CN110723207B - Intelligent automobile model prediction steering controller based on model reconstruction and control method thereof - Google Patents
Intelligent automobile model prediction steering controller based on model reconstruction and control method thereof Download PDFInfo
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Abstract
The invention discloses an intelligent automobile model prediction steering controller based on model reconstruction and a control method thereof. The invention comprehensively uses the inverse model and the model predictive control method, uses the reconstructed new model and the reconstructed left inverse model to observe data, reasonably designs the model predictive controller, has definite purpose and simple method, can conveniently observe yaw velocity response data, inhibits multiple interference factors, overcomes unmodeled dynamics and obtains high-performance control effect.
Description
Technical Field
The invention relates to the field of intelligent automobile steering control, in particular to an intelligent automobile model prediction steering controller based on model reconstruction and a control method thereof.
Background
The intelligent automobile is a high and new technology product based on an environment perception technology, a computer technology, an information technology and an intelligent control technology, and the transformation and upgrading of the automobile industry under the background of a new technological revolution are a process for gradually realizing the intellectualization of the automobile product. As the intelligent automobile of a plurality of new technology application carriers in the future, a brand new possibility is provided for the automobile industry to effectively solve the problems of safety, energy and environmental protection: the intelligent automobile fuel consumption and emission control system comprises accident avoidance and optimized fuel consumption and emission control under the intelligent running state of an automobile, energy consumption saving and emission reduction under the intelligent traffic mode, the automobile utilization rate under the brand-new business mode is obviously improved, and the like, so that the automobile industry can meet various requirements of safety, comfort and the like under the requirements of energy and environmental protection, and a healthy automobile society is built.
The dynamic model of the automobile is a nonlinear and strongly coupled multivariable time-varying system, the intelligent automobile steering control is a nonlinear control problem with strong coupling, multiple interference and unmodeled dynamics, the intelligent automobile steering control has a very important position in the whole control process of the intelligent automobile, the traditional control algorithm temporarily does not well coordinate and solve the problems of parameter nonlinearity and time-varying characteristics in the steering control process, the control parameters with good effect at medium and low speed are often poor in control effect at high speed, and the control parameters suitable for the snake-shaped path are possibly not suitable for the double-shift line path. Meanwhile, due to the strong coupling characteristic among the state variables, the indexes of the intelligent automobile such as quick response, safety and comfort are difficult to take into account. The modern society puts forward higher and higher requirements on the control performance of the intelligent automobile, and on the basis of intelligently sensing the internal and external environment parameters of the automobile, how to reasonably reconstruct a steering control model, solve the coupling problem between state variables, inhibit multiple interference factors, overcome unmodeled dynamics and improve the steering control performance of the intelligent automobile is a problem to be solved urgently.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent automobile model prediction steering controller based on model reconstruction and a control method thereof.
The invention discloses an intelligent automobile model prediction steering controller based on model reconstruction, which adopts the technical scheme that: the intelligent automobile steering system comprises an external environment sensing module, an automobile parameter measuring module, an intelligent automobile original model, an intelligent automobile right inverse model, an intelligent automobile left inverse model, a model prediction steering control module and a steering execution module.
