CN106882080B - Differential steering system and adaptive neural network fault-tolerant control method thereof - Google Patents
Differential steering system and adaptive neural network fault-tolerant control method thereof Download PDFInfo
- Publication number
- CN106882080B CN106882080B CN201710028294.2A CN201710028294A CN106882080B CN 106882080 B CN106882080 B CN 106882080B CN 201710028294 A CN201710028294 A CN 201710028294A CN 106882080 B CN106882080 B CN 106882080B
- Authority
- CN
- China
- Prior art keywords
- neural network
- adaptive neural
- tolerant control
- network fault
- steering system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 99
- 238000000034 method Methods 0.000 title claims abstract description 37
- 230000003044 adaptive effect Effects 0.000 title claims description 54
- 230000008569 process Effects 0.000 claims abstract description 5
- 210000002569 neuron Anatomy 0.000 claims description 14
- 238000003860 storage Methods 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 4
- 238000013016 damping Methods 0.000 claims description 3
- 238000012905 input function Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- 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
- B60L15/2036—Electric differentials, e.g. for supporting steering vehicles
-
- 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
- B60L15/32—Control or regulation of multiple-unit electrically-propelled vehicles
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D11/00—Steering non-deflectable wheels; Steering endless tracks or the like
- B62D11/001—Steering non-deflectable wheels; Steering endless tracks or the like control systems
- B62D11/003—Electric or electronic control systems
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D11/00—Steering non-deflectable wheels; Steering endless tracks or the like
- B62D11/02—Steering non-deflectable wheels; Steering endless tracks or the like by differentially driving ground-engaging elements on opposite vehicle sides
- B62D11/04—Steering non-deflectable wheels; Steering endless tracks or the like by differentially driving ground-engaging elements on opposite vehicle sides by means of separate power sources
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Power Engineering (AREA)
- Automation & Control Theory (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Steering Control In Accordance With Driving Conditions (AREA)
Abstract
The invention discloses a differential steering system and a fault-tolerant control method of a self-adaptive neural network thereof. In the running process, the whole vehicle electronic control unit acquires steering wheel rotation angle, yaw rate and vehicle speed signals in real time, calculates the difference value between the ideal yaw rate and the actual yaw rate, recalculates the output torque of the hub motor through the designed self-adaptive neural network controller, transmits the torque signals to the motor controller, and sends current signals to four hub motors through the motor controller to complete steering stability control under the normal and failure conditions of the hub motors. The invention can improve the reliability of the differential steering system and the stability and safety of the automobile during running.
Description
Technical Field
The invention relates to the technical field of four-wheel steering, in particular to a differential steering system and a fault-tolerant control method of a self-adaptive neural network thereof.
Background
For conventional vehicles, clutches, transmissions, propeller shafts, differentials, and even transfer cases are indispensable, and these components are heavy, complex in structure, and have problems of requiring periodic maintenance and failure rate. But the in-wheel motor solves this problem well. Besides the simpler structure, the vehicle driven by the hub motor can obtain better space utilization rate, and meanwhile, the transmission efficiency is high.
Because the wheel hub motor has the characteristic of independent driving of a single wheel, a front drive, rear drive or four-drive driving mode is easy to realize. Meanwhile, the hub motor can adjust the torque or the rotating speed of the left wheel and the right wheel to realize differential steering, so that the turning radius of the vehicle is greatly reduced, and the in-situ steering can be almost realized under special conditions.
But the hub motor may have a failure condition and reliability is problematic. How to guarantee the stability of the automobile under the condition of failure of the hub motor.
Disclosure of Invention
The invention aims to solve the technical problem of providing a differential steering system and a self-adaptive neural network fault-tolerant control method thereof aiming at the defects related to the background technology.
