CN112394734A - Vehicle track tracking control method based on linear model predictive control algorithm - Google Patents
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Abstract
The invention discloses a vehicle track tracking control method based on a linear model predictive control algorithm, which comprises the following steps: s1, collecting vehicle state information, vehicle positioning information and a reference track; s2, filtering the collected items; s3, acquiring a real-time target point of the vehicle; s4, obtaining the lateral error and the heading angle error of the vehicle; s5, establishing a linear vehicle dynamic model; s6, obtaining the steering angle and the rotating speed of the steering wheel of the vehicle; s7, respectively carrying out amplitude limiting and filtering on the rotation angle of the vehicle steering wheel and the rotation speed of the vehicle steering wheel; and S8, sending the result to the controlled vehicle to realize the track tracking control. The invention uses the linear model predictive control algorithm on the basis of the linear vehicle dynamics model, and improves the real-time performance of control. Meanwhile, the invention fully considers the dynamic characteristics of the vehicle and the sudden change of the vehicle control quantity, improves the vehicle track tracking capability and ensures the stability of the controlled vehicle in middle and high-speed running.
Description
Technical Field
The invention relates to a vehicle control technology, in particular to a vehicle track tracking control method based on a linear model predictive control algorithm, and belongs to the technical field of vehicle automatic driving.
Background
With the continuous progress of automatic driving technology in recent years, the hardware level of various systems corresponding to the technology is greatly improved.
Taking a conventional vehicle track tracking control system as an example, in the actual running process of the system, a track tracking controller in the system calculates control quantities such as a steering angle of a steering wheel of the vehicle, a rotating speed of the steering wheel and the like according to vehicle positioning information, vehicle state information and the like and by referring to selected track points in the track, and then sends the control quantities to a vehicle to be controlled, so that the track tracking of the vehicle is realized. In the field of current automatic driving, a vehicle trajectory tracking controller is often constructed based on control algorithms such as proportional-integral-derivative (PID) control, slip-film (SMC) control, Model Predictive Control (MPC), and the like, so as to realize an expected trajectory of a vehicle. The vehicle track tracking control system enables the vehicle to run according to the expected track, and the running stability and safety of the vehicle are guaranteed. As such, research related to vehicle trajectory tracking control is also becoming an industry focus.
The unmanned vehicle path tracking controller is divided into a pre-aiming control part and a compensation control part, the pre-aiming control part simulates a driver to pre-aim the road environment information in front in the vehicle driving process and then determines steering of a steering wheel according to the road curvature degree, and the compensation control part corrects the vehicle which deviates from an original lane when the vehicle encounters interference. However, after practical use, researchers have found that the following problems exist with this approach: 1. the dynamic characteristics of the vehicle are not considered in the controller, and the method has larger track tracking error in a high-dynamic and high-speed scene. 2. The PID controller and the Fuzzy controller utilized by the controller belong to model-free controllers, and the robustness of the PID controller and the Fuzzy controller in the aspect of track tracking control is poor.
The Fujie 28952nd Zhao Ke just and the like design a track tracking control algorithm aiming at a front wheel steering intelligent automobile in an intelligent automobile track tracking algorithm based on MPC (multi-media personal computer). The algorithm is based on a vehicle kinematic model, a vehicle track tracking state space equation is established, a model predictive control algorithm is adopted, the minimum dynamic tracking deviation under the consideration of riding comfort is taken as a control target, the control constraint and the state constraint are added to the model under the consideration of the actual condition of the vehicle, and the optimal control of intelligent vehicle track tracking with the constraint is realized through rolling optimization and feedback correction. However, also, the following problems still exist in this solution: 1. the trajectory tracking control algorithm is based on a vehicle kinematic model, the vehicle dynamics characteristics are obvious under the condition of medium-speed or high-speed running of the vehicle, and at the moment, the error of the trajectory tracking control algorithm is large, and the safe running of the vehicle cannot be guaranteed. 2. The track tracking control algorithm does not consider the stationarity of the controlled variable, and the controlled variable is easy to generate sudden change in the running process of the vehicle, so that the vehicle shakes.
