CN-122008905-A - Multi-motor cooperative control method and system for tunnel transport vehicle based on virtual main shaft

CN122008905ACN 122008905 ACN122008905 ACN 122008905ACN-122008905-A

Abstract

The invention discloses a tunnel transport vehicle multi-motor cooperative control method based on a virtual main shaft, and belongs to the technical field of tunnel engineering vehicle control. The method comprises the steps of acquiring multisource state information in the running process of a tunnel transport vehicle in real time, preprocessing acquired signals, generating a dynamic virtual main shaft rotating speed reference value and a dynamic virtual main shaft torque reference value according to processed vehicle state and gradient information, taking the dynamic virtual main shaft rotating speed reference value and the dynamic virtual main shaft torque reference value as unified references for cooperative control of the whole vehicle, calculating rotating speed deviation and torque deviation of each motor relative to the virtual main shaft, fusing the rotating speed deviation and the torque deviation into comprehensive deviation, dynamically adjusting participation weights of the motors according to the comprehensive deviation of the motors, and executing double closed loop cooperative control of a speed loop and a torque loop on each driving motor based on the adjusted weights. The invention solves the problems of low synchronous precision, uneven torque distribution and easy overload or slipping of multiple motors under the working condition of large-gradient heavy-load climbing of the tunnel in the prior art, and remarkably improves the traction stability and the operation safety of the tunnel material transport vehicle.

Inventors

ZHANG BOQIANG
WU XINPING
Tong Yihang
TIAN HUALIANG
CAI TAO
ZHANG XIAOPENG
ZHANG XUN
ZHOU YOU
ZHANG QIANG
ZOU ZHEN

Assignees

河南工业大学
郑州新大方重工科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260410

Claims (10)

