CN113778074A - Feedback correction method and apparatus for model predictive control of autonomous vehicles - Google Patents
Feedback correction method and apparatus for model predictive control of autonomous vehicles Download PDFInfo
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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
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Abstract
The invention discloses a feedback correction method and a feedback correction device for model predictive control of an automatic driving vehicle, and relates to the technical field of computers. One embodiment of the method comprises: carrying out linearization processing on a kinematic model of the vehicle to obtain an equivalent interference signal; determining a statistical characteristic value of the equivalent interference signal, and setting an initial value of a state covariance matrix of the extended Kalman filter; and determining a feedback correction result of the state value of the current control period of the vehicle according to the state value of the current control period of the vehicle, the control instruction of the previous control period and the statistical characteristic value of the equivalent interference signal. The implementation mode can enable the model predictive control algorithm to accurately predict and control the future state and the dynamic behavior of the vehicle, and greatly improves the precision and the robustness of the control system.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a feedback correction method and a feedback correction device for model predictive control of an automatic driving vehicle.
Background
In addition to the qualities of stability, accuracy and quickness in following a desired trajectory, the controller for an autonomous vehicle needs to be robust, i.e., resistant to uncertainty factors. In the driving process of the automatic driving vehicle, along with various uncertain factors, such as external factors including road bumpiness, stone rolling, positioning data or accidental inaccuracy of a high-precision map, internal factors including model mismatching, modeling error after linearization and the like, all of the factors can cause inaccuracy or jump of the vehicle state, and the factors are coupled to a control system to further cause transverse and longitudinal oscillation of the vehicle and even continuous divergence of the vehicle.
The traditional feedback correction method used in the model predictive control of the automatic driving vehicle is to directly compare the actual state with the predicted state of the automatic driving vehicle and then to carry out predictive control by taking the difference value as a compensation quantity, or to directly use a white noise signal for equivalence in Kalman filtering. However, when the interference factor is too large or the model mismatch is severe, the compensation amount cannot be accurately compensated by the conventional feedback correction method, and finally, the steady-state deviation of the control system is too large or diverged, so that the robustness and the control accuracy of the controller are low.
Disclosure of Invention
In view of this, embodiments of the present invention provide a feedback correction method and apparatus for model predictive control of an autonomous vehicle, which enable a model predictive control algorithm to make relatively accurate prediction and control on a future state and a dynamic behavior of the vehicle, thereby greatly improving the accuracy and robustness of a control system.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a feedback correction method for predictive control of an autonomous vehicle model.
A feedback correction method for model predictive control of an autonomous vehicle, comprising:
carrying out linearization processing on a kinematic model of the vehicle to obtain an equivalent interference signal;
determining a statistical characteristic value of the equivalent interference signal, and setting an initial value of a state covariance matrix of the extended Kalman filter;
and determining a feedback correction result of the state value of the current control period of the vehicle according to the state value of the current control period of the vehicle, the control instruction of the previous control period and the statistical characteristic value of the equivalent interference signal.
Optionally, the obtaining the equivalent interference signal by performing linearization on the kinematic model of the vehicle includes:
performing linearization processing by performing first-order Taylor expansion on a kinematic model of the vehicle;
and obtaining an equivalent interference signal according to the linearized kinematic model, wherein the equivalent interference signal comprises process noise and a model mismatch equivalent interference signal.
Optionally, the kinematic model of the vehicle is:
wherein x isk、yk、vk、θkRepresents a state variable, ak、δkThe control variable is represented by a number of control variables,
representing process noise of each state variable, T representing a control period, and L representing a vehicle wheel base;
state transition matrix phi obtained after first-order Taylor expansion of kinematic model of vehiclekControl matrix gammakMoment of equivalent interference signal with model mismatchMatrix of
Respectively as follows:
then, the equivalent interference signal Δ WkComprises the following steps:
optionally, determining the statistical characteristic value of the equivalent interference signal includes:
determining a mean and a variance of the equivalent interference signal, wherein the mean E (Δ W) of the equivalent interference signalk) Comprises the following steps:
w is to bekThe mean and variance of the noise signal, which is considered to be gaussian-distributed, are:
E(Wk)＝[μx μy μv μθ]T；
wherein, mux、μy、μv、μθRespectively, the mean values of the variables x, y, v, theta,
process noise variances for variables x, y, v, θ, respectively;
then, the equivalent interference signal Δ WkVariance of (Δ Q)kComprises the following steps:
△Qk＝E[(△Wk-E(△Wk))(△Wk-E(△Wk))T]+Q+E[2(△Wk-E(△Wk))(Wk-E(Wk))T]。
optionally, if the equivalent interference signal Δ W is usedkConversion to white noise with mean zero
Then:
wherein the content of the first and second substances,
has a mean value of 0 and a variance of:
optionally, the feedback correction result of the state value of the current control cycle of the vehicle is calculated by the following formula:
Xk+1/k＝φkXk+ΓkUk-1+E(△Wk)；
wherein, XkFor updated state variable value [ x ]k yk vk θk]T，Uk-1Is the control instruction [ a ] of the last control cyclek-1 δk-1]T。Xk+1/kIs a predicted state variable that is a function of,i.e. the feedback correction result.