In view of the current research situation and the problems in the research, how to provide a novel vehicle trajectory tracking control method in consideration of the dynamic characteristics of the vehicle and the improvement of the vehicle trajectory tracking capability is a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, an object of the present invention is to provide a vehicle trajectory tracking control method based on a linear model predictive control algorithm, which is as follows.
A vehicle track tracking control method based on a linear model predictive control algorithm comprises the following steps:
s1, respectively collecting vehicle state information and vehicle positioning information of a controlled vehicle, and collecting a preset running track of the vehicle as a reference track;
s2, performing Kalman filtering processing on the vehicle state information and the vehicle positioning information respectively, removing sensor noise signals in the vehicle state information and the vehicle positioning information to obtain denoised vehicle state information and vehicle positioning information, performing track filtering processing on the reference track to remove noise points in the reference track to obtain a denoised reference track;
s3, selecting a track point closest to the mass center of the vehicle from the de-noised reference track as a real-time target point of the vehicle according to the de-noised vehicle state information and the vehicle positioning information;
s4, obtaining a vehicle transverse error and a vehicle course angle error through an error analysis algorithm according to the vehicle real-time target point and the de-noised vehicle positioning information;
s5, establishing a vehicle dynamic model aiming at the current running state of the vehicle according to the denoised vehicle state information and the vehicle positioning information, and carrying out linearization and discretization processing on the vehicle dynamic model by using a Taylor formula and an Euler discrete mapping method to obtain a linear vehicle dynamic model;
s6, calculating to obtain a vehicle steering wheel corner and a vehicle steering wheel rotating speed by using a model predictive control algorithm according to the vehicle transverse error, the vehicle course angle error, the denoised vehicle state information and vehicle positioning information and the linear vehicle dynamics model;
s7, performing amplitude limiting filtering on the steering angle of the vehicle steering wheel and the rotating speed of the vehicle steering wheel respectively, removing abnormal points in the control quantity, and obtaining the processed steering angle of the vehicle steering wheel and the processed rotating speed of the vehicle steering wheel;
and S8, sending the processed steering wheel angle and the vehicle steering wheel rotating speed of the vehicle to the controlled vehicle, and realizing the track following control of the vehicle.
Preferably, S1 includes the steps of:
s11, collecting vehicle state information from an internal CAN bus of the controlled vehicle through a CAN communication module, wherein the vehicle state information comprises longitudinal information, steering information and other information;
s12, acquiring vehicle positioning information from the vehicle-mounted integrated navigation module through the communication interface, wherein the vehicle positioning information comprises angle information, angular velocity information, acceleration information, position information and speed information;
and S13, acquiring a preset running track of the vehicle as a reference track through a vehicle navigation system.
Preferably, it is characterized in that:
the longitudinal information comprises longitudinal speed, longitudinal acceleration, wheel speed of a left front wheel, average wheel speed of the wheel, driving direction of the wheel and a wheel speed pulse signal;
the steering information comprises a steering wheel angle, a steering wheel rotating speed, a lateral acceleration and a yaw angular velocity;
the other information comprises gear information, accelerator depth, brake pedal state, pedal brake depth and EPB electronic hand brake state;
the angle information comprises a roll angle, a pitch angle and an azimuth angle;
the angular velocity information comprises roll angle rate, pitch angle rate and azimuth angle rate;
the acceleration information comprises longitudinal acceleration, transverse acceleration and vertical acceleration;
the location information includes longitude, latitude, and altitude;
the speed information includes a northbound speed, an eastern speed, and a groundbound speed.
