This invention belongs to the technical field of intelligent vehicle control and particularly relates to an extension lane-keeping control method with the variable vehicle speed of an intelligent vehicle.
Intelligent vehicles have become an important carrier and meeting the requirements of safe, efficient, and intelligent transportation development have become the main targets for their development and research. Specifically, electric intelligent vehicles have a great effect on environmental pollution, energy efficiency, and traffic congestion. Among them, the lane-keeping technology of intelligent vehicles has gradually become one of the hot topics of research in the road driving process, especially curve keeping and high-speed lane-keeping performance.
To achieve self-awareness, independent decision-making, and autonomous execution to ensure safe driving, lane-keeping control of intelligent vehicles is based on the common vehicle platform, architecture computer, vision sensor, automatic control actuator, and signal communication equipment. Most common vehicles are front-wheel drive, and fire lateral control accuracy of the vehicle and the safety stability of the vehicle are ensured by adjusting the front wheel angle. The lane-keeping is based on a visual sensor, such as a camera. The lane line information is extracted through lane line detection, the position of the vehicle in the lane is acquired, and the front wheel angle to be executed at the next moment is determined based on the lane line and vehicle position information. There are two main methods of control: the pie-shooting reference system and the non-pre-attack reference system. The pre-shooting reference system mainly takes as input the road curvature at the front of the vehicle according to the lateral error or heading error between the vehicle and the desired path. To meet the control target, a feedback control system robust for the vehicle dynamic parameters is designed through various feedback control methods, such as a reference system based on a vision sensor like radar or a camera. The non-pre-attack reference system calculates a physical quantity describing the vehicle motion, such as the vehicle yaw rate, based on the desired path near the vehicle, and then designs a feedback control system for tracking. This invention is based on the pre-shooting control method. A plurality of the desired vehicle states at the front point completes the design of the extension lane-keeping control method for multi-state feedback.
From the current main research contents, the control precision and stability of intelligent vehicles lane-keeping control under large curves and high speed are hot topics of research. This invention is aimed at the control accuracy of intelligent vehicles lane-keeping in variable speeds, and an extension adaptive lane-keeping control method with variable vehicle speeds is proposed.
This invention applies the extension control method to the intelligent vehicle lane-keeping control method to ensure that the vehicle always moves within the lane range during the movement of the vehicle. The control objective of the lane-keeping is to ensure that the distance between the left lane line and the right lane line of the vehicle is equal and the heading error is zero. The upper layer extension controller of the invention adaptively adjusts the lower layer control coefficients according to the current integral square of error with time index (ISTE) of the lane-keeping. The lower layer extension controller consists of two parts, the speed extension controller and the deviation tracking extension controller, and changes the constraint domain boundary range according to the vehicle speed change, which realizes the lane-keeping control function of the intelligent vehicle with a variable speed.
The beneficial effects of this invention can be summarized as follows:
The invention is further described below with reference to the figures.
As shown in
Step 1: Establish a Three-Degree-of-Freedom Dynamic Model
The invention adopts a three-degree-of-freedom vehicle dynamics model, including longitudinal motion, lateral motion, and yaw motion.
where m is the vehicle mass; x is the longitudinal displacement; φ is the yaw angle; δf is the front wheel angle: {dot over (φ)} is the yaw rate: y is the lateral displacement; Iz is the yaw moment of inertia around Z-axis; Fx is the longitudinal force of vehicle; Fy is the lateral force of vehicle; Mz is the yaw moment; Fcf and Fcr is the lateral force of front tires and rear tires, respectively, related to the lateral force, corner stiffness, and slope angle of tries; Flf and Flr is the longitudinal force of front and rear tires, respectively, related to the longitudinal stiffness and slip ratio of tires; Fxf and Fxr is the front and rear force of x-axis, respectively; Fy and Fyr is the front and rear force of the y-axis; a is the distance of the front wheel axle from the center of gravity; and b is the distance of rear-wheel axle from center of gravity.
The preview error during the path tracking process of the vehicle includes the heading error and the lateral position error at the pre-shooting point. As shown in
According to the geometric relationship in the figure:
{dot over (y)}
L
={dot over (x)}φ
h
−{dot over (y)}−{dot over (φ)}L (2)
{dot over (φ)}h={dot over (x)}ρ−{dot over (φ)} (3)
Step 2: Lane Line Fitting Calculation
Lane line fitting functions use a quadratic polynomial equation based on the road curvature ρ and the distance of the vehicle from the left line and right line DL, Dr, respectively. The lane line equation for the curve can be obtained as follows:
where ρ is the road curvature; DL, Dr is the distance of the vehicle from the left line and right line, respectively; φρ is the heading angle of lane line: y1 is left line fitting function; and y2 is right line fitting function.
Considering that the heading error angle of the vehicle ranges from −1 rad to 1 rad, the lane line curvature setting range is set between −0.12/m and 0.12/m.
