CN113547932B - Torque optimal distribution control system, unmanned carrier and control method thereof - Google Patents
Torque optimal distribution control system, unmanned carrier and control method thereof Download PDFInfo
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- CN113547932B CN113547932B CN202110896883.9A CN202110896883A CN113547932B CN 113547932 B CN113547932 B CN 113547932B CN 202110896883 A CN202110896883 A CN 202110896883A CN 113547932 B CN113547932 B CN 113547932B
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
- B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2220/00—Electrical machine types; Structures or applications thereof
- B60L2220/40—Electrical machine applications
- B60L2220/42—Electrical machine applications with use of more than one motor
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2220/00—Electrical machine types; Structures or applications thereof
- B60L2220/40—Electrical machine applications
- B60L2220/44—Wheel Hub motors, i.e. integrated in the wheel hub
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/72—Electric energy management in electromobility
Abstract
The invention discloses a torque optimal distribution control system, an unmanned carrier and a control method thereof, and belongs to the technical field of control systems. The system comprises an upper layer controller, a middle layer controller and a bottom layer controller; the upper layer controller and the middle layer controller, the upper layer controller and the bottom layer controller, and the middle layer controller and the bottom layer controller are electrically and mechanically connected; the middle layer controller comprises a torque distribution controller and a slip rate similar controller connected with the torque distribution controller; the slip rate similar controller is connected with a hub motor of the unmanned carrier; the bottom layer controller comprises an observer; the observer is connected with the unmanned carrier. The torque optimizing distribution control strategy based on the neural network PID improves the running stability of the unmanned carrier while reducing the energy consumption of the unmanned carrier.
Description
Technical Field
The invention relates to a torque optimal distribution control system, an unmanned carrier and a control method thereof, and belongs to the technical field of control systems.
Background
The distributed driving introduces a new implementation form for dynamic control of the electric automobile, has the advantages of short driving chain, high transmission efficiency, independent control among all wheels, capability of distributing the torque of each wheel according to any proportion and the like, has important significance for improving the safety and stability, energy conservation and other movement performances of the vehicle, and is an ideal movement platform of the intelligent vehicle. In a great background of increasingly prominent environmental and energy problems, distributed driving electric vehicles are becoming a development hot spot in academia.
The unmanned carrier in the intelligent mill adopts the distributed drive mode mostly, in carrying out the transport task in-process, because load size and position change, arouse that different drive wheels bear different positive pressures, lead to AGV barycenter position to change, influence unmanned carrier's stability and economic nature. The torque distribution control of the hub motor of the distributed drive automobile is a key factor for determining the stability, dynamic performance and economy of the whole automobile, and in order to ensure the running stability and economy of the distributed drive automobile and fully exert the independent and controllable advantages of four-wheel drive torque, an active control strategy is adopted for torque distribution.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a torque optimal distribution control system, an unmanned carrier and a control method thereof, so as to solve the technical problems of unreasonable torque distribution and poor stability of the unmanned carrier in the prior art.
In order to solve the technical problems, the invention is realized by adopting the following scheme:
the invention provides a torque optimal distribution control system, which comprises an upper layer controller, a middle layer controller and a bottom layer controller; the upper layer controller and the middle layer controller, the upper layer controller and the bottom layer controller, and the middle layer controller and the bottom layer controller are electrically and mechanically connected; the middle layer controller comprises a torque distribution controller and a slip rate similar controller connected with the torque distribution controller; the slip rate similar controller is connected with a hub motor of the unmanned carrier; the bottom layer controller comprises an observer; the observer is connected with the unmanned carrier.
The invention also provides the unmanned carrier, and the torque optimal distribution control system is adopted to carry out torque optimal distribution control on the whole carrier.