Optionally, the method further comprises:
carrying out linearization processing on an output model of the extended Kalman filter to obtain observation noise;
determining a statistical characteristic value of the observed noise;
and updating the extended Kalman filter according to the state value of the current control cycle of the vehicle, the statistical characteristic value of the equivalent interference signal and the statistical characteristic value of the observation noise.
Optionally, the output model is:
Yk＝HXk+Vk；
wherein, the H matrix is a four-dimensional unit matrix: h ═ I4；
The above
Respectively observing noise of each state of the vehicle;
the statistical eigenvalues of the observed noise include a variance matrix R of the observed noise, and:
wherein the content of the first and second substances,
the observed noise variance of the variables x, y, v, θ, respectively.
Optionally, the updating of the extended kalman filter comprises:
and the covariance prediction of the Kalman filter, Kalman filtering gain calculation, state variable estimation value updating and covariance matrix updating.
Optionally, the covariance prediction of the kalman filter is calculated by the following equation:
Pk+1/k＝φkPkφk T+△Qk；
wherein, PkRepresenting a priori estimates of covariance, Pk+1/kFor a posteriori estimation of the covariance, phikIs a state transition matrix;
the kalman filter gain K is calculated by the following equation:
Kk+1＝Pk+1/k(Pk+1/k+R)-1；
the state variable estimate update is calculated by the following equation:
Xk+1/k+1＝Xk+1/k+Kk+1(Yk-Xk+1/k)，
wherein, YkVariable value of state variable [ x ] for real-time observation of vehiclek yk vk θk]；
The covariance matrix update is calculated by the following equation:
Pk+1/k+1＝Pk+1/k-Kk+1Pk+1/k)。
according to another aspect of an embodiment of the present invention, a feedback correction apparatus for model predictive control of an autonomous vehicle is provided.
A feedback correction apparatus for model predictive control of an autonomous vehicle, comprising:
the model processing module is used for carrying out linearization processing on the kinematic model of the vehicle to obtain an equivalent interference signal;
the signal processing module is used for determining a statistical characteristic value of the equivalent interference signal and setting an initial value of a state covariance matrix of the extended Kalman filter;
and the state correction module is used for determining a feedback correction result of the state value of the current control cycle of the vehicle according to the state value of the current control cycle of the vehicle, the control instruction of the previous control cycle and the statistical characteristic value of the equivalent interference signal.
Optionally, the model processing module is further configured to:
performing linearization processing by performing first-order Taylor expansion on a kinematic model of the vehicle;
and obtaining an equivalent interference signal according to the linearized kinematic model, wherein the equivalent interference signal comprises process noise and a model mismatch equivalent interference signal.
Optionally, the kinematic model of the vehicle is:
wherein x isk、yk、vk、θkRepresents a state variable, ak、δkThe control variable is represented by a number of control variables,
representing process noise of each state variable, T representing a control period, and L representing a vehicle wheel base;
state transition matrix phi obtained after first-order Taylor expansion of kinematic model of vehiclekControl matrix gammakAnd model mismatch equivalent interference signal matrix
Respectively as follows:
then, the equivalent interference signal Δ WkComprises the following steps:
optionally, the signal processing module is further configured to:
determining a mean and a variance of the equivalent interference signal, wherein the mean E (Δ W) of the equivalent interference signalk) Comprises the following steps:
w is to bekThe mean and variance of the noise signal, which is considered to be gaussian-distributed, are:
E(Wk)＝[μx μy μv μθ]T；
wherein, mux、μy、μv、μθRespectively, the mean values of the variables x, y, v, theta,
process noise variances for variables x, y, v, θ, respectively;
then, the equivalent interference signal Δ WkVariance of (Δ Q)kComprises the following steps:
△Qk＝E[(△Wk-E(△Wk))(△Wk-E(△Wk))T]+Q+E[2(△Wk-E(△Wk))(Wk-E(Wk))T]。
optionally, if the equivalent interference signal Δ W is usedkConversion to white noise with mean zero
Then:
wherein the content of the first and second substances,
has a mean value of 0 and a variance of:
optionally, the feedback correction result of the state value of the current control cycle of the vehicle is calculated by the following formula:
Xk+1/k＝φkXk+ΓkUk-1+E(△Wk)；
wherein, XkFor updated state variable value [ x ]k yk vk θk]T，Uk-1Is the control instruction [ a ] of the last control cyclek-1 δk-1]T。Xk+1/kIs the predicted state variable, i.e. the feedback correction result.
