axis inclination sensor collects the real-time body inclination angle at a frequency of 200Hz, for example, the initial state =1.2°, the body is inclined forward; the laser displacement sensor collects the real-time length of each outrigger at a frequency of 200Hz, for example, the left front outrigger =1.2m, and the right rear outrigger =0.8m; The inclination error is calculated; for example, the initial error e(0)=1.2°-0°=1.2°, because 0° is taken as the ideal horizontal state; the error change rate is also calculated; the error change rate is obtained by dividing the inclination angle difference between two adjacent samplings by the time interval; Step 3: PD control amount calculation and target length conversion are performed; Among them, the decision control module calls the PD control algorithm formula: ; wherein Kp is the proportional coefficient, used to amplify the influence of the current error, and Kd is the differential coefficient, used to suppress the error change rate; The decision control module converts the control output u(t) into the target extension amount of each outrigger according to the body mechanics model; for example, when the body is inclined forward, the front outrigger needs to be extended and the rear outrigger needs to be shortened to balance the inclination angle, and the conversion formula is: ; Among them, =0.01, in m / °, indicating that the outrigger needs to be extended by 0.01m when the body inclination angle changes by 1°, is the horizontal distance from the outrigger to the body center of gravity; Step 4: The execution module drives the outrigger extension; When the decision control module converts the target extension amount into the control instruction of the electro-hydraulic servo system; The outrigger target length is calculated; According to the difference between the target length and the current length, the current signal required by the proportional valve is calculated, and the formula is: ; wherein v max is the maximum extension speed; T 采样To control the sampling period; After receiving the current signal, the execution module generates a control command and controls the proportional directional valve to open with a response time of ≤15ms. The hydraulic cylinder extends and retracts at a speed of 40mm / s, driving the left front outrigger to extend and the right rear outrigger to retract. Among them, there is a limit position protection, and the right rear outrigger is locked after retracting to 0.5m. Step 5: Perform real-time feedback verification and overshoot suppression; During real-time feedback verification, the perception module updates the vehicle's real-time tilt angle at a 5ms cycle. and the real-time length of each leg The decision control module recalculates the error e(t) and control quantity u(t) every 10ms and dynamically adjusts the proportional valve current; it can be implemented according to preset adjustment rules. When performing overshoot suppression, if the real-time body roll angle near Overshoot occurs, differential term It generates a reverse control quantity to counteract the excessive adjustment of the proportional term, so that the tilt angle converges smoothly; Step 6: Determine if the goal has been achieved and dynamically switch to the dynamic compensation phase; Among them, the vehicle body tilt angle of three consecutive samples ≤±0.3°, and the outrigger extension error ≤±0.3mm, for example, the laser displacement sensor feedback L1(5)=1.48m, relative to the target =1.48m consistent, the decision control module determines that the initial leveling stage is completed and outputs the initial leveling completion signal; Based on the initial leveling completion signal, the system enters the dynamic compensation stage and switches to adaptive PID control according to real-time disturbance data. In this embodiment of the invention, the PD control algorithm and the coordinated operation of multiple modules enable rapid and precise adjustment of the outrigger extension and retraction, laying a stable foundation for the subsequent dynamic compensation stage.

[0021] Dynamic compensation phase: When a dynamic disturbance is detected to be greater than the standard threshold, for example... >0.5g, switch to adaptive PID control, adjust according to real-time load changes. Adjust PID parameters online, such as the proportional coefficient K. p Increase by 1.5 times, integral coefficient K i Reduced to 1 / 3 of the original value to suppress vehicle vibration caused by sudden load changes; Steady-state optimization phase: When the temperature is ≤±0.1° and the real-time load change ΔFi≤5%, the LQR optimal control algorithm is used to fine-tune the support force of the outriggers, so that the load balance of each outrigger is increased to more than 95%. In the implementation of adaptive PID control, the parameter adjustment rules are as follows: proportionality coefficient ,in, α is the initial proportional gain, and α is the load sensitivity coefficient, where 0.1 ≤ α ≤ 0.3; Integral coefficient ,in, β is the initial integral coefficient, and β is the sensitivity coefficient for the rate of change of tilt angle, 0.5≤β≤1.0; Differential coefficients ;in, This is a sign function for the rate of change of the inclination angle, with +1 for forward inclination and -1 for backward inclination; PID control quantity calculation: ; in, ; ; The above rules enable the PID parameters to adapt to dynamic loads and disturbances in real time.

[0022] In this embodiment of the invention, the three-stage control strategy resolves the contradiction between speed and accuracy in traditional systems by decoupling phased objectives; online parameter adjustment rules enable the decision control module to independently adapt to different operating conditions. Furthermore, the collaboration between the decision control module and the execution module achieves refined adjustment of load balancing.

