CN115782587A - Active fault-tolerant control method based on single-motor failure lateral dynamics cluster analysis - Google Patents
Active fault-tolerant control method based on single-motor failure lateral dynamics cluster analysis Download PDFInfo
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Abstract
The invention relates to an active fault-tolerant control method based on single-motor failure transverse dynamics cluster analysis. The method comprises the following steps: s1, selecting yaw velocity and centroid slip angle to represent the lateral stability of the vehicle, and setting different failure factors and pedal opening degrees to obtain the response conditions of the current yaw velocity and centroid slip angle of the fault vehicle; clustering and analyzing boundaries of participation of lateral stability and dynamic control; s2, analyzing an expected yaw velocity and a centroid slip angle based on the steering wheel angle and pedal opening input, and designing upper-layer control of an additional yaw moment and a total longitudinal force control law; and S3, selecting a boundary and upper-layer control output according to the performance, and establishing fault-tolerant control lower-layer control of optimal moment solving. The invention considers the boundary setting problem of the transverse stability and the longitudinal power control participation of the vehicle after the vehicle is invalid; the residual torque output capacity of the fault motor is fully utilized, and the vehicle still has good stability and power performance after the motor fails.
Description
Technical Field
The invention belongs to the field of distributed multi-axis vehicle driving system control, and particularly relates to an active fault-tolerant control method based on single-motor failure transverse dynamics cluster analysis.
Background
The fault-tolerant control of failure of a driving motor of a distributed electric automobile is a hotspot of current research, and the current fault-tolerant control strategies mainly comprise two types: (1) model-free fault-tolerant control; (2) model-based fault tolerant control. The model-free fault-tolerant control is a control method based on human input and output signals of a system. The fault-tolerant control based on the model simplifies the automobile into a linear or non-linear automobile model, and realizes the fault-tolerant control by a vehicle dynamics method.
The main problems of the existing fault-tolerant control strategy are as follows: 1. the method for completely setting the output torque of the failure motor to zero based on the rule and compensating the output torque of the failure motor through the residual motor does not consider the condition that the motor is not completely failed, cannot fully utilize the residual torque of the failed motor, and enables the other motors on the same side to output long-term work under a large load, thereby increasing the risk of normal motor failure.
2. Although the output torque of the motor is not completely set to zero by the active fault-tolerant control considering the failure factor (not completely failed), the problem of boundary setting for the participation of the lateral stable control and the longitudinal dynamic control of the vehicle after the vehicle fails is not considered, the fault-tolerant control is frequently used due to the fact that the fault degree is not high, and the situation that no solution exists when the stability and the dynamic property are simultaneously ensured due to the existence of two equality rigid constraints.
Disclosure of Invention
The invention aims to provide an active fault-tolerant control method based on single-motor failure lateral dynamics cluster analysis. The method can effectively solve the problem that the driving intention can be well tracked after the failure of the distributed vehicle single motor, can well coordinate the participation degree of dynamic performance and transverse stability, and ensures that the vehicle still has good stability and dynamic performance after the failure of the driving motor.
The technical solution for realizing the purpose of the invention is as follows: an active fault-tolerant control method based on single-motor failure transverse dynamic cluster analysis comprises the following steps:
step S1: selecting yaw angular velocity and centroid slip angle to represent the lateral stability of the vehicle, and obtaining the response conditions of the current yaw angular velocity and centroid slip angle of the fault vehicle by setting different failure factors and pedal opening degrees; clustering and analyzing a boundary where the lateral stability control and the dynamic control participate;
step S2: analyzing an expected yaw velocity and a centroid side slip angle based on the steering wheel turning angle and pedal opening input of a driver, designing yaw moment tracking control by adopting sliding mode control, designing longitudinal force tracking control based on PI control, and obtaining upper-layer control of an additional yaw moment and a total longitudinal force control law;
and step S3: and establishing fault-tolerant control lower-layer control based on optimal moment solution of a quadratic programming algorithm according to the performance selection boundary in the step S1 and the upper-layer control output in the step S2, taking the upper-layer control output in the step S2 as the input of the lower-layer control, and taking the performance selection boundary in the step S1 as the basis for selecting a lower-layer objective function and a constraint set.