The external environment sensing module is used for acquiring lane information, traffic signs, nearby vehicles and pedestrian information of automobile driving and transmitting the information to the model prediction steering control module;
the automobile parameter measuring module is used for acquiring longitudinal speed u, transverse speed v and front wheel corner delta information of automobile running and transmitting the information to the model prediction steering control module;
the intelligent automobile original model
The two-degree-of-freedom coupling model containing nonlinear parameters comprises the following parameters: automobile front wheel to mass center distance a, rear wheel to mass center distance b and front wheel side deflection rigidity k1Rear wheel side yaw stiffness k2Mass m and moment of inertia I of automobilezFront wheel turning angle delta, longitudinal speed u, lateral speed v, yaw rate omegar；
The intelligent automobile original model
The input variable of (1) is the front wheel steering angle delta, and the control variable is the yaw rate omegarAnd a lateral velocity v, the output variable being the lateral velocity v;
the intelligent automobile right inverse model is composed of an adaptive neural fuzzy inference system (ANFIS (1)) and integrators s-1The adaptive neuro-fuzzy inference system (ANFIS (1)) comprises two inputs and one output, and is constructed according to a front wheel rotation angle delta, a transverse speed v and a transverse speed first derivative obtained in the running process of an intelligent automobile original model
The input variables of the intelligent automobile right inverse model are the transverse speed v and the first derivative of the transverse speed
The output variable is a front wheel steering angle delta;
the intelligent automobile left inverse model is constructed by an adaptive neural fuzzy inference system (ANFIS (2)) and a differentiator s, the adaptive neural fuzzy inference system (ANFIS (2)) comprises three inputs and one output, and the adaptive neural fuzzy inference system (ANFIS (2)) is constructed according to an intelligent automobile original model
Front wheel corner delta and yaw velocity omega obtained in running processrLateral velocity v and first derivative of lateral velocity
The input variables of the intelligent automobile left inverse model are front wheel turning angle delta, transverse speed v and first derivative of the transverse speed
The output variable being yaw rate omegar；
The intelligent automobile right inverse model is placed in the intelligent automobile original model in a series connection mode
On the left side of the model, the intelligent automobile model is reconstructed into a standard first-order transfer function new model G(s) ═ s-1The input variable of the new model is the first derivative of the lateral speed of the intelligent automobile
The output variable is the transverse speed v of the intelligent automobile;
the intelligent automobile left inverse model is placed on the right side of the intelligent automobile original model in a series connection mode to form an intelligent automobile yaw velocity observation model;
the model prediction steering control module comprises a steering decision sub-module and a steering control sub-module;
the steering decision sub-module takes lane information and traffic signs sent by the external environment sensing module as a decision background, takes nearby vehicle and pedestrian information sent by the external environment sensing module as obstacle information, calculates a steering motion track according to longitudinal speed, transverse speed and front wheel steering angle information sent by the vehicle parameter measuring module, determines whether to steer or not by taking the safety distance between the intelligent vehicle and the obstacle as a necessary condition, and plans the steering motion track by adopting a pre-aiming following method as a model to predict and control the output of the steering control sub-module when the safety distance allows to be steered;
the steering control submodule is designed according to the reconstructed new model and comprises a prediction equation, a constraint condition and an objective function, wherein the prediction equation predicts a time domain N through design according to the discretized reconstructed new modelpControl time domain NcAnd a sampling time T, wherein the constraint conditions comprise front wheel rotation angle delta constraint, longitudinal speed u constraint and yaw speed omegarConstraint, said objective function including error e (k) information, control input
Information and error correction factor h.
The prediction equation is specifically designed as follows:
in the prediction equation, vp(k +1| k) is the prediction output,
for the control input at the moment k, h is an error correction coefficient, v (k) is the output of the reconstructed new model of the intelligent automobile at the moment k, vm(k) Is a standard model G(s) ═ s-1Output at time k, e (k) ═ v (k) — vm(k) Is the error at time k, predicting the time domain NpControl time domain N20cThe sampling time T is 0.05 s;
the constraint conditions are specifically designed as follows:
front wheel steering angle constraint: delta is between 10 degrees and 10 degrees, delta is between 0.8 degrees and 0.8 degrees;
and (3) longitudinal speed constraint: u is more than 0 and less than or equal to 70 km/h;
and (3) restricting the yaw velocity: -5.0 °/s ≦ ωr≤5.0°/s；
The objective function is specifically designed as follows:
in the objective function, vp(k +1| k) is the prediction output, vr(k +1) inputting a reference trajectory,
h is an error correction coefficient, e (k) is equal to v (k) -vm(k) Is the error of k time, v (k) is the output of the reconstructed new model of the intelligent automobile at the k time, vm(k) Is a standard model G(s) ═ s-1Output at time k.
And the steering execution module drives the steering execution mechanism according to the control signal transmitted by the model prediction steering control module, so that the intelligent automobile can steer autonomously.
