CN115782587B - Active fault-tolerant control method based on single motor failure transverse dynamics cluster analysis - Google Patents
Active fault-tolerant control method based on single motor failure transverse dynamics cluster analysis Download PDFInfo
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Abstract
The invention discloses an active fault-tolerant control method based on single motor failure transverse dynamics cluster analysis. The method comprises the following steps: s1, selecting a yaw rate and a centroid side deflection angle to represent the transverse stability of a vehicle, and obtaining the response conditions of the current yaw rate and the centroid side deflection angle of the fault vehicle by setting different failure factors and pedal opening; clustering analysis is carried out on boundaries of the lateral stability and dynamic control participation; s2, analyzing expected yaw rate and centroid side deviation angle based on steering wheel rotation angle and pedal opening degree input, and designing and obtaining upper control of an additional yaw moment and a total longitudinal force control law; s3, selecting a boundary and upper control output according to the performance, and establishing fault-tolerant control lower control of optimal moment solution. The invention considers the problem of boundary setting of vehicle transverse stability and longitudinal power control participation after vehicle failure; 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 control of a distributed multi-axis vehicle driving system, and particularly relates to an active fault-tolerant control method based on single motor failure transverse dynamics cluster analysis.
Background
Fault-tolerant control of failure of a driving motor of a distributed electric automobile is a hotspot studied at present, and the existing fault-tolerant control strategies mainly have 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 system man-in-transmission and output signals. The fault-tolerant control based on the model simplifies the automobile into a linear or nonlinear automobile model, and realizes the fault-tolerant control by a vehicle dynamics method.
The existing fault-tolerant control strategy mainly has the following problems: 1. the method for completely setting the output torque of the failure motor to zero based on rules and compensating by the residual motor does not consider the condition that the motor fails completely, the residual torque of the failure motor cannot be fully utilized, the output of the other motors on the same side works under a large load for a long time, and the risk of failure of the normal motor is increased.
2. The active fault-tolerant control taking into consideration the failure factor (incomplete failure) does not set the motor output torque to be zero completely, but the problem of boundary setting of the vehicle transverse stable control and the longitudinal power control after the vehicle is failed is not considered, the fault-tolerant control is not used frequently due to the fact that the fault degree is less frequently, and the situation of no solution exists when the stability and the power performance are ensured simultaneously due to the fact that two equality rigidity constraints exist.
Disclosure of Invention
The invention aims to provide an active fault-tolerant control method based on single motor failure transverse dynamics cluster analysis. The method can effectively solve the problem that the single motor of the distributed vehicle can track the driving intention well after failure, coordinate the participation degree of dynamic property and transverse stability well, and ensure that the vehicle has good stability and dynamic property after 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 dynamics cluster analysis comprises the following steps:
Step S1: selecting a yaw rate and a centroid side deflection angle to represent the transverse stability of the vehicle, and obtaining the response conditions of the current yaw rate and the centroid side deflection angle of the fault vehicle by setting different failure factors and pedal opening; clustering and analyzing boundaries involved in lateral stability control and dynamic control;
Step S2: analyzing expected yaw rate and centroid side deviation angle based on steering wheel angle and pedal opening degree input of a driver, adopting sliding mode control to design yaw moment tracking control, and based on PI control to design longitudinal force tracking control, so as to obtain upper control of an additional yaw moment and a total longitudinal force control law;
Step S3: establishing fault-tolerant control lower control based on optimal moment solution of a quadratic programming algorithm according to the performance selection boundary in the step S1 and the upper control output in the step S2, taking the upper control output in the step S2 as the input of the lower control, and taking the performance selection boundary in the step S1 as the basis of lower objective function and constraint set selection.
Further, the specific process of step S1 is as follows:
a failure factor h is introduced aiming at the motor failure degree, and the pedal opening degree And (5) representing the driving and braking condition, and selecting a failure factor and pedal opening as influencing factors.
Selecting a yaw rate and a centroid side deviation angle to represent the lateral stability of the vehicle; data acquisition is carried out on the response conditions of the current yaw rate and the centroid slip angle of the fault vehicle by setting different failure factors and pedal opening degrees; after all data are acquired offline, the influence change degree of yaw rate, centroid side deviation angle along with failure factors and pedal opening is obtained respectively;
k-means clustering is carried out on the acquired data, the number of clusters is set to be 2, the absolute value of the yaw rate and the centroid side deviation angle value is small, the dynamic characteristic of the data is represented as a safety class with less serious yaw, and the clustering centroid is a dynamic clustering centroid; the absolute value of the yaw angle value of the yaw rate centroid is larger, the dynamic characteristic of the yaw rate centroid is represented as a dangerous class with serious yaw, and the cluster centroid is a stability cluster centroid.
