CN110687797B - Self-adaptive MPC parking transverse control method based on position and posture - Google Patents

Self-adaptive MPC parking transverse control method based on position and posture Download PDF

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CN110687797B
CN110687797B CN201911094383.2A CN201911094383A CN110687797B CN 110687797 B CN110687797 B CN 110687797B CN 201911094383 A CN201911094383 A CN 201911094383A CN 110687797 B CN110687797 B CN 110687797B
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parking
weight coefficient
yaw angle
speed
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秦兆博
陈亮
王晓伟
秦晓辉
谢国涛
徐彪
边有钢
胡满江
陈鑫
丁荣军
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Hunan University
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    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
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Abstract

The invention discloses a self-adaptive MPC parking transverse control method based on position and posture, and belongs to the technical field of intelligent vehicle transverse control. The method comprises the following steps: step 1, setting a parking speed range and selecting a prediction step length and sampling time according to the parking speed; step 2, designing a model predictive control algorithm based on a nonlinear vehicle kinematics model and calculating a transverse distance deviation and a yaw angle deviation; step 3, designing a performance index function according to the transverse distance deviation and the yaw angle deviation obtained in the step 2; and 4, designing a fuzzy controller for adaptively adjusting the weight coefficient, wherein the fuzzy controller adjusts the weight coefficient of the performance index function in real time according to the vehicle speed and the transverse distance deviation. The invention not only can effectively control the vehicle posture and solve the problem of vehicle body incorrectness after the vehicle enters the parking space, but also can realize high-precision control only by less prediction step length and reduce the calculation burden.

Description

Self-adaptive MPC parking transverse control method based on position and posture
Technical Field
The invention relates to the technical field of intelligent vehicle transverse control, in particular to a self-adaptive MPC parking transverse control method based on position and posture.
Background
In recent years, the amount of automobile storage has been increasing, the traffic technology capability has been increasing, and the demand for an auxiliary driving technology for automobiles has also been increasing. Due to control limitations of parking and limitations of a vehicle body structure, the vehicle is required to be parked according to the specifications of parking spaces. Based on the restrictions, the main vehicle and other vehicles are prevented from colliding, and the safe storage of the vehicles is realized, which is the difficulty of the automatic parking technology development. The path planning method for parking generally uses curves, spirals, etc. to plan the path, but only considers the constraint conditions of the vehicle itself, such as the minimum turning radius of the vehicle. The parking process of the vehicle is completed in limited space resources based on actual road conditions and environments, the limited space needs to be fully utilized, and high-precision control needs to be realized to achieve the purpose. Not only must ensure higher error precision, but also must ensure better vehicle body posture.
In an automatic parking scene, although some common vehicle transverse control methods can achieve high error accuracy, the expected posture tracking effect of a vehicle is poor, collision is easy to happen in the parking process, parking fails or a path needs to be planned again for parking, particularly in a parallel parking scene, the path is planned to use a curve, and if the posture of a vehicle body is not adjusted timely, the vehicle is placed incorrectly after entering a parking space, and the vehicle is difficult to adjust again.
Disclosure of Invention
The invention aims to provide a position and attitude based adaptive MPC parking lateral control method to overcome the defects of the prior art, so that an automatic parking path tracking controller has high error precision and excellent expected attitude tracking capability. Under the premise that the parking path is planned, the parking process is completed once without readjustment.
In order to achieve the above object, the present invention provides a method for adaptive MPC parking lateral control based on position and attitude, comprising:
step 1, setting a parking speed range and selecting a prediction step length and sampling time according to the parking speed;
step 2, designing a model predictive control algorithm based on a nonlinear vehicle kinematics model and calculating a transverse distance deviation and a yaw angle deviation;
step 3, designing a performance index function according to the transverse distance deviation and the yaw angle deviation obtained in the step 2;
and 4, designing a fuzzy controller for adaptively adjusting the weight coefficient, wherein the fuzzy controller adjusts the weight coefficient of the performance index function in real time according to the vehicle speed and the transverse distance deviation.