Optionally, the model processing module is further configured to: carrying out linearization processing on an output model of the extended Kalman filter to obtain observation noise;
the signal processing module is further configured to: determining a statistical characteristic value of the observed noise;
the apparatus further comprises a filter update module to: and updating the extended Kalman filter according to the state value of the current control cycle of the vehicle, the statistical characteristic value of the equivalent interference signal and the statistical characteristic value of the observation noise.
Optionally, the output model is:
Yk＝HXk+Vk；
wherein, the H matrix is a four-dimensional unit matrix: h ═ I4；
The above
Respectively observing noise of each state of the vehicle;
the statistical eigenvalues of the observed noise include a variance matrix R of the observed noise, and:
wherein the content of the first and second substances,
the observed noise variance of the variables x, y, v, θ, respectively.
Optionally, the updating of the extended kalman filter comprises:
and the covariance prediction of the Kalman filter, Kalman filtering gain calculation, state variable estimation value updating and covariance matrix updating.
Optionally, the covariance prediction of the kalman filter is calculated by the following equation:
Pk+1/k＝φkPkφk T+△Qk；
wherein, PkRepresenting assistantA priori estimate of the difference, Pk+1/kFor a posteriori estimation of the covariance, phikIs a state transition matrix;
the kalman filter gain K is calculated by the following equation:
Kk+1＝Pk+1/k(Pk+1/k+R)-1；
the state variable estimate update is calculated by the following equation:
Xk+1/k+1＝Xk+1/k+Kk+1(Yk-Xk+1/k)，
wherein, YkVariable value of state variable [ x ] for real-time observation of vehiclek yk vk θk]；
The covariance matrix update is calculated by the following equation:
Pk+1/k+1＝Pk+1/k-Kk+1Pk+1/k)。
according to yet another aspect of an embodiment of the present invention, an electronic device for feedback correction of model predictive control of an autonomous vehicle is provided.
An electronic device for feedback correction for model predictive control of an autonomous vehicle, comprising: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the feedback correction method for model predictive control of an autonomous vehicle provided by an embodiment of the invention.
According to yet another aspect of embodiments of the present invention, a computer-readable medium is provided.
A computer readable medium having stored thereon a computer program which, when executed by a processor, implements a feedback correction method for model predictive control of an autonomous vehicle as provided by embodiments of the present invention.
One embodiment of the above invention has the following advantages or benefits: obtaining an equivalent interference signal by carrying out linearization processing on a kinematic model of the vehicle; determining a statistical characteristic value of the equivalent interference signal, and setting an initial value of a state covariance matrix of the extended Kalman filter; the technical means for determining the feedback correction result of the state value of the current control period of the vehicle according to the state value of the current control period of the vehicle, the control instruction of the previous control period and the statistical characteristic value of the equivalent interference signal can obtain the equivalent interference signal by carrying out linearization processing on a kinematic model of the vehicle, comprehensively consider the instability phenomenon caused by various uncertain factors, uniformly and equivalently use various noise signals and errors as the interference signal and estimate the statistical characteristics of the interference signal, carry out feedback correction by combining with the actual state value of the current control period of the vehicle, try to make a model prediction control algorithm accurately predict and control the future state and the dynamic behavior of the vehicle, and greatly improve the precision and the robustness of a control system.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a functional block diagram of an implementation of model predictive control of an autonomous vehicle;
FIG. 2 is a schematic diagram of the main steps of a feedback correction method for model predictive control of an autonomous vehicle according to an embodiment of the invention;
FIG. 3 is a schematic flow chart illustrating an implementation of an extended Kalman filter according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the main blocks of a feedback correction arrangement for model predictive control of an autonomous vehicle in accordance with an embodiment of the invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 6 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In order to overcome the defects of the traditional feedback correction method in the prior art, the method estimates and corrects the real state of the vehicle by estimating the statistical characteristics of disturbance signals and applying an Extended Kalman Filter (EKF) technology, wherein specific variables comprise the current position, the course angle and the speed of the vehicle, and then feeds the corrected state back to the model prediction controller, thereby improving the robustness and the control precision of the controller.
Predictive control is a specific type of control method. It is a control that predicts the future output of the system using the prediction model and the historical data, future inputs of the system. And optimizing a certain performance index in a rolling limited time interval to obtain feedback correction control. The model prediction control of the invention comprises three links of model prediction, rolling optimization and feedback correction, and the implementation schematic block diagram of the model prediction control is shown in figure 1. The control model predicted in the embodiment of the present invention may be a vehicle kinematics model or a dynamics model, or a model generated by identification through a least square method or a neural network method, but it is conceivable that the model generated in any way is not an accurate mathematical model, and for uncertain factors such as model mismatch, nonlinearity, time variation, and disturbance existing in the system, the prediction based on the above models cannot be realized to conform to the real situation, such as existence of constant steady state deviation, excessively slow response time, and the like. The rolling optimization process is to determine future control output in an optimal mode through a certain performance index, the performance index relates to the future behavior of the system, the link is rolling optimization performed on the basis of a prediction model, and for various uncertain interference factors, the control output obtained through the rolling optimization can deviate from the expected control effect.