The technical scheme adopted by the intelligent automobile model prediction steering control method based on model reconstruction comprises the following steps:
step 1) simplifying and equivalence are carried out on an intelligent automobile to obtain a two-degree-of-freedom intelligent automobile original model, wherein an input variable is a front wheel corner delta, and an output variable is a transverse speed v;
step 2) analyzing and deducing the original intelligent automobile model, and determining the input variables of the right inverse model as the transverse velocity v and the first derivative of the transverse velocity
The output variable is a front wheel steering angle delta;
step 3) analyzing and deducing the original model of the intelligent automobile, and determining the input variables of the left inverse model of the intelligent automobile to be the corner delta and the transverse speed of the front wheelDegree v and first derivative of lateral velocity
The output variable being yaw rate omegar；
Step 4) constructing an intelligent automobile right inverse model by using an adaptive neural fuzzy inference system ANFIS (1) and an integrator, wherein the parameter determination method of the ANFIS (1) is to determine a step excitation signal delta of a front wheel corner*Adding the data to the input end of the original intelligent automobile model; acquisition of an excitation signal delta*And a lateral velocity v; off-line solving of first derivative of obtained transverse velocity v signal
Using formed training sample sets
Training the ANFIS (1) to determine parameters of the ANFIS (1);
step 5) constructing an intelligent automobile left inverse model by using an adaptive neural fuzzy inference system ANFIS (2) and a differentiator, wherein the parameter determination method of the ANFIS (2) is to determine a step excitation signal delta of a front wheel corner*Adding the data to the input end of the original intelligent automobile model; acquisition of an excitation signal delta*Lateral velocity v and yaw rate ωr(ii) a Off-line solving of first derivative of obtained transverse velocity v signal
Using formed training sample sets
Training the ANFIS (2) to determine parameters of the ANFIS (2);
step 6) reconstructing a first-order transfer function new model G(s) -s according to the intelligent automobile right inverse model and the intelligent automobile original model-1And designing a model prediction steering control module, and taking a yaw velocity observation value output by the intelligent automobile left-inverse model as a constraint condition of a steering control submodule.
The invention is based on the structure intelligenceThe right inverse model and the left inverse model of the original automobile model can be used for reconstructing the intelligent automobile model into a simple first-order transfer function new model G(s) ═ s-1The observation of the yaw angular velocity of the intelligent automobile is realized by constructing a left inverse model of an original model of the intelligent automobile, and then the model predictive steering controller is designed, so that the efficient control of the steering process of the intelligent automobile is realized.
The invention has the beneficial effects that:
1. the right inverse model of the intelligent automobile original model is constructed, the intelligent automobile right inverse model and the intelligent automobile original model are compounded, the intelligent automobile model is reconstructed into a standard first-order transfer function new model, and the problem of simplifying and controlling the intelligent automobile complex model is solved;
2. the invention constructs a left inverse model of the original intelligent automobile model, and the left inverse model is matched with the original intelligent automobile model parameters, thereby realizing the real-time observation of the key parameter yaw velocity and providing constraint condition parameters for model prediction control;
3. the invention comprehensively uses the inverse model and the model predictive control method, uses the reconstructed simplified new model and the reconstructed left inverse model to observe data, reasonably designs the model predictive controller, has clear purpose and simple method, can conveniently observe yaw velocity response data, inhibits multiple interference factors, overcomes unmodeled dynamics and obtains high-performance control effect.
Drawings
Fig. 1 is a simplified equivalent diagram of an intelligent automobile.
FIG. 2 is a diagram of a right inverse model of an intelligent vehicle.
FIG. 3 is a diagram of a new model for reconstructing a first order transfer function.
Fig. 4 is a left inverse model diagram of the intelligent automobile.
FIG. 5 is a diagram of an intelligent vehicle yaw rate observation model.
FIG. 6 is a block diagram of an intelligent vehicle model predictive steering control based on model reconstruction.
Detailed Description
The invention will be further explained with reference to the drawings.