The invention also provides a control method of the torque optimal distribution control system, which comprises the following steps: the upper controller determines the torque required by the vehicle according to the cargo carrying capacity of the unmanned carrier and determines the driving mode of the unmanned carrier according to the required torque; the slip rate similar controller of the middle-layer controller adjusts the torque according to a PIDNN algorithm under the condition that the driving force allowance and the driving mode determined by the upper-layer controller are met, and feeds back the adjusted torque to the torque distribution controller; the torque distribution controller calculates the expected torque of each wheel hub motor according to the required torque of the upper controller and the adjustment torque, and feeds back the expected torque to each wheel hub motor; and an observer of the bottom layer controller detects the actual torque of the driving wheels of the unmanned carrier and compares the actual torque with the expected torque output by the torque distribution controller to perform closed-loop feedback tracking control.
Preferably, the method further comprises the following steps: when the carrying capacity is smaller than 20kg and the mass center positions of the unmanned carrier and the goods are close to the front axle, the unmanned carrier adopts a front wheel driving mode; when the cargo carrying capacity is smaller than 20kg and the mass center positions of the unmanned carrier and the cargo are close to the rear axle, the unmanned carrier adopts a rear wheel driving mode; when the carrying capacity is not less than 20kg, the unmanned carrier adopts a four-wheel drive mode.
Preferably, the method further comprises the following steps: the slip rate similar controller calculates the slip rate of the driving wheel according to the speed and the wheel speed fed back by the driving wheel: when the minimum value of the slip rate of the driving wheel is larger than the threshold value of the slip rate of the driving wheel, outputting the adjustment torque of the driving wheel; when the minimum value of the driving wheel slip rate is not more than the threshold value of the driving wheel slip rate and the difference of the driving wheel slip rates is not more than the preset threshold value of the difference of the driving wheel slip rates, the driving wheel adjusting torque is zero; and outputting the driving wheel adjustment torque when the minimum value of the driving wheel slip rate is not more than the threshold value of the driving wheel slip rate and the difference of the driving wheel slip rates exceeds the preset threshold value of the difference of the driving wheel slip rates.
Preferably, the method further comprises the following steps: when the change rate of the actual torque and the expected torque is less than 0.1%, the actual torque of the driving wheel is unchanged; when the change rate of the actual torque from the desired torque is not less than 0.1%, the actual torque of the driving wheel is adjusted.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention provides a torque distribution control strategy based on a neural network PID, and designs a layered controller, so that the energy consumption of an unmanned carrier is reduced and the running stability of the unmanned carrier is improved;
2. the PINDD-based control strategy has practical application value, stronger robustness and adaptability, and the requirements of AGV torque distribution control are well met.
Drawings
FIG. 1 is a schematic flow chart of a torque optimizing distribution control system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a flow chart of a slip ratio approaching controller according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a PIDNN algorithm according to an embodiment of the present invention;
FIG. 4 is a graph illustrating an efficiency characteristic of an in-wheel motor according to an embodiment of the present invention;
FIG. 5 is a diagram of total power of a driving wheel under a low-load simulation experiment according to an embodiment of the present invention;
FIG. 6 is a graph of lateral offset distances of an unmanned truck under a low-load simulation experiment provided by an embodiment of the invention;
FIG. 7 is a diagram of total power of a driving wheel under a high-load simulation experiment provided by an embodiment of the invention;
FIG. 8 is a graph of lateral offset distances of an unmanned truck under a low-load simulation experiment provided by an embodiment of the invention;
FIG. 9 is a graph comparing total power of experimental and simulated driving wheels under low load conditions provided by an embodiment of the present invention;
FIG. 10 is a graph comparing the lateral offset distances of an experimental and simulated unmanned truck under low load conditions provided by an embodiment of the present invention;
FIG. 11 is a graph showing the comparison of experimental and simulated total power of a driving wheel under high load conditions provided by an embodiment of the present invention;
fig. 12 is a graph comparing the lateral offset distances of the experimental and simulated unmanned carrier under the high load condition provided by the embodiment of the invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Example 1
The embodiment provides a torque optimal distribution control system, which aims at the straight running working condition of an AGV (automatic guided vehicle) and combines the characteristic that the torques of driving wheels are independently controllable in a distributed manner, so that the energy consumption of an unmanned carrier is reduced, the running stability of the unmanned carrier is improved, a PIDNN-based torque optimal distribution layered control strategy is provided, and a layered controller is provided, wherein the layered controller comprises an upper controller, a middle controller and a bottom controller which are mutually and electrically connected as shown in a flow diagram of fig. 1; the upper controller selects a driving mode, the middle controller comprises a torque distribution controller and a slip rate similar controller connected with the torque distribution controller, and the slip rate similar controller is connected with a hub motor of the unmanned carrier to adjust and distribute the torque; the bottom layer controller comprises an observer; the observer is connected with the unmanned carrier and is used for detecting the control effect of torque distribution of the unmanned carrier on the middle-layer controller and making feedback adjustment and control according to actual conditions.