The invention adopts the following technical scheme for solving the technical problems:
a differential steering system comprises a steering wheel angle sensor, a steering wheel, a steering column, a rack-and-pinion steering gear, first to fourth wheels, first to fourth hub motors, a front axle, a whole vehicle electronic control unit, a storage battery pack, a vehicle speed sensor, a yaw rate sensor, a rear axle and a motor control unit;
one end of the steering column is fixedly connected with the steering wheel, and the other end of the steering column is connected with the front shaft through a rack-and-pinion steering gear;
the steering wheel angle sensor is arranged on the steering column and used for acquiring the steering wheel angle;
the vehicle speed sensor and the yaw rate sensor are arranged on the vehicle and are respectively used for acquiring the vehicle speed and the yaw rate of the vehicle;
the first wheel and the second wheel are respectively arranged at two ends of the front axle, and the third wheel and the fourth wheel are respectively arranged at two ends of the rear axle;
the first to fourth hub motors are respectively and correspondingly arranged on the first to fourth wheels and are used for driving the first to fourth wheels;
the storage battery pack is arranged on the automobile and is used for supplying power;
the whole vehicle electronic control unit is respectively and electrically connected with the steering wheel angle sensor, the vehicle speed sensor, the yaw rate sensor, the motor controller and the storage battery pack, and is used for calculating the moment of the four hub motors according to the data measured by the steering wheel angle sensor, the vehicle speed sensor and the yaw rate sensor, generating corresponding current signals and transmitting the current signals to the motor controller;
the motor controller is electrically connected with the four hub motors and the storage battery respectively and is used for controlling the four hub motors to work according to the received current signals.
The invention also discloses a self-adaptive neural network fault-tolerant control method based on the differential steering system, which comprises the following steps:
step 1), calculating the relation between the ideal yaw rate and the steering wheel angle;
step 2), establishing a state space model of the differential steering system;
step 3), a state space model of a self-adaptive neural network fault-tolerant control system of the differential steering system is established based on the state space model of the differential steering system, and a state space model of the self-adaptive neural network fault-tolerant control system of the differential steering system under the condition that a wheel hub motor fails is established based on the state space model of the self-adaptive neural network fault-tolerant control system of the differential steering system;
step 4), establishing a reference model and an inverse model of the adaptive neural network fault-tolerant control system;
step 5), building a neural network compensator of the adaptive neural network fault-tolerant control system based on a reference model, an inverse model and the relation between the ideal yaw rate and the steering wheel angle of the adaptive neural network fault-tolerant control system;
step 6), establishing an adaptive neural network controller of the adaptive neural network fault-tolerant control system based on a neural network compensator of the adaptive neural network fault-tolerant control system;
and 7) carrying out self-adaptive adjustment on the error between the reference model and the output of the self-adaptive neural network fault-tolerant control system under the fault of the hub motor based on the self-adaptive neural network controller.
As a further advantage of the adaptive neural network fault-tolerant control method of the differential steering systemThe ideal yaw rate ω according to step 1) r * And steering wheel angle theta sw The relation is:
wherein:
a 0 ＝k f k r (a+b) 2 +(k r b-k f a)mu 2 ；b 0 ＝k f k r (a+b) u; l is the axial distance between the front axle and the rear axle; u is the speed of the car; k (K) s Adjusting parameters for a preset yaw rate; k (k) f 、k r The lateral deflection rigidity of the front wheel and the rear wheel respectively; a is the axle distance from the mass center to the front axle; b is the axle distance from the mass center to the rear axle; m is the mass of the whole vehicle.
As a further optimization scheme of the adaptive neural network fault-tolerant control method of the differential steering system, the state space model of the differential steering system in the step 2) is as follows:
in the method, in the process of the invention,
δ f is the front wheel corner; beta is the centroid slip angle; omega r Is yaw rate; d is a half wheelbase; j (J) s Equivalent moment of inertia for steering wheel; g is the gear ratio of the gear rack steering gear; i is the whole vehicleMoment of inertia about the z-axis; b (B) s R is the tire radius, which is the equivalent damping of the steering wheel; d, d 2 The tire drag torque; d, d 1 A transverse offset moment for the kingpin; t (T) sw Torque applied to the steering wheel for the driver; t (T) fl 、T fr 、T rl 、T rr The output torques of the front left, front right, rear left and rear right hub motors are respectively.