Further, the specific process of step S1 is as follows:
introducing failure factor h and pedal opening degree aiming at motor fault degree
And (4) representing the driving and braking conditions, and selecting failure factors and pedal opening as influencing factors.
Selecting yaw angular velocity and a centroid slip angle to represent the lateral stability of the vehicle; acquiring data of response conditions of the current yaw velocity and the centroid sideslip angle of the fault vehicle by setting different failure factors and pedal opening degrees; after all data are acquired off line, the influence change degrees of the yaw velocity and the centroid side slip angle along with the failure factor and the pedal opening degree are respectively obtained;
performing K-means clustering processing on the acquired data, setting the clustering number to be 2, and setting the absolute values of the numerical values of the yaw angular velocity and the centroid sideslip angle to be small, wherein the dynamic characteristics of the data are expressed that the data are not serious in yaw and serve as safety classes, and the clustering centroid of the data is dynamic clustering centroid; the absolute value of the yaw angular velocity mass center and the lateral deviation angle is large, the dynamic characteristic of the yaw angular velocity mass center is that the yaw is serious and serves as a dangerous class, and the clustering mass center of the yaw angular velocity mass center is a stability clustering mass center.
Furthermore, the clustering number is set to be a plurality, and the corresponding failure degree is also divided into a plurality of corresponding grades according to different corresponding classification numbers.
Further, step S2 specifically includes the following steps:
step S21: the transverse expected value tracking control design steps are as follows:
firstly, simplifying a vehicle into a two-degree-of-freedom model, and deducing the expected yaw velocity w of the vehicle in a steady state according to the two-degree-of-freedom dynamic model of the vehicle d Angle of lateral deviation from centroid d ；
Where c is 1 、c 2 、c 3 、c 4 、c 5 Is a constant related to vehicle mass, tire cornering stiffness, wheel base, δ is the wheel angle, u is the longitudinal vehicle speed, w d Desired yaw rate, beta d The desired centroid slip angle;
step S22: designing sliding mode control as a tracking control algorithm, tracking the expected yaw angular velocity and the centroid slip angle in the step S21, and designing the following steps:
designing a sliding mode surface switching function s as follows:
e＝w-w d +ε 1 (β-β d ) (2)
e is the combined deviation of the yaw angular velocity and the centroid slip angle, epsilon 1 The weight of the centroid slip angle is obtained, c is a normal number, the larger the value of c is, the shorter the time for the system to reach a steady state is, t is integral time, w is the current yaw velocity, and beta is the current centroid slip angle;
using exponential approach rate
In the formula of 2 K is a positive number and is a parameter of the controller;
eliminating the slip form flutter phenomenon by adopting a boundary layer method; when the system is outside the boundary layer, the switch control is used, and when inside the boundary layer, the sign function is replaced by a saturation function, namely:
here, φ is the boundary layer thickness;
substituting the control rule function into the first derivative of the sliding mode switching function to obtain the expected value of the additional yaw moment containing the sliding mode control variable as follows:
here, the number of the first and second electrodes,
is the sum of the longitudinal force of each wheel to the moment of the center of mass, I z For the moment of inertia of the vehicle about the Z axis, M zd An additional yaw moment;
step S23: the design steps of longitudinal expected value tracking control are as follows:
the longitudinal force demand can be calculated as follows:
n is the number of driving motors T max The maximum torque of the motor;
the degree of opening of the pedal is set as,
v is the current longitudinal speed of the vehicle, R is the tire radius, F xd Is the total longitudinal force.
