CN-121990467-A - Underground monorail crane prediction control method and system based on digital road spectrum

CN121990467ACN 121990467 ACN121990467 ACN 121990467ACN-121990467-A

Abstract

The invention is suitable for the technical field of automatic control of auxiliary transportation equipment for mines, and provides a method and a system for predicting and controlling underground monorail crane based on digital road spectrum, wherein the method comprises the steps of acquiring positions and road spectrum; the method has the beneficial effects that speed self-adaptive correction is realized through road spectrum pre-reading and parameter on-line identification, smooth torque is output by combining compound prediction control, and pure electric drive stable speed regulation is realized by the monorail crane. According to the scheme, unstable slip under complex working conditions is effectively overcome, the safety risk of triggering mechanical braking intervention due to speed runaway is avoided, and the response speed, running stability and intrinsic safety level of the whole system are remarkably improved.

Inventors

JIANG FAN
TANG SHUAI
ZHU ZHENCAI
LI YUE
ZHOU GONGBO
ZHOU PING
YAN XIAODONG
DONG XIAOWEI
NI YUN

Assignees

中国矿业大学

Dates

Publication Date: 20260508
Application Date: 20260408

Claims (7)

1. The underground monorail crane prediction control method based on the digital road spectrum is characterized by comprising the following steps of: acquiring a position and a road spectrum, namely acquiring the absolute position of a locomotive, inquiring a speed map to acquire the reference speed and the wet skid coefficient of a current section, and reading the gradient sequence of each section in a preset distance in front; on-line identification of parameters, namely, when the traction working condition and the acceleration are larger than a set threshold value, on-line identification of the total mass and the rail surface adhesion coefficient of the locomotive is carried out; calculating a correction coefficient according to the total mass of the locomotive, the rail surface adhesion coefficient and the curvature radius of the current section, and correcting the reference speed to obtain a target speed; A composite torque command generation step of calculating a torque command by adopting supercoiled sliding mode feedback control and feedforward control based on a pre-aiming gradient; The drive execution and braking cooperation step is that the torque command is sent to the traction drive unit for execution and the hydraulic brake unit is completely released during normal operation, and the intervention of the hydraulic brake unit is controlled only in a limited emergency or parking state.
2. The method for predicting and controlling the underground monorail crane based on the digital road spectrum according to claim 1, wherein the step of obtaining the position and the road spectrum specifically comprises the following steps: Collecting pulse signals output by a rotary encoder arranged at the shaft end of a traction motor so as to accumulate relative driving mileage; reading absolute mileage coordinate values in RFID tags pre-buried in the side walls of the roadway through a passive RFID card reader arranged at the bottom of the vehicle body; fusing the relative driving mileage calculated by the pulse signals with the absolute mileage coordinate values, correcting and calculating the real-time absolute position of the locomotive; Inquiring and extracting a reference speed and a wet sliding coefficient of a current characteristic section from a speed map of the built-in vehicle controller by taking the real-time absolute position as an index; And taking the current position as a starting point, pre-reading the gradient sequence of each section within the set distance in front, and storing the gradient sequence in a cache for standby.
3. The method for predicting and controlling the underground monorail crane based on the digital road spectrum according to claim 1, wherein the step of on-line identifying the parameters specifically comprises the following steps: Collecting the rotating speed of a traction motor in real time and performing differential calculation to obtain the acceleration of the locomotive; Acquiring a longitudinal inclination angle of a current track through an inclination sensor on a vehicle body so as to determine a current gradient; when the traction working condition is met and the acceleration is larger than the set threshold value, calculating and obtaining an original estimated value of the total mass of the locomotive based on the following dynamics equation : ; In the formula, Is the total mass of the locomotive, For the acceleration of the locomotive, For the traction force to be applied, In order to achieve a basic resistance to operation, The acceleration of the gravity is that, Is the longitudinal inclination angle of the current track; the original estimated value is processed Smoothing by a moving average filter to output the final total mass of the locomotive; and estimating the available adhesion coefficient of the current rail surface in real time, and comparing the adhesion coefficient with a threshold value of a preset typical value to judge whether the current rail surface enters a wet sliding state or not.
4. The method for predicting and controlling a downhole monorail crane based on a digital road spectrum according to claim 1, wherein the step of dynamically correcting the target speed comprises: calculating a load correction coefficient limited in a preset range according to the recognized and output total mass of the locomotive : ; In the formula, Is the rated full-load mass of the locomotive, Is the total mass of the locomotive; calculating an adhesion correction coefficient of not more than 1 according to the real-time estimated rail surface adhesion coefficient : ; In the formula, For the real-time estimated rail adhesion coefficient, Ideal adhesion coefficients in the velocity map; Extracting radius of curvature of current segment Combining with a preset safety coefficient of locomotive overturning stability With the acceleration of gravity Calculating the maximum safe passing speed ; And based on the maximum safe passing speed Reference speed to current section Calculating curvature correction coefficient : ; Multiplying the reference speed by the load correction factor Adhesion correction coefficient Curvature correction coefficient Generating the target speed after comprehensive dynamic correction : ; The target speed is updated once per fixed period and sent to the traction drive unit as a speed closed loop setpoint.
5. The method for predictive control of an underground monorail crane based on digital road spectroscopy of claim 1, wherein the step of generating the compound torque command specifically comprises: calculating a feedback torque component of a speed loop based on a supercoiled sliding mode control algorithm according to the rotation speed tracking error of the target speed and the actual rotation speed; determining the total lag time of the system through a step response test, and setting the pretightening time to be 1.5 to 2.5 times of the total lag time; Calculating a pretightening distance based on the current vehicle speed and the pretightening time, and extracting a corresponding gradient value at the front pretightening distance from a cache; and calculating a feedforward torque required for overcoming gradient resistance according to the corresponding gradient value, and superposing the feedback torque component and the feedforward torque to generate a final torque command.
6. A digital road spectrum-based underground monorail crane predictive control system, characterized in that the digital road spectrum-based underground monorail crane predictive control method as claimed in any one of claims 1 to 5 is applied, the system comprising: The vehicle-mounted controller module is internally provided with a speed map taking roadway mileage as an index, and is used for executing a control algorithm, comprehensively planning data of each module and resolving a target speed and torque instruction; the positioning unit module is connected with the vehicle-mounted controller module and comprises a rotary encoder and a passive RFID card reader, and is used for acquiring the absolute position of the locomotive in a roadway in real time; The state sensing unit module comprises a current sensor and an inclination sensor and is used for collecting the running state and the track environment parameters of the locomotive and transmitting data to the vehicle-mounted controller module for on-line identification.
7. The digital road spectrum based downhole monorail crane predictive control system of claim 6, further comprising: The traction driving unit module comprises an explosion-proof frequency converter and a permanent magnet synchronous traction motor, and is used for receiving a torque command sent by the vehicle-mounted controller module and independently completing power driving and speed adjustment of the monorail crane under normal operation conditions; The hydraulic braking unit module is controlled by the vehicle-mounted controller module and is used for intervening braking when an emergency stop button is pressed, serious faults occur or the vehicle is in a parking state, and the hydraulic braking unit module is kept completely released in the speed adjusting process of normal operation.