Further, the number of clusters is set to be a plurality, and corresponding failure degrees are also classified into corresponding several levels according to the different corresponding classification numbers.
Further, the step S2 specifically includes the following steps:
step S21: the design steps of the transverse expected value tracking control are as follows:
Firstly, simplifying a vehicle into a two-degree-of-freedom model, and deducing a desired yaw rate w d and a centroid side deflection angle beta d of the vehicle in a steady state according to the two-degree-of-freedom dynamics model of the vehicle;
Where c 1、c2、c3、c4、c5 is a constant related to vehicle mass, tire cornering stiffness, wheelbase, δ is wheel angle, u is longitudinal vehicle speed, w d is desired yaw rate, β d is desired centroid cornering angle;
step S22: the sliding mode control is designed as a tracking control algorithm, the expected yaw rate and the centroid slip angle in the tracking step S21 are tracked, and the design steps are as follows:
the design sliding mode surface switching function s is:
e＝w-wd+ε1(β-βd) (2)
e is the combined deviation of the yaw rate and the centroid side deflection angle, epsilon 1 is the centroid side deflection angle weight, c is the normal number, the larger the value of c is, the shorter the system reaches steady state, t is the integration time, w is the current yaw rate, and beta is the current centroid side deflection angle;
By using an exponential approach rate
Wherein epsilon 2 and k are positive numbers and are parameters of the controller;
Eliminating the vibration phenomenon of the sliding mode by adopting a boundary layer method; when the system is outside the boundary layer, switching control is used, and when the boundary layer is inside, a saturation function is used instead of a sign function, namely:
Here, phi is the boundary layer thickness;
Substituting the control law function into the first derivative of the slip-mode switching function yields the desired value of the additional yaw moment comprising the slip-mode control variable as:
Here the number of the elements is the number, For the sum of the moment of each wheel longitudinal force to the centroid, I z is the moment of inertia of the vehicle about the Z-axis, M zd is the additional yaw moment;
Step S23: the longitudinal expected value tracking control design steps are as follows:
The longitudinal force demand may be calculated as follows:
n is the number of driving motors, and T max is the maximum torque of the motors; For pedal opening,/> V is the current longitudinal speed of the vehicle, R is the tire radius, and F xd is the total longitudinal force.
Further, the step S3 specifically includes the following steps:
Step S31: acquiring yaw rate and centroid side deviation angle information of a current vehicle, calculating Euclidean distance d 1 between the dynamic clustering centroid and the stability clustering centroid analyzed in the step S1, and 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 rate and centroid side deflection angle of the vehicle, w 1、β1 is the dynamic cluster centroid, w 2、β2 is the stability cluster centroid, d 1 is the Euclidean distance of the dynamic cluster centroid, and d 2 is the Euclidean distance of the dynamic cluster centroid;
Step S32: after the current optimization performance requirement is acquired, the implementation steps of the quadratic programming optimal moment solution are as follows:
the target control amount V d generated by the upper layer controller is
Vd＝[Fxd Mzl]T (9)
Here, V d is the upper layer controller output, and the target amount of the lower layer controller is controlled;
The motor output vector x is:
x＝[T1f T1r T2f T2r T3f T3r....TNf TNr]T (10)
wherein x is the motor output vector, T Nf is the torque of the driving motor on the left side of the N-th shaft, and T Nr is the torque of the driving motor on the right side of the N-th shaft.
Ideally, the relationship between the lower-layer target control amount V d and the motor output vector x is:
Wherein x min,xmax is the lower bound and the upper bound of the motor output vector constraint, and B is a control coefficient matrix;
The writing matrix pattern is:
Wherein b is the wheel track;
the mathematical description of a general control allocation problem is expressed as an optimization problem:
Wherein J (x) is a cost function;
After the motor fails, redefining a constraint boundary of a fault motor, and when a certain motor i fails, defining the constraint boundary corresponding to the fault motor as follows:
-hxmax≤xi≤hxmax (14)
wherein h is a failure factor;
finally, the optimal torque distribution problem after failure can be described as:
moment distribution results required by different performances can be obtained by setting different objective functions, so that fault tolerance is realized.