Further, step S3 specifically includes the following steps:
step S31: acquiring the information of the yaw angular velocity and the centroid slip angle of the current vehicle, and calculating the Euclidean distance d from the dynamic clustering centroid analyzed in the step S1 1 Euclidean distance d from stable cluster centroid 2 Judging the optimization performance requirement of the current vehicle; the Euclidean distance calculation formula is as follows:
wherein w and beta are the current yaw angular velocity and the centroid slip angle of the vehicle, and w 1 、β 1 Is dynamic clustering centroid, w 2 、β 2 Clustering centroid for stability, d 1 Euclidean distance, d, of dynamic clustering centroid 2 Euclidean distance which is dynamic clustering centroid;
step S32: after the current optimization performance requirement is obtained, the implementation steps of solving the optimal moment of the quadratic programming are as follows:
target control quantity V generated by upper controller d Is composed of
V d ＝[F xd M zl ] T (9)
Here, V d For upper layer controlOutputting a controller, and controlling the target quantity of a lower layer controller;
the motor output vector x is:
x＝[T 1f T 1r T 2f T 2r T 3f T 3r ....T Nf T Nr ] T (10)
wherein x is the motor output vector, T Nf Torque of the driving motor on the left side of the Nth shaft, T Nr The moment of the driving motor at the right side of the Nth shaft.
Ideally, the lower layer target control quantity V d The relationship with the motor output vector x is:
wherein x is min ，x max Respectively a lower bound and an upper bound of the output vector constraint of the motor, and B is a control coefficient matrix;
written in a matrix pattern as:
wherein b is a wheel track;
the mathematical description for a general control distribution problem is expressed as an optimization problem:
wherein J (x) is a cost function;
after the motor fails, redefining the constraint boundary of the fault motor, and when a certain motor i fails, defining the constraint boundary corresponding to the fault motor as follows:
-hx max ≤x i ≤hx max (14)
wherein h is a failure factor;
finally, the problem of optimal torque distribution after failure can be described as:
the torque distribution results required by different performances can be obtained by setting different objective functions, and the fault tolerance is realized.
Compared with the prior art, the invention has the remarkable advantages that:
1. the boundary setting problem of the participation of the driving motor to the transverse stable control and the longitudinal power control of the vehicle after the failure is considered, the fault-tolerant control interference can be avoided for small faults in the range of the stable area of the vehicle, the use frequency of the fault-tolerant control is reduced, and the stability and the dynamic switching requirement of the vehicle can be optimally ensured according to the current running state of the vehicle.
2. And compared with a method of not considering the residual output torque of the fault motor, the method can avoid the condition that other motors on the same side work under a large load for a long time, thereby increasing the risk of failure of other normal motors.
Drawings
FIG. 1 is a flow chart of the single motor failure feature cluster analysis of the present invention
FIG. 2 is a general block diagram of single motor failure fault tolerant control of the present invention
FIG. 3 is a flow chart of the solution of the optimal moment solution of the lower control quadratic programming of the present invention.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
The invention relates to an active fault-tolerant control method based on single-motor failure transverse dynamic cluster analysis, which mainly comprises the following steps:
step S1: the yaw angular velocity and the centroid slip angle are selected to represent the lateral stability of the vehicle, and the response conditions of the current yaw angular velocity and the centroid slip angle of the fault vehicle are obtained by setting different failure factors and pedal opening degrees. And clustering to obtain the boundary of the participation of the lateral stability control and the dynamic control.
Step S2: the method comprises the steps of analyzing an expected yaw velocity and a centroid side slip angle based on the steering wheel turning angle and pedal opening input of a driver, designing yaw moment tracking control by adopting sliding mode control, designing longitudinal force tracking control based on PI control, and obtaining upper-layer control of an additional yaw moment and a total longitudinal force control law.
And step S3: and (3) establishing fault-tolerant control lower-layer control based on optimal moment solution of a quadratic programming algorithm according to the performance selection boundary in the S1 and the upper-layer control output in the S2, taking the upper-layer control output in the S2 as the input of the lower-layer control, and taking the performance selection boundary in the S1 as the basis for selecting a lower-layer objective function and a constraint set.
Specifically, as shown in fig. 1, the transverse dynamic failure characteristic analysis process in step S1 is as follows, and a failure factor h is introduced according to the degree of motor failure, and the failure factor represents the damage condition of the driving motor. Pedal opening degree
And (4) representing the driving and braking conditions, and selecting failure factors and pedal opening as influencing factors.
And selecting the yaw velocity and the centroid slip angle to represent the lateral stability of the vehicle. And by setting different failure factors and pedal opening degrees, data acquisition is carried out on the response conditions of the current yaw velocity and the centroid sideslip angle of the fault vehicle. After all data are acquired off line, the influence change degrees of the yaw velocity and the centroid side slip angle along with the failure factor and the pedal opening degree are respectively obtained. Then, the acquired data is subjected to K-means clustering processing, and the number of clusters is set to 2 (a plurality of cluster numbers can be set, and the corresponding failure degrees are also divided into several grades according to different corresponding classification numbers). The absolute values of the numerical values of the yaw angular velocity and the centroid sideslip angle are small, the dynamic characteristics of the sensor are represented as that the sensor does not have serious yaw and serves as a safety class, and the clustering centroid of the sensor is a dynamic clustering centroid; the absolute value of the yaw angular velocity mass center and the lateral deviation angle is large, the dynamic characteristic of the yaw angular velocity mass center is that the yaw is serious and serves as a dangerous class, and the clustering mass center of the yaw angular velocity mass center is a stability clustering mass center.
As shown in fig. 2, the upper layer fault-tolerant control design step in step S2 includes control of horizontal and vertical expected value tracking.
The transverse expected value tracking control design steps are as follows:
firstly, simplifying the vehicle into a two-degree-of-freedom model, and deducing the expected yaw velocity w of the vehicle in a steady state according to the two-degree-of-freedom dynamic model of the vehicle d Angle of lateral deviation from centroid d 。
Where c is 1 、c 2 、c 3 、c 4 、c 5 Is a constant related to vehicle mass, tire cornering stiffness, wheel base, δ is wheel angle, u is longitudinal vehicle speed, w d Desired yaw rate, beta d The desired centroid slip angle;
then, in order to track the above expected yaw rate and centroid slip angle, sliding mode control is designed as a tracking control algorithm, and the design steps are as follows:
designing a sliding mode surface switching function s as follows:
e＝w-w d +ε 1 (β-β d ) (2)
where e is the combined yaw rate and centroid yaw angle deviation, ε 1 The weight of the centroid slip angle is obtained, c is a normal number, the larger the value of c is, the shorter the time of the system reaching the steady state is, t is the integral time, w is the current yaw angular velocity, and beta is the current centroid slip angle;
using an exponential approach rate:
in the formula of 2 K is a positive number and is a parameter of the controller;
and eliminating the flutter of the sliding mode by adopting a boundary layer method. When the system is outside the boundary layer, switch control is used, and when the system is inside the boundary layer, a saturation function is used instead of a sign function, namely:
where φ is the boundary layer thickness.
Substituting the control rule function into the first derivative of the sliding mode switching function to obtain the expected value of the additional yaw moment containing the sliding mode control variable as follows:
here, the first and second liquid crystal display panels are,
is the sum of the moments of the longitudinal force of each wheel over the center of mass, I z For the moment of inertia of the vehicle about the Z axis, M zd For additional yaw moment: (ii) a
The longitudinal expected value tracking control design steps are as follows,
the longitudinal force demand can be calculated as follows
Wherein N is the number of driving motors, T max The maximum torque of the motor;
is the opening degree of the pedal, phi is more than 0 and less than 1,
v is the current longitudinal speed of the vehicle, R is the tire radius, F xd Is the total longitudinal force;
as shown in fig. 3, the implementation steps of the fault-tolerant control lower layer control designed according to the performance selection boundary in S1 and the upper layer control output in S2 in step S3 are as follows:
firstly, the yaw angular velocity and the centroid slip angle information of the current vehicle are obtained, the Euclidean distance between the dynamic clustering centroid and the stable clustering centroid analyzed in the step S1 is calculated, and the optimization performance requirement of the current vehicle is judged. The Euclidean distance calculation formula is as follows:
wherein w and beta are the current yaw angular velocity and the centroid slip angle of the vehicle, and w 1 、β 1 Is dynamic clustering centroid, w 2 、β 2 Clustering centroid for stability, d 1 Euclidean distance, d, of dynamic clustering centroid 2 Euclidean distance which is dynamic clustering centroid;
after the current optimization performance requirement is obtained, the implementation steps of solving the optimal moment of the quadratic programming are as follows:
target control quantity V generated by upper controller d Is composed of
V d ＝[F xd M zl ] T (9)
Here, V d The target quantity of the lower layer controller is controlled for the output of the upper layer controller;
the system control input x is:
x＝[T 1f T 1r T 2f T 2r T 3f T 3γ ....T Nf T Nr ] T (10)
wherein x is the motor output vector, T Nf Torque of the drive motor on the left side of the Nth axis, T Nr The moment of the driving motor at the right side of the Nth shaft.