Based on the above situation, the feedback correction link is a supplement and online correction for various uncertain factors existing in the above two links, and the link is also a link mainly designed by the invention. The invention comprehensively considers the unstable phenomenon caused by the uncertain factors, uniformly and equivalently converts various noise signals and errors into disturbance signals and estimates the statistical characteristics of the disturbance signals, utilizes the EKF technology to estimate in combination with the actual state of the vehicle, tries to make a model predictive control algorithm accurately predict and control the future state and dynamic behavior of the vehicle, and further improves the robustness and the control precision of the vehicle controller.
According to one aspect of the invention, a feedback correction method for model predictive control of an autonomous vehicle is provided. Fig. 2 is a schematic diagram of the main steps of a feedback correction method for the model predictive control of an autonomous vehicle according to an embodiment of the invention. As shown in fig. 2, the feedback correction method for the model predictive control of the autonomous vehicle according to the embodiment of the present invention mainly includes steps S201 to S203 as follows.
Step S201: carrying out linearization processing on a kinematic model of the vehicle to obtain an equivalent interference signal;
step S202: determining a statistical characteristic value of the equivalent interference signal, and setting an initial value of a state covariance matrix of the extended Kalman filter;
step S203: and determining a feedback correction result of the state value of the current control period of the vehicle according to the state value of the current control period of the vehicle, the control instruction of the previous control period and the statistical characteristic value of the equivalent interference signal.
According to the steps S201 to S203, the equivalent interference signal can be obtained by carrying out linearization processing on the kinematic model of the vehicle, the instability phenomenon caused by various uncertain factors is comprehensively considered, various noise signals and errors are uniformly equivalent to the interference signal, the statistical characteristic of the interference signal is estimated, feedback correction is carried out by combining the actual state value of the current control period of the vehicle, the model predictive control algorithm is tried to accurately predict and control the future state and dynamic behavior of the vehicle, and the precision and the robustness of the control system are greatly improved.
According to an embodiment of the present invention, when a kinematic model of a vehicle is linearized to obtain an equivalent interference signal, the method may specifically be performed according to the following steps:
performing linearization processing by performing first-order Taylor expansion on a kinematic model of the vehicle;
and obtaining an equivalent interference signal according to the linearized kinematic model, wherein the equivalent interference signal comprises process noise and a model mismatch equivalent interference signal.
For the nonlinear model, the general model is as follows:
Xk+1＝fk(Xk,Uk)+Wk；
Yk＝hk(Xk)+Vk；
wherein, XkRepresents a state variable, UkDenotes a controlled variable, WkRepresenting process noise interference, YkRepresents output, VkRepresenting observation noise, fk、hkRepresenting functions, at least one of which is non-linear.
To estimate the state of a nonlinear system, the error caused by its linearization process and W can be usedk、VkEqually as interference or perturbation. First, a nonlinear model first-order Taylor expansion is performed on a variable to be linearized at a working point of the variable, as follows:
here, P, P0For the variables to be linearized and the operating point, [ phi ]kIs a state transition matrix, ΓkFor the control matrix, HkIs an observation matrix. Then, one can get:
above, Delta Wk、△VkThe method is an equivalent interference signal after the model mismatch and the noise interference are integrated.
In the present invention, the vehicle model is a kinematic model:
wherein x isk、yk、vk、θkRepresents a state variable, ak、δkThe control variable is represented by a number of control variables,
represents the process noise of each state variable, T represents the control period, and L is the vehicle wheel base.
Combining the related formula for carrying out linearization processing on the general nonlinear model, and carrying out first-order Taylor expansion on the kinematic model of the vehicle to obtain state transitionMatrix phikControl matrix gammakAnd model mismatch equivalent interference signal matrix
Respectively as follows:
here, the number of the first and second electrodes,
for the model mismatch equivalent interference signal, all the equivalent interference signals arekComprises the following steps:
in the present invention, the output model:
Yk＝HXk+Vk；
wherein, the H matrix is a four-dimensional unit matrix: h ═ I4；
In the above-mentioned manner,
respectively, the observed noise of each state of the vehicle.
According to the introduction, the state model and the output model of the extended Kalman filter can be obtainedLine linearization processing to obtain equivalent interference signal DeltaWkAnd observation noise Vk. Then, in the implementation process, in order to correct the acquired state variables (including the position coordinates, the speed and the heading angle of the vehicle) of the vehicle in the current control period, the equivalent interference signal Δ W needs to be correctedkFurther processing is carried out, specifically, a statistical characteristic value of the equivalent interference signal is determined first.