1. The tunnel transport vehicle multi-motor cooperative control method based on the virtual main shaft is characterized by comprising the following steps of: Acquiring multi-source state information in the running process of the tunnel transport vehicle, wherein the multi-source state information at least comprises the rotating speed and the rotating torque of each driving motor, gradient information of a vehicle running road surface, longitudinal acceleration information and load distribution information of each shaft or each carriage; Generating a virtual main shaft rotating speed reference value and a virtual main shaft torque reference value according to the load distribution information and the gradient information; Calculating the rotation speed deviation of the rotation speed of each motor and the virtual spindle rotation speed reference value, and the torque deviation of the torque of each motor and the virtual spindle torque reference value, and fusing the rotation speed deviation and the torque deviation of each motor into the comprehensive deviation of the motor; adjusting the participation weight of each motor according to the comprehensive deviation of each motor, wherein the motor with larger comprehensive deviation obtains lower weight; And carrying out weighted compensation on the comprehensive deviation based on the adjusted participation weight, and executing cooperative control on each motor according to a double closed-loop structure of the outer rotating speed ring and the inner torque ring.
2. The method of claim 1, wherein the multi-source status information further comprises image information acquired by a camera and point cloud information acquired by a lidar; Preprocessing the multi-source state information, wherein the preprocessing comprises the following steps: Filtering the high-frequency signals by adopting a low-pass filter; Removing abnormal data points by adopting sliding window median filtering and combining with a statistical criterion; Performing state estimation by adopting self-adaptive Kalman filtering aiming at gradient information and longitudinal acceleration information; and mapping the processed information to a preset interval by adopting a normalization method, wherein the mapping boundary of the normalization method is dynamically adjusted according to the gradient information and the load distribution information.
3. The method of claim 2, wherein the process noise covariance of the adaptive kalman filter is dynamically adjusted according to the following conditions: and when the gradient angle is larger than 7 degrees, or the maximum relative deviation between each axle load and the average load is larger than 20 percent, or the longitudinal acceleration fluctuation amplitude exceeds a preset acceleration fluctuation threshold value, increasing the process noise covariance.
4. The method according to claim 1, wherein the virtual spindle rotation speed reference value is generated by: determining the weight of each motor according to the load of the corresponding shaft of each motor; Multiplying the real-time rotating speeds of all motors by the corresponding weighting weights, summing the multiplied real-time rotating speeds, and dividing the multiplied real-time rotating speeds by the sum of all the weighting weights to obtain a basic synchronous rotating speed; Calculating the product of the speed compensation coefficient, the gravity acceleration and the sine value of the current gradient angle, and dividing the product by the effective radius of the wheel to obtain a gradient compensation term; Adding the basic synchronous rotating speed and the gradient compensation term to obtain the virtual main shaft rotating speed reference value; Wherein the value range of the speed compensation coefficient is 0.05-0.15.
5. The method according to claim 1, wherein the virtual spindle torque reference is generated by: Estimating a total torque demand according to the total mass of the vehicle, the gradient information and the rolling resistance coefficient; Dividing the total torque demand by the total number of motors to obtain basic distribution torque; Calculating the maximum relative deviation between the load of each shaft and the average load as load distribution unevenness; And determining a torque adjustment coefficient according to the load distribution unevenness, multiplying the difference between the real-time torque of each motor and the average torque of all motors by the torque adjustment coefficient, and then superposing the basic distribution torque to obtain the virtual main shaft torque reference value.
6. The method according to claim 1, wherein when the rotational speed deviation and the torque deviation of each motor are fused into the integrated deviation of the motor, the rotational speed deviation and the torque deviation are multiplied by different weight coefficients respectively and then summed, and the weight coefficient of the rotational speed deviation is larger than the weight coefficient of the torque deviation; The weight coefficient of the rotating speed deviation is in a range of 0.6-0.8, and the weight coefficient of the torque deviation is in a range of 0.2-0.4.
7. The method of claim 1, wherein the integrated weight of each motor (w_i) is determined as follows: Wherein, the For an initial weight determined from the load of the i-th motor's corresponding shaft, For the integrated deviation of the i-th motor, For the maximum value of the integrated deviation of all the motors, The value range is 0.3-0.7 for attenuation sensitivity coefficient.
8. The method of claim 1, wherein the dual closed loop structure of the outer rotating speed ring and the inner torque ring is: Taking the virtual main shaft rotating speed reference value as a given value of the rotating speed outer ring, taking the real-time rotating speed of the motor as a feedback value of the rotating speed outer ring, and taking the output of the rotating speed outer ring as a torque given intermediate value of the torque inner ring; and taking the sum of the given intermediate value of the torque and the compensation term as a given value of the torque inner ring, taking the real-time torque of the motor as a feedback value of the torque inner ring, and outputting a motor driving instruction by the torque inner ring.
9. The method of claim 8, wherein the compensation term comprises a rotational speed deviation feedforward compensation term and a torque balancing compensation term: the rotating speed deviation feedforward compensation term is the result obtained by performing proportional, integral and differential operations on the rotating speed deviation and multiplying the result by the comprehensive weight of the motor; The torque balance compensation term is that the torque deviation is multiplied by a preset balance proportion coefficient and then multiplied by the comprehensive weight of the motor; And taking the sum of the torque given intermediate value, the rotating speed deviation feedforward compensation term and the torque balance compensation term as a given value of a torque inner ring.
10. A virtual spindle based tunnel transporter multi-motor cooperative control system for performing the method of any one of claims 1 to 9, comprising: The data acquisition and signal processing module is used for acquiring multi-source state information in the running process of the tunnel transport vehicle in real time and carrying out filtering, denoising and normalization preprocessing on the multi-source state information; The virtual spindle construction module is connected with the data acquisition and signal processing module and is used for generating a virtual spindle rotating speed reference value and a virtual spindle torque reference value according to the preprocessed multi-source state information; the motor deviation calculation module is connected with the virtual spindle construction module and is used for calculating the rotation speed deviation and the torque deviation of each motor according to the virtual spindle rotation speed reference value, the virtual spindle torque reference value and the real-time rotation speed and the real-time torque of each motor and fusing the rotation speed deviation and the torque deviation into comprehensive deviation; The motor weight adjusting module is connected with the motor deviation calculating module and is used for dynamically adjusting the participation weight of each motor according to the comprehensive deviation of each motor, wherein the motor with larger comprehensive deviation obtains lower weight; And the cooperative control execution module is respectively connected with the virtual main shaft construction module, the motor weight adjustment module and each driving motor and is used for executing cooperative control on each motor according to a double closed-loop structure of the outer rotating speed ring and the inner torque ring based on the virtual main shaft reference value and the adjusted participation weight and outputting motor driving instructions.