As shown in FIG. 1, the original model of the intelligent automobileThrough simplified equivalence, the original model of the intelligent automobile is a two-degree-of-freedom model
The original intelligent automobile model comprises the following parameters: automobile front wheel to mass center distance a, rear wheel to mass center distance b and front wheel side deflection rigidity k1Rear wheel side yaw stiffness k2Mass m and moment of inertia I of automobilezFront wheel turning angle delta, longitudinal speed u, lateral speed v, yaw rate omegarThe input variable of the original model of the intelligent automobile is the front wheel rotation angle delta, and the control variable is the yaw velocity omegarAnd a lateral velocity v, the output variable being the lateral velocity v;
as shown in FIG. 2, the intelligent vehicle right inverse model is composed of an integrator and an adaptive neuro-fuzzy inference system ANFIS (1), and the adaptive neuro-fuzzy inference system ANFIS (1) is composed of an intelligent vehicle original model
Front wheel turning angle delta, lateral speed v and lateral speed first derivative obtained in operation process
The input variables of the right inverse model of the intelligent automobile are the transverse velocity v and the first derivative of the transverse velocity
The output variable is a front wheel steering angle delta;
as shown in fig. 3, a new first-order transfer function model is reconstructed, and the intelligent automobile right inverse model is placed in the intelligent automobile original model in a series connection mode
On the left side of the model, the intelligent automobile model is reconstructed into a standard first-order transfer function new model G(s) ═ s-1The input variable of the new model is the first derivative of the lateral speed of the intelligent automobile
The output variable is the transverse speed v of the intelligent automobile;
as shown in FIG. 4, the intelligent automobile left inverse model is composed of a differentiator and an adaptive neuro-fuzzy inference system ANFIS (2), and the adaptive neuro-fuzzy inference system ANFIS (2) is based on the intelligent automobile original model
Front wheel corner delta and yaw velocity omega obtained in running processrLateral velocity v and first derivative of lateral velocity
The input variables of the intelligent automobile left inverse model are front wheel rotation angle delta, transverse speed v and first derivative of the transverse speed
The output variable being yaw rate omegar；
As shown in FIG. 5, the intelligent automobile yaw velocity observation model and the intelligent automobile left inverse model are placed in the intelligent automobile original model in a series connection mode
And (4) forming an intelligent automobile yaw rate observation model.
As shown in fig. 6, an intelligent automobile model prediction steering controller based on model reconstruction includes an external environment sensing module, an automobile parameter measuring module, an intelligent automobile original model, an intelligent automobile right inverse model, an intelligent automobile left inverse model, a model prediction steering control module, and a steering execution module;
the external environment perception module comprises a camera and a radar, the camera is installed at the front end and the rear end of the intelligent automobile, the radar is installed around the intelligent automobile, the camera and the radar coordinate together to realize the perception of lane information, traffic signs, nearby vehicles and pedestrian information, and the information is transmitted to the model prediction steering control module;
the automobile parameter measuring module comprises a GPS navigation system and a steering wheel corner measuring sensor, and is used for respectively acquiring longitudinal speed u, transverse speed v and front wheel corner delta information of automobile driving and transmitting the information to the model prediction steering control module;
the model prediction steering control module comprises a steering decision sub-module and a steering control sub-module; the steering decision sub-module is used for deciding whether to steer or not according to the automobile running parameters sent by the external environment sensing module and the automobile parameter measuring module, and giving a given value of a steering control parameter of the model predictive control steering control sub-module when deciding to steer; the steering control submodule is designed according to the reconstructed new model and comprises a prediction equation, a constraint condition and an objective function, wherein the prediction equation is a time domain N predicted through design according to the discretized reconstructed new modelpControl time domain NcAnd the sampling time T, wherein the constraint conditions comprise front wheel corner delta constraint, longitudinal speed u constraint and yaw speed omegarConstraining, objective function including error e (k) information, control input
Information and error correction factor h.
The steering execution module drives the steering execution mechanism according to the control signal transmitted by the model prediction steering control module to realize the autonomous steering of the intelligent automobile;
the finally formed intelligent automobile model prediction steering controller based on model reconstruction comprises: the intelligent automobile steering system comprises an external environment sensing module, an automobile parameter measuring module, a model prediction steering control module, an intelligent automobile right inverse model and an intelligent automobile left inverse model.
An intelligent automobile model prediction steering control method based on model reconstruction comprises the following steps:
step 1) simplifying and equivalence are carried out on the intelligent automobile to obtain a two-degree-of-freedom intelligent automobile original model
The input variable being the front wheel steering angle delta and the control variable being the yaw angleSpeed omegarAnd a lateral velocity v, the output variable being the lateral velocity v;
step 2) for the original model of the intelligent automobile
Analyzing and deducing to ensure that the right inverse model meets the right inverse condition, wherein the