According to an embodiment of the present invention, determining the statistical characteristic value of the equivalent interference signal comprises:
determining a mean and a variance of the equivalent interference signal, wherein the mean E (Δ W) of the equivalent interference signalk) Comprises the following steps:
in general, W iskThe mean and variance of the noise signal, which is considered to be gaussian-distributed, are:
E(Wk)＝[μx μy μv μθ]T；
wherein, mux、μy、μv、μθRespectively, the mean values of the variables x, y, v, theta,
process noise variances for variables x, y, v, θ, respectively;
then, the equivalent interference signal Δ WkVariance of (Δ Q)kComprises the following steps:
△Qk＝E[(△Wk-E(△Wk))(△Wk-E(△Wk))T]+Q+E[2(△Wk-E(△Wk))(Wk-E(Wk))T]；
wherein the equivalent interference signal Δ WkVariance of (Δ Q)kIs Δ WkAnd E (. DELTA.W)k) Both perturb the integrated variance.
For the design of the extended kalman filter EKF, according to an embodiment of the invention, Δ W will be used herekConversion to white noise with mean zero
If the equivalent interference signal is DeltaWkConversion to white noise with mean zero
Then:
the linearized system state equation is equivalent to:
wherein the content of the first and second substances,
has a mean value of 0 and a variance of:
meanwhile, an observation noise variance matrix can be set:
wherein the content of the first and second substances,
the variance of the observed noise of the variables x, y, v and theta;
setting an initial value of a state covariance matrix P estimated by an EKF filter:
setting a vehicle home position X0＝[x0 y0 v0 θ0]TIn the invention, the initial state is a vehicle state obtained by fusing the inertial measurement unit IMU, the laser radar and the wheel speed meter.
Then, the state value of the current control cycle of the vehicle can be corrected. Specifically, the feedback correction result of the state value of the vehicle current control period is calculated by the following formula:
Xk+1/k＝φkXk+ΓkUk-1+E(△Wk)；
wherein, XkFor updated state variable value [ x ]k yk vk θk]T，Uk-1Is the control instruction [ a ] of the last control cyclek-1 δk-1]T。Xk+1/kIs the predicted state variable, i.e. the feedback correction result.
According to the technical scheme of the invention, after the feedback correction is carried out on the state variable of the vehicle, the output model of the extended Kalman filter can be subjected to linearization processing to obtain observation noise; determining a statistical characteristic value of the observed noise; and updating the extended Kalman filter according to the state value of the current control cycle of the vehicle, the statistical characteristic value of the equivalent interference signal and the statistical characteristic value of the observation noise.
Specifically, as described above, the output model in the embodiment of the present invention is:
Yk＝HXk+Vk；
wherein, the H matrix is a four-dimensional unit matrix: h ═ I4；
The above
Respectively observing noise of each state of the vehicle;
and, setting a statistical feature value of the observation noise to include a variance matrix R of the observation noise, and:
wherein the content of the first and second substances,
the observed noise variance of the variables x, y, v, θ, respectively.
Then, when performing the update of the extended kalman filter, the following updates may be mainly included:
and the covariance prediction of the Kalman filter, Kalman filtering gain calculation, state variable estimation value updating and covariance matrix updating.
Specifically, the covariance prediction of the kalman filter is calculated by the following equation:
Pk+1/k＝φkPkφk T+△Qk；
wherein, PkRepresenting a priori estimates of covariance, Pk+1/kFor a posteriori estimation of the covariance, phikIs a state transition matrix;
the kalman filter gain K is calculated by the following equation:
Kk+1＝Pk+1/k(Pk+1/k+R)-1；
the state variable estimate update is calculated by the following equation:
Xk+1/k+1＝Xk+1/k+Kk+1(Yk-Xk+1/k)，
wherein, YkVariable value of state variable [ x ] for real-time observation of vehiclek yk vk θk]；
The covariance matrix update is calculated by the following equation:
Pk+1/k+1＝Pk+1/k-Kk+1Pk+1/k)。
the invention applies the extended Kalman filtering technology to the feedback correction link of the control system, wherein the instability phenomenon caused by various uncertain factors is comprehensively considered, the instability phenomenon is equivalent to a disturbance signal, the statistical characteristic of the disturbance signal is estimated, and the precision and the robustness of the control system are greatly improved.
Fig. 3 is a schematic flow chart of an implementation of the extended kalman filter according to the embodiment of the present invention. In an embodiment of the invention, an extended Kalman filtering technique is applied for feedback correction of model predictive control of an autonomous vehicle. The method mainly comprises model linearization processing, estimation of the statistical characteristic of an interference signal, and the design of an extended Kalman filter. As shown in fig. 3, the implementation flow of the extended kalman filter according to the embodiment of the present invention is mainly as follows:
1. acquiring the current state of the vehicle, including the position, the speed, the course angle and the like of the vehicle, so as to construct a state model and an output model of the extended Kalman filter;
2. the nonlinear vehicle kinematic model is linearized, and then a linearized key matrix, such as an equivalent interference signal in the embodiment of the invention, is obtained;
3. estimating statistical characteristics of the equivalent interference signal, such as mean value and variance;
4. initializing an initial value of a variance matrix R of observation noise and a state covariance matrix P estimated by a filter;
5. predicting the vehicle state to obtain a predicted value corresponding to the current state of the vehicle as a feedback correction result, and then outputting the feedback correction result to a controller for performing prediction control on the vehicle;
6. carrying out covariance prediction and Kalman filtering gain calculation;
7. and updating the vehicle state and the covariance matrix.