Compared with the prior art, the invention has the remarkable advantages that:
1. Considering the boundary setting problem of the participation of the drive motor in the transverse stable control and the longitudinal power control of the vehicle after the failure, fault-tolerant control interference can be omitted for some small fault problems in the range of the stable region of the vehicle, the use frequency of the fault-tolerant control is reduced, and the stability and the power switching requirement of the vehicle can be optimally ensured according to the current running state of the vehicle.
2. And the failure factor is introduced to adjust the constraint boundary, the residual torque output capacity of the fault motor is fully utilized, and compared with a method which does not consider the residual output torque of the fault motor, the method can avoid that the other motors on the same side work under a large load for a long time, thereby increasing the failure risk 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 a single motor failure fault tolerant control of the present invention
FIG. 3 is a solution flow chart of the lower-level control quadratic programming optimal moment solution of the invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
The invention relates to an active fault-tolerant control method based on single motor failure transverse dynamics cluster analysis, which mainly comprises the following steps:
Step S1: and selecting the yaw rate and the centroid side deflection angle to represent the transverse stability of the vehicle, and obtaining the response conditions of the current yaw rate and the centroid side deflection angle of the fault vehicle by setting different failure factors and pedal opening degrees. The cluster analysis shows the boundary between the lateral stability control and the dynamic control.
Step S2: and analyzing the expected yaw rate and the centroid side deviation angle based on the steering wheel angle and pedal opening degree input of the driver, adopting sliding mode control to design yaw moment tracking control, and designing longitudinal force tracking control based on PI control to obtain upper control of an additional yaw moment and a total longitudinal force control law.
Step S3: and establishing fault-tolerant control lower control based on optimal moment solution of a quadratic programming algorithm according to the performance selection boundary in S1 and the upper control output in S2, taking the upper control output in S2 as the input of the lower control, and taking the performance selection boundary in S1 as the basis of lower objective function and constraint set selection.
Specifically, as shown in fig. 1, the analysis process of the transverse dynamics failure characteristic in step S1 is as follows, and a failure factor h is introduced for the motor failure degree, and the failure factor characterizes the damage condition of the driving motor. Pedal openingAnd (5) representing the driving and braking condition, and selecting a failure factor and pedal opening as influencing factors.
And selecting a yaw rate and a centroid side offset angle to represent the lateral stability of the vehicle. And data acquisition is carried out on the response conditions of the current yaw rate and the centroid slip angle of the fault vehicle by setting different failure factors and pedal opening degrees. After all the data are acquired offline, the change degree of the yaw rate and the centroid side deviation angle along with the influence of the failure factor and the pedal opening is respectively obtained. And then carrying out K-means clustering processing on the acquired data, setting the clustering number to be 2 (a plurality of clustering numbers can be set, and corresponding failure degrees are also classified into a plurality of grades according to different corresponding classification numbers). The absolute value of the yaw rate and the centroid side deviation angle value is small, the dynamic characteristics of the yaw rate and the centroid side deviation angle value are represented as a safety class with non-serious yaw, and the clustering centroid is a dynamic clustering centroid; the absolute value of the yaw angle value of the yaw rate centroid is larger, the dynamic characteristic of the yaw rate centroid is represented as a dangerous class with serious yaw, and the cluster centroid is a stability cluster centroid.
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 design steps of the transverse expected value tracking control are as follows:
Firstly, simplifying the vehicle into a two-degree-of-freedom model, and deriving the expected yaw rate w d and the expected centroid side slip angle beta d of the vehicle in a steady state according to the two-degree-of-freedom dynamics model of the vehicle.
Where c 1、c2、c3、c4、c5 is a constant related to vehicle mass, tire cornering stiffness, wheelbase, δ is wheel angle, u is longitudinal vehicle speed, w d is desired yaw rate, β d is desired centroid cornering angle;
then, in order to track the above desired yaw rate and centroid slip angle, a sliding mode control is designed as a tracking control algorithm, and the design steps are as follows:
the design sliding mode surface switching function s is:
e＝w-wd+ε1(β-βd) (2)
Here, e is the combined deviation of the yaw rate and the centroid side deflection angle, epsilon 1 is the centroid side deflection angle weight, c is the normal number, the larger the value of c is, the shorter the time of the system reaching the steady state is, t is the integration time, w is the current yaw rate, and beta is the current centroid side deflection angle;
The index approach rate is adopted:
Wherein epsilon 2 and k are positive numbers and are parameters of the controller;
and eliminating the sliding mode flutter phenomenon by adopting a boundary layer method. When the system is outside the boundary layer, switching control is used, and when the boundary layer is inside, a saturation function is used instead of a sign function, namely:
where phi is the boundary layer thickness.
