Intelligent railway vehicle hinge device pressure closed-loop control algorithm

Abstract

According to the pressure closed-loop control algorithm of the intelligent railway vehicle hinge device, the vehicle speed, the angle and the angular speed serve as input signals, the pressure of a damper hydraulic cylinder serves as a feedback signal, meanwhile, the corresponding relation between the input signals and the feedback signal is established through a fuzzy rule, PID closed-loop control is carried out, and the control precision is improved. The damping force is changed in a self-adaptive mode so as to restrain the displacement amount of yawing motion between compartments of the intelligent railway vehicle. Complex modeling is avoided, robustness of a control system is enhanced, closed-loop control is carried out by monitoring actuator oil cylinder pressure based on the fuzzy rule, and when the pressure does not meet a target value, input current of a proportional valve is adjusted through a PID closed-loop algorithm to carry out self-adaptive control on the oil cylinder pressure. The purpose of adaptively changing the damping force to restrain the large yaw motion between the carriages of the intelligent railway vehicle is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent rail articulated vehicles, and in particular to a pressure closed-loop control algorithm for an articulated device of an intelligent rail vehicle. Background Art

[0002] As a new transportation product, the intelligent tram combines the advantages of modern trams and buses. This new mode of transportation has revolutionized traditional perceptions of urban transportation, offering new options and experiences to address travel difficulties in large and medium-sized cities. This new mode of transportation utilizes a three-carriage structure with two motors and one trailer, interconnected by articulated joints. These joints, with symmetrically mounted hydraulic dampers on both sides, ensure the intelligent tram's straight-line stability and prevent the front and rear cars from collapsing during sharp turns. This is crucial for ensuring passenger safety and comfort.

[0003] After searching, CN108639093A discloses a vehicle body articulation device and rotation angle control method for a self-guided virtual rail train, which can limit the longitudinal, lateral and vertical displacement of the vehicle body, allow three-way rotation between the two vehicle bodies, and at the same time play the role of transferring load. After the distance between vehicles is reduced and the length of the through-passage is shortened, the vehicle space utilization rate is improved while ensuring that the vehicle has a smaller turning radius. When the vehicle moves through a curve, while monitoring the rotation angle, the articulation device is autonomously controlled, so that the turning angle can be adjusted. This solution mainly focuses on the description of the composition of the articulation control system, and does not involve specific algorithms.

[0004] CN220764508U discloses an articulated device with a front and rear frame structure hinged via a turntable bearing. This shortens the longitudinal dimensions of the front and rear frames and reduces the diameter of the turntable bearing. This increases passenger space and improves carrying capacity while maintaining the same vehicle length. Three independent angle sensors are used. The internal angle sensor transmits an angle signal to the airbag control unit (ACU) within the articulated device. The ACU then distributes and controls the damping of the hydraulic cylinders, ensuring smoother rotation. Simultaneously, two other vehicle-wide angle sensors measure the angle between the front and rear carriages connected to the articulated device in real time and transmit this signal to the vehicle's steering system, where it participates in steering calculations for more precise steering angle control, ensuring safe and smooth operation. This solution collects the angle signal and feeds it to the control unit, which then distributes the damping force, implementing open-loop control.

[0005] The controllers in the existing technology have few input signal types, simple control strategies, weak adaptability to complex and diverse working conditions, adopt open-loop control, lack feedback on the control effect, and are difficult to guarantee the control effect.

[0006] In summary, the invention of a pressure closed-loop control algorithm for intelligent rail trams has great economic significance and practical value. Summary of the Invention

[0007] The purpose of the present invention is to address the deficiencies of the existing technology and design a pressure closed-loop control algorithm for the articulated device of an intelligent rail vehicle. A fuzzy method is used to construct the correspondence rules between the actuator cylinder pressure and the vehicle speed, the intelligent rail vehicle turning angle and angular velocity, thereby avoiding complicated modeling and enhancing the robustness of the control system. At the same time, based on this fuzzy rule, the actuator cylinder pressure is monitored for closed-loop control. When the pressure does not meet the target value, the proportional valve input current is adjusted through the PID closed-loop algorithm to adaptively control the cylinder pressure, thereby achieving the purpose of adaptively changing the damping force to suppress large yaw motions between the carriages of the intelligent rail vehicle.

[0008] In order to achieve the above objectives, the specific technical solutions of the present invention are as follows: Disclosed is a pressure closed-loop control algorithm for an articulated joint device of an intelligent rail vehicle. The algorithm uses vehicle speed, angle, and angular velocity as input signals, and the damper cylinder pressure as feedback signal. Fuzzy rules are used to establish a corresponding relationship between the input and feedback signals, and PID closed-loop control is performed to adaptively change the damping force to suppress the displacement of the yaw motion between the carriages of the intelligent rail vehicle.

[0009] The pressure closed-loop control algorithm for an intelligent rail vehicle articulated device disclosed in the present invention addresses the problems of a small number of controller input signal types, a simple control strategy, and weak adaptability to complex and diverse working conditions. The controller input signal is increased from a vehicle speed signal to a vehicle speed signal, a rotation angle signal, and an angular velocity signal, and fuzzy rules are used to establish a corresponding relationship between the input signal and the damper cylinder pressure. This algorithm fully considers working conditions with different vehicle speeds, different rotation angle signals, and different angular velocity signals, while avoiding complicated modeling and calibration, enriching the control strategy and improving the control system's adaptability to diverse working conditions.

[0010] In order to solve the problem of open-loop control, lack of feedback on the control effect, and difficulty in ensuring the control effect; the damper cylinder pressure is used as the feedback control quantity, and the PID pressure closed-loop algorithm is adopted for control; the damper cylinder pressure is directly monitored to ensure the accurate control of the damping force and improve the robustness of the control system.

[0011] Furthermore, the fuzzy algorithm of the fuzzy rules is stored in the fuzzy controller. When the input variables of the fuzzy controller are the deviation between the actual data and the set data and the deviation change rate, the two input variables are fuzzified and divided into multiple fuzzy subsets. Fuzzy control rules are formulated based on actual control experience to obtain the actual control quantity for adjustment in actual use. In the PID algorithm, the control quantity is obtained through the calculation of the three links of proportional (P), integral (I) and differential (D). The proportional link quickly adjusts the control output according to the size of the deviation; the integral link is used to eliminate the steady-state error of the system; the differential link can predict the deviation change trend and adjust the control output in advance so that the system can quickly and stably reach the set data.

[0012] Further, the following steps are included: S1. Collect the corresponding damper cylinder pressure data when the target damping force is achieved at different vehicle speeds, angles, and angular velocities of the articulated device, establish fuzzy rules, and fuzzify the input variables; S2. While the vehicle is running, the articulation controller obtains the current vehicle speed and articulation angle signals, calculates the articulation angular velocity signal, and determines the current damper cylinder target pressure based on fuzzy rules; S3. The articulated device controller collects the current damper cylinder pressure and uses the PID algorithm to adjust the proportional valve opening by adjusting the proportional valve input voltage until the cylinder pressure reaches the target pressure.

[0013] Furthermore, step S1 specifically includes: S11. Using the articulation angle sensor and pressure sensor to collect a certain amount of damper cylinder pressure data corresponding to different vehicle speeds, angles, and angular velocities, the corresponding damper cylinder pressure data collected when the target damping force is achieved at different vehicle speeds, angles, and angular velocities is used to iterate the fuzzy rule control table for each input variable and the damper cylinder pressure; S12. Setting several fuzzy subset domains for input variables such as vehicle speed, angle, and angular velocity signal; S13. Scaling the input variable value from the basic domain to the fuzzy subset domain; S14. Obtain fuzzy language values ​​through membership functions or fuzzy variable assignment tables.

[0014] Furthermore, the fuzzy rules segment the vehicle speed, angle, and angular velocity into fuzzy subsets.

[0015] Furthermore, step S2 specifically includes: S21. Obtain the fuzzy language value of the output variable through the fuzzy rule table based on the fuzzy language value of each input variable; S22. Obtain the value of the output variable in the fuzzy subset domain through the fuzzy variable assignment table; S23. The output variable language value is transformed into the target cylinder pressure through the maximum membership method and the center of gravity method transformation principle.

[0016] Furthermore, step S3 is specifically as follows: after obtaining the target pressure, the controller receives the damper cylinder pressure signal, compares the obtained damper cylinder pressure signal with the target pressure signal, and continuously adjusts the damper cylinder input voltage through the PID algorithm until the damper cylinder pressure reaches the target pressure, thereby achieving the goal of damping force control of the intelligent rail vehicle articulation device.

[0017] The pressure closed-loop control algorithm of the intelligent rail vehicle articulated device of the present invention is used to directly monitor the damper cylinder pressure and perform closed-loop control, thereby ensuring accurate control of the damping force.

[0018] Compared with the prior art, the pressure closed-loop control algorithm of the intelligent rail vehicle articulated device of the present invention has the following advantages: The pressure closed-loop control algorithm for the intelligent rail vehicle articulation device of the present invention adopts a fuzzy method to construct the correspondence rules between the actuator cylinder pressure and the vehicle speed, the intelligent rail vehicle turning angle and angular velocity, fully considering the working conditions of different vehicle speeds, different turning angle signals and angular velocity signals, while avoiding complicated modeling and calibration, enriching the control strategy, improving the control system's adaptability to various working conditions, and enhancing the robustness of the control system; at the same time, directly monitoring the damper cylinder pressure and performing closed-loop control to ensure accurate control of the damping force. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a schematic block diagram of the overall system of the pressure closed-loop control algorithm for the intelligent rail vehicle articulation device of the present invention.

[0020] Figure 2 This is a control system block diagram of the pressure closed-loop control algorithm for the intelligent rail vehicle articulation device described in the present invention.

[0021] Figure 3 This is a flow chart of the pressure closed-loop control algorithm for the intelligent rail vehicle articulation device described in the present invention. DETAILED DESCRIPTION

[0022] Below with reference to the embodiments and appended Figure 1-Figure 3 The present invention is further described. Example 1

[0023] like Figure 1-Figure 3 As shown in FIG, the pressure closed-loop control algorithm of the intelligent rail vehicle articulation device of this embodiment uses vehicle speed, angle, and angular velocity as input signals, and the pressure of the damper cylinder as feedback signal. At the same time, a corresponding relationship between the input signal and the feedback signal is established through fuzzy rules to perform PID closed-loop control, thereby adaptively changing the damping force to suppress the displacement of the yawing motion between the carriages of the intelligent rail vehicle.

[0024] Specifically, the fuzzy algorithm of the fuzzy rules is stored in the fuzzy controller (articulated device controller). When the input variables of the fuzzy controller are the deviation between the actual data and the set data and the deviation change rate, the two input variables are fuzzified and divided into multiple fuzzy subsets. Fuzzy control rules are formulated based on actual control experience to obtain the actual control quantity for adjustment in actual use. In the PID algorithm, the control quantity is obtained through the calculation of the three links of proportion (P), integration (I) and differentiation (D). The proportional link quickly adjusts the control output according to the size of the deviation; the integral link is used to eliminate the steady-state error of the system; the differential link can predict the deviation change trend and adjust the control output in advance so that the system can quickly and stably reach the set data.

[0025] The pressure closed-loop control algorithm of the intelligent rail vehicle articulation device includes the following steps: S1. Collect the corresponding damper cylinder pressure data when achieving the target damping force at a certain number of different vehicle speeds, angles, and angular velocities of the articulated device, establish fuzzy rules, and fuzzify the input variables; S11. Using the articulation angle sensor and pressure sensor to collect a certain amount of damper cylinder pressure data corresponding to different vehicle speeds, angles, and angular velocities, the corresponding damper cylinder pressure data collected when the target damping force is achieved at different vehicle speeds, angles, and angular velocities is used to iterate the fuzzy rule control table for each input variable and the damper cylinder pressure; S12. Setting several fuzzy subset domains for input variables such as vehicle speed, angle, and angular velocity signal; S13. Scaling the input variable value from the basic domain to the fuzzy subset domain; S14. Obtain fuzzy language values ​​through membership functions or fuzzy variable assignment tables.

[0026] like Figure 1 As shown, the articulated joint is the connecting structure of intelligent rail vehicles. It is a link mechanism with two degrees of freedom: vertical and horizontal rotation. Standard interfaces are provided at both ends for connection to standard locomotives, standard power cars, and standard trailer cars. The articulated joint is connected to the damper cylinder, which contains a pressure sensor and a hydraulic proportional valve. This valve converts an input electrical signal into force or displacement, thereby continuously controlling parameters such as pressure and flow. A hydraulic valve converts an input electrical signal into force or displacement, thereby continuously controlling parameters such as pressure and flow.

[0027] S2. When the vehicle is running, the articulation device controller obtains the vehicle speed and articulation angle signals controlled by the current vehicle controller, calculates the articulation angular velocity signal, and determines the current damper cylinder target pressure based on the fuzzy rule; S21. Obtain the fuzzy language value of the output variable through the fuzzy rule table based on the fuzzy language value of each input variable; S22. Obtain the value of the output variable in the fuzzy subset domain through the fuzzy variable assignment table; S23. The output variable language value is transformed into the target cylinder pressure through transformation principles such as the maximum membership method and the center of gravity method.

[0028] In this embodiment, the vehicle speed of 0km / h-120km / h is divided into five fuzzy sets, namely, "low speed (0km / h < v < 25km / h)", "medium-low speed (25km / h ≤ v < 50km / h)", "medium speed (50km / h ≤ v < 75km / h)", "medium-high speed (75km / h ≤ v < 100km / h)" and "high speed (100km / h ≤ v ≤ 120km / h)", and the angle of 0°-60° is divided into five fuzzy sets, namely, "small turning angle (0° < θ < 10°)", "small turning angle (10° ≤ θ < 20°)", "medium ... The angular velocity of 0rad / s-10rad / s is divided into five fuzzy sets, namely, “small angular velocity (0rad / s<ω<2rad / s)”, “small angular velocity (2rad / s≤ω<4rad / s)”, “medium angular velocity (4rad / s≤ω<6rad / s)”, “large angular velocity (6rad / s≤ω<8rad / s)” and “large angular velocity (8rad / s≤ω≤10rad / s)”.

[0029] According to the articulated device test bench, the hydraulic cylinder pressure data corresponding to different vehicle speeds, angles and angular velocities are obtained, and the membership functions and fuzzy variable assignment tables of the input variables for different fuzzy sets are set. During the operation of the articulated device, the controller receives the vehicle speed and angle signals, calculates the angular velocity signal, and fuzzifies the signal input through the membership function. The fuzzy language value of the input signal is obtained through the fuzzy variable assignment table to obtain the target pressure fuzzy language value, and the target pressure fuzzy language value is converted into the target hydraulic cylinder pressure through the maximum membership method.

[0030] S3. The articulated joint controller collects the current damper cylinder pressure and uses a PID controller to adjust the proportional valve opening by adjusting the proportional valve input voltage until the cylinder pressure reaches the target pressure. Specifically, after obtaining the target pressure, the controller receives the damper cylinder pressure signal, compares the obtained damper cylinder pressure signal with the target pressure signal, and continuously adjusts the damper cylinder input voltage using a PID algorithm until the damper cylinder pressure reaches the target pressure, thereby achieving the goal of controlling the damping force of the intelligent rail vehicle articulated joint.

[0031] As another alternative, the input variables are not limited to the variables such as vehicle speed, angle and angular velocity described in the scheme, but can be other easily conceivable forms (angular acceleration, heading angle, etc.).

[0032] As another alternative, the acquisition of the cylinder pressure target value is not limited to the fuzzy algorithm described in the scheme, but can also be a particle swarm algorithm, artificial intelligence related algorithm, etc.

[0033] The present invention adopts a fuzzy method to construct the correspondence rules between the actuator cylinder pressure and the vehicle speed, the turning angle and angular velocity of the intelligent rail vehicle, avoiding complicated modeling while enhancing the robustness of the control system. At the same time, based on this fuzzy rule, the actuator cylinder pressure is monitored for closed-loop control. When the pressure does not meet the target value, the proportional valve input current is adjusted through the PID closed-loop algorithm to adaptively control the cylinder pressure, thereby achieving the purpose of adaptively changing the damping force to suppress large yaw motion between the carriages of the intelligent rail vehicle.

[0034] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A pressure closed-loop control algorithm for an intelligent rail vehicle articulation device, characterized in that: The vehicle speed, angle and angular velocity are used as input signals, and the pressure of the damper cylinder is used as feedback signal. At the same time, the corresponding relationship between the input signal and the feedback signal is established through fuzzy rules, and PID closed-loop control is performed to achieve adaptive change of the damping force to suppress the displacement of the yawing motion between the carriages of the intelligent rail vehicle.

2. The pressure closed-loop control algorithm of the intelligent rail vehicle articulation device according to claim 1 is characterized in that: The fuzzy algorithm of the fuzzy rules is stored in the fuzzy controller. When the input variables of the fuzzy controller are the deviation between the actual data and the set data and the rate of change of the deviation, the two input variables are fuzzified and divided into multiple fuzzy subsets. Fuzzy control rules are formulated based on actual control experience to obtain the actual control quantity for adjustment in actual use. In the PID algorithm, the control quantity is obtained through the calculation of the three links of proportional (P), integral (I) and differential (D). The proportional link quickly adjusts the control output according to the size of the deviation.

3. The pressure closed-loop control algorithm of the intelligent rail vehicle articulation device according to claim 2 is characterized in that: The following steps are involved: S1. Collect the corresponding damper cylinder pressure data when the target damping force is achieved at different vehicle speeds, angles, and angular velocities of the articulated device, establish fuzzy rules, and fuzzify the input variables; S2. While the vehicle is running, the articulation controller obtains the current vehicle speed and articulation angle signals, calculates the articulation angular velocity, and determines the current damper cylinder target pressure based on fuzzy rules; S3. The articulated device controller collects the current damper cylinder pressure and uses the PID algorithm to adjust the proportional valve opening by adjusting the proportional valve input voltage until the cylinder pressure reaches the target pressure.

4. The pressure closed-loop control algorithm for the intelligent rail vehicle articulation device according to claim 3 is characterized in that: Step S1 specifically includes: S11. Using the articulation angle sensor and pressure sensor to collect a certain amount of damper cylinder pressure data corresponding to different vehicle speeds, angles, and angular velocities, the corresponding damper cylinder pressure data collected when the target damping force is achieved at different vehicle speeds, angles, and angular velocities is used to iterate the fuzzy rule control table for each input variable and the damper cylinder pressure; S12. Set several fuzzy subset domains for the vehicle speed, angle, and angular velocity signal input variable values; S13. Scaling the input variable value from the basic domain to the fuzzy subset domain; S14. Obtain fuzzy language values ​​through membership functions or fuzzy variable assignment tables.

5. The pressure closed-loop control algorithm for the intelligent rail vehicle articulation device according to claim 4 is characterized in that: The fuzzy rules divide vehicle speed, angle and angular velocity into segments to establish fuzzy subsets.

6. The pressure closed-loop control algorithm for the intelligent rail vehicle articulation device according to claim 3 is characterized in that: Step S2 specifically includes: S21. Obtain the fuzzy language value of the output variable through the fuzzy rule table based on the fuzzy language value of each input variable; S22. Obtain the value of the output variable in the fuzzy subset domain through the fuzzy variable assignment table; S23. The output variable language value is transformed into the target cylinder pressure through the maximum membership method and the center of gravity method transformation principle.

7. The pressure closed-loop control algorithm for the intelligent rail vehicle articulation device according to claim 3 is characterized in that: Step S3 is specifically as follows: after obtaining the target pressure, the controller receives the damper cylinder pressure signal, compares the obtained damper cylinder pressure signal with the target pressure signal, and continuously adjusts the damper cylinder input voltage through the PID algorithm until the damper cylinder pressure reaches the target pressure, thereby achieving the goal of damping force control of the intelligent rail vehicle articulated device.

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