Fig. 4 is a schematic diagram of the main blocks of a feedback correction apparatus for model predictive control of an autonomous vehicle according to an embodiment of the present invention. As shown in fig. 4, a feedback correction apparatus 400 for model predictive control of an autonomous vehicle according to an embodiment of the present invention mainly includes a model processing module 401, a signal processing module 402, and a state correction module 403.
The model processing module 401 is configured to perform linearization processing on a kinematic model of a vehicle to obtain an equivalent interference signal;
a signal processing module 402, configured to determine a statistical characteristic value of the equivalent interference signal, and set an initial value of a state covariance matrix of the extended kalman filter;
and a state correction module 403, configured to determine a feedback correction result of the state value of the current control cycle of the vehicle according to the state value of the current control cycle of the vehicle, the control instruction of the previous control cycle, and the statistical characteristic value of the equivalent interference signal.
According to an embodiment of the present invention, the model processing module 401 may further be configured to:
performing linearization processing by performing first-order Taylor expansion on a kinematic model of the vehicle;
and obtaining an equivalent interference signal according to the linearized kinematic model, wherein the equivalent interference signal comprises process noise and a model mismatch equivalent interference signal.
According to one embodiment of the invention, the kinematic model of the vehicle is:
wherein x isk、yk、vk、θkRepresents a state variable, ak、δkThe control variable is represented by a number of control variables,
representing process noise of each state variable, T representing a control period, and L representing a vehicle wheel base;
state transition matrix phi obtained after first-order Taylor expansion of kinematic model of vehiclekControl matrix gammakAnd model mismatch equivalent interference signal matrix
Respectively as follows:
then, the equivalent interference signal Δ WkComprises the following steps:
according to one embodiment of the present invention, the signal processing module is further configured to:
determining a mean and a variance of the equivalent interference signal, wherein the mean E (Δ W) of the equivalent interference signalk) Comprises the following steps:
w is to bekThe mean and variance of the noise signal, which is considered to be gaussian-distributed, are:
E(Wk)＝[μx μy μv μθ]T；
wherein, mux、μy、μv、μθRespectively, the mean values of the variables x, y, v, theta,
process noise variances for variables x, y, v, θ, respectively;
then, the equivalent interference signal Δ WkVariance of (Δ Q)kComprises the following steps:
△Qk＝E[(△Wk-E(△Wk))(△Wk-E(△Wk))T]+Q+E[2(△Wk-E(△Wk))(Wk-E(Wk))T]。
according to the embodiment of the invention, if the equivalent interference signal Δ W is usedkConversion to white noise with mean zero
Then:
wherein the content of the first and second substances,
has a mean value of 0 and a variance of:
according to one embodiment of the present invention, the feedback correction result of the state value of the current control cycle of the vehicle is calculated by the following formula:
Xk+1/k＝φkXk+ΓkUk-1+E(△Wk)；
wherein, XkFor updated state variable value [ x ]k yk vk θk]T，Uk-1Is the control instruction [ a ] of the last control cyclek-1 δk-1]T。Xk+1/kIs the predicted state variable, i.e. the feedback correction result.
According to another embodiment of the present invention, the model processing module 401 may be further configured to: carrying out linearization processing on an output model of the extended Kalman filter to obtain observation noise;
the signal processing module 402 may also be configured to: determining a statistical characteristic value of the observed noise;
the feedback correction apparatus 400 for the model predictive control of the autonomous vehicle may further include a filter update module (not shown in the drawings) for: and updating the extended Kalman filter according to the state value of the current control cycle of the vehicle, the statistical characteristic value of the equivalent interference signal and the statistical characteristic value of the observation noise.
According to an embodiment of the invention, the output model is:
Yk＝HXk+Vk；
wherein, the H matrix is a four-dimensional unit matrix: h ═ I4；
The above
Respectively observing noise of each state of the vehicle;
the statistical eigenvalues of the observed noise include a variance matrix R of the observed noise, and:
wherein the content of the first and second substances,
the observed noise variance of the variables x, y, v, θ, respectively.
In an embodiment of the invention, the updating of the extended kalman filter comprises:
and the covariance prediction of the Kalman filter, Kalman filtering gain calculation, state variable estimation value updating and covariance matrix updating.
