CN114200925A - Tractor path tracking control method and system based on adaptive time domain model prediction - Google Patents

Tractor path tracking control method and system based on adaptive time domain model prediction Download PDF

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CN114200925A
CN114200925A CN202111324994.9A CN202111324994A CN114200925A CN 114200925 A CN114200925 A CN 114200925A CN 202111324994 A CN202111324994 A CN 202111324994A CN 114200925 A CN114200925 A CN 114200925A
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魏新华
胡珉珉
王爱臣
吴抒航
汪岸哲
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Jiangsu University
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    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
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Abstract

The invention provides a tractor path tracking control method and system based on adaptive time domain model prediction.A time domain optimization module finds out the optimal solution of a prediction time domain and a control time domain, corrects the solved prediction time domain and control time domain, and updates the prediction time domain and the control time domain in an adaptive time domain model prediction controller by using the corrected prediction time domain and control time domain; the adaptive time domain model predictive controller optimizes and solves the advancing speed and the front wheel corner according to the current position, the course angle, the reference position, the reference course angle, the reference front wheel corner, the current speed, the corrected predictive time domain, the corrected control time domain and the system constraint; when the current vehicle speed reaches the forward vehicle speed, finishing acceleration and deceleration; when the actual front wheel steering angle reaches the front wheel steering angle, the steering is finished; and realizing the path tracking control of the tractor. The invention adaptively adjusts the prediction time domain and the control time domain according to the vehicle speed and the curvature of the planned path, thereby improving the path tracking effect and adaptability.

Description

Tractor path tracking control method and system based on adaptive time domain model prediction
Technical Field
The invention belongs to the technical field of automatic driving of vehicles, and particularly relates to a tractor path tracking control method and system based on adaptive time domain model prediction.
Background
With the continuous development of intelligent agriculture, automatic driving of agricultural vehicles is more and more concerned by people. The key to the automatic driving of agricultural vehicles is path planning and path tracking control. Path tracking is an important part of automatic driving, and the quality of the path tracking directly influences whether the whole driving system can complete a given task.
The model prediction control is applied to path tracking due to good tracking accuracy and hysteresis suppression capability, but the influence of vehicle speed and path curvature change is less considered in the traditional model prediction, a fixed prediction time domain and a control time domain are mostly adopted, the adaptability to the environment is poor, and the expected tracking effect is difficult to achieve.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a tractor path tracking control method and system based on adaptive time domain model prediction, which adaptively adjusts the prediction time domain and the control time domain according to the speed and the curvature of a planned path, and improves the adaptability of the control method to different paths and the path tracking effect.
The present invention achieves the above-described object by the following technical means.
A tractor path tracking control method based on adaptive time domain model prediction specifically comprises the following steps:
the time domain optimization module searches a corresponding prediction time domain N according to the current vehicle speed vpAnd control time domain NcThe optimal solution of (2); the time domain optimization module is then based on the reference path curvature KrFor the solved prediction time domain NpAnd control time domain NcCorrecting;
using the modified prediction time domain NpcAnd control time domain NccUpdating a prediction time domain and a control time domain in the adaptive time domain model prediction controller;
the adaptive time domain model prediction controller predicts the controller according to the current position (x, y) and the course angle
Figure BDA0003346631990000011
Reference position (x)r，yr) Reference course angle
Figure BDA0003346631990000012
Reference front wheel corner deltafrCurrent vehicle speed v, and corrected prediction time domain NpcCorrected control time domain NccAnd the system constraint optimization solves the advancing vehicle speed vmAngle delta with front wheelm；
The current vehicle speed v reaches the advancing vehicle speed vmWhen the speed is reduced, the acceleration and deceleration are finished; the actual front wheel turning angle delta reaches the front wheel turning angle deltamWhen the steering is finished, the steering is finished; and realizing the path tracking control of the tractor.
Further, the solved prediction time domain NpAnd control time domain NcThe correction is carried out as follows:
Npc＝Round(γ*Kr+Np)
Ncc＝Round(∈*Kr+Nc)
γ≥∈
in the formula, NpcAnd NccThe corrected prediction time domain and the control time domain are respectively, gamma is a curvature gain coefficient of the prediction time domain, and epsilon is a curvature gain coefficient of the control time domain.
Further, a corresponding prediction time domain N is foundpAnd control time domain NcThe optimal solution adopts an improved particle swarm optimization algorithm, and the fitness function of the improved particle swarm optimization algorithm is as follows:
Figure BDA0003346631990000021
Np≥Nc＞0
wherein alpha and beta are weight coefficients,
Figure BDA0003346631990000022
is the average lateral error, Δ δ, of the path trackingvIs the maximum value of the nose wheel steering angle increment during tracking.
Further, the forward vehicle speed vmAngle delta with front wheelmComprises the following steps:
Figure BDA0003346631990000023
wherein:
Figure BDA0003346631990000024
the control amount output for the kth time at time t,
Figure BDA0003346631990000025
and delta u (k) is a control increment for the control quantity output at the k-1 th time at the time t.
Further, the reference course angle
Figure BDA0003346631990000026
Wherein (x)rn，yrn) Is the reference position at the next time.
Further, the reference front wheel turning angle
Figure BDA0003346631990000027
Wherein
Figure BDA00033466319900000211
The reference course angle at the next moment, and l is the wheel base of the agricultural vehicle.
Further, the path curvature
Figure BDA0003346631990000029
Wherein
Figure BDA00033466319900000210
Are each yrFor xrFirst and second derivatives.
A tractor path tracking control system based on adaptive time domain model prediction, comprising:
the navigation positioning module is used for acquiring the current position, the course angle and the current speed of the tractor;
the time domain optimization module is used for correcting the prediction time domain and the control time domain;
the adaptive time domain model prediction controller is used for solving the advancing speed and the front wheel rotation angle;
the corner sensor feeds back the actual front wheel corner in real time;
and the action executing mechanism realizes the path tracking of the tractor according to the advancing speed and the front wheel turning angle.
The invention has the beneficial effects that: the time domain optimization module finds out the optimal solution of the prediction time domain and the control time domain, modifies the solved prediction time domain and control time domain, and updates the prediction time domain and the control time domain in the adaptive time domain model prediction controller by utilizing the modified prediction time domain and control time domain; the invention realizes the real-time online adjustment of the prediction time domain and the control time domain, ensures the adaptability of the controller to the vehicle speed and curvature change, and improves the precision and the stability of path tracking.
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FIG. 1 is a schematic structural diagram of a tractor path tracking control system based on adaptive time domain model prediction according to the present invention;
FIG. 2 is a flow chart of a tractor path tracking control method based on adaptive time domain model prediction according to the present invention;
FIG. 3 is a schematic illustration of the tractor path tracking according to the present invention;
FIG. 4 is a flow chart of an improved particle swarm algorithm according to the present invention;
FIG. 5 is a diagram of a vehicle kinematic model according to the present invention.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.
As shown in FIG. 1, the tractor path tracking control system based on adaptive time domain model prediction of the present invention comprises a navigation positioning module, a driving control module, an action executing mechanism and a state feedback module (including a navigation positioning module and a corner sensor installed on the front wheel of the tractor), wherein the driving control module comprises a time domain optimization module and an adaptive time domain model prediction controller.
The navigation positioning module collects the information of the running state of the tractor in real time, including the current position (x, y) and the course angle
Figure BDA0003346631990000037
And a current vehicle speed v, defining a path point on the planned path closest to the current position (x, y) of the tractor as a reference position (x)r，yr) The course angle of the path point corresponding to the reference position is the reference course angle
Figure BDA0003346631990000032
The front wheel corner of the path point corresponding to the reference position is a reference front wheel corner deltafrThe path curvature of the path point corresponding to the reference position is the reference path curvature Kr. The navigation positioning module sends the collected tractor running state information to the running control module, wherein the current speed v and the reference path curvature KrThe current position (x, y) and the course angle are transmitted to a time domain optimization module
Figure BDA0003346631990000033
Reference position (x)r，yr) Reference course angle
Figure BDA0003346631990000034
Reference front wheel corner deltafrTransmitting the current vehicle speed v to the adaptive time domain model predictive controller; the time domain optimization module firstly obtains a prediction time domain N of a corresponding vehicle speed by adopting an improved particle swarm algorithm according to the current vehicle speed vpAnd control time domain NcThe time domain optimization module then follows the reference path curvature KrFor the prediction time domain NpAnd control time domain NcMaking a correction and using the corrected predicted time domain NpcAnd control time domain NccUpdating a prediction time domain and a control time domain of the adaptive time domain model prediction controller; the adaptive time domain model prediction controller is based on a vehicle kinematic model and according to the current position (x, y) and the course angle
Figure BDA0003346631990000035
Reference position (x)r，yr) Reference course angle
Figure BDA0003346631990000036
Reference front wheel corner deltafrCurrent vehicle speed v, and corrected prediction time domain NpcControl time domain NccOptimizing the system constraint to obtain the forward speed vmAngle delta with front wheelm. The action executing mechanism is specifically as follows: an electronic accelerator, an electronic brake and a steering actuating mechanism, wherein the electronic accelerator and the brake are driven according to the advancing speed vmOutputting corresponding throttle and brake voltage values, and steering the actuating mechanism according to the front wheel rotation angle deltamRotate the corresponding angle. The navigation positioning module feeds back the actual current speed v of the tractor to the action executing mechanism in real time, and the current speed v reaches the final advancing speed vmEnding acceleration and deceleration; the rotation angle sensor arranged on the front wheel of the tractor feeds back the actual front wheel rotation angle delta to the action executing mechanism in real time, and the actual front wheel rotation angle delta reaches the final front wheel rotation angle deltamThe steering is over.
As shown in fig. 2, the tractor path tracking control method based on adaptive time domain model prediction of the present invention specifically includes the following steps:
step (1), the navigation positioning module collects the tractor running state information in real time and sends the information to the running control module
As shown in fig. 3, a field coordinate system XOY is established with the long side of the field as the X-axis, the wide side of the field as the Y-axis, and the intersection of the length and the width as the origin of coordinates; the curve D is a part of the planned path and is formed by connecting a plurality of reference path points; defining the center of mass of the tractor as the current position (X, y) of the tractor, and defining the included angle between the tractor body and the X axis as a course angle
Figure BDA0003346631990000041
The reference path point on the planned path closest to the current position (x, y) of the tractor is the reference position (x)r，yr) The transverse error delta X in the driving process is the shortest distance from the current position (X, y) of the tractor to the planned path; the course angle of the reference position corresponding to the reference path point is the reference course angle
Figure BDA0003346631990000042
The reference position corresponds to the front wheel corner of the path point asReference front wheel corner deltafrThe path curvature of the path point corresponding to the reference position is the reference path curvature KrAnd, and:
Figure BDA0003346631990000043
Figure BDA0003346631990000044
Figure BDA0003346631990000045
wherein, the position of the reference path point which is second near to the current position (x, y) of the tractor on the planned path is (x)rn，yrn) I.e., the reference position at the next time,
Figure BDA0003346631990000046
the reference course angle is the next moment,
Figure BDA0003346631990000047
are each yrFor xrFirst and second derivatives.
Step (2), the time domain optimization module firstly adopts an improved particle swarm optimization algorithm to search a corresponding prediction time domain N according to the current vehicle speed vpAnd control time domain NcThe optimal solution of (2);
as shown in FIG. 4, during each iteration, the particle passes through the individual extrema
Figure BDA0003346631990000048
Group extremum
Figure BDA0003346631990000049
And inertia factor
Figure BDA00033466319900000410
Update its own speed
Figure BDA00033466319900000411
And position
Figure BDA00033466319900000412
The update formula is as follows:
Figure BDA00033466319900000413
Figure BDA00033466319900000414
Figure BDA00033466319900000415
wherein i is 1, 2, n, n is the total number of particles; d is the current iteration number; c. C1And c2Is a learning factor; r is1And r2Is a random number between (0, 1); omegamax、ωminIs a preset maximum inertia coefficient and a preset minimum inertia coefficient;
Figure BDA00033466319900000416
Figure BDA00033466319900000417
i.e. the minimum fitness value of all particles at iteration d;
Figure BDA00033466319900000418
is the average fitness value of all particles at iteration d times; the fitness function is:
Figure BDA0003346631990000051
Np≥Nc＞0
wherein α and β are weight coefficients;
Figure BDA0003346631990000052
the average lateral error of the path tracking reflects the accuracy of the control system; delta deltavThe maximum value of the front wheel steering angle increment in the tracking process reflects the stability of the control system.
Inertia factor
Figure BDA0003346631990000053
The convergence of the particle swarm optimization is greatly influenced, a large inertia factor is beneficial to global search, and a small inertia factor is beneficial to local search; the fixed inertia factor has a plurality of defects in the whole solving process. Therefore, an inertia factor which is adjusted in real time along with the fitness is introduced, and the optimization speed of the algorithm is improved. The smaller the fitness is, the closer the optimal solution is, the inertia factor is reduced, and the local search capability is improved; on the contrary, the larger the fitness is, the longer the distance from the optimal solution is, the inertia factor is increased, and the global search capability is improved.
Averagely dividing the range from the lowest stable speed to the highest speed of the tractor into 10 intervals, and respectively obtaining corresponding prediction time domains N based on an improved particle swarm algorithmpAnd control time domain NcAnd (5) optimal solution.
Step (3), the time domain optimization module is used for optimizing the time domain according to the curvature K of the reference pathrTime domain prediction N for improved particle swarm algorithm solutionpAnd control time domain NcAnd (5) correcting:
Npc＝Round(γ*Kr+Np)
Ncc＝Round(∈*Kr+Nc)
γ≥∈
in the formula, NpcAnd NccThe corrected prediction time domain and the control time domain are respectively, gamma is a curvature gain coefficient of the prediction time domain, and epsilon is a curvature gain coefficient of the control time domain.
Finally, the corrected prediction time domain N is usedpcAnd control time domain NccAnd updating a prediction time domain and a control time domain in the adaptive time domain model prediction controller.
Step (4), self-adapting time domain modeBased on vehicle kinematic model, the prediction controller is based on current position (x, y) and course angle
Figure BDA0003346631990000054
Reference position (x)r，yr) Reference course angle
Figure BDA0003346631990000055
Reference front wheel corner deltafrCurrent vehicle speed v, and corrected prediction time domain NpcControl time domain NccAnd the system constraint optimization solves the advancing vehicle speed vmAngle delta with front wheelmThe concrete modeling and calculating process is as follows:
as shown in fig. 5, in the field coordinate system, the vehicle kinematics equation is established:
Figure BDA0003346631990000056
wherein x and y are respectively the transverse coordinate and the longitudinal coordinate of the tractor barycenter z,
Figure BDA0003346631990000057
in the case of a longitudinal speed, the speed,
Figure BDA0003346631990000058
in order to be the transverse velocity,
Figure BDA0003346631990000059
is the course angular velocity, l is the wheelbase, deltafIs a front wheel corner, OzIs the steering center.
For a given reference path, which may be described by the trajectory of the tractor, each point on it satisfies the vehicle kinematics equation described above, with r representing the reference quantity, generally in the form of:
Figure BDA0003346631990000061
setting a reference vehicle speed v of a tractorrIs constant.
With the vehicle kinematic equation at the reference point (x)r，yr) The taylor expansion is performed and the high order terms are ignored, so that:
Figure BDA0003346631990000062
subtracting the taylor expansion from the vehicle kinematic equation at the reference point yields:
Figure BDA0003346631990000063
discretizing the vehicle kinematic model to obtain a final vehicle kinematic model:
Figure BDA0003346631990000064
wherein k is a discrete variable,
Figure BDA0003346631990000065
in order to be a state variable, the state variable,
Figure BDA0003346631990000066
for controlling variables, state quantity transfer matrices
Figure BDA0003346631990000067
Control quantity transfer matrix
Figure BDA0003346631990000068
T is the sampling period.
State variable
Figure BDA0003346631990000069
And a control variable
Figure BDA00033466319900000610
Constructed as new state quantities
Figure BDA00033466319900000611
Where ζ (k | t) is the state quantity of the kth sample at time t,
Figure BDA00033466319900000612
obtaining a new state space expression for the control quantity output at the k-1 th time at the t moment:
Figure BDA00033466319900000613
Figure BDA00033466319900000614
in the formula (I), the compound is shown in the specification,
Figure BDA00033466319900000615
eta is output quantity for control increment, state quantity transfer matrix
Figure BDA00033466319900000616
Figure BDA00033466319900000617
Controlling incremental transfer matrices
Figure BDA00033466319900000618
Output quantity transfer matrix
Figure BDA00033466319900000619
Ck，tAnd I are all identity matrixes.
For convenience of calculation, order
Figure BDA00033466319900000620
Using modified prediction time domain NpcAnd control time domain NccAnd updating a prediction time domain and a control time domain in the prediction model. The output of the adaptive model predictive controller at time t is:
Y(t)＝Ψtζ(k|t)+ΘtΔU(t)
wherein the output quantity is predicted
Figure BDA0003346631990000071
State quantity prediction parameter
Figure BDA0003346631990000072
Controlling a sequence of increments
Figure BDA0003346631990000073
Controlling incremental sequence prediction parameters
Figure BDA0003346631990000074
Taking the control increment as a state quantity of a target function of the adaptive time domain model prediction controller, and introducing a relaxation factor to avoid the condition of no feasible solution; the target function expression of the model predictive control is as follows:
Figure BDA0003346631990000075
in the formula etarefFor the output quantities referenced, Q, F and ρ are the weight matrices, and ε is the relaxation factor.
In the path tracking process, constraints are required to be applied to the control quantity and the control increment, and the constraint conditions are as follows:
Figure BDA0003346631990000076
Δumin(t+j)≤Δu(t+j)≤Δumax(t+j)，j＝0，1...，Ncc-1
in the formula (I), the compound is shown in the specification,
Figure BDA0003346631990000077
and
Figure BDA0003346631990000078
Δ u being the maximum value of the control quantityminAnd Δ uxaxThe control increment is the maximum value.
Converting an objective function J (k) with constraint solving into a linear quadratic programming problem with constraint solving to obtain an optimal control increment sequence delta U (t) of a moment t in a control time domain, acting a first element delta u (k | t) of the sequence on a model prediction controller, and solving an advancing vehicle speed v by the model prediction controllermAngle delta with front wheelm：
Figure BDA0003346631990000079
Step (5), predicting the forward speed v output by the controller according to the adaptive time domain modelmAngle delta with front wheelmThe electronic throttle and the electronic brake output corresponding throttle and brake voltage values, and the steering actuating mechanism rotates by a corresponding angle.
The navigation positioning module feeds back the actual current speed v of the tractor in real time, and the current speed v reaches the final advancing speed vmEnding acceleration and deceleration; the rotation angle sensor arranged on the front wheel of the tractor feeds back the actual front wheel rotation angle delta in real time, and the actual front wheel rotation angle delta reaches the final front wheel rotation angle deltamEnding the steering; thereby realizing path following control of the tractor.
The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.

Claims (8)

1. A tractor path tracking control method based on adaptive time domain model prediction is characterized in that:
the time domain optimization module searches a corresponding prediction time domain N according to the current vehicle speed vpAnd control time domain NcThe optimal solution of (2); the time domain optimization module is then based on the reference path curvature KrFor the solved prediction time domain NpAnd control time domain NcCorrecting;
by usingModified prediction time domain NpcAnd control time domain NccUpdating a prediction time domain and a control time domain in the adaptive time domain model prediction controller;
the adaptive time domain model prediction controller predicts the controller according to the current position (x, y) and the course angle
Figure FDA0003346631980000011
Reference position (x)r，yr) Reference course angle
Figure FDA0003346631980000012
Reference front wheel corner deltafrCurrent vehicle speed v, and corrected prediction time domain NpcCorrected control time domain NccAnd the system constraint optimization solves the advancing vehicle speed vmAngle delta with front wheelm；
The current vehicle speed v reaches the advancing vehicle speed vmWhen the speed is reduced, the acceleration and deceleration are finished; the actual front wheel turning angle delta reaches the front wheel turning angle deltamWhen the steering is finished, the steering is finished; and realizing the path tracking control of the tractor.
2. The adaptive time domain model prediction-based tractor path tracking control method according to claim 1, characterized in that the solved prediction time domain N ispAnd control time domain NcThe correction is carried out as follows:
Npc＝Round(γ*Kr+Np)
Ncc＝Round(∈*Kr+Nc)
γ≥∈
in the formula, NpcAnd NccThe corrected prediction time domain and the control time domain are respectively, gamma is a curvature gain coefficient of the prediction time domain, and epsilon is a curvature gain coefficient of the control time domain.
3. The adaptive time domain model prediction-based tractor path tracking control method according to claim 1, characterized in that a corresponding prediction time domain N is foundpAnd control time domain NcIs adopted to changeEntering a particle swarm optimization algorithm, and improving a fitness function of the particle swarm optimization algorithm as follows:
Figure FDA0003346631980000013
Np≥Nc＞0
wherein alpha and beta are weight coefficients,
Figure FDA0003346631980000014
is the average lateral error, Δ δ, of the path trackingvIs the maximum value of the nose wheel steering angle increment during tracking.
4. The adaptive time domain model prediction-based tractor path following control method according to claim 1, characterized in that the forward vehicle speed vmAngle delta with front wheelmComprises the following steps:
Figure FDA0003346631980000015
wherein:
Figure FDA0003346631980000016
the control amount output for the kth time at time t,
Figure FDA0003346631980000017
and delta u (k) is a control increment for the control quantity output at the k-1 th time at the time t.
5. The adaptive time domain model prediction-based tractor path tracking control method as claimed in claim 1, wherein the reference heading angle
Figure FDA0003346631980000021
Wherein (x)rn，yrn) Is the reference position at the next time.
6. The adaptive time domain model prediction-based tractor path tracking control method according to claim 5, characterized in that the reference front wheel turning angle
Figure FDA0003346631980000022
Wherein
Figure FDA0003346631980000023
The reference course angle at the next moment, and l is the wheel base of the agricultural vehicle.
7. The adaptive time domain model prediction-based tractor path tracking control method according to claim 5, characterized in that the path curvature
Figure FDA0003346631980000024
Wherein
Figure FDA0003346631980000025
Are each yrFor xrFirst and second derivatives.
8. A control system implementing the adaptive time domain model prediction based tractor path tracking control method of any one of claims 1-7, comprising:
the navigation positioning module is used for acquiring the current position, the course angle and the current speed of the tractor;
the time domain optimization module is used for correcting the prediction time domain and the control time domain;
the adaptive time domain model prediction controller is used for solving the advancing speed and the front wheel rotation angle;
the corner sensor feeds back the actual front wheel corner in real time;
and the action executing mechanism realizes the path tracking of the tractor according to the advancing speed and the front wheel turning angle.
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