As a further optimization scheme of the adaptive neural network fault-tolerant control method of the differential steering system, the state space model of the adaptive neural network fault-tolerant control system of the differential steering system in the step 3) is as follows:
where f (x (t))=ax (t); g (x (t))=λb; h (x (t))=cx (t); t is time;
λ 1 、λ 2 、λ 3 、λ 4 the probability of failure of the front left, front right, rear left and rear right hub motors is respectively.
As a further optimization scheme of the adaptive neural network fault-tolerant control method of the differential steering system, the state space model of the adaptive neural network fault-tolerant control system of the differential steering system under the condition that the wheel hub motor fails in step 3) is as follows:
where σ (x (t), u (t), w (t)) is a disturbance input function in the event of a hub motor failure.
As a further optimization scheme of the adaptive neural network fault-tolerant control method of the differential steering system, the reference model of the adaptive neural network fault-tolerant control system in the step 4) is as follows:
wherein: x is x m (t) is a state vector of the reference model; u (u) m (t) input control vector for reference model, y m (t) is the output vector of the reference model; a is that m ＝A；B m ＝λB；C m ＝C。
As a further optimization scheme of the adaptive neural network fault-tolerant control method of the differential steering system, the inverse model of the adaptive neural network fault-tolerant control system in the step 4) is as follows:
u(t)＝g -1 (t)[v(t)-f(x)]
wherein: v (t) is a given tracking response.
As a further optimization scheme of the adaptive neural network fault-tolerant control method of the differential steering system, the neural network compensator in the step 5) is as follows:
wherein: delta is the inverse model error; y is s Is the output of the s-th layer neural network; w (w) is Weights for the ith neuron to the s-th layer neuron; g i (x) Output value for the ith neuron; i is a natural number which is more than or equal to 1 and less than or equal to n, n is the number of neurons, and s is the number of layers of the current neural network.
As a further optimization scheme of the adaptive neural network fault-tolerant control method of the differential steering system, the adaptive neural network controller in the step 6) is as follows:
wherein: u (u) eer (t) is the compensation error of the inner loop system; k (K) p Is a parameter matrix; u (u) NN Is the output of the adaptive neural network controller.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
1. according to the steering wheel angle, yaw rate and vehicle speed signals acquired by the electronic control unit in real time, calculating the difference value between the ideal yaw rate and the actual yaw rate, recalculating the output torque of the wheel hub motor through a designed self-adaptive neural network controller, and sending a current signal to the wheel hub motor by the ECU to complete the steering stability control of the wheel hub motor under the normal and failure conditions;
2. the provided control method is simple, convenient and reliable, and meanwhile, the influence of an inverse model error and a nonlinear factor caused by the fault of the steering system is effectively overcome by utilizing the neural network, so that the real-time following control of the differential steering model is realized;
3. the method can ensure the accurate tracking model output of the differential steering system under the fault condition without knowing the position and the size of the fault in advance or carrying out parameter identification on the system, thereby achieving ideal dynamic performance.
Drawings
FIG. 1 is a schematic diagram of a differential steering system according to the present invention;
fig. 2 is a flow chart of the fault-tolerant control method of the adaptive neural network in the present invention.