Preferably, the track filtering processing on the reference track in S2 is performed to remove noise points in the reference track, so as to obtain a denoised reference track, and the method includes the following steps:
s21, vehicle motion simulation is carried out on the reference track by using the linear vehicle motion model, all track points which do not accord with vehicle motion characteristics in the reference track are searched, if no track point which does not accord with the linear vehicle motion model exists, the denoised reference track is obtained, and if track points which do not accord with the linear vehicle motion model exist, S22 is executed in sequence;
s22, respectively selecting front adjacent points and rear adjacent points of the track points which do not accord with the linear vehicle motion model from the reference track, obtaining intermediate points of the front adjacent points and the rear adjacent points by using a binomial difference method, and replacing the track points which do not accord with the linear vehicle motion model from the reference track by using the intermediate points to obtain the de-noised reference track.
Preferably, S5 includes the steps of:
s51, establishing a vehicle dynamic model aiming at the current running state of the vehicle by using the vehicle state information and the vehicle positioning information after denoising;
s52, carrying out Taylor expansion on the vehicle dynamic model by using a Taylor formula, and neglecting a derivative term which is not lower than the second order in the expansion in the Taylor expansion process to obtain a non-discrete linear vehicle dynamic model;
and S53, discretizing the non-discrete linear vehicle dynamic model by using an Euler discrete mapping method to obtain the linear vehicle dynamic model.
Preferably, S6 includes the steps of:
s61, establishing a model prediction matrix under the current vehicle running state according to the linear vehicle dynamics model;
s62, establishing a vehicle state matrix at the current moment according to the transverse error, the course angle error, the denoised vehicle state information and the vehicle positioning information;
s63, according to the model prediction matrix, combining the vehicle state information at the current moment, setting the loss function of the model prediction control algorithm, establishing the following optimization problem,
st.δmin≤u(t+k|t)≤δmax,k＝0,1,K,Hc-1,
Δδmin≤Δu(t+k|t)≤Δδmax,k＝0,1,K,Hc-1,
ε≥0，
wherein t and k are respectively time t and time k,
as vehicle state information, Δ U as control time domain HcThe inner steering wheel rotation variable, eta is the system output quantity, delta y is the control quantity change quantity, Q, R is the output weight matrix and the control quantity weight matrix, deltamin、δmaxMaximum and minimum steering angles, Δ δ, of the steering wheel of the vehicle, respectivelymin、ΔδmaxThe minimum and maximum rotation speeds of the steering wheel are respectively;
s64, transforming the optimization problem into a quadratic programming problem by matrix transforming the optimization problem,
st.ΔUmin≤ΔU(t)≤ΔUmax,
Umin≤U(t)≤Umax，
wherein, Delta Umin、ΔUmaxMinimum and maximum constraints, U, respectively, controlling the variationmin、UmaxRespectively a minimum constraint and a maximum constraint of the control quantity;
s65, solving the quadratic programming problem by using a Lagrange method to obtain the steering angle variation of the steering wheel of the vehicle;
and S66, summing the steering wheel angle variable quantity of the vehicle to obtain the steering wheel angle of the vehicle, and dividing the steering wheel angle variable quantity of the vehicle by sampling time to obtain the rotating speed of the steering wheel of the vehicle.
Compared with the prior art, the invention has the advantages that:
the vehicle track tracking control method provided by the invention is based on a linear vehicle dynamics model and uses a linear model predictive control algorithm, so that the real-time performance of control is obviously improved. Meanwhile, in the method, the dynamic characteristics of the vehicle and the sudden change of the vehicle control quantity are fully considered, so that the vehicle track tracking capability of the method is improved, and the stability of the controlled vehicle in the middle and high-speed running is ensured to the maximum extent.
In addition, the invention also provides a reference basis for other technical schemes in the same field, can be expanded and extended on the basis of the reference basis, is applied to other technical schemes related to vehicle track tracking control, and has high use and popularization values.
The following detailed description of the embodiments of the present invention is provided in connection with the accompanying drawings for the purpose of facilitating understanding and understanding of the technical solutions of the present invention.
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FIG. 1 is a schematic overview of the process of the present invention;
fig. 2 is a flowchart of a vehicle trajectory tracking control section in the present invention.