Step 3: Upper Layer ISTE Controller Design
1) Control Index (ISTE) Extension Set
The control index (ISTE) reflects the control effect, and the control target of lane-keeping ensures that the intelligent vehicle moves in the range of the lane line. In addition, it should make the lateral error yL and heading error φh equal to zero. Therefore, in this event, the control index should consider the errors mentioned above. The calculation method of the extension control index adopts the principle of integrating the time multiplied by the square of the error. The specific expression is as follows:
ISTE
y=∫0TstyL2dt
where ISTEy is the control index of the lateral position error, and Ts is the adjustment time;
ISTE
φ=∫0Tstφh2dt
where ISTEφ is the control index of heading error, and Ts is the adjustment time.
The upper layer ISTE extension controller selects the control indexes ISTEy and ISTEφ as the feature quantities and builds the extension set SISTE(ISTEy, ISTEφ).
2) Control Index (ISTE) Domain Boundary
The extension control index ISTE is the integral form of the error multiplied by time, and the result varies within the range of [0, +∞). Therefore, the classical domain boundary of the control effect is expressed as follows:
aop and bop are the classical domain constraint boundaries of the control index extension set, the values can be expressed as follows:
a
op=∫0Tst·ryop2dt
b
op=∫0Tst·rφop2dt,
where ryop is the classical domain constraint range for the lateral positional error, rφop is classical domain constraint range for heading error, and the two values are related to the values of the lower layer extension controller, which can adaptively adjust along with the vehicle speed.
The extension domain boundary of control index is as follows:
ap and bp are the extension domain constraint boundaries of the control index extension set, the values can be expressed as follows:
a
p=∫0Tst·ryp2dt
b
p=∫0Tst·rφp2dt,
where ryp is the classical domain constraint range for lateral positional error, rφp is extension domain constraint range for heading error, and the two values are related to the values of lower layer extension controller, which can adaptively adjust with the vehicle speed.
3) Calculation of Correlation Function for the Control Index (ISTE)
In this event, to calculate the value, the correlation function of the control index (ISTE) adopts a dimensionality reduction method.
The extension distance of point P and the classical domain O, P1 and the extension domain P1, P2 are expressed as [P, O, P1] and [P, P1, P2], respectively. Those values can be obtained as follows:
Then, the correlation function KISTE(P) of the control index can be expressed as follows:
where
[P,P1,P2,O,P1]=[P,P1,P2]−[P,O,P1]
4) Upper Layer Extension Controller Decision
An expert knowledge base is used in the upper layer extension controller decision, including five expert pieces of knowledge as follows:
a. When KISTE(P)≥0, the control satisfies the control requirements and maintains the original control coefficient.
b. When −1≤KISTE(P)<0, the control needs further improvement, and it is necessary to continue changing the control coefficient in the lower controller.
c. When KISTE(P)<−1, there is control failure.
d. When the lower characteristic state stays for a long time in the second measurement mode (i.e., the critical steady-state), it indicates that the control quantity changes little, and the control coefficient in the measurement mode should be appropriately increased to accelerate the development of the characteristic state to the steady state.
e. When the current control effect is worse than the last control effect, the coefficient in the measurement mode is returned to the previous control coefficient, and the control coefficient is appropriately reduced.
The decision result is set to:
When KISTE(P)≥0, select expert knowledge a;
When −1≤KISTE(P)<0, select three expert pieces of knowledge b, d, or e;
When KISTE(P)<−1, select expert knowledge c.
Step 4: Lower Speed Extension Controller Design
The lower layer speed extension controller feature quantity selects the deviation ev
The velocity feature quantity classical domain boundary is expressed as follows:
where ev
The velocity feature quantity extension domain boundary is expressed as follows:
where ev
Then, the non-domain can be defined as the remaining domains except for the classical domain and extension domain.
The extension set domain boundary of the speed extension controller is shown in
The speed extension association function Kv
The classic domain extension distance is:
M
v
0=√{square root over (ev
the extension domain extension distance is:
M
v
=√{square root over (ev
Moreover, the extension distance of the real-time feature state and the best state can be expressed as follows:
|Sv
When Sv
K
v
(S)=1−|Sv
else,
K
v
(S)=(Mv
Therefore, the velocity feature quantity correlation function is as follows:
The output calculation of speed extension controller is:
When Kv
The output longitudinal tire force Fx of the controller is as follows:
F
x
=−K
v
e
v
,
where Kv is state feedback gain coefficient.
When −1≤Kv
F
x
=−K
v
e
v
+K
vc
·K
v
(S)·sgn(ev
where Kvc is an additional output term gain coefficient, and sgn(ev
When Kv
Therefore, the output longitudinal force Fx of controller is as follows:
Step 5: Lower Layer Error Tracking Extension Controller Design
1) Error Tracking Extension Feature Quantities Extraction and Domain Bounding
The lower layer error tracking extension controller selects the preview lateral position error yL and heading error φh as the extension feature quantities, which form a two-dimensional feature state set denoted as S(yL, φh). The control target should ensure the lateral error and heading error is zero when tracking the desired path for the lateral control of intelligent vehicles. The feature quantities extension set division of the lower layer controller is shown in
According to Extenics theory, the classical domain and extension domain for the feature quantities are ensured. Moreover, they can be expressed as follows:
For the classic domain,
where yLom and φhom are the classical domain boundaries of the feature set S(yL, φh).