In the figure, a 1-steering wheel angle sensor, a 2-steering wheel, a 3-steering column, a 4-left front wheel and hub motor, a 5-rack-and-pinion steering gear, a 6-right front wheel and hub motor, a 7-front axle, an 8-whole vehicle electronic control unit, a 9-storage battery pack, a 10-left rear wheel and hub motor, a 11-right rear wheel and hub motor, a 12-vehicle speed sensor, a 13-yaw rate sensor, a 14-rear axle and a 15-motor controller.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings:
as shown in fig. 1, the present invention has developed a differential steering system including a steering wheel angle sensor 1, a steering wheel 2, a steering column 3, a rack-and-pinion steering gear 5, first to fourth wheels, first to fourth hub motors, a front axle 7, a complete vehicle electronic control unit 8, a battery pack 9, a vehicle speed sensor 12, a yaw rate sensor 13, a rear axle 14, and a motor control unit 15;
one end of the steering column 3 is fixedly connected with the steering wheel 2, and the other end of the steering column is connected with the front shaft 7 through a rack-and-pinion steering gear 5;
the steering wheel angle sensor 1 is arranged on the steering column 3 and is used for acquiring a steering wheel angle;
the vehicle speed sensor 12 and the yaw rate sensor 13 are arranged on the vehicle and are respectively used for acquiring the vehicle speed and the yaw rate of the vehicle;
the first wheel and the second wheel are respectively arranged at two ends of the front axle 7, and the third wheel and the fourth wheel are respectively arranged at two ends of the rear axle 14;
the first to fourth hub motors are respectively and correspondingly arranged on the first to fourth wheels and are used for driving the first to fourth wheels;
the storage battery pack 9 is arranged on the automobile and is used for supplying power;
the whole vehicle electronic control unit 8 is respectively and electrically connected with the steering wheel angle sensor 1, the vehicle speed sensor 12, the yaw rate sensor 13, the motor controller 15 and the storage battery pack 9, and is used for calculating the moment of the four hub motors according to the data measured by the steering wheel angle sensor 1, the vehicle speed sensor 12 and the yaw rate sensor 13, generating corresponding current signals and transmitting the corresponding current signals to the motor controller 15;
the motor controller 15 is electrically connected with the four hub motors and the storage battery 9 respectively, and is used for controlling the four hub motors to work according to the received current signals.
As shown in fig. 2, the invention also discloses a self-adaptive neural network fault-tolerant control method based on the differential steering system, which is characterized by comprising the following steps:
step 1), calculating an ideal yaw rate ω r * And steering wheel angle theta sw Relationship:
wherein:
a 0 ＝k 1 k 2 (a+b) 2 +(k 2 b-k 1 a)mu 2 ；b 0 ＝k 1 k 2 (a+b) u; l is the axial distance between the front axle and the rear axle; u is the speed of the car; k (K) s The range of the yaw rate adjusting parameter is selected according to the preference of a driver, and the range is preferably 0.12-0.37; k (k) 1 、k 2 The lateral deflection rigidity of the front wheel and the rear wheel respectively; a is the axle distance from the mass center to the front axle; b is the axle distance from the mass center to the rear axle; m is the mass of the whole vehicle.
Step 2), establishing a differential steering system state space model:
the differential steering system state space model is as follows:
in the method, in the process of the invention,
δ f is the front wheel corner; beta is the centroid slip angle; omega r Is yaw rate; d is a half wheelbase; j (J) s Equivalent moment of inertia for steering wheel; g is the gear ratio of the gear rack steering gear; i is the moment of inertia of the whole vehicle around the z axis; b (B) s R is the tire radius, which is the equivalent damping of the steering wheel; d, d 2 The tire drag torque; d, d 1 A transverse offset moment for the kingpin; t (T) sw Torque applied to the steering wheel for the driver; t (T) fl 、T fr 、T rl 、T rr Respectively are provided withThe output torque of the hub motors is the output torque of the hub motors at the front left, the front right, the rear left and the rear right.
And 3) establishing a state space model of the self-adaptive neural network fault-tolerant control system based on the state space model of the differential steering system, and establishing a state space model of the self-adaptive neural network fault-tolerant control system of the differential steering system under the condition that the hub motor fails based on the state space model of the self-adaptive neural network fault-tolerant control system of the differential steering system.
Firstly, establishing a state space model of a self-adaptive neural network fault-tolerant control system of a differential steering system:
where f (x (t))=ax (t); g (x (t))=λb; h (x (t))=cx (t); t is time;
λ 1 、λ 2 、λ 3 、λ 4 the probability of failure of the front left, front right, rear left and rear right hub motors is respectively.