Description

Multi-motor cooperative control method and system for tunnel transport vehicle based on virtual main shaft Technical Field The invention relates to a tunnel transport vehicle multi-motor cooperative control method and system based on a virtual main shaft, and belongs to the technical field of tunnel engineering vehicle control. Background Under the background that underground space development scale is continuously enlarged, the requirements of tunnel engineering on material transportation equipment are higher and higher, and particularly in heavy-gradient tunnel construction, a special tunnel transportation vehicle with multiple sections of marshalling is commonly adopted for heavy-load material transportation, a plurality of transmission shafts are usually arranged in each carriage of the vehicle, each shaft is driven by an independent driving motor to form a typical multi-motor distributed driving system, and in theory, the framework can provide larger traction power redundancy and also has certain driving flexibility. However, in practical engineering application, particularly under complex working conditions such as continuous ascending, abrupt gradient change, uneven load distribution and the like, the existing multi-motor cooperative control method has some common problems. The common multi-motor cooperative control strategy in the engineering at present mainly comprises a master-slave control structure, a parallel control structure and a control structure based on deviation coupling, wherein the master-slave control structure uses one motor as a reference, the other motors follow the output of the master-slave control structure, the structure is simple, errors are amplified step by step once the master motor is disturbed, the parallel control structure is that the motors are independently regulated and lack of an effective coordination mechanism, the deviation coupling control structure introduces error feedback among the motors, but on long-marshalling vehicles with multiple motors and multiple carriages, the calculation complexity is high, and the severe dynamic changes of gradient and load are still difficult to deal with. With respect to heavy-duty, multi-axis, long-range consist vehicles such as tunnel trucks, the prior art routes are generally faced with several technical dilemmas: First, the control architecture lacks a globally uniform reference. Most of the existing multi-motor systems adopt a distributed independent adjustment mode, and each motor controller can only carry out closed-loop adjustment according to local information such as rotating speed and current detected by the motor controllers. The lack of effective cooperative signal channels among the motors can cause difficulty in forming integral coordination of a whole vehicle driving unit under the scene of multi-carriage and multi-shaft driving, and particularly in the climbing process, the stress state difference of each shaft is obvious, and the local closed loop can not reflect the real power requirement of the whole vehicle. And secondly, the load distribution strategy is difficult to adapt to the dynamic coupling of the gradient and the load. The conventional method generally distributes torque based on a static or simplified load model, such as average distribution according to rated power or distribution according to fixed proportion, but in actual tunnel construction, vehicles need to face dynamic factors such as continuously changing large-gradient pavement, material distribution difference between carriages, material gravity center deviation in the climbing process and the like, and the factors can enable real-time load of each driving shaft to present nonlinear and spatial distributed changes, while the conventional method lacks real-time fusion capability of gradient information and load distribution information, and is difficult to respond to load changes quickly and differentially. Under the existing architecture, due to the fact that an effective global synchronous reference and load estimation mechanism is not available, the output states of all motors are easy to generate obvious differences, some motors can be in an overload or light-load state for a long time, torque oscillation can occur due to accumulation of synchronous errors, and the unbalanced operation mode can further amplify the synchronous errors, easily cause wheel slip or single-shaft overload and influence traction stability and operation safety of the whole vehicle. In general, under typical working conditions such as long gradient of a tunnel, heavy load climbing and the like, the existing multi-motor cooperative control method mainly has the following problems that a unified reference standard capable of reflecting gradient change and load distribution dynamics simultaneously is lacking, rotation speed synchronization and torque distribution are often processed separately, an effective cooperative adjustment mechanism is difficult to form, part