Ideally, the lower layer target control quantity V d The relationship with the motor output vector x is:
wherein x is min ，x max The lower bound and the upper bound of the output vector constraint of the motor are respectively, and B is a control coefficient matrix.
Written in a matrix pattern as:
wherein b is a wheel track;
the mathematical description for a general control distribution problem can be expressed as an optimization problem:
in the formula, J (x) is a cost function.
After the motor fails, redefining the constraint boundary of the fault motor, and when a certain motor i fails, defining the constraint boundary corresponding to the fault motor as follows:
-hx max ≤x i ≤hx max (14)
wherein h is a failure factor.
Finally, the problem of optimal torque distribution after failure can be described as:
the torque distribution results required by different performances can be obtained by setting different objective functions, and the fault tolerance is realized.
Claims (5)
1. An active fault-tolerant control method based on single-motor failure lateral dynamics cluster analysis is characterized by comprising the following steps:
step S1: selecting yaw angular velocity and centroid slip angle to represent the lateral stability of the vehicle, and obtaining the response conditions of the current yaw angular velocity and centroid slip angle of the fault vehicle by setting different failure factors and pedal opening degrees; clustering and analyzing a boundary in which the lateral stability control and the dynamic control participate;
step S2: analyzing an expected yaw velocity and a centroid side slip angle based on the steering wheel turning angle and pedal opening input of a driver, designing yaw moment tracking control by adopting sliding mode control, designing longitudinal force tracking control based on PI control, and obtaining upper-layer control of an additional yaw moment and a total longitudinal force control law;
and step S3: and establishing fault-tolerant control lower-layer control based on optimal moment solution of a quadratic programming algorithm according to the performance selection boundary in the step S1 and the upper-layer control output in the step S2, taking the upper-layer control output in the step S2 as the input of the lower-layer control, and taking the performance selection boundary in the step S1 as the basis for selecting a lower-layer objective function and a constraint set.
2. The method according to claim 1, wherein the step S1 comprises the following steps:
introducing failure factor h and pedal opening degree aiming at motor fault degree
Representing the driving and braking conditions, and selecting failure factors and pedal opening as influencing factors;
selecting yaw angular velocity and a centroid slip angle to represent the lateral stability of the vehicle; acquiring data of response conditions of the current yaw velocity and the centroid sideslip angle of the fault vehicle by setting different failure factors and pedal opening degrees; after all data are acquired off line, the influence change degrees of the yaw angular velocity and the centroid slip angle along with the failure factor and the pedal opening degree are respectively obtained;
performing K-means clustering processing on the acquired data, setting the clustering number to be 2, and setting the absolute values of the numerical values of the yaw angular velocity and the centroid sideslip angle to be small, wherein the dynamic characteristics of the data are expressed that the data are not serious in yaw and serve as safety classes, and the clustering centroid of the data is dynamic clustering centroid; the absolute value of the yaw angular velocity mass center and the lateral deviation angle is large, the dynamic characteristic of the yaw angular velocity mass center is that the yaw is serious and serves as a dangerous class, and the clustering mass center of the yaw angular velocity mass center is a stability clustering mass center.
3. The method according to claim 2, wherein the number of clusters is set to be plural, and the corresponding failure degrees are classified into corresponding several grades according to the corresponding classification numbers.