Based on the state space model of the self-adaptive neural network fault-tolerant control system of the differential steering system, when the differential system fails, the state space model of the differential steering system with the self-adaptive neural network fault-tolerant function is as follows:
wherein sigma (x (t), u (t), w (t)) is a disturbance input function in the case of a fault;
step 4), establishing a reference model and an inverse model of the adaptive neural network fault-tolerant control system;
firstly, establishing a reference model of the adaptive neural network fault-tolerant control system:
wherein: x is x m (t) is a state vector of the reference model; u (u) m (t) input control vector for reference model, y m (t) is the output vector of the reference model; a is that m ＝A；B m ＝λB；C m ＝C。
Secondly, establishing an inverse model of the adaptive neural network fault-tolerant control system:
u(t)＝g -1 (t)[v(t)-f(x)]
wherein: v (t) is a given tracking response.
Step 5), based on the reference model, the inverse model and the relation between the ideal yaw rate and the steering wheel angle of the adaptive neural network fault-tolerant control system, the neural network compensator for establishing the adaptive neural network fault-tolerant control system can be expressed as:
wherein: delta is the inverse model error; y is s Is the output of the s-th layer neural network; w (w) is Weights for the ith neuron to the s-th layer neuron; g i (x) Output value for the ith neuron; i is a natural number which is more than or equal to 1 and less than or equal to n, n is the number of neurons, and s is the number of layers of the current neural network.
Step 6), based on the neural network compensator of the adaptive neural network fault-tolerant control system, the adaptive neural network controller of the adaptive neural network fault-tolerant control system is established as follows:
wherein: u (u) eer (t) is the compensation error of the inner loop system; k (K) p Is a parameter matrix; u (u) NN Is the output of the adaptive neural network controller.
And 7) carrying out self-adaptive adjustment on the error between the reference model and the output of the self-adaptive neural network fault-tolerant control system under the fault of the hub motor based on the self-adaptive neural network regulator.
As can be seen from the structure diagram of the adaptive neural network controller of the present invention in fig. 2, the relationship between the inverse model error Δ and the input and system output of the inverse model is:
the following performance indexes are defined:
wherein: y is js Output for the jth neuron; e, e j Is the jth neuron error.
First of all, the compensation errors u in each case are trained off-line eer So that the output of the network approximates u eer Thereby completing the function of feedback compensation.
On the basis of offline learning, data of the differential steering system during operation are collected in real time, parameters are updated by using an online self-adaptive learning algorithm, and in order to improve the stability of the self-adaptive neural network fault-tolerant control system, the neural network weight is adjusted by adopting the following online self-adaptive learning algorithm:
wherein: w () is a weight; t, P is a positive definite matrix; q is a radial basis function.
In the running process, the electronic control unit acquires steering wheel rotation angle, yaw rate and vehicle speed signals in real time, calculates the difference value between the ideal yaw rate and the actual yaw rate, recalculates the output torque of the wheel hub motor through the designed self-adaptive neural network controller, and sends a current signal to the wheel hub motor through the motor control unit to complete steering stability control under the normal and failure conditions of the wheel hub motor, thereby realizing the four-wheel steering system with the self-adaptive neural network fault-tolerant control function and the control method thereof.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
While the foregoing is directed to embodiments of the present invention, other and further details of the invention may be had by the present invention, it should be understood that the foregoing description is merely illustrative of the present invention and that no limitations are intended to the scope of the invention, except insofar as modifications, equivalents, improvements or modifications are within the spirit and principles of the invention.