[0023] The execution module is used to drive the outriggers to move according to control commands, achieving balance and anti-shake; specifically: The execution module includes an electro-hydraulic proportional servo unit, an actuator closed-loop control logic unit, and a dynamic anti-jitter unit; Among them, the electro-hydraulic proportional servo unit consists of a proportional directional valve, a hydraulic cylinder and a pressure compensator, with a flow control accuracy of ±0.5L / min, and is used to drive the extension and retraction of the outriggers; The actuator closed-loop control logic unit is used for feedback from a laser displacement sensor. As input, the PID control output from the decision control module adjusts the opening of the proportional valve to achieve a leg length tracking error of ≤±0.3mm; In this embodiment of the invention, the combination of a proportional directional valve and a high-precision hydraulic cylinder ensures rapid and accurate tracking of decision commands; high-frequency damping compensation can be independent of the conventional control process and quickly intervene when strong disturbances are detected, reducing the risk of vehicle rollover.

[0024] Dynamic image stabilization unit, used when detecting... When the pressure is >1.0g, switch to damping compensation mode. The proportional valve is quickly opened and closed to generate dynamic back pressure in the cylinder chamber to suppress high-frequency vibration of the outrigger, such as amplitude ≤±1mm.

[0025] A safety protection module is configured to monitor system abnormalities and trigger a protection mechanism; in particular, The safety protection module comprises a hardware protection unit, a software protection unit and a fault diagnosis unit; The hardware protection unit: electromagnetic limit switches are arranged on the leg telescopic sleeve, and a redundant overflow valve is arranged on the hydraulic system, for example, the opening pressure is less than or equal to 40 MPa, to prevent damage caused by overstroke or overpressure; The software protection unit is configured to preset safety thresholds, for example, the maximum leg extension length = 4.5 m, the maximum support force = 70 MPa, if the control instruction exceeds the threshold, the system will automatically cut off and trigger an audible and light alarm; The fault diagnosis unit is configured to check sensor data and monitor the state of the actuator, for example, if the difference between the two inclination sensors is greater than 0.1°, the system is determined to be invalid; if the valve current is greater than 18 mA but the cylinder has no displacement, the system is determined to be stuck, and after identifying the fault, the system immediately outputs a full valve closing instruction to stop all actions.

[0026] It should be noted that the independent arrangement of the electromagnetic limit switch and the redundant overflow valve avoids mechanical damage caused by abnormal control instructions or actuator failures; In addition, the independent operation of the threshold cutting and the fault mutual check can quickly identify and respond to faults in the early stage, avoiding false actions of the execution module.

[0027] The parallel operation of the safety protection module and the perception / decision / execution module realizes the control-protection dual-track mechanism, and improves the active supervision and control effect of the risk.

[0028] In the embodiments of the present application, through the technical advantages of independent operation of each module, the high-precision data of the perception module, the intelligent strategy of the decision control module, the precise action of the execution module, the risk prevention and control of the safety module, and the deep cooperation of data-strategy-action closed loop, control-protection dual-track and scene adaptation optimization, the whole process efficient linkage from data acquisition-dynamic control-safety guarantee is realized, and the dynamic load adaptation ability, balance control precision and system reliability are effectively improved.

[0029] In several embodiments provided by the present application, it should be understood that the disclosed system can be implemented in other manners. For example, the above-described embodiments of the application are merely schematic; for example, the division of the modules is merely a logical function division, and there can be another division manner in actual implementation.

[0030] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0031] In addition, each functional module in various embodiments of the present application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The integrated module can be realized in the form of hardware or in the form of hardware plus software functional module.

[0032] Embodiment 2 On the basis of the above-mentioned embodiment 1, the intelligent leg automatic balancing control method of this embodiment 2 further comprises the specific steps as shown in the figure: Figure 2 S1, collecting the attitude, load, displacement and dynamic disturbance data of the legs and the vehicle body; S2, processing and analyzing the collected data through a multi-stage cooperative strategy to generate control instructions, and adjusting the control parameters by using a self-adaptive PID algorithm combined with a sensor error compensation model; S3, driving the leg action according to the control instructions. Specifically, in the step of collecting the attitude, load, displacement and dynamic disturbance data of the legs and the vehicle body,

[0033] the overall inclination of the vehicle body is measured according to a multi-axis inclination sensor and the inclination of a single leg relative to the horizontal plane ; the real-time support force of each leg is collected by a strain pressure sensor ; the leg extension length is measured by a laser displacement sensor ; dynamic disturbance is captured by a three-axis acceleration sensor ; the sensor communicates with the decision control module through a CAN FD bus and sends the collected data to the decision control module.

[0034] Preferably, it further comprises the step of monitoring system abnormalities and triggering a protection mechanism.

[0035] The implementation of the method depends on the system of embodiment 1, and the specific technical content involved has been introduced in embodiment 1 and will not be repeated here.

[0036] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the essential characteristics of the present application.

[0037] ​It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit it. Although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. An intelligent outrigger automatic balancing control system, characterized in that, Comprise: A perception module for collecting attitude, load, displacement and dynamic disturbance data of the outrigger and the vehicle body; A decision control module for processing and analyzing the collected data through a multi-stage collaborative strategy, generating control instructions, and adjusting control parameters using an adaptive PID algorithm combined with a sensor error compensation model; An execution module for driving the outrigger to act according to the control instructions.