Description

Underground monorail crane prediction control method and system based on digital road spectrum Technical Field The invention belongs to the technical field of automatic control of auxiliary transportation equipment for mines, and particularly relates to a method and a system for predictive control of an underground monorail crane based on a digital road spectrum. Background The underground monorail crane is used as key equipment of an auxiliary coal mine transportation system and bears important tasks of transporting materials, equipment and personnel along a roadway. With the advancement of intelligent construction of coal mines, unmanned and self-adaptive control technology of monorail cranes becomes an industry research hotspot. However, the underground roadway environment is extremely complex, and various severe working conditions such as long-distance undulating ramp, small-curvature radius bend, abrupt change of the surface state of the track and the like exist, so that the autonomous safety and stable operation of the monorail crane are provided with serious challenges. The existing monorail crane control system generally lacks advanced perception capability on the road condition in front, and can not adaptively adjust target speed and driving instructions according to dynamically-changed vehicle load and real-time rail surface adhesion state. When the vehicle is driven into a long downhill slope or a road section with low attachment coefficient, the speed is easy to be out of control and exceeds a safety threshold value, and finally the emergency locking of the safety braking system is triggered. The passive protection mode of 'out of control-emergency stop' not only causes low transportation efficiency, but also causes accumulated damage to key transmission parts due to frequent emergency braking impact, thereby fundamentally restricting the running smoothness, the safety reliability and the service life of core parts of the whole system. Disclosure of Invention The invention aims to provide a method and a system for predicting and controlling an underground monorail crane based on a digital road spectrum, and aims to solve the problems in the background art. The invention discloses a method for controlling underground monorail crane prediction based on digital road spectrum, which comprises the steps of obtaining the absolute position of a locomotive, inquiring a speed map to obtain the reference speed and the wet skid coefficient of the current section, and reading the gradient sequence of each section in a preset distance. And the step of parameter on-line identification, namely, when the traction working condition and the acceleration are larger than the set threshold value, on-line identification of the total mass and the rail surface adhesion coefficient of the locomotive is carried out. And a target speed dynamic correction step, namely calculating a correction coefficient according to the total mass of the locomotive, the rail surface adhesion coefficient and the curvature radius of the current section, and correcting the reference speed to obtain the target speed. And a composite torque command generating step, namely calculating a torque command by adopting supercoiled sliding mode feedback control and feedforward control based on a pre-aiming gradient. The drive execution and braking cooperation step is that the torque command is sent to the traction drive unit for execution and the hydraulic brake unit is completely released during normal operation, and the intervention of the hydraulic brake unit is controlled only in a limited emergency or parking state. The method comprises the steps of acquiring a position and a road spectrum, wherein the step of acquiring the position and the road spectrum specifically comprises the step of acquiring pulse signals output by a rotary encoder arranged at the shaft end of a traction motor so as to accumulate relative driving mileage. And reading absolute mileage coordinate values in the RFID tags pre-buried in the side walls of the roadway through a passive RFID card reader arranged at the bottom of the vehicle body. And fusing the relative driving mileage calculated by the pulse signals with the absolute mileage coordinate values, correcting and calculating the real-time absolute position of the locomotive. And inquiring and extracting the reference speed and the wet sliding coefficient of the current characteristic section from a speed map built in the vehicle-mounted controller by taking the real-time absolute position as an index. And taking the current position as a starting point, pre-reading the gradient sequence of each section within the set distance in front, and storing the gradient sequence in a cache for standby. As a still further scheme of the invention, the parameter on-line identification step specifically comprises the steps of collecting the rotating speed of the traction motor in real time and performing differential calculation to