According to the embodiment, through the layered PIDNN-based torque distribution control strategy, the energy consumption of the unmanned carrier is reduced, and meanwhile, the running stability of the unmanned carrier is greatly improved.
Example 2
The embodiment provides an unmanned carrier, adopts the torque distribution control system in the above embodiment 1, controls the torque distribution of the whole car, improves the economy and stability of the unmanned carrier, and effectively reduces the energy consumption of the whole car.
Example 3
The embodiment provides a torque optimizing distribution control method, as shown in fig. 1, in an upper layer controller, the upper layer controller firstly determines the magnitude of the torque required by the vehicle according to the load capacity of the AGV, specifically: when the carrying capacity is smaller than 20kg and the mass center positions of the unmanned carrier and the goods are close to the front axle, the unmanned carrier adopts a front wheel driving mode; when the cargo carrying capacity is smaller than 20kg and the mass center positions of the unmanned carrier and the cargo are close to the rear axle, the unmanned carrier adopts a rear wheel driving mode; when the carrying capacity is not less than 20kg, the unmanned carrier adopts a four-wheel drive mode.
The four wheel hub motors adopted by the unmanned carrier are wheel hub motors of the same model, so that the efficiency characteristics are basically consistent, the wheel hub motor efficiency characteristic diagram shown in fig. 4 is measured by utilizing different rotating speeds and rotating speeds, the motor efficiency is different under different rotating speeds, the change of the rotating speed has little influence on the motor efficiency under the same rotating speed, and the change of the rotating speed has obvious influence on the motor efficiency under the same rotating speed. According to the distribution of the motor efficiency, the hub motor efficiency is lower when the torque is smaller and larger, and the efficiency of the middle area is higher, namely, the hub motor is higher when the hub motor is under the medium load condition. Therefore, the upper controller determines an unmanned carrier driving mode according to the required torque, if the torque requirement is small, front wheel or rear wheel driving is selected (if the mass center position is close to the front axle, front wheel driving is selected, or rear wheel driving is selected), so that the torque of a motor working point is increased to approach to a high-efficiency area; if the torque demand is large, four-wheel drive is adopted, so that the efficiency is prevented from being reduced due to overlarge torque of a single motor.
As shown in fig. 1 and 2, the intermediate layer controller merges the torque distribution controller and the slip ratio approaching controller, and aims at approaching the slip ratio of the driving wheels in the range allowed by the driving force and under the condition of meeting the driving mode determined by the upper layer controller, when the slip ratio of the driving wheels is greatly different, the slip ratio approaching controller acts to adjust the torque and output the adjusted torqueΔT i AndΔT j ，ΔT i andΔT j representing the adjustment torque of the different driving wheels.
The slip rate similar controller based on PIDNN calculates the slip rate of each driving wheel according to the speed and the speed of the current driving wheel feedbackS i 、S j When the slip rate similar controller acts, the adjusting torque is fed back to the torque distribution controller, the driving wheel torque is adjusted, and when the slip rate similar controller does not act, feedback is not needed. Wherein, the liquid crystal display device comprises a liquid crystal display device,S i 、S j andT i 、T j （i≠j) Respectively representing slip ratios of different driving wheels and output desired torque.