Further, in step 1, the setting of the range of the parking vehicle speed and the selection of the prediction step length and the sampling time according to the vehicle speed are specifically:
step 11, setting a vehicle speed range in a parking state in the longitudinal controller: 0< | v | is less than or equal to 6km/h, and the vehicle speed v is specified to be positive when the vehicle moves forwards, and the vehicle speed v is specified to be negative when the vehicle moves backwards;
step 12, selecting the optimal prediction step size N according to the current speed valuepAnd sampling time delta t, wherein the sampling time delta t is selected to be 0.1s and is 0<Selecting a prediction step length N when | v | is less than or equal to 3.6km/hpA, when 3.6<Selecting a prediction step length N when | v | is less than or equal to 6km/hpB, wherein 5. ltoreq. a<b≤15；
Further, in step 2, the designing a model predictive control algorithm based on the nonlinear vehicle kinematics model and calculating the lateral distance deviation and the yaw angle deviation specifically include:
step 21, selecting a vehicle transverse control nonlinear kinematics model as follows:
Figure BDA0002267846360000021
wherein (x, y) is the center coordinates of the rear axle of the vehicle,
Figure BDA0002267846360000022
the yaw angle of the vehicle body, v the central speed of the rear axle of the vehicle, delta the corner of the front wheel of the vehicle and l the wheelbase.
Step 22, the calculation formula of the transverse distance deviation e is as follows:
Figure BDA0002267846360000023
wherein x and y are central coordinates of the rear axle of the vehicle, klB is the intercept of the corresponding waypoint tangent;
step 24, yaw angle deviation eyawThe formula of (c) is as follows:
when the vehicle backs up:
Figure BDA0002267846360000031
when the vehicle advances:
Figure BDA0002267846360000032
in the formula (I), the compound is shown in the specification,
Figure BDA0002267846360000033
and theta is the heading angle of the corresponding road point, namely the expected yaw angle.
Further, in step 3, the designing a performance indicator function according to the lateral distance deviation and the yaw angle deviation obtained in step 2 specifically includes:
step 31, determining a performance index function according to the transverse distance deviation, the yaw angle deviation and the control quantity increment:
Figure BDA0002267846360000034
in the formula, E is a sequence for predicting the transverse distance deviation in the time domain; Δ u is the control amount increment; eyawIs a sequence of prediction of yaw angle deviations in the time domain; q, R, W are weight coefficients, respectively. The first term of the expression reflects the expected road of the vehicleThe following ability of the path reflects the requirement of the vehicle on the stable change of the control quantity, and the third reflects the following ability of the vehicle on the expected posture.
Further, in step 4, the designing of the fuzzy controller for adaptively adjusting the weight coefficient includes adjusting the weight coefficient of the performance index function in real time according to the vehicle speed and the lateral distance deviation, and specifically includes:
step 41, converting the absolute value | v | of the vehicle speed into a fuzzy set of [0,1.5] and dividing the fuzzy set into two domains, wherein the domains are { S, L }, and S, L is respectively called as positive small and positive large; converting the lateral distance deviation e into a fuzzy set of [0,0.5] and dividing the fuzzy set into 8 domains, namely { Z, B1, B2, B3, B4, B5, B6 and B7 }; converting the weight coefficient Q into a fuzzy set of [500,2000] and dividing the fuzzy set into 8 domains, namely { B1, B2, B3, B4, B5, B6, B7 and B8}, converting the weight coefficient W into [0,350] and dividing the fuzzy set into 8 domains, namely { Z, B1, B2, B3, B4, B5, B6 and B7}, wherein Z, B1, B2, B3, B4, B5, B6, B7 and B8 are respectively called zero, very small, large and large; and R is a weight coefficient of the increment of the controlled variable, and represents the requirement of smooth change of the controlled variable, and the R is set to be a constant 1 because the vehicle speed is low.
And 42, determining a fuzzy rule by adopting an expert experience method, wherein a weight coefficient Q represents the following ability of the vehicle to the expected path, and a weight coefficient W reflects the following ability of the vehicle to the expected posture. When the vehicle is farther from the desired path, the weight coefficient Q should be set larger and the weight coefficient W should be set smaller in order to make the lateral distance deviation converge quickly; when the transverse error of the vehicle is converged within a certain range, properly reducing the weight coefficient Q and increasing the weight coefficient W, so that the controller can ensure the control of the posture of the vehicle body while the vehicle tracks the path;
step 43, the fuzzy rule inference table of the weight coefficient Q is:
Figure BDA0002267846360000041
step 44, the fuzzy rule inference table of the weight coefficient Q is:
Figure BDA0002267846360000042
the invention has the beneficial effects that: the invention not only can effectively control the vehicle posture and solve the problem of vehicle body incorrectness after the vehicle enters the parking space, but also can realize high-precision control only by less prediction step length and reduce the calculation burden. The invention has wide applicability and can be suitable for the transverse control of vehicle parking and parking under various parking conditions such as parallel, vertical and oblique parking conditions.
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FIG. 1 is an overall block diagram of the present invention for performing parking path tracking;
FIG. 2 is a diagram of a path tracking simulation result under a parallel parking condition;
fig. 3 is a diagram of simulation results when the vehicle completely enters the parking space.
Detailed Description
The invention will be further described in detail with reference to the following examples, which are given in the accompanying drawings.
A self-adaptive MPC parking transverse control method based on position and posture is shown in figure 1, wherein delta is the optimal numerical solution of front wheel steering angle, x, y,
Figure BDA0002267846360000043
In order to identify the latest vehicle state obtained by the system and act on the system as the initial state of the vehicle in the next prediction time domain, the position and attitude based adaptive MPC parking lateral control method comprises the following steps:
step 1, setting a parking speed range and selecting a prediction step length and sampling time according to the parking speed;
step 2, designing a model predictive control algorithm based on a nonlinear vehicle kinematics model and calculating a transverse distance deviation and a yaw angle deviation;
step 3, designing a performance index function according to the transverse distance deviation and the yaw angle deviation obtained in the step 2;
and 4, designing a fuzzy controller for adaptively adjusting the weight coefficient, wherein the fuzzy controller adjusts the weight coefficient of the performance index function in real time according to the vehicle speed and the transverse distance deviation.
In the step 1, the setting of the range of the parking speed and the selection of the prediction step length and the sampling time according to the parking speed are specifically as follows:
step 11, setting a vehicle speed range in a parking state in the longitudinal controller: 0< | v | is less than or equal to 6km/h, and the vehicle speed v is specified to be positive when the vehicle moves forwards, and the vehicle speed v is specified to be negative when the vehicle moves backwards;
step 12, selecting the optimal prediction step size N according to the current speed valuepAnd sampling time delta t, wherein the sampling time delta t is selected to be 0.1s and is 0<Selecting a prediction step length N when | v | is less than or equal to 3.6km/hpA, when 3.6<Selecting a prediction step length N when | v | is less than or equal to 6km/hpB, wherein 5. ltoreq. a<b≤15。
In the step 2, designing a model predictive control algorithm based on the nonlinear vehicle kinematics model and calculating the lateral distance deviation and the yaw angle deviation specifically comprises:
step 21, selecting a vehicle transverse control nonlinear kinematics model as follows:
Figure BDA0002267846360000051
in the formula, v is the central speed of the rear axle of the vehicle, delta is the corner of the front wheel of the vehicle, and l is the wheel base.
The prediction model in the controller is:
Figure BDA0002267846360000052
Figure BDA0002267846360000053
Figure BDA0002267846360000054
wherein x (k), y (k),
Figure BDA0002267846360000055
Delta (k) is respectively a horizontal coordinate, a vertical coordinate, a yaw angle and a front wheel rotating angle of the vehicle under an inertia coordinate system at the k-th sampling moment, v is the actual vehicle speed, delta t is the sampling time, and l is the wheelbase.
Step 22, the calculation formula of the transverse distance deviation e is as follows:
Figure BDA0002267846360000056
wherein x and y are central coordinates of the rear axle of the vehicle, klB is the intercept of the corresponding waypoint tangent;
step 24, yaw angle deviation eyawThe formula of (c) is as follows:
when the vehicle backs up:
Figure BDA0002267846360000061
when the vehicle advances:
Figure BDA0002267846360000062
in the formula (I), the compound is shown in the specification,
Figure BDA0002267846360000063
the yaw angle of the vehicle body when the vehicle is reversed, and theta is the course angle of the corresponding road point, namely the expected yaw angle.
Further, in step 3, the designing a performance indicator function according to the lateral distance deviation and the yaw angle deviation obtained in step 2 specifically includes:
step 31, determining a performance index function according to the transverse distance deviation, the yaw angle deviation and the control quantity increment:
Figure BDA0002267846360000064
in the formula (I), the compound is shown in the specification,e is a sequence for predicting the transverse distance deviation in the time domain; Δ u is the control amount increment; eyawIs a sequence of prediction of yaw angle deviations in the time domain; q, R, W are weight coefficients, respectively. The first term of the expression reflects the following capacity of the vehicle to the expected path, the second term reflects the requirement of the vehicle for smooth change of the control quantity, and the third term reflects the following capacity of the vehicle to the expected posture.
Determining the constraint conditions of the optimization problem:
Figure BDA0002267846360000065
according to the performance index function determined in step 51, the controller needs to solve the constrained optimization problem in each cycle
Further, in step 4, the designing of the fuzzy controller for adaptively adjusting the weight coefficient includes adjusting the weight coefficient of the performance index function in real time according to the vehicle speed and the lateral distance deviation, and specifically includes:
step 41, converting the absolute value | v | of the vehicle speed into a fuzzy set of [0,1.5] and dividing the fuzzy set into two domains, wherein the domains are { S, L }, and S, L is respectively called as positive small and positive large; converting the lateral distance deviation e into a fuzzy set of [0,0.5] and dividing the fuzzy set into 8 domains, namely { Z, B1, B2, B3, B4, B5, B6 and B7 }; converting the weight coefficient Q into a fuzzy set of [500,2000] and dividing the fuzzy set into 8 domains, namely { B1, B2, B3, B4, B5, B6, B7 and B8}, converting the weight coefficient W into [0,350] and dividing the fuzzy set into 8 domains, namely { Z, B1, B2, B3, B4, B5, B6 and B7}, wherein Z, B1, B2, B3, B4, B5, B6, B7 and B8 are respectively called zero, very small, large and large; and R is a weight coefficient of the increment of the controlled variable, and represents the requirement of smooth change of the controlled variable, and the R is set to be a constant 1 because the vehicle speed is low.
And 42, determining a fuzzy rule by adopting an expert experience method, wherein a weight coefficient Q represents the following ability of the vehicle to the expected path, and a weight coefficient W reflects the following ability of the vehicle to the expected posture. When the vehicle is farther from the desired path, the weight coefficient Q should be set larger and the weight coefficient W should be set smaller in order to make the lateral distance deviation converge quickly; when the deviation of the transverse distance of the vehicle is converged within a certain range, properly reducing the weight coefficient Q and increasing the weight coefficient W, so that the controller can ensure the control of the posture of the vehicle body while the vehicle tracks the path;
step 43, the fuzzy rule inference table of the weight coefficient Q is:
Figure BDA0002267846360000071
step 44, the fuzzy rule inference table of the weight coefficient Q is:
Figure BDA0002267846360000072
so alright realize to the control of vehicle gesture, solved the vehicle and got into the wrong problem of automobile body behind the parking stall, and only need less prediction step length just can realize high accuracy control moreover, reduced the calculation burden, through the adjustment to weight coefficient Q and weight coefficient W, under the prerequisite of having played to the path tracking of parkking, still realized the adjustment to the automobile body gesture for when final vehicle stopped to put in the parking stall, the vehicle can be held in the parking stall completely.
An example of a simulation is presented below:
in specific implementation, in order to verify the performance effect of the method, a simulation experiment is carried out by taking automatic parking and backing path tracking as an example;
taking the axle distance L as 2.8m,
the controller design parameters are as follows:
vehicle speed v ═ 1m/s
Wheelbase l 2.6m
Sampling time Δ t is 0.1s
Predicting step size Np＝8
Initial value delta of front wheel rotation angle in each sampling period0＝0
Control quantity constraint-0.44 ≤ u ≤ 0.44(rad)
Delta u is more than or equal to 0.08 and less than or equal to 0.08(rad)
The simulation was performed using Matlab, fig. 2 is a comparison of the reference path and the tracking path, and fig. 3 is a graph of the trajectory after the vehicle has completely entered the parking space, almost parallel to the expected path, indicating that the vehicle body has been aligned with an error of only 3 cm.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.

Claims (5)

1. A method for adaptive MPC parking lateral control based on position and attitude is characterized by comprising the following steps:
step 1, setting a parking speed range and selecting a prediction step length and sampling time according to the parking speed;
step 2, designing a model predictive control algorithm based on the nonlinear vehicle kinematics model, and calculating a transverse distance deviation and a yaw angle deviation by using the control algorithm;
step 3, designing a performance index function according to the transverse distance deviation and the yaw angle deviation obtained in the step 2;
step 4, designing a fuzzy controller for self-adaptively adjusting the weight coefficient, and adjusting the weight coefficient of the performance index function by using the fuzzy controller according to the vehicle speed and the transverse distance deviation;
the transverse distance deviation in the step 2 is calculated by the following formula:
Figure FDA0003401500490000011
wherein x and y are central coordinates of the rear axle of the vehicle, klB is the intercept of the corresponding waypoint tangent;
the yaw angle deviation in the step 2 is calculated by the following formula:
when the vehicle backs up:
Figure FDA0003401500490000012
when the vehicle advances:
Figure FDA0003401500490000013
in the formula (I), the compound is shown in the specification,
Figure FDA0003401500490000014
the yaw angle of the vehicle body is shown, and theta is a course angle of a corresponding road point, namely a desired yaw angle;
the designing of the performance index function according to the lateral distance deviation and the yaw angle deviation obtained in the step 2 in the step 3 specifically comprises:
Figure FDA0003401500490000015
in the formula, E is a sequence for predicting the transverse distance deviation in the time domain; Δ u is the control amount increment; eyawIs a sequence of prediction of yaw angle deviations in the time domain; q, R, W are weight matrices, respectively.
2. The adaptive MPC location and attitude based parking lateral control method of claim 1, wherein: in the step 1, the setting of the range of the parking speed and the selection of the prediction step length and the sampling time according to the parking speed are specifically as follows:
step 11, setting a vehicle speed range in a parking state in the longitudinal controller: the absolute value of v is more than 0 and less than or equal to 6km/h, and the vehicle speed v is specified to be positive when the vehicle moves forwards and negative when the vehicle moves backwards;
step 12, selecting the optimal prediction step size N according to the current speed valuepAnd sampling time delta t, selecting the sampling time delta t to be 0.1s, and selecting the prediction step length N when | v | is more than 0 and less than or equal to 3.6km/hpWhen the absolute value of v is less than or equal to 6km/h and is more than 3.6, selecting a prediction step length NpB, wherein a is more than 5 and less than b and less than 15。
3. The adaptive MPC location and attitude based parking lateral control method of claim 1 or claim 2, wherein: the model designed based on the nonlinear vehicle kinematics model in the step 2 specifically comprises the following steps:
Figure FDA0003401500490000021
wherein (x, y) is the center coordinates of the rear axle of the vehicle,
Figure FDA0003401500490000022
the yaw angle of the vehicle body, v the central speed of the rear axle of the vehicle, delta the corner of the front wheel of the vehicle and l the wheelbase.
4. The adaptive MPC location and attitude based parking lateral control method of claim 1 or claim 2, wherein: the fuzzy controller in the step 4 is designed as follows:
step 41, converting the absolute value v of the vehicle speed into a fuzzy set of [0,1.5] and dividing the fuzzy set into two domains, wherein the domains are { S, L }, and S, L are respectively called as positive small and positive large; converting the lateral distance deviation e into a fuzzy set of [0,0.5] and dividing the fuzzy set into 8 domains, namely { Z, B1, B2, B3, B4, B5, B6 and B7 }; converting the weight coefficient Q into a fuzzy set of [500,2000] and dividing the fuzzy set into 8 domains, namely { B1, B2, B3, B4, B5, B6, B7 and B8}, converting the weight coefficient W into [0,350] and dividing the fuzzy set into 8 domains, namely { Z, B1, B2, B3, B4, B5, B6 and B7}, wherein Z, B1, B2, B3, B4, B5, B6, B7 and B8 are respectively called as zero, very small, large and large; and R is a weight coefficient of the increment of the controlled variable, and represents the requirement of stable change of the controlled variable, and the fuzzy controller is formed by setting R to be a constant 1 because the vehicle speed is low.
5. The adaptive MPC location and attitude based parking lateral control method of claim 4, wherein: the concrete steps of adjusting the weight coefficient of the performance index function by the fuzzy controller in the step 4 are as follows:
step 42, determining a fuzzy rule by adopting an expert experience method, setting a weight coefficient Q to represent the following ability of the vehicle to the expected path, and setting a weight coefficient W to reflect the following ability of the vehicle to the expected attitude;
in step 43, the values of the weighting factor Q and the weighting factor W are adjusted according to the vehicle speed and the lateral distance deviation.
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