In one of the alternative embodiments of the invention, the covariance prediction of the kalman filter is calculated by the following equation:
Pk+1/k＝φkPkφk T+△Qk；
wherein, PkRepresenting a priori estimates of covariance, Pk+1/kFor a posteriori estimation of the covariance, phikIs a state transition matrix;
the kalman filter gain K is calculated by the following equation:
Kk+1＝Pk+1/k(Pk+1/k+R)-1；
the state variable estimate update is calculated by the following equation:
Xk+1/k+1＝Xk+1/k+Kk+1(Yk-Xk+1/k)，
wherein, YkVariable value of state variable [ x ] for real-time observation of vehiclek yk vk θk]；
The covariance matrix update is calculated by the following equation:
Pk+1/k+1＝Pk+1/k-Kk+1Pk+1/k)。
according to the technical scheme of the embodiment of the invention, the equivalent interference signal is obtained by carrying out linearization processing on the kinematic model of the vehicle; determining a statistical characteristic value of the equivalent interference signal, and setting an initial value of a state covariance matrix of the extended Kalman filter; the technical means for determining the feedback correction result of the state value of the current control period of the vehicle according to the state value of the current control period of the vehicle, the control instruction of the previous control period and the statistical characteristic value of the equivalent interference signal can obtain the equivalent interference signal by carrying out linearization processing on a kinematic model of the vehicle, comprehensively consider the instability phenomenon caused by various uncertain factors, uniformly and equivalently use various noise signals and errors as the interference signal and estimate the statistical characteristics of the interference signal, carry out feedback correction by combining with the actual state value of the current control period of the vehicle, try to make a model prediction control algorithm accurately predict and control the future state and the dynamic behavior of the vehicle, and greatly improve the precision and the robustness of a control system.
Fig. 5 illustrates an exemplary system architecture 500 to which the feedback correction method for model predictive control of an autonomous vehicle or the feedback correction apparatus for model predictive control of an autonomous vehicle of embodiments of the invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. Various applications, such as navigation-type applications, location information collection-type applications, positioning-type applications, etc. (for example only), may be installed on the terminal devices 501, 502, 503.
The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server (for example only) providing estimation predictions of current state parameters of the vehicle collected by the user using the terminal devices 501, 502, 503. The background management server can carry out linearization processing on the kinematic model of the vehicle to obtain an equivalent interference signal; determining a statistical characteristic value of the equivalent interference signal, and setting an initial value of a state covariance matrix of the extended Kalman filter; and determining a feedback correction result of the state value of the current control cycle of the vehicle according to the state value of the current control cycle of the vehicle, the control instruction of the previous control cycle and the statistical characteristic value of the equivalent interference signal, and feeding back a processing result (such as a feedback correction result, which is only an example) to the terminal equipment.
It should be noted that the feedback correction method for the model predictive control of the autonomous vehicle provided by the embodiment of the present invention is generally executed by the server 505, and accordingly, the feedback correction means for the model predictive control of the autonomous vehicle is generally provided in the server 505.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 6, a block diagram of a computer system 600 suitable for use with a terminal device or server implementing an embodiment of the invention is shown. The terminal device or the server shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present invention may be implemented by software, or may be implemented by hardware. The described units or modules may also be provided in a processor, and may be described as: a processor includes a model processing module, a signal processing module, and a state correction module. The names of the units or modules do not in some cases form a limitation on the units or modules themselves, and for example, the model processing module may also be described as a "module for linearizing a kinematic model of a vehicle to obtain an equivalent interference signal".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: carrying out linearization processing on a kinematic model of the vehicle to obtain an equivalent interference signal; determining a statistical characteristic value of the equivalent interference signal, and setting an initial value of a state covariance matrix of the extended Kalman filter; and determining a feedback correction result of the state value of the current control period of the vehicle according to the state value of the current control period of the vehicle, the control instruction of the previous control period and the statistical characteristic value of the equivalent interference signal.
According to the technical scheme of the embodiment of the invention, the equivalent interference signal is obtained by carrying out linearization processing on the kinematic model of the vehicle; determining a statistical characteristic value of the equivalent interference signal, and setting an initial value of a state covariance matrix of the extended Kalman filter; the technical means for determining the feedback correction result of the state value of the current control period of the vehicle according to the state value of the current control period of the vehicle, the control instruction of the previous control period and the statistical characteristic value of the equivalent interference signal can obtain the equivalent interference signal by carrying out linearization processing on a kinematic model of the vehicle, comprehensively consider the instability phenomenon caused by various uncertain factors, uniformly and equivalently use various noise signals and errors as the interference signal and estimate the statistical characteristics of the interference signal, carry out feedback correction by combining with the actual state value of the current control period of the vehicle, try to make a model prediction control algorithm accurately predict and control the future state and the dynamic behavior of the vehicle, and greatly improve the precision and the robustness of a control system.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (13)
1. A feedback correction method for model predictive control of an autonomous vehicle, comprising:
carrying out linearization processing on a kinematic model of the vehicle to obtain an equivalent interference signal;
determining a statistical characteristic value of the equivalent interference signal, and setting an initial value of a state covariance matrix of the extended Kalman filter;
and determining a feedback correction result of the state value of the current control period of the vehicle according to the state value of the current control period of the vehicle, the control instruction of the previous control period and the statistical characteristic value of the equivalent interference signal.