For the extension domain,
where yLm and φhm are the classical domain boundaries of the feature set S(yL, φh).
Then, the non-domain can be defined as the remaining domains except for the classical domain and extension domain of the feature set S(yL, φh).
2) Correlation Function of Lower Layer Extension Controller
For the lateral control of intelligent vehicles, the control target should ensure that the lateral error and heading error are zero when tracking the desired path. The optimal state is Slow0=(0,0).
In the process of vehicle motion, the real-time feature quantities are marked as S(yL, φh), and then the extension distance of the real-time state quantities and the optimal point is as follows:
|SSlow0|=√{square root over (k1yL2+k2φh2)}, (22)
where k1 and k2 are the real-time state quantities and optimal state point extension weighting coefficients; the coefficients are usually 1.
The extension distance of the classic domain is as follows:
M
eo=√{square root over (yLom2+φhom2)}. (23)
The extension distance of extension domain is as follows:
M
e=√{square root over (yLm2+φhm2)}. (24)
If the real-time feature state quantity S(yL, φh) is located in the classic domain Rlow_os, then the correlation function is as follows:
K
low(S)=1−|SSlow0|/Meo (25)
Else,
K
low(S)=(Meo−|SSlow0|)/(Me−Meo). (26)
In summary, the correlation function is as follows:
3) Measure Mode Recognition of the Lower-Layer Controller
The measurement mode recognition of the system characteristic quantity S(yL, φh) is determined according to the above the value of correlation function Klow (S). The measurement mode recognition rules are described below.
IF Klow(S)≥0, THEN the measurement mode of the real-time feature state quantity S(yL, φh) is in the classical domain and the measurement mode state is marked as Mlow_1. Under this state, the error tracking control is easy, and the control process is very stable, and it is a fully controllable state;
IF −1≤Klow(S)<0, THEN the measurement mode of the real-time feature state quantity S(yL, φh) is in the extension domain and the measurement mode state is marked as Mlow_2. When the error tracking control difficulty is increasing, the error of the lateral position error and the heading error are larger, the control quantity and the control quantity change speed need to be increased, and the control process is a critical steady state; ELSE, the real-time feature state quantity S(yL, φh) is in the non-domain and the measurement mode state is marked as Mlow_3. The error of the lane-keeping control is much larger and the vehicle even skids off the lane. The control process is an extremely unstable state.
4) Output Front-Wheel Angle of the Lower Layer Controller
When the state is in mode Mlow_1, the state is in the stable state, and the output front-wheel steeling angle is as follows:
δf=−KlowCM1S (28)
where KlowCM1 is the state feedback coefficient of the measurement mode Mlow_1 related to the characteristic quantity S, and KlowCM1=[Klow_c1 Klow_c1]T, where Klow_c1 and Klow_c1 are the state feedback coefficients related to the feature quantity yL and feature quantity φh. The invention adopts a pole placement method to select the state feedback coefficients and S is [yL φh]T.
When the state is in mode Mlow_2, the state is in a critical instability state and in the controllable range. The controller can re-adjust the system to a steady-state by controlling the additional output. The output steering angle is as follows:
δf=−KlowCM1{S+KlowC·Klow(S)·[sgn(S)]}. (29)
KlowC is an additional output term gain coefficient in the measurement mode Mlow_2. To ensure that additional outputs enable the system to return to a relatively steady state, the coefficient is manually adjusted based on measurement mode Mlow_1.
Here,
KlowC·Klow(S)·[sgn(S)] is the additional output additional output term. This term combines the value of the correlation function of the lower layer controller that embodies the adjustment difficulty of the vehicle moving along the centerline of the lane during lane-keeping control. Therefore, the value of the additional output of the controller is changed in real time according to the control difficulty by changing the correlation function value.
δf=0 (31)
When the state is in measurement mode Mlow_3, the error from the lane during the lane-keeping process is very large, and the lane-keeping control fails. If the vehicle wants to return to the original lane, then the front wheel corner output value is instantly large. In the case of a first vehicle speed, vehicle movement has great safety hazards under the large front wheel angle input, which should be avoided as much as possible in the control process. This situation rarely exists due to the current Chinese road planning size.
In summary, the output front wheel steering angle of lower layer deviation tracking extension controller based on characteristic quantity S is as follows:
The output of the above controller is fed back to the vehicle model, and the relevant parameters in the model are adjusted in real time so that the vehicle can adjust the lane tracking status in teal time.
The series of detailed descriptions set forth above are merely illustrative of the possible embodiments of the present invention, and they are not intended to limit the scope of the present invention. Changes are intended to be included within the scope of the invention.
Number | Date | Country | Kind |
---|---|---|---|
201811373199.7 | Nov 2018 | CN | national |
Filing Document | Filing Date | Country | Kind |
---|---|---|---|
PCT/CN2019/075504 | 2/20/2019 | WO | 00 |