The slip ratio similar control system calculates the slip ratio of the driving wheel according to the vehicle speed and the wheel speed, judges whether the slip ratio of the driving wheel is overlarge at the same time or not, and whether the slip ratio of the driving wheel is similar or not, redistributes the torque according to the judging result, and ensures the economy and the running stability of the whole vehicle. When the slip rate of the driving wheel is minimumS min Greater than the slip ratio thresholdS m At that time, the torque of the drive wheel is adjusted according to the output of the PIDNN algorithmΔT i ，Simultaneously adjusting the torque of the driving wheel; if the slip rate of the driving wheel is minimumS min Not too bigAt the slip rate thresholdS m And the difference between the slip ratios of the driving wheels does not exceed a set slip ratio difference thresholdΔSThe torque of the driving wheel does not need to be adjusted; if the slip rate of the driving wheel is minimumS min Not greater than the slip ratio thresholdS m And the difference between the slip ratios of the driving wheels exceeds a set slip ratio difference thresholdΔSTorque adjustment of the associated drive wheel is required.
As shown in fig. 3, which is a schematic diagram of a design structure of a PIDNN algorithm, the neural network PID has a three-layer forward network structure including an input layer, an hidden layer, and an output layer. The sub-network is the basic form of PID control of the neural network, the multi-control-quantity neural network can be regarded as the combination of a plurality of neural network sub-networks, a plurality of parallel sub-networks are independent from each other and are mutually connected through network weights.
The input layers are respectively a system given value and a feedback value, and are respectively the difference threshold value of the slip rate of the driving wheel in the inventionΔ SSlip ratio difference from the driving wheelS i －S j The hidden layer 3 neurons are respectively a proportional element P, an integral element I and a differential element D, and the output layer comprises a neuron which is used for receiving the calculation result of the hidden layer and adding the calculation result, and finally outputting the calculated control law. The neural network PID calculates errors according to the objective function, continuously corrects the network weight of each layer by adopting a gradient correction method, calculates the neuron input of each layer, and then carries out iterative update operation according to each neuron update method to finally obtain proper control rate. Regulating torque of output motor in this contextΔT i 。ω pq For the input layer to connect weights with the hidden layer,ω qk for the implicit layer to connect weights with the output layer,p=1，2；q=1, 2,3, y is the control output.
1) Input layer
X op For the input layer to output a value, X p for the input value of the system,nfor the sequence number of the sampled data value, there are:
2) Hidden layer
The hidden layer input values are:
the output values are:
proportional neurons:
integrating neurons:
differential neurons:
wherein, the liquid crystal display device comprises a liquid crystal display device,the neuron output value calculated in the previous operation step in the calculation process is obtained.
3) Output layer
The output is:
the observer of the lower controller detects the control effect of the middle controller on the hub motor through the whole vehicle model, compares the control effect with the expected torque obtained by the torque distribution controller, and when the change rate of the actual torque and the expected torque is less than 0.1%, the actual torque of the driving wheel is unchanged; when the change rate of the actual torque from the desired torque is not less than 0.1%, the actual torque of the driving wheel is adjusted. The torque of the final driving wheel is deviated from the expected torque due to the reasons of algorithm, execution structure and the like, and the lower controller realizes closed-loop tracking feedback control through an observer.
In addition, the invention also provides a simulation analysis experiment applying the torque optimal distribution control system and method, and the simulation analysis experiment is respectively used for carrying out joint simulation through CarSim and Matlab/Simulink under the working conditions of low load and high load, and comparing with the torque average distribution of a driving wheel, and researching the feasibility of the torque distribution control strategy based on the PID of the neural network in the aspects of improving the running stability of the unmanned carrier and reducing the energy consumption of the whole carrier. The parameters of the distributed driving unmanned carrier are shown in table 1.