2. The method of claim 1, wherein linearizing the kinematic model of the vehicle to obtain the equivalent interference signal comprises:
performing linearization processing by performing first-order Taylor expansion on a kinematic model of the vehicle;
and obtaining an equivalent interference signal according to the linearized kinematic model, wherein the equivalent interference signal comprises process noise and a model mismatch equivalent interference signal.
3. The method of claim 2, wherein the kinematic model of the vehicle is:
wherein x isk、yk、vk、θkRepresents a state variable, ak、δkThe control variable is represented by a number of control variables,
representing process noise of each state variable, T representing a control period, and L representing a vehicle wheel base;
state transition matrix phi obtained after first-order Taylor expansion of kinematic model of vehiclekControl matrix gammakAnd model mismatch equivalent interference signal matrix
Respectively as follows:
then, the equivalent interference signal Δ WkComprises the following steps:
4. the method of claim 3, wherein determining the statistical signature of the equivalent interference signal comprises:
determining a mean and a variance of the equivalent interference signal, wherein the mean E (Δ W) of the equivalent interference signalk) Comprises the following steps:
w is to bekThe mean and variance of the noise signal, which is considered to be gaussian-distributed, are:
E(Wk)＝[μx μy μv μθ]T；
wherein, mux、μy、μv、μθRespectively, the mean values of the variables x, y, v, theta,
process noise variances for variables x, y, v, θ, respectively;
then, the equivalent interference signal Δ WkVariance of (Δ Q)kComprises the following steps:
△Qk＝E[(△Wk-E(△Wk))(△Wk-E(△Wk))T]+Q+E[2(△Wk-E(△Wk))(Wk-E(Wk))T]。
5. the method of claim 4, wherein the equivalent interference signal Δ W is obtained if the equivalent interference signal Δ W is usedkConversion to white noise with mean zero
Then:
wherein the content of the first and second substances,
has a mean value of 0 and a variance of:
6. the method according to claim 5, characterized in that the feedback correction result of the state value of the vehicle's current control period is calculated by the following formula:
Xk+1/k＝φkXk+ΓkUk-1+E(△Wk)；
wherein, XkFor updated state variable value [ x ]k yk vk θk]T，Uk-1Is the control instruction [ a ] of the last control cyclek-1 δk-1]T。Xk+1/kIs the predicted state variable, i.e. the feedback correction result.
7. The method of claim 1, further comprising:
carrying out linearization processing on an output model of the extended Kalman filter to obtain observation noise;
determining a statistical characteristic value of the observed noise;
and updating the extended Kalman filter according to the state value of the current control cycle of the vehicle, the statistical characteristic value of the equivalent interference signal and the statistical characteristic value of the observation noise.
8. The method of claim 7, wherein the output model is:
Yk＝HXk+Vk；
wherein, the H matrix is a four-dimensional unit matrix:
the above
Respectively observing noise of each state of the vehicle;
the statistical eigenvalues of the observed noise include a variance matrix R of the observed noise, and:
wherein the content of the first and second substances,
the observed noise variance of the variables x, y, v, θ, respectively.
9. The method of claim 7, wherein the updating of the extended Kalman filter comprises:
and the covariance prediction of the Kalman filter, Kalman filtering gain calculation, state variable estimation value updating and covariance matrix updating.
10. The method of claim 9, wherein the covariance prediction of the kalman filter is calculated by the following equation:
Pk+1/k＝φkPkφk T+△Qk；
wherein, PkRepresenting a priori estimates of covariance, Pk+1/kFor a posteriori estimation of the covariance, phikIs a state transition matrix;
the kalman filter gain K is calculated by the following equation:
Kk+1＝Pk+1/k(Pk+1/k+R)-1；
the state variable estimate update is calculated by the following equation:
Xk+1/k+1＝Xk+1/k+Kk+1(Yk-Xk+1/k)，
wherein, YkVariable value of state variable [ x ] for real-time observation of vehiclek yk vk θk]；
The covariance matrix update is calculated by the following equation:
Pk+1/k+1＝Pk+1/k-Kk+1Pk+1/k)。
11. a feedback correction apparatus for model predictive control of an autonomous vehicle, comprising:
the model processing module is used for carrying out linearization processing on the kinematic model of the vehicle to obtain an equivalent interference signal;
the signal processing module is used for determining a statistical characteristic value of the equivalent interference signal and setting an initial value of a state covariance matrix of the extended Kalman filter;
and the state correction module is used for determining a feedback correction result of the state value of the current control cycle of the vehicle according to the state value of the current control cycle of the vehicle, the control instruction of the previous control cycle and the statistical characteristic value of the equivalent interference signal.
12. An electronic device for feedback correction for model predictive control of an autonomous vehicle, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-10.
13. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-10.
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