4. The method according to claim 2, wherein step S2 comprises in particular the steps of:
step S21: the transverse expected value tracking control design steps are as follows:
firstly, simplifying the vehicle into a two-degree-of-freedom model, and deducing the expected yaw velocity w of the vehicle in a steady state according to the two-degree-of-freedom dynamic model of the vehicle d Angle of deviation from centroid beta d ；
Where c is 1 、c 2 、c 3 、c 4 、c 5 Is a constant related to vehicle mass, tire cornering stiffness, wheel base, δ is wheel angle, u is longitudinal vehicle speed, w d Desired yaw rate, beta d The desired centroid slip angle;
step S22: designing sliding mode control as a tracking control algorithm, tracking the expected yaw velocity and the centroid slip angle in the step S21, and designing the following steps:
designing a sliding mode surface switching function s as follows:
e＝w-w d +ε 1 (β-β d ) (2)
e is the combined deviation of the yaw angular velocity and the centroid slip angle, epsilon 1 The weight of the centroid slip angle is obtained, c is a normal number, the larger the value of c is, the shorter the time for the system to reach a steady state is, t is integral time, w is the current yaw velocity, and beta is the current centroid slip angle;
using exponential approach rate
In the formula epsilon 2 K is a positive number and is a parameter of the controller;
eliminating the slip form flutter phenomenon by adopting a boundary layer method; when the system is outside the boundary layer, the switch control is used, and when inside the boundary layer, the sign function is replaced by a saturation function, namely:
here, φ is the boundary layer thickness;
substituting the control rule function into the first derivative of the sliding mode switching function to obtain the expected value of the additional yaw moment containing the sliding mode control variable as follows:
here, the first and second liquid crystal display panels are,
is the sum of the moments of the longitudinal force of each wheel over the center of mass, I z For the moment of inertia of the vehicle about the Z axis, M zd An additional yaw moment;
step S23: the design steps of longitudinal expected value tracking control are as follows:
the longitudinal force demand can be calculated as follows:
n is the number of driving motors T max The maximum torque of the motor;
the degree of opening of the pedal is set as,
v is the current longitudinal speed of the vehicle, R is the tire radius, F xd Is the total longitudinal force.
5. The method according to claim 4, wherein step S3 comprises the following steps:
step S31: acquiring the information of the yaw angular velocity and the centroid slip angle of the current vehicle, and calculating the Euclidean distance d from the dynamic clustering centroid analyzed in the step S1 1 Euclidean distance d from stable cluster centroid 2 Judging the optimization performance requirement of the current vehicle; the Euclidean distance calculation formula is as follows:
wherein w and beta are the current yaw angular velocity and the centroid slip angle of the vehicle, and w 1 、β 1 Is dynamic clustering centroid, w 2 、β 2 Clustering centroid for stability, d 1 Euclidean distance, d, of dynamic clustering centroid 2 Euclidean distance which is dynamic clustering centroid;
step S32: after the current optimization performance requirement is obtained, the implementation steps of solving the optimal moment of the quadratic programming are as follows:
target control quantity V generated by upper controller d Is composed of
V d ＝[F xd M zd ] T (9)
Here, V d The target quantity of the lower layer controller is controlled for the output of the upper layer controller;
the motor output vector x is:
x＝[T 1f T 1r T 2f T 2r T 3f T 3r ..T Nf T Nr ] T (10)
wherein x is the motor output vector, T Nf Torque of the drive motor on the left side of the Nth axis, T Nr The moment of the driving motor at the right side of the Nth shaft;
ideally, the lower layer target control quantity V d The relationship with the motor output vector x is:
wherein x is min ，x max Respectively a lower bound and an upper bound of the output vector constraint of the motor, and B is a control coefficient matrix;
written in a matrix pattern as:
wherein b is a wheel track;
the mathematical description for a general control distribution problem is expressed as an optimization problem:
wherein J (x) is a cost function;
after the motor fails, redefining the constraint boundary of the fault motor, and when a certain motor i fails, defining the constraint boundary corresponding to the fault motor as follows:
-hx max ≤x i ≤hx max (14)
wherein h is a failure factor;
finally, the problem of optimal torque distribution after failure can be described as:
the torque distribution results required by different performances can be obtained by setting different objective functions, and the fault tolerance is realized.
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| CN110789350A (en) * | 2019-11-20 | 2020-02-14 | 电子科技大学 | Fault-tolerant control method for four-wheel drive electric vehicle |
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| CN114735076A (en) * | 2022-05-19 | 2022-07-12 | 吉林大学 | Fault-tolerant control method for differential cooperative steer-by-wire |
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