Table 1 distributed drive unmanned truck parameters
| Whole car preparation mass/kg | 148 |
| Front-rear wheelbase/m | 1.26 |
| Track/m | 0.73 |
| Tire radius/m | 0.10 |
| Barycenter height/m in unloaded state | 0.36 |
| Centroid to front axle distance/m in unloaded condition | 0.69 |
| Centroid to rear axle distance/m in no-load condition | 0.57 |
Low load simulation experiment:
the speed of the unmanned carrier is fixed to be 20km/h, the weight of the carrier is 6.8kg, the cargo position is closer to the front wheels, the carrier runs straight on a good horizontal road surface, torque optimization distribution simulation based on the PID of the neural network is carried out on the unmanned carrier, the simulation result is compared with the torque average distribution effect, the simulation result is shown in fig. 5 and 6, the torque demand of the whole carrier is small under the low-load working condition, the center of mass position of the whole carrier is close to the front axles, and the possibility that a motor works in a high-efficiency area is increased. From fig. 5, it can be seen that the total power of torque distribution input such as the optimal distribution ratio is reduced by 17.63%, which means that under the low-load working condition, the optimal distribution can achieve the optimal target of the motor utilization efficiency, reduce the energy consumption and improve the economical efficiency of the unmanned carrier. As can be seen from fig. 6, when the X-axis position of the truck is 125m, after the optimized allocation, the Y-axis position is reduced from 0.1285m, where torque is equally allocated, to 0.0495m, and the lateral deviation distance is significantly reduced. On the basis, compared with an equidistant distribution control strategy, the torque distribution control strategy based on the neural network PID adopted by the invention realizes the goal that the slip rate of each driving wheel is similar, and improves the running stability of the unmanned carrier.
High load simulation experiment:
the speed of the unmanned carrier is fixed to be 20km/h, the weight of the carrier is 27.2kg, the carrier runs straight on a good horizontal road surface, torque optimization distribution simulation based on a neural network PID is carried out on the unmanned carrier, the torque optimization distribution simulation effect is compared with the torque average distribution simulation effect, the torque demand of the whole carrier is large under a high-load working condition, motor efficiency reduction caused by overlarge torque of a single motor is prevented, and the unmanned carrier adopts four-wheel drive. As can be obtained from fig. 7, compared with equal torque distribution, the optimized torque distribution reduces the total power of the driving wheels by 15.54%, which means that under the high-load working condition, the optimized torque distribution can achieve the optimal target of the utilization efficiency of the motor, and the energy consumption is reduced; as can be seen from fig. 8, in the two allocation methods, the optimized allocation control of the rotation speed reduces the lateral deviation distance of the unmanned carrier by 61.39%, and on the basis, the slip rate of each driving rate is kept similar, so that the stability of the vehicle is good.
In addition, the invention also provides a torque optimizing distribution control system and method based on the neural network PID, and an algorithm verification experiment for proving the validity and the correctness of the simulation result, and particularly develops a distributed driving unmanned carrier model machine, wherein the model machine is provided with an STM32F407 as a lower computer, an embedded mini industrial personal computer is an upper computer, and the motor driver of the unmanned carrier driving, steering and braking system is controlled to control the operation of each motor so as to realize the movement control of the unmanned carrier. The position and posture information of the vehicle such as the two-dimensional position, the speed, the yaw rate and the like of the vehicle are determined through a high-precision INS inertial navigation system, real-time parameters such as the rotating speed and the rotating torque of the motor of the hub are acquired through a motor encoder and a torque sensor respectively, signals are acquired in real time through an STM32F407 singlechip and fed back to an upper computer, the upper computer sends control instructions to the singlechip through serial communication, and the singlechip directly controls the corresponding motor according to the commands.
In order to simulate the intelligent factory carrying environment, algorithm verification experiments are carried out under the working conditions of low load and high load respectively, the speed of the vehicle is 20km/h, the parameters of the unmanned carrier are consistent with those used in the upper section simulation, in the experimental process, in order to reduce errors, a plurality of groups of experiments are carried out, the average value is obtained, the effect of the torque optimization distribution control strategy is compared and verified, the experimental result is basically consistent with the simulation result, and the accuracy of the algorithm is verified no matter the total power of the driving wheels or the deviation of the transverse deviation distance of the model machine of the unmanned carrier and the slippage rate of each driving wheel is within 5.8%.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (1)
1. The torque optimal distribution control method is characterized in that the torque optimal distribution control system comprises an upper layer controller, a middle layer controller and a bottom layer controller; the upper layer controller and the middle layer controller, the upper layer controller and the bottom layer controller, and the middle layer controller and the bottom layer controller are electrically and mechanically connected;
the middle layer controller comprises a torque distribution controller and a slip rate similar controller connected with the torque distribution controller; the slip rate similar controller is connected with a hub motor of the unmanned carrier;
the bottom layer controller comprises an observer; the observer is connected with the unmanned carrier;
the control method of the torque optimal distribution control system comprises the following steps:
the upper controller determines the torque required by the vehicle according to the cargo carrying capacity of the unmanned carrier and determines the driving mode of the unmanned carrier according to the required torque;
when the carrying capacity is smaller than 20kg and the mass center positions of the unmanned carrier and the goods are close to the front axle, the unmanned carrier adopts a front wheel driving mode; when the cargo carrying capacity is smaller than 20kg and the mass center positions of the unmanned carrier and the cargo are close to the rear axle, the unmanned carrier adopts a rear wheel driving mode;
when the cargo carrying capacity is not less than 20kg, the unmanned carrier adopts a four-wheel drive mode;
the slip rate similar controller of the middle-layer controller adjusts the torque according to a PIDNN algorithm under the condition that the driving force allowance and the driving mode determined by the upper-layer controller are met, and feeds back the adjusted torque to the torque distribution controller; the torque distribution controller calculates the expected torque of each wheel hub motor according to the required torque of the upper controller and the adjustment torque, and feeds back the expected torque to each wheel hub motor;
the slip rate similar controller calculates the slip rate of the driving wheel according to the speed and the wheel speed fed back by the driving wheel:
when the minimum value of the slip rate of the driving wheel is larger than the threshold value of the slip rate of the driving wheel, outputting the adjustment torque of the driving wheel;
when the minimum value of the driving wheel slip rate is not more than the threshold value of the driving wheel slip rate and the difference of the driving wheel slip rates is not more than the preset threshold value of the difference of the driving wheel slip rates, the driving wheel adjusting torque is zero;
outputting a driving wheel adjustment torque when the minimum value of the driving wheel slip rate is not greater than the threshold value of the driving wheel slip rate and the difference of the driving wheel slip rates exceeds the preset threshold value of the difference of the driving wheel slip rates;
the observer of the bottom layer controller detects the actual torque of the driving wheel of the unmanned carrier and compares the actual torque with the expected torque output by the torque distribution controller to perform closed-loop feedback tracking control;
when the change rate of the actual torque and the expected torque is less than 0.1%, the actual torque of the driving wheel is unchanged;
when the change rate of the actual torque from the desired torque is not less than 0.1%, the actual torque of the driving wheel is adjusted.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| JPH11159365A (en) * | 1997-11-26 | 1999-06-15 | Nissan Motor Co Ltd | Slip ratio control device for automobile |
| CN109606133A (en) * | 2019-01-16 | 2019-04-12 | 浙江科技学院 | Distributed-driving electric automobile torque vector control method based on bilayer control |
| CN111746295A (en) * | 2019-03-29 | 2020-10-09 | 北京新能源汽车股份有限公司 | Distributed drive control method and device for electric automobile |
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- 2021-08-05 CN CN202110896883.9A patent/CN113547932B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11159365A (en) * | 1997-11-26 | 1999-06-15 | Nissan Motor Co Ltd | Slip ratio control device for automobile |
| CN109606133A (en) * | 2019-01-16 | 2019-04-12 | 浙江科技学院 | Distributed-driving electric automobile torque vector control method based on bilayer control |
| CN111746295A (en) * | 2019-03-29 | 2020-10-09 | 北京新能源汽车股份有限公司 | Distributed drive control method and device for electric automobile |
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