Substituting the control law function into the first derivative of the slip-mode switching function yields the desired value of the additional yaw moment comprising the slip-mode control variable as:
Here the number of the elements is the number, For the sum of the moment of each wheel longitudinal force to the centroid, I z is the moment of inertia of the vehicle about the Z-axis, M zd is the additional yaw moment: ;
The longitudinal expectation value tracking control design steps are as follows,
The longitudinal force demand may be calculated as follows
Wherein N is the number of driving motors, and T max is the maximum torque of the motors; Is the pedal opening degree, phi is more than 0 and less than 1,
V is the current longitudinal speed of the vehicle, R is the tire radius, and F xd is the total longitudinal force;
As shown in fig. 3, the implementation steps of selecting a boundary according to the performance in S1 and designing the fault-tolerant control lower control by the upper control output in S2 in step S3 are as follows:
Firstly, the yaw rate and centroid side deviation angle information of the current vehicle are obtained, euclidean distance between dynamic clustering centroids and stability clustering centroids 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 rate and centroid side deflection angle of the vehicle, w 1、β1 is the dynamic cluster centroid, w 2、β2 is the stability cluster centroid, d 1 is the Euclidean distance of the dynamic cluster centroid, and d 2 is the Euclidean distance of the dynamic cluster centroid;
after the current optimization performance requirement is acquired, the implementation steps of the quadratic programming optimal moment solution are as follows:
the target control amount V d generated by the upper layer controller is
Vd＝[Fxd Mzl]T (9)
Here, V d is the upper layer controller output, and the target amount of the lower layer controller is controlled;
The system control input x is:
x＝[T1f T1r T2f T2r T3f T3γ....TNf TNr]T (10)
wherein x is the motor output vector, T Nf is the torque of the driving motor on the left side of the N-th shaft, and T Nr is the torque of the driving motor on the right side of the N-th shaft.
Ideally, the relationship between the lower-layer target control amount V d and the motor output vector x is:
Wherein x min,xmax is the lower bound and the upper bound of the motor output vector constraint, and B is the control coefficient matrix.
The writing matrix pattern is:
Wherein b is the wheel track;
The mathematical description of a general control allocation problem can be expressed as an optimization problem:
Where J (x) is a cost function.
After the motor fails, redefining a constraint boundary of a fault motor, and when a certain motor i fails, defining the constraint boundary corresponding to the fault motor as follows:
-hxmax≤xi≤hxmax (14)
Wherein h is a failure factor.
Finally, the optimal torque distribution problem after failure can be described as:
moment distribution results required by different performances can be obtained by setting different objective functions, so that fault tolerance is realized.
Claims (3)
1. An active fault-tolerant control method based on single motor failure transverse dynamics cluster analysis is characterized by comprising the following steps:
Step S1: selecting a yaw rate and a centroid side deflection angle to represent the transverse stability of the vehicle, and obtaining the response conditions of the current yaw rate and the centroid side deflection angle of the fault vehicle by setting different failure factors and pedal opening; clustering and analyzing boundaries involved in lateral stability control and dynamic control;
Step S2: analyzing expected yaw rate and centroid side deviation angle based on steering wheel angle and pedal opening degree input of a driver, adopting sliding mode control to design yaw moment tracking control, and based on PI control to design longitudinal force tracking control, so as to obtain upper control of an additional yaw moment and a total longitudinal force control law;
Step S3: establishing fault-tolerant control lower control based on optimal moment solution of a quadratic programming algorithm according to the performance selection boundary in the step S1 and the upper control output in the step S2, taking the upper control output in the step S2 as the input of the lower control, and taking the performance selection boundary in the step S1 as the basis of lower objective function and constraint set selection;
the step S2 specifically comprises the following steps:
step S21: the design steps of the transverse expected value tracking control are as follows:
Firstly, simplifying a vehicle into a two-degree-of-freedom model, and deducing a desired yaw rate w d and a centroid side deflection angle beta d of the vehicle in a steady state according to the two-degree-of-freedom dynamics model of the vehicle;
Where c 1、c2、c3、c4、c5 is a constant related to vehicle mass, tire cornering stiffness, wheelbase, δ is wheel angle, u is longitudinal vehicle speed, w d is desired yaw rate, β d is desired centroid cornering angle;
step S22: the sliding mode control is designed as a tracking control algorithm, the expected yaw rate and the centroid slip angle in the tracking step S21 are tracked, and the design steps are as follows:
the design sliding mode surface switching function s is:
e＝w-wd+ε1(β-βd)
e is the combined deviation of the yaw rate and the centroid side deflection angle, epsilon 1 is the centroid side deflection angle weight, c is the normal number, the larger the value of c is, the shorter the system reaches steady state, t is the integration time, w is the current yaw rate, and beta is the current centroid side deflection angle;
By using an exponential approach rate
Wherein epsilon 2 and k are positive numbers and are parameters of the controller;
Eliminating the vibration phenomenon of the sliding mode by adopting a boundary layer method; when the system is outside the boundary layer, switching control is used, and when the boundary layer is inside, a saturation function is used instead of a sign function, namely:
Here, phi is the boundary layer thickness;
Substituting the control law function into the first derivative of the slip-mode switching function yields the desired value of the additional yaw moment comprising the slip-mode control variable as:
Here the number of the elements is the number, For the sum of the moment of each wheel longitudinal force to the centroid, I z is the moment of inertia of the vehicle about the Z-axis, M zd is the additional yaw moment;
Step S23: the longitudinal expected value tracking control design steps are as follows:
The longitudinal force demand may be calculated as follows:
n is the number of driving motors, and T max is the maximum torque of the motors; For pedal opening,/>
V is the current longitudinal speed of the vehicle, R is the tire radius, and F xd is the total longitudinal force;
The step S3 specifically comprises the following steps:
Step S31: acquiring yaw rate and centroid side deviation angle information of a current vehicle, calculating Euclidean distance d 1 between the dynamic clustering centroid and the stability clustering centroid analyzed in the step S1, and 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 rate and centroid side deflection angle of the vehicle, w 1、β1 is the dynamic cluster centroid, w 2、β2 is the stability cluster centroid, d 1 is the Euclidean distance of the dynamic cluster centroid, and d 2 is the Euclidean distance of the dynamic cluster centroid;
Step S32: after the current optimization performance requirement is acquired, the implementation steps of the quadratic programming optimal moment solution are as follows:
the target control amount V d generated by the upper layer controller is
Vd＝[Fxd Mzd]T
Here, V d is the upper layer controller output, and the target amount of the lower layer controller is controlled;
The motor output vector x is:
x＝[T1f T1r T2f T2r T3f T3r…TNf TNr]T
Wherein x is a motor output vector, T Nf is the torque of the driving motor on the left side of the N-th shaft, and T Nr is the torque of the driving motor on the right side of the N-th shaft;
ideally, the relationship between the lower-layer target control amount V d and the motor output vector x is:
Vd＝Bx
xmin≤x≤xmax
Wherein x min,xmax is the lower bound and the upper bound of the motor output vector constraint, and B is a control coefficient matrix;
The writing matrix pattern is:
Wherein b is the wheel track;
the mathematical description of a general control allocation problem is expressed as an optimization problem:
Wherein J (x) is a cost function;
After the motor fails, redefining a constraint boundary of a fault motor, and when a certain motor i fails, defining the constraint boundary corresponding to the fault motor as follows:
-hxmax≤xi≤hxmax
wherein h is a failure factor;
finally, the optimal torque distribution problem after failure can be described as:
moment distribution results required by different performances can be obtained by setting different objective functions, so that fault tolerance is realized.
2. The method according to claim 1, wherein the step S1 is performed as follows:
a failure factor h is introduced aiming at the motor failure degree, and the pedal opening degree Representing the driving and braking condition, and selecting a failure factor and pedal opening as influencing factors;
selecting a yaw rate and a centroid side deviation angle to represent the lateral stability of the vehicle; data acquisition is carried out on the response conditions of the current yaw rate and the centroid slip angle of the fault vehicle by setting different failure factors and pedal opening degrees; after all data are acquired offline, the influence change degree of yaw rate, centroid side deviation angle along with failure factors and pedal opening is obtained respectively;
k-means clustering is carried out on the acquired data, the number of clusters is set to be 2, the absolute value of the yaw rate and the centroid side deviation angle value is small, the dynamic characteristic of the data is represented as a safety class with less serious yaw, and the clustering centroid is a dynamic clustering centroid; the absolute value of the yaw angle value of the yaw rate centroid is larger, the dynamic characteristic of the yaw rate centroid is represented as a dangerous class with serious yaw, and the cluster centroid is a stability cluster centroid.
3. A method according to claim 2, characterized in that the number of clusters is set to be plural, and the corresponding degree of failure is also classified into corresponding classes according to the number of corresponding classifications.
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| CN109733205A (en) * | 2018-12-10 | 2019-05-10 | 江苏大学 | A kind of wheel hub electric car direct yaw moment control method with fault tolerance |
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