2. The intelligent outrigger automatic balance control system according to claim 1, wherein, The perception module comprises: Multi-axis tilt sensor for measuring the overall tilt of a vehicle body and the tilt of a single leg relative to horizontal ; Strain pressure sensor for collecting real-time support force of each leg ; Laser displacement sensor for measuring outrigger extension length ; Tri-axial acceleration sensor for capturing dynamic disturbances ; Each sensor communicates with the decision control module through a CAN FD bus.

3. The intelligent outrigger automatic balance control system according to claim 2, wherein, The perception module further comprises a sensor error compensation unit for performing a composite compensation method of Kalman filtering combined with neural network correction, comprising: Kalman filtering of the original inclination and pressure data to suppress high-frequency noise; Training a BP neural network based on historical calibration data, with sensor temperature, usage time and environmental humidity as input, and sensor zero drift and sensitivity error compensation values as output, and correcting using the compensation values.

4. The intelligent leg automatic balancing control system of claim 3, wherein, The multi-stage collaborative strategy of the decision control module includes an initial leveling stage, a dynamic compensation stage and a steady-state optimization stage; Initial leveling stage: based on vehicle body tilt angle With the primary control objective, a PD control algorithm is employed to rapidly adjust the outrigger extension and retraction. It converges to within ±0.3° within ≤5s; Dynamic compensation stage: when a dynamic disturbance greater than a standard threshold is detected, switch to adaptive PID control, adjust PID parameters online according to real-time load changes to suppress vehicle body shaking caused by sudden load changes; Steady-state optimization phase: in When the steady-state error is less than or equal to ±0.1° and the real-time load change ΔFi is less than or equal to 5%, the LQR optimal control algorithm is used to fine-tune the support force of the support leg.

5. The intelligent outrigger automatic balance control system according to claim 4, wherein, The parameter adjustment rules involved in the adaptive PID control when implemented are: proportionality factor wherein, is an initial proportionality factor, a is a load sensitivity factor, 0.1 < a < 0.3; integral coefficient wherein is an initial integral coefficient, β is a rate of change of inclination sensitivity coefficient, 0.5≤β≤1.0; derivative coefficient ; wherein is a sign function of the rate of change of the tilt angle.

6. The intelligent leg automatic balancing control system of claim 5, wherein, PID control amount calculation: ; wherein ; ; Through the above rules, the PID parameters realize real-time self-adaptation to dynamic load and disturbance.

7. The intelligent outrigger automatic balance control system according to claim 6, wherein, The execution module includes an electro-hydraulic proportional servo unit, an actuator closed-loop control logic unit and a dynamic anti-shake unit: The electro-hydraulic proportional servo unit, composed of a proportional directional valve, a hydraulic cylinder and a pressure compensator, is used to drive the outrigger to extend and retract; An actuator closed-loop control logic unit is used to adjust the proportional valve opening degree by the PID control amount output by the decision control module For input, the PID control amount output by the decision control module adjusts the proportional valve opening degree; A dynamic anti-shake unit is configured to switch to a damping compensation mode when a detection result is When the value is greater than 1.0 g, the damping compensation mode is switched to, and a proportional valve is rapidly opened and closed to generate a dynamic back pressure in a cylinder cavity, thereby suppressing high-frequency shaking of the outrigger.

8. The intelligent outrigger automatic balance control system according to claim 7, wherein, The system further comprises a safety protection module for monitoring system abnormalities and triggering protection mechanisms; wherein, The safety protection module includes a hardware protection unit, a software protection unit and a fault diagnosis unit; The hardware protection unit includes: electromagnetic limit switches arranged on the outrigger extension sleeve, and a redundant overflow valve for the hydraulic system; The software protection unit is used to preset safety thresholds, and if the control instructions exceed the thresholds, it will automatically cut off and trigger an audible and visual alarm; The fault diagnosis unit is used to identify faults by mutual checking of sensor data and monitoring of actuator status, and immediately outputs a full valve closing instruction to stop all actions.

9. An intelligent leg automatic balancing control method, characterized in that, Comprise: Collecting attitude, load, displacement and dynamic disturbance data of the outrigger and the vehicle body; Processing and analyzing the collected data through a multi-stage collaborative strategy, generating control instructions, and adjusting control parameters using an adaptive PID algorithm combined with a sensor error compensation model; Driving the outrigger to act according to the control instructions.

10. The intelligent leg automatic balancing control method of claim 9, wherein, In the step of collecting attitude, load, displacement and dynamic disturbance data of the outrigger and the vehicle body, measuring the overall inclination of the vehicle body according to a multi-axis inclination sensor and the inclination of the single leg with respect to the horizontal ; Strain gauge pressure sensors collect real-time support forces of each leg ; Laser displacement sensor measures outrigger extension length ; Tri-axial acceleration sensor captures dynamic disturbances .

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