CN116540527B - Mining truck model prediction speed change track tracking control method - Google Patents
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Abstract
The invention discloses a mining truck model prediction speed change track tracking control method, which comprises the following steps: according to the information of the reference track of the planning layer and the vehicle state, a curvature speed matching table is established, and a matching table of speed and the forward looking distance of the vehicle is set; according to the mining truck dynamics model, a mining truck transverse tracking dynamics error model is established; discretizing a vehicle dynamics error model, and designing a model prediction controller; searching matching points in the reference track through the collected real-time pose information of the vehicle; when the vehicle runs, the model prediction controller gives the corresponding vehicle speed according to the curvature of the position of the detected forward looking distance; the lower layer steering executing mechanism carries out steering control according to the rotation angle given by the model predictive controller, and the lower layer PID controller carries out longitudinal speed control according to the speed given by the model predictive controller. The invention realizes accurate track tracking of the track, avoids abrupt change of the vehicle actuating mechanism, and improves the transportation efficiency and the driving safety.
Description
Technical Field
The invention relates to the field of unmanned control, in particular to a truck model predictive speed change track tracking control method.
Background
The mining truck, namely the mining truck for short, is transportation equipment widely applied to open-air mining areas. Different from the track tracking of the traditional unmanned vehicle, the transportation environment of the unmanned mine truck is severe, and the road is more tortuous. Therefore, many unmanned technologies cannot be directly applied, special track tracking control is required to be researched aiming at special transportation environments and transportation requirements of mining areas so as to meet the safe and efficient unmanned transportation of the mining areas, and therefore, the track tracking research of unmanned mining cards has great social and economic values. The existing unmanned mine card track tracking control method mainly comprises PID, pure tracking, LQR, model predictive control and the like.
The PID is a controller which is mature in industry and widely used, is simple and effective in control and is widely used until now, but has a great disadvantage that parameters of the PID are often debugged by combining experience, the debugging is time-consuming, and the workload is extremely large; the pure tracking algorithm is simple and practical, has good robustness to road curvature disturbance, but the tracking performance of the pure tracking algorithm is seriously dependent on the selection of the forward looking distance, and the optimal value is difficult to acquire. Furthermore, pure tracking algorithms are based on simple geometric models, and do not take into account vehicle dynamics and steering actuator dynamics; LQR is one application of modern control theory, and calculates the optimal control quantity in the whole time domain through an established vehicle model, but the problem of unstable vehicle running caused by abrupt change of the control quantity is easily caused due to the fact that the constraint problem of the control quantity and the control increment is not considered; model predictive control is capable of considering more complex vehicle dynamics models and has better capability of handling constraints, and is therefore widely used in academia and even industry in recent years. However, most of the existing model-based predictive track tracking control methods are based on ideal road environments, do not consider the tortuous environments of mining roads, mainly take fixed speed, and cannot guarantee the transportation efficiency and driving safety of vehicles at the same time.
Therefore, development of a track tracking method which can consider vehicle dynamics characteristics and can add control quantity and control increment constraint is urgently needed to ensure accuracy of track tracking of a mining truck and stability of vehicle running; a controller that can be changed in speed according to the change of the road environment of the mining area is also considered to ensure the transportation efficiency of the mining card.
Disclosure of Invention
The invention aims to: the invention aims to provide a mining truck model prediction variable speed track tracking control method for realizing stable and accurate track tracking of an unmanned mining truck and improving transportation efficiency.
The technical scheme is as follows: the invention relates to a mining truck model prediction speed change track tracking control method, which comprises the following steps:
s1, establishing a curvature speed matching table of a curvature interval and a corresponding vehicle speed according to the reference track of the planning layer and the information of the vehicle state, and setting a matching table of the speed and the vehicle forward looking distance;
s2, establishing a mining truck transverse tracking dynamics error model according to the mining truck dynamics model;
s3, discretizing a vehicle dynamics error model, and deducing a model prediction controller for carrying out soft constraint on the variable quantity of the control quantity by constructing a new state quantity;
s4, searching matching points in the reference track through the acquired real-time pose information of the vehicle; when the vehicle runs, the model predictive controller gives out a constrained steering wheel angle and gives out a corresponding vehicle speed according to the curvature of the position of the detected forward looking distance;
and S5, the lower steering executing mechanism performs steering control according to the steering angle given by the model predictive controller, and the lower PID controller performs longitudinal speed control according to the speed given by the model predictive controller.
In step S1, a curvature speed matching table of a curvature section and a corresponding vehicle speed is established, and the implementation steps of the matching table of the set speed and the vehicle forward looking distance are as follows:
s101, receiving reference track information of a planning layer, wherein the reference track information comprises: transverse position x under geodetic coordinate system r Longitudinal position y r Yaw angle θ r And curvature k r ；
S102, acquiring vehicle state data through a vehicle body sensor and transmitting the data through CAN communication, wherein the acquired vehicle state data comprises: vehicle speed, vehicle position, vehicle yaw angle, engine speed;
s103, setting a minimum turning radius according to the mining area road construction standard, and establishing a curvature speed matching table;
s104, according to different current speeds, matching different forward looking distances to obtain a speed forward looking distance matching table.
Further, in step S2, the specific steps for establishing a vehicle dynamics error model of the lateral and yaw errors of the mining truck are as follows:
s201, establishing a mining truck dynamics model, wherein the expression is as follows:
wherein v is y Is the transverse speed of the mine truck,is the yaw angle of the mine truck, C αf Is the cornering stiffness of the front wheel tyre of the mine truck, C αr Is the cornering stiffness of the rear wheel tire of the mining truck, a is the distance from the mass center to the front wheel, b is the distance from the mass center to the rear wheel, I z The yaw moment of inertia, delta is the front wheel angle; m is the mass of the ore card, v x Is the longitudinal speed of the mine truck;
s202, a mining truck transverse tracking dynamics error model is established, and the expression is:
wherein e d Is the transverse error between the position of the vehicle and the position of the projection point on the reference track under the natural coordinate system,is the transverse error change rate; />Under the geodetic coordinate system, the yaw angle error between the mine truck yaw angle and the projection point,/->Is the yaw angle error change rate;
further, the mining truck transverse tracking dynamics error model is recorded as a state equation:
wherein the method comprises the steps of， u＝δ。
Further, in step S3, the model predictive controller implements the steps of:
s301, discretizing a state equation by mixing a neutral point Euler method and a forward Euler method, wherein discretization is carried out to obtain:
wherein,e is the identity matrix, ">dt is the sampling time;
s302, constructing a new state quantity xi (k) as follows:
ξ(k)＝[X(k) u(k-1)] T
s303, designing a model predictive trajectory tracking controller, wherein the specific steps are as follows:
s3031, obtaining a new state space expression according to the new state quantity constructed in the step S302:
s3032, let the prediction time domain be N P Control the time domain to be N C ，N P ≥N C The prediction equation of the system is obtained by the new state space expression:
Y＝ψξ(k)+ΘΔU
wherein Y is system N P Predictive output of the prediction time domain, ζ (k) is the system state at k time, ψ is the output matrix, Δu is the system controlThe input of the time domain is made, Θ is the direct transfer matrix;
s3033, designing a cost function, wherein the expression is as follows:
wherein Q is a weight matrix of the system prediction output state, R is a weight matrix of the system control variable variation, and epsilon is a relaxation factor; η (k+i, k) is the predicted output of the system at time k+i, η ref (k+i, k) is a prediction time domain reference value of the system at the moment k+i, and ρ is a relaxation factor weight; Δu (k+i, k) is the input variable of the system control time domain at time k+i;
s3034, designing constraint, wherein the expression is as follows:
wherein u is min Is the minimum value of the front wheel rotation angle, u max Is the maximum value of the front wheel rotation angle, deltau min 、Δu max Respectively the minimum and maximum values of the front wheel steering angle variation;
s3035, optimizing and solving the cost function, converting the cost function and the constraint into a form required by standard quadratic solution, wherein the method specifically comprises the following steps:
wherein,
U min 、U max respectively, minimum and maximum of control quantity constraint, deltaU min 、ΔU max Respectively areThe minimum and maximum values of the control variable quantity change quantity, M is the upper bound of the relaxation factor; H. f (f) T Is a standard quadratic coefficient matrix.
Further, in step S4, when the vehicle is running, the model predictive controller gives a constrained steering wheel angle, and according to the curvature at the detected forward looking distance, gives the corresponding vehicle speed as follows:
s401, searching for a matching point (x) in the reference track according to the current vehicle position information (x, y) r ,y r )；
S402, inquiring a speed forward looking distance matching table according to the matching point and the current vehicle speed, and determining a forward looking distance L;
s403, obtaining a forward-looking curvature K on the reference track according to the forward-looking distance and the matching point;
s404, inquiring a curvature speed matching table according to the forward-looking curvature K to obtain the expected speed V of the mining truck;
s405, updating parameters of a model predictive controller according to the expected speed V, wherein the model predictive controller gives the expected steering wheel angle delta.
In step S5, the lower PID controller gives a control amount required for controlling the longitudinal speed according to the desired speed given in step S4 and the current own vehicle speed, and gives an accelerator or a brake amount required for the final mining truck in combination with the accelerator and the brake calibration table, so as to control the vehicle speed.
Compared with the prior art, the invention has the following remarkable effects:
1. aiming at the track tracking problem of the mining area unmanned mining truck, the invention designs a model prediction controller based on curvature speed change by considering the vehicle dynamics model, the control quantity and the control increment problem, and the model prediction controller runs at a low speed on a poor road (with large curvature), runs at a high speed on a good road (with small curvature), effectively controls the input smoothness, so that the vehicle can track accurately and run stably;
2. the invention introduces road curvature, designs a speed curvature matching table, changes a time-varying model predictive control MPC into a local time-invariant model predictive control MPC, reduces the calculated amount, can effectively carry out variable speed running according to road environment, and improves the transportation efficiency of the mining truck.
Drawings
FIG. 1 is a block diagram of a model predictive variable speed trajectory tracking control based on road curvature of the present invention;
FIG. 2 is a flow chart of the model predictive variable speed trajectory tracking based on road curvature of the present invention;
FIG. 3 is an explanatory diagram of road conditions;
FIG. 4 is a graph of track following effects;
fig. 5 is a mining truck travel speed graph.
Detailed Description
The invention is described in further detail below with reference to the drawings and the detailed description.
The invention relates to a model predictive speed-changing track tracking control method based on road curvature, which is shown in fig. 1 as a control block diagram and comprises the following steps:
step 1, a control center receives a reference track of a planning layer and information of a collected vehicle state, a curvature speed matching table of a curvature interval and a corresponding vehicle speed is established, and a front view distance and a rear view distance of the vehicle are set; the specific contents are as follows:
step 101, the control center receiving the reference track information of the planning layer includes: transverse position x under geodetic coordinate system r Longitudinal position y r Yaw angle θ r And curvature k r 。
Step 102, acquiring vehicle state data through a vehicle body sensor and transmitting the data through CAN communication, wherein the acquired vehicle state data comprises: vehicle speed, vehicle position, vehicle yaw angle, engine speed.
Step 103, according to the mining area road construction standard, the minimum turning radius is 15 meters, and the requirements of the unmanned mining truck when setting the curvature speed matching table are more strict, so that the limit turning radius is less than or equal to 20 meters, and the specific curvature speed matching table is shown in table 1.
Table 1 curvature speed matching table
Step 104, the forward looking distance is: starting from a matching point of the vehicle on a reference track, the reference track is about to execute a distance L; different forward looking distances are matched according to different current speeds, and specific speed forward looking distance matching is shown in table 2.
Table 2 speed front view distance matching table
Step 2, establishing a vehicle dynamics linear error model about the transverse and yaw angle errors of the mining truck; the method specifically comprises the following steps:
step 201, mining truck dynamics model is:
wherein v is y Is the transverse speed of the mine truck,is the yaw angle of the mine truck, C αf Is the cornering stiffness of the front wheel tyre of the mine truck, C αr Is the cornering stiffness of the rear wheel tire of the mining truck, a is the distance from the mass center to the front wheel, b is the distance from the mass center to the rear wheel, I z The yaw moment of inertia, delta is the front wheel angle; m is the mass of the ore card, v x Is the longitudinal speed of the mine truck.
Step 202, a mining truck transverse tracking dynamics error model is as follows:
wherein e d Is the transverse error between the position of the vehicle and the position of the projection point on the reference track under the natural coordinate system,is the transverse error change rate; />Under the geodetic coordinate system, the yaw angle error of the yaw angle and the projection point of the mine truck,/is>Is the yaw angle error rate of change.
Further, the equation (2) is written as the state equation:
wherein,u＝δ。
step 3, discretizing the established vehicle dynamics error model, and deducing a model prediction controller capable of carrying out soft constraint on the control quantity variation by constructing a new state quantity; the method specifically comprises the following steps:
step 301, discretizing the formula (3) into discretized form by mixing the mid-point euler method and the forward euler method:
wherein,e is identity matrix, b= Bdt, dt is sampling time;
step 302, a new state quantity ζ (k) is constructed as follows:
ξ(k)＝[X(k) u(k-1)] T (5)
wherein,
step 303, designing a model predictive trajectory tracking controller, which specifically includes the following steps:
step 3031, obtaining a new state space expression according to the new state quantity constructed in step 302:
o represents a zero matrix;
step 3032, let the prediction time domain be N P Control the time domain to be N C (N P ≥N C ) The predictive equation for the system obtainable from equation (6) is:
Y＝ψξ(k)+ΘΔU (7)
y is a system N P The prediction output of the prediction time domain, ζ (k), is the system state at time k, ψ is the output matrix, Δu is the input of the system control time domain, and Θ is the direct transfer matrix.
Step 3033, the cost function is designed to:
wherein Q is a weight matrix of a system prediction output state, R is a weight matrix of a system control quantity variation, and ρ is a relaxation factor weight; η (k+i, k) is the predicted output of the system at time k+i, η ref (k+i, k) is a predicted time domain reference value of the system at time k+i; Δu (k+i, k) is the input variable of the system control time domain at time k+i, and epsilon is the relaxation factor.
In step 3034, the constraints are designed as:
wherein u is min Is the minimum value of the front wheel rotation angle, u max Is the maximum value of the front wheel rotation angle; deltau min 、Δu max Respectively the minimum and maximum values of the front wheel steering angle variation;
step 3035, optimizing and solving a cost function: converting the cost function of formula (8) and the constraint of formula (9) into a form required by standard quadratic form, specifically:
wherein,H、f T is a standard quadratic coefficient matrix:
f T ＝[2E T QΘ O]；
U min 、U max respectively, minimum and maximum of control quantity constraint, deltaU min 、ΔU max The minimum and maximum values of the control amount variation are respectively, and M is the upper bound of the relaxation factor.
And 4, searching a matching point in a reference track through the acquired real-time pose information of the vehicle, wherein the model prediction controller gives a constrained steering wheel angle when the vehicle runs, and gives a corresponding speed according to a curvature speed matching table according to the curvature at the position of the detection front viewing distance:
step 401, searching for a matching point (x) in the reference track according to the current vehicle position information (x, y) r ,y r )；
Step 402, according to the matching point and the current vehicle speed, inquiring a speed forward looking distance matching table, and determining a forward looking distance L;
step 403, obtaining a forward looking curvature K on the reference track according to the forward looking distance obtained in step 403;
step 404, inquiring a curvature speed matching table according to the forward-looking curvature K obtained in the step 403 to obtain the expected speed V of the mining truck;
step 405, updating parameters of a model predictive controller according to the expected speed V obtained in step 404, wherein the model predictive controller gives an expected steering wheel angle delta;
and 5, steering control is carried out by the lower steering executing mechanism according to the rotation angle given by the controller, and longitudinal speed control is carried out by the lower PID controller according to the speed given by the controller.
Step 501, steering control is carried out by a steering mechanism according to a desired steering angle delta given by a model predictive controller;
step 502, the lower layer longitudinal PID controller performs speed control according to the expected speed given by the curvature matching table;
the method is suitable for unmanned driving. In order to better understand the present invention, a method for model predictive transmission trajectory tracking control based on road curvature will be described in detail with reference to specific examples.
The present embodiment considers the road conditions shown in fig. 3 as follows:
the aim of this embodiment is to make the mining truck travel at variable speeds following the road trajectory shown in fig. 3. The five-pointed star in fig. 3 represents the current vehicle location and is described in more detail below in conjunction with the algorithm flow chart of fig. 2.
Step C1, the vehicle searches a matching point in the reference track through a matching point algorithm according to the current position and the reference track, and if the found matching point in the third diagram is the position of an arrow 1;
step C2, the vehicle inquires a set speed forward looking distance matching table according to the current speed to obtain a forward looking distance, wherein the forward looking distance is L in FIG. 3;
step C3, the model controller inquires a preset curvature speed matching table according to the curvature K at the front viewing distance L, namely the curvature indicated by an arrow 2 in fig. 3, and obtains a desired speed v from the curvature speed matching table, wherein the desired speed is 10km/h;
step C4, the model predictive controller transmits the expected speed v obtained in the step C3 to a lower-layer longitudinal speed PID controller, and then the model predictive controller updates own system parameters, performs optimization solution to obtain a steering wheel angle delta expected to turn, and transmits the steering wheel angle delta to a lower-layer steering executing mechanism;
step C5, the PID controller gives out the control quantity required by longitudinal speed control according to the expected speed given by the step C4 and the current own vehicle speed, and combines the throttle and the brake calibration table to give out the throttle or brake quantity required by the final mining truck, thereby achieving vehicle speed control; the steering mechanism performs steering control according to the desired steering angle given in step C4.
The invention can be found from fig. 4 to have a very good track following effect, and fig. 5 to be able to run at variable speeds according to road conditions. In summary, the model predictive controller based on curvature speed change designed by the invention runs at a low speed on a poor road (with large curvature) and runs at a high speed on a good road (with small curvature).
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (2)
1. A mining truck model prediction speed change track tracking control method is characterized by comprising the following steps:
s1, according to the information of the reference track and the vehicle state of a planning layer received by a control center, a curvature speed matching table of a curvature section and a corresponding vehicle speed is established, and a matching table of the speed and the front viewing distance of the vehicle is set; the implementation steps are as follows:
s101, receiving reference track information of a planning layer, wherein the reference track information comprises: transverse position x under geodetic coordinate system r Longitudinal position y r Yaw angle θ r And curvature k r ；
S102, acquiring vehicle state data through a vehicle body sensor and transmitting the data through CAN communication, wherein the acquired vehicle state data comprises: vehicle speed, vehicle position, vehicle yaw angle, engine speed;
s103, setting a minimum turning radius according to the mining area road construction standard, and establishing a curvature speed matching table;
s104, matching different forward looking distances according to different current speeds to obtain a speed forward looking distance matching table;
s2, establishing a mining truck transverse tracking dynamics error model according to the mining truck dynamics model; the implementation steps are as follows:
s201, establishing a mining truck dynamics model, wherein the expression is as follows:
wherein v is y Is the transverse speed of the mining truck,is the yaw angle of the mining truck, C αf Is the cornering stiffness of front wheel tires of the mining truck, C αr Is the cornering stiffness of the rear wheel tyre of the mining truck, a is the distance from the mass center to the front wheel, b is the distance from the mass center to the rear wheel, I z The yaw moment of inertia, delta is the front wheel angle; m is the mass of the mining truck, v x The longitudinal speed of the mining truck;
s202, a mining truck transverse tracking dynamics error model is established, and the expression is:
wherein e d Is the transverse error between the position of the vehicle and the position of the projection point on the reference track under the natural coordinate system,is the transverse error change rate; />Under the geodetic coordinate system, yaw angle error of the mining truck at yaw angle and projection point, +.>For yaw angle error rate of change；
Further, the mining truck transverse tracking dynamics error model is recorded as a state equation:
wherein, u＝δ；
s3, discretizing a vehicle dynamics error model, and deducing a model prediction controller for carrying out soft constraint on the variable quantity of the control quantity by constructing a new state quantity;
s4, searching matching points in the reference track through the acquired real-time pose information of the vehicle; when the vehicle runs, the model predictive controller gives out a constrained steering wheel angle and gives out a corresponding vehicle speed according to the curvature of the position of the detected forward looking distance;
s5, the lower steering executing mechanism performs steering control according to the steering angle given by the model predictive controller, and the lower PID controller performs longitudinal speed control according to the speed given by the model predictive controller;
in step S3, the implementation steps of the model predictive controller are as follows:
s301, discretizing a state equation by mixing a neutral point Euler method and a forward Euler method, wherein discretization is carried out to obtain:
wherein,e is the identity matrix, ">dt is the sampling time;
s302, constructing a new state quantity xi (k) as follows:
ξ(k)＝[X(k) u(k-1)] T
s303, designing a model predictive trajectory tracking controller, wherein the specific steps are as follows:
s3031, obtaining a new state space expression according to the new state quantity constructed in the step S302:
s3032, let the prediction time domain be N P Control the time domain to be N C ，N P ≥N C The prediction equation of the system is obtained by the new state space expression:
Y＝ψξ(k)+ΘΔU
wherein Y is system N P Predictive output of a prediction time domain, ζ (k) is a system state at k moment, ψ is an output matrix, Δu is input of a system control time domain, and Θ is a direct transfer matrix;
s3033, designing a cost function, wherein the expression is as follows:
wherein Q is a weight matrix of the system prediction output state, R is a weight matrix of the system control variable variation, and epsilon is a relaxation factor; η (k+i, k) is the predicted output of the system at time k+i, η ref (k+i, k) is a prediction time domain reference value of the system at the moment k+i, and ρ is a relaxation factor weight; Δu (k+i, k) is the input variable of the system control time domain at time k+i;
s3034, designing constraint, wherein the expression is as follows:
wherein u is min Is the minimum value of the front wheel rotation angle, u max Is the maximum value of the front wheel rotation angle, deltau min 、Δu max Respectively the minimum and maximum values of the front wheel steering angle variation;
s3035, optimizing and solving the cost function, converting the cost function and the constraint into a form required by standard quadratic solution, wherein the method specifically comprises the following steps:
wherein,
U min 、U max respectively, minimum and maximum of control quantity constraint, deltaU min 、ΔU max Respectively the minimum and maximum values of the control variable variation, M is the upper bound of the relaxation factor; H. f (f) T Is a standard quadratic coefficient matrix;
in step S4, when the vehicle is running, the model predictive controller gives the constrained steering wheel angle, and gives the corresponding vehicle speed according to the curvature at the detected forward looking distance, as follows:
s401, searching for a matching point (x) in the reference track according to the current vehicle position information (x, y) r ,y r )；
S402, inquiring a speed forward looking distance matching table according to the matching point and the current vehicle speed, and determining a forward looking distance L;
s403, obtaining a forward-looking curvature K on the reference track according to the forward-looking distance and the matching point;
s404, inquiring a curvature speed matching table according to the forward-looking curvature K to obtain the expected speed V of the mining truck;
s405, updating parameters of a model predictive controller according to the expected speed V, wherein the model predictive controller gives the expected steering wheel angle delta.
2. The mining truck model predictive speed-change track tracking control method according to claim 1, wherein in the step S5, the lower layer PID controller gives the control amount required for longitudinal speed control according to the expected speed given in the step S4 and the current own vehicle speed, and gives the throttle or brake amount required for the final mining truck in combination with a throttle and brake calibration table to control the vehicle speed.
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