Detailed Description
The invention discloses a vehicle track tracking control method based on a linear model predictive control algorithm, which is characterized in that vehicle state, positioning information and reference track are filtered, target point matching, error analysis, a vehicle dynamics model and a model predictive control algorithm are carried out to obtain a vehicle steering wheel corner and a vehicle steering wheel rotating speed, filtering is carried out again, and finally the vehicle steering wheel corner and the vehicle steering wheel rotating speed are sent to a controlled vehicle, so that vehicle track tracking control based on the model predictive control algorithm is realized. The contents are as follows.
As shown in fig. 1 and fig. 2, a vehicle trajectory tracking control method based on a linear model predictive control algorithm includes the following steps:
and S1, respectively collecting the vehicle state information and the vehicle positioning information of the controlled vehicle, and collecting the preset running track of the vehicle as a reference track.
S1 specifically includes the following steps:
and S11, collecting vehicle state information from an internal CAN bus of the controlled vehicle through the CAN communication module, wherein the vehicle state information comprises longitudinal information, steering information and other information.
The longitudinal information here includes longitudinal speed, longitudinal acceleration, wheel speed of the left front wheel, wheel average speed, wheel direction of travel, and wheel speed pulse signals; the steering information comprises a steering wheel angle, a steering wheel rotating speed, a lateral acceleration and a yaw angular velocity; the other information includes gear information, accelerator depth, Brake pedal state, pedal Brake depth, and EPB (Electrical Park Brake) electronic hand Brake state.
And S12, acquiring vehicle positioning information from the vehicle-mounted integrated navigation module through the communication interface, wherein the vehicle positioning information comprises angle information, angular velocity information, acceleration information, position information and speed information.
Here, the angle information includes roll angle, pitch angle, and azimuth angle; the angular velocity information comprises roll angle rate, pitch angle rate and azimuth angle rate; the acceleration information comprises longitudinal acceleration, transverse acceleration and vertical acceleration; the location information includes longitude, latitude, and altitude; the speed information includes a northbound speed, an eastern speed, and a groundbound speed.
And S13, acquiring a preset running track of the vehicle as a reference track through a vehicle navigation system.
S2, in order to prevent noise of an external sensor from reducing the tracking control precision of the vehicle track, Kalman filtering processing needs to be respectively carried out on vehicle state information and vehicle positioning information, sensor noise signals in the vehicle state information and the vehicle positioning information are removed, and the vehicle state information and the vehicle positioning information after denoising are obtained; in order to ensure the effectiveness of the reference trajectory, trajectory filtering processing needs to be performed on the reference trajectory to remove noise points in the reference trajectory, so as to obtain a denoised reference trajectory.
It should be noted that, in the step S2, performing trajectory filtering processing on the reference trajectory to remove noise points in the reference trajectory to obtain a denoised reference trajectory specifically includes the following steps:
and S21, performing vehicle motion simulation on the reference track by using the linear vehicle motion model, retrieving all track points which do not accord with the vehicle kinematics characteristic in the reference track, if no track point which does not accord with the linear vehicle motion model exists, obtaining the denoised reference track, and if track points which do not accord with the linear vehicle motion model exist, executing S22 in sequence.
S22, respectively selecting front adjacent points and rear adjacent points of the track points which do not accord with the linear vehicle motion model from the reference track, obtaining intermediate points of the front adjacent points and the rear adjacent points by using a binomial difference method, and replacing the track points which do not accord with the linear vehicle motion model from the reference track by using the intermediate points to obtain the de-noised reference track.
And S3, selecting a track point closest to the mass center of the vehicle from the de-noised reference track as a real-time target point of the vehicle according to the de-noised vehicle state information and the vehicle positioning information.
And S4, obtaining the vehicle transverse error and the vehicle course angle error through an error analysis algorithm according to the vehicle real-time target point and the de-noised vehicle positioning information.
S5, according to the denoised vehicle state information and the vehicle positioning information, a vehicle dynamic model is established according to the current running state of the vehicle, and the linear vehicle dynamic model is obtained by carrying out linearization and discretization on the vehicle dynamic model by using a Taylor formula and an Euler discrete mapping method.
S5 specifically includes the following steps:
and S51, establishing a vehicle dynamic model aiming at the current running state of the vehicle by using the denoised vehicle state information and the vehicle positioning information.
S52, carrying out Taylor expansion on the vehicle dynamic model by using a Taylor formula, wherein a derivative which is not lower than the second order in the expansion is ignored in the Taylor expansion process, and then the non-discrete linear vehicle dynamic model is obtained.
And S53, discretizing the non-discrete linear vehicle dynamic model by using an Euler discrete mapping method to obtain the linear vehicle dynamic model.
And S6, calculating to obtain the steering wheel angle and the steering wheel rotating speed of the vehicle by using a model predictive control algorithm according to the lateral error of the vehicle, the heading angle error of the vehicle, the vehicle state information after noise removal, the vehicle positioning information and the linear vehicle dynamics model.
S6 specifically includes the following steps:
and S61, establishing a model prediction matrix under the current vehicle running state according to the linear vehicle dynamic model.
And S62, establishing a vehicle state matrix at the current moment according to the transverse error, the heading angle error, the denoised vehicle state information and the vehicle positioning information.
S63, according to the model prediction matrix, combining the vehicle state information at the current moment, setting the loss function of the model prediction control algorithm, establishing the following optimization problem,
st.δmin≤u(t+k|t)≤δmax,k＝0,1,K,Hc-1,
Δδmin≤Δu(t+k|t)≤Δδmax,k＝0,1,K,Hc-1,
ε≥0，
wherein t and k are respectively time t and time k,
as vehicle state information, Δ U as control time domain HcThe inner steering wheel rotation variable, eta the system output quantity, delta u the control quantity change quantity, Q, R the output weight matrix and the control quantity weight matrix, deltamin、δmaxMaximum and minimum steering angles, Δ δ, of the steering wheel of the vehicle, respectivelymin、ΔδmaxThe minimum and maximum rotation speeds of the steering wheel are respectively;
the right side of the above equation is composed of the sum of three terms, wherein the first term is an output state term, and the time domain H is predicted according to the first termpCalculating the value of the item by the internal system output quantity and the output weight matrix Q, wherein the item reflects the tracking capability of the system on the reference track; the second term is a control input term according to the control time domain HcThe value of the term is calculated by the internal control variable quantity delta u and the control weight R, the term limits the change of the control quantity, and the stability of control is ensured. The third term is a relaxation term, and the loss function is guaranteed to have an optimal solution.
S64, transforming the optimization problem into a quadratic programming problem by matrix transforming the optimization problem,
st.ΔUmin≤ΔU(t)≤ΔUmax,
Umin≤U(t)≤Umax，
wherein, Delta Umin、ΔUmaxMinimum and maximum constraints, U, respectively, controlling the variationmin、UmaxRespectively a minimum constraint and a maximum constraint for the control quantity.
And S65, solving the quadratic programming problem by using a Lagrange method to obtain the steering angle variation of the steering wheel of the vehicle.
And S66, summing the steering wheel angle variable quantity of the vehicle to obtain the steering wheel angle of the vehicle, and dividing the steering wheel angle variable quantity of the vehicle by sampling time to obtain the rotating speed of the steering wheel of the vehicle. S7, performing amplitude limiting filtering on the steering angle of the vehicle steering wheel and the rotating speed of the vehicle steering wheel respectively, removing abnormal points in the control quantity, and obtaining the processed steering angle of the vehicle steering wheel and the processed rotating speed of the vehicle steering wheel;
and S8, sending the processed steering wheel angle and the vehicle steering wheel rotating speed of the vehicle to the controlled vehicle, and realizing the track following control of the vehicle.
In summary, the vehicle trajectory tracking control method provided by the invention uses a linear model predictive control algorithm based on a linear vehicle dynamics model, and remarkably improves the real-time performance of control. Meanwhile, in the method, the dynamic characteristics of the vehicle and the sudden change of the vehicle control quantity are fully considered, so that the vehicle track tracking capability of the method is improved, and the stability of the controlled vehicle in the middle and high-speed running is ensured to the maximum extent.
In addition, the invention also provides a reference basis for other technical schemes in the same field, can be expanded and extended on the basis of the reference basis, is applied to other technical schemes related to vehicle track tracking control, and has high use and popularization values.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference signs in the claims are not intended to be construed as limiting the claim concerned.
Finally, it should be understood that although the present description refers to embodiments, not every embodiment contains only a single technical solution, and such description is for clarity only, and those skilled in the art should integrate the description, and the technical solutions in the embodiments can be appropriately combined to form other embodiments understood by those skilled in the art.
Claims (6)
1. A vehicle track tracking control method based on a linear model predictive control algorithm is characterized by comprising the following steps:
s1, respectively collecting vehicle state information and vehicle positioning information of a controlled vehicle, and collecting a preset running track of the vehicle as a reference track;
s2, performing Kalman filtering processing on the vehicle state information and the vehicle positioning information respectively, removing sensor noise signals in the vehicle state information and the vehicle positioning information to obtain denoised vehicle state information and vehicle positioning information, performing track filtering processing on the reference track to remove noise points in the reference track to obtain a denoised reference track;
s3, selecting a track point closest to the mass center of the vehicle from the de-noised reference track as a real-time target point of the vehicle according to the de-noised vehicle state information and the vehicle positioning information;
s4, obtaining a vehicle transverse error and a vehicle course angle error through an error analysis algorithm according to the vehicle real-time target point and the de-noised vehicle positioning information;
s5, establishing a vehicle dynamic model aiming at the current running state of the vehicle according to the denoised vehicle state information and the vehicle positioning information, and carrying out linearization and discretization processing on the vehicle dynamic model by using a Taylor formula and an Euler discrete mapping method to obtain a linear vehicle dynamic model;
s6, calculating to obtain a vehicle steering wheel corner and a vehicle steering wheel rotating speed by using a model predictive control algorithm according to the vehicle transverse error, the vehicle course angle error, the denoised vehicle state information and vehicle positioning information and the linear vehicle dynamics model;
s7, performing amplitude limiting filtering on the steering angle of the vehicle steering wheel and the rotating speed of the vehicle steering wheel respectively, removing abnormal points in the control quantity, and obtaining the processed steering angle of the vehicle steering wheel and the processed rotating speed of the vehicle steering wheel;
and S8, sending the processed steering wheel angle and the vehicle steering wheel rotating speed of the vehicle to the controlled vehicle, and realizing the track following control of the vehicle.
2. The linear-model predictive control algorithm-based vehicle trajectory tracking control method according to claim 1, wherein S1 includes the steps of:
s11, collecting vehicle state information from an internal CAN bus of the controlled vehicle through a CAN communication module, wherein the vehicle state information comprises longitudinal information, steering information and other information;
s12, acquiring vehicle positioning information from the vehicle-mounted integrated navigation module through the communication interface, wherein the vehicle positioning information comprises angle information, angular velocity information, acceleration information, position information and speed information;
and S13, acquiring a preset running track of the vehicle as a reference track through a vehicle navigation system.
3. The vehicle trajectory tracking control method based on the linear model predictive control algorithm according to claim 2, characterized in that:
the longitudinal information comprises longitudinal speed, longitudinal acceleration, wheel speed of a left front wheel, average wheel speed of the wheel, driving direction of the wheel and a wheel speed pulse signal;
the steering information comprises a steering wheel angle, a steering wheel rotating speed, a lateral acceleration and a yaw angular velocity;
the other information comprises gear information, accelerator depth, brake pedal state, pedal brake depth and EPB electronic hand brake state;
the angle information comprises a roll angle, a pitch angle and an azimuth angle;
the angular velocity information comprises roll angle rate, pitch angle rate and azimuth angle rate;
the acceleration information comprises longitudinal acceleration, transverse acceleration and vertical acceleration;
the location information includes longitude, latitude, and altitude;
the speed information includes a northbound speed, an eastern speed, and a groundbound speed.
4. The method as claimed in claim 1, wherein the step of performing trajectory filtering on the reference trajectory to remove noise points in the reference trajectory to obtain a de-noised reference trajectory in S2 includes the following steps:
s21, vehicle motion simulation is carried out on the reference track by using the linear vehicle motion model, all track points which do not accord with vehicle motion characteristics in the reference track are searched, if no track point which does not accord with the linear vehicle motion model exists, the denoised reference track is obtained, and if track points which do not accord with the linear vehicle motion model exist, S22 is executed in sequence;
s22, respectively selecting front adjacent points and rear adjacent points of the track points which do not accord with the linear vehicle motion model from the reference track, obtaining intermediate points of the front adjacent points and the rear adjacent points by using a binomial difference method, and replacing the track points which do not accord with the linear vehicle motion model from the reference track by using the intermediate points to obtain the de-noised reference track.
5. The linear-model predictive control algorithm-based vehicle trajectory tracking control method according to claim 1, wherein S5 includes the steps of:
s51, establishing a vehicle dynamic model aiming at the current running state of the vehicle by using the vehicle state information and the vehicle positioning information after denoising;
s52, carrying out Taylor expansion on the vehicle dynamic model by using a Taylor formula, and neglecting a derivative term which is not lower than the second order in the expansion in the Taylor expansion process to obtain a non-discrete linear vehicle dynamic model;
and S53, discretizing the non-discrete linear vehicle dynamic model by using an Euler discrete mapping method to obtain the linear vehicle dynamic model.
6. The linear-model predictive control algorithm-based vehicle trajectory tracking control method according to claim 1, wherein S6 includes the steps of:
s61, establishing a model prediction matrix under the current vehicle running state according to the linear vehicle dynamics model;
s62, establishing a vehicle state matrix at the current moment according to the transverse error, the course angle error, the denoised vehicle state information and the vehicle positioning information;
s63, according to the model prediction matrix, combining the vehicle state information at the current moment, setting the loss function of the model prediction control algorithm, establishing the following optimization problem,
st.δmin≤u(t+k|t)≤δmax,k＝0,1,K,Hc-1,
Δδmin≤Δu(t+k|t)≤Δδmax,k＝0,1,K,Hc-1,
ε≥0，
wherein t and k are respectively time t and time k,
as vehicle state information, Δ U as control time domain HcThe inner steering wheel rotation variable, eta the system output quantity, delta u the control quantity change quantity, Q, R the output weight matrix and the control quantity weight matrix, deltamin、δmaxMaximum and minimum steering angles, Δ δ, of the steering wheel of the vehicle, respectivelymin、ΔδmaxThe minimum and maximum rotation speeds of the steering wheel are respectively;
s64, transforming the optimization problem into a quadratic programming problem by matrix transforming the optimization problem,
st.ΔUmin≤ΔU(t)≤ΔUmax,
Umin≤U(t)≤Umax，
wherein, Delta Umin、ΔUmaxMinimum and maximum constraints, U, respectively, controlling the variationmin、UmaxRespectively a minimum constraint and a maximum constraint of the control quantity;
s65, solving the quadratic programming problem by using a Lagrange method to obtain the steering angle variation of the steering wheel of the vehicle;
and S66, summing the steering wheel angle variable quantity of the vehicle to obtain the steering wheel angle of the vehicle, and dividing the steering wheel angle variable quantity of the vehicle by sampling time to obtain the rotating speed of the steering wheel of the vehicle.
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Cited By (3)
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| CN113968278A (en) * | 2021-11-17 | 2022-01-25 | 广州文远知行科技有限公司 | Vehicle steering wheel correction method and device, electronic equipment and storage medium |
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