Claims (9)
1. An adaptive neural network fault-tolerant control method of a differential steering system comprises a steering wheel angle sensor (1), a steering wheel (2), a steering column (3), a rack-and-pinion steering gear (5), first to fourth wheels, first to fourth hub motors, a front axle (7), a whole vehicle electronic control unit (8), a storage battery pack (9), a vehicle speed sensor (12), a yaw rate sensor (13), a rear axle (14) and a motor controller (15);
one end of the steering column (3) is fixedly connected with the steering wheel (2), and the other end of the steering column is connected with the front shaft (7) through a rack-and-pinion steering gear (5);
the steering wheel angle sensor (1) is arranged on the steering column (3) and is used for acquiring the steering wheel angle;
the vehicle speed sensor (12) and the yaw rate sensor (13) are arranged on the vehicle and are respectively used for acquiring the vehicle speed and the yaw rate of the vehicle;
the first wheel and the second wheel are respectively arranged at two ends of the front axle (7), and the third wheel and the fourth wheel are respectively arranged at two ends of the rear axle (14);
the first to fourth hub motors are respectively and correspondingly arranged on the first to fourth wheels and are used for driving the first to fourth wheels;
the storage battery pack (9) is arranged on the automobile and is used for supplying power;
the whole vehicle electronic control unit (8) is respectively and electrically connected with the steering wheel angle sensor (1), the vehicle speed sensor (12), the yaw rate sensor (13), the motor controller (15) and the storage battery pack (9), and is used for calculating the moment of the four hub motors according to the data measured by the steering wheel angle sensor (1), the vehicle speed sensor (12) and the yaw rate sensor (13) and generating corresponding current signals to be transmitted to the motor controller (15);
the motor controller (15) is respectively and electrically connected with the four hub motors and the storage battery (9) and is used for controlling the four hub motors to work according to the received current signals;
the self-adaptive neural network fault-tolerant control method of the differential steering system is characterized by comprising the following steps of:
step 1), calculating the relation between the ideal yaw rate and the steering wheel angle;
step 2), establishing a state space model of the differential steering system;
step 3), a state space model of a self-adaptive neural network fault-tolerant control system of the differential steering system is established based on the state space model of the differential steering system, and a state space model of the self-adaptive neural network fault-tolerant control system of the differential steering system under the condition that a wheel hub motor fails is established based on the state space model of the self-adaptive neural network fault-tolerant control system of the differential steering system;
step 4), establishing a reference model and an inverse model of the adaptive neural network fault-tolerant control system;
step 5), building a neural network compensator of the adaptive neural network fault-tolerant control system based on a reference model, an inverse model and the relation between the ideal yaw rate and the steering wheel angle of the adaptive neural network fault-tolerant control system;
step 6), establishing an adaptive neural network controller of the adaptive neural network fault-tolerant control system based on a neural network compensator of the adaptive neural network fault-tolerant control system;
and 7) carrying out self-adaptive adjustment on the error between the reference model and the output of the self-adaptive neural network fault-tolerant control system under the fault of the hub motor based on the self-adaptive neural network controller.
2. The adaptive neural network fault-tolerant control method of a differential steering system according to claim 1, wherein the ideal yaw rate ω of step 1) is r * And steering wheel angle theta sw The relation is:
wherein:
a 0 ＝k f k r (a+b) 2 +(k r b-k f a)mu 2 ；b 0 ＝k f k r (a+b) u; l is the axial distance between the front axle and the rear axle; u is the speed of the car; k (K) s Adjusting parameters for a preset yaw rate; k (k) f 、k r The lateral deflection rigidity of the front wheel and the rear wheel respectively; a is the axle distance from the mass center to the front axle; b is the axle distance from the mass center to the rear axle; m is the mass of the whole vehicle.
3. The adaptive neural network fault-tolerant control method of a differential steering system according to claim 2, wherein the state space model of the differential steering system in step 2) is:
in the method, in the process of the invention,
θ f is the front wheel corner; beta is the centroid slip angle; omega r Is yaw rate; d is a half wheelbase; j (J) s Equivalent moment of inertia for steering wheel; g is the gear ratio of the gear rack steering gear; i is the moment of inertia of the whole vehicle around the z axis; b (B) s R is the tire radius, which is the equivalent damping of the steering wheel; d, d 2 The tire drag torque; d, d 1 A transverse offset moment for the kingpin; t (T) sw Torque applied to the steering wheel for the driver; t (T) fl 、T fr 、T rl 、T rr The output torques of the front left, front right, rear left and rear right hub motors are respectively.
4. The method for adaptive neural network fault-tolerant control of a differential steering system according to claim 3, wherein the state space model of the adaptive neural network fault-tolerant control of a differential steering system in step 3) is:
where f (x (t))=ax (t); g (x (t))=λb; h (x (t))=cx (t); t is time;
λ 1 、λ 2 、λ 3 、λ 4 the probability of failure of the front left, front right, rear left and rear right hub motors is respectively.
5. The adaptive neural network fault-tolerant control method of a differential steering system according to claim 4, wherein the state space model of the adaptive neural network fault-tolerant control system of the differential steering system in the case of a failure of the in-wheel motor in step 3) is:
where σ (x (t), u (t), w (t)) is a disturbance input function in the event of a hub motor failure.
6. The adaptive neural network fault-tolerant control method of a differential steering system according to claim 5, wherein the reference model of the adaptive neural network fault-tolerant control system in step 4) is:
wherein: x is x m (t) is a state vector of the reference model; u (u) m (t) input control vector for reference model, y m (t) is the output vector of the reference model; a is that m ＝A；B m ＝λB；C m ＝C。
7. The adaptive neural network fault-tolerant control method of a differential steering system according to claim 6, wherein the inverse model of the adaptive neural network fault-tolerant control system in step 4) is:
u(t)＝g -1 (t)[v(t)-f(x)]
wherein: v (t) is a given tracking response.
8. The adaptive neural network fault-tolerant control method of a differential steering system according to claim 7, wherein the neural network compensator in step 5) is:
wherein: delta is the inverse model error; y is s Is the output of the s-th layer neural network; w (w) is Weights for the ith neuron to the s-th layer neuron; g i (x) Output value for the ith neuron; i is a natural number which is more than or equal to 1 and less than or equal to n, n is the number of neurons, and s is the number of layers of the current neural network.
9. The adaptive neural network fault-tolerant control method of a differential steering system according to claim 8, wherein the adaptive neural network controller in step 6) is:
wherein: u (u) eer (t) is the compensation error of the inner loop system; k (K) p Is a parameter matrix; u (u) NN Is the output of the adaptive neural network controller.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710028294.2A CN106882080B (en) | 2017-01-16 | 2017-01-16 | Differential steering system and adaptive neural network fault-tolerant control method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710028294.2A CN106882080B (en) | 2017-01-16 | 2017-01-16 | Differential steering system and adaptive neural network fault-tolerant control method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106882080A CN106882080A (en) | 2017-06-23 |
| CN106882080B true CN106882080B (en) | 2023-05-23 |
Family
ID=59176604
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710028294.2A Active CN106882080B (en) | 2017-01-16 | 2017-01-16 | Differential steering system and adaptive neural network fault-tolerant control method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106882080B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107458457A (en) * | 2017-07-06 | 2017-12-12 | 江苏速度智能科技有限公司 | Low damage control system and 360 degree of low damage body chassis and its control method |
| CN109050661B (en) * | 2018-09-20 | 2020-04-17 | 合肥工业大学 | Coordinated control method and coordinated control device for electronic differential and active differential steering |
| CN109808511B (en) * | 2019-03-15 | 2020-12-11 | 北京航空航天大学 | Six-wheel driving force distribution method, device, equipment and medium |
| CN110758117B (en) * | 2019-10-31 | 2022-07-12 | 南京航空航天大学 | Intelligent fault-tolerant control system for electric wheel automobile driver and working method thereof |
| CN111824256B (en) * | 2020-07-13 | 2021-10-01 | 南京航空航天大学 | Steer-by-wire system with adaptive fault-tolerant control function and control method thereof |
| CN112026777B (en) * | 2020-07-23 | 2021-09-17 | 南京航空航天大学 | Vehicle composite steering system and mode switching control method thereof |
| CN112519873B (en) * | 2020-07-28 | 2022-04-26 | 江苏大学 | Active fault-tolerant control algorithm and system for four-wheel independent steer-by-wire electric vehicle actuating mechanism |
| CN113386583A (en) * | 2021-07-30 | 2021-09-14 | 重庆电子工程职业学院 | Automobile hub motor differential control system and method |
| CN114013501B (en) * | 2021-11-25 | 2022-09-30 | 南京航空航天大学 | Electro-hydraulic steering fault-tolerant control method and terminal |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006007824A (en) * | 2004-06-22 | 2006-01-12 | Honda Motor Co Ltd | Vehicle controller |
| CN102522945B (en) * | 2012-01-10 | 2014-06-25 | 江苏大学 | Polyphase motor fault-tolerant control method and system based on multi-neural-network inverse model |
| CN103587576B (en) * | 2013-12-06 | 2015-09-02 | 中国石油大学(华东) | A kind of Power-driven automobile steering-by-wire system and control method |
| CN105774902B (en) * | 2016-03-08 | 2018-02-06 | 南京航空航天大学 | A kind of automobile power steering control device and control method with fault tolerance |
| CN106080753B (en) * | 2016-06-14 | 2018-04-06 | 宁波工程学院 | A kind of Electric Motor Wheel steering control system and its control method for merging active steering, power-assisted steering and direct yaw moment control function |
| CN206537164U (en) * | 2017-01-16 | 2017-10-03 | 南京航空航天大学 | A kind of differential steering system |
-
2017
- 2017-01-16 CN CN201710028294.2A patent/CN106882080B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN106882080A (en) | 2017-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106882080B (en) | Differential steering system and adaptive neural network fault-tolerant control method thereof | |
| CN206537164U (en) | A kind of differential steering system | |
| CN105774902B (en) | A kind of automobile power steering control device and control method with fault tolerance | |
| CN107685767B (en) | Multiaxis wheel-hub motor driven vehicle rear-wheel steering-by-wire driving device and forward method | |
| CN108422901B (en) | A kind of In-wheel-motor driving wheel of vehicle torque Multipurpose Optimal Method optimal based on vehicle comprehensive performance | |
| CN110126643B (en) | Control method and system for distributed driving electric automobile in motor failure state | |
| CN108340967B (en) | Method for controlling yaw stability during steering of multi-wheel independent driving electric vehicle | |
| CN110466604B (en) | Control method for differential driving steering and stability of electric automobile driven by hub motor | |
| CN109094640B (en) | Wheel-driven electric automobile steer-by-wire system and control method | |
| CN107738691B (en) | Timely four-wheel-drive composite steering system and multi-objective optimization method thereof | |
| CN111645755B (en) | Control method and device | |
| CN108163044A (en) | The steering redundancy of four motorized wheels electric vehicle and integrated control system and method | |
| CN105966263B (en) | A kind of electric wheel truck differential steering road feel control method of In-wheel motor driving | |
| CN110901630A (en) | Method and system for controlling lateral stability of semi-trailer train | |
| CN112026777B (en) | Vehicle composite steering system and mode switching control method thereof | |
| CN113968205B (en) | Multi-shaft electric drive vehicle-based compound brake fault-tolerant control method and vehicle | |
| CN111546907B (en) | Wheel speed distribution method for four-wheel steering hub motor driven vehicle | |
| CN114701565B (en) | Four-wheel steering independent wheel steering driving device and control method thereof | |
| CN115489318A (en) | Fault diagnosis and failure control method for distributed drive electric automobile | |
| CN115723590A (en) | Energy-saving torque vector control method for hub motor driven automobile | |
| CN109849898A (en) | Vehicle yaw stability control method based on genetic algorithm hybrid optimization GPC | |
| CN111824256B (en) | Steer-by-wire system with adaptive fault-tolerant control function and control method thereof | |
| CN209739155U (en) | electronic round of system of electronic round of line control four-wheel initiative steering | |
| CN115848162B (en) | Control method for six-wheel independent electrically-driven unmanned vehicle differential steering | |
| JP4030871B2 (en) | Electric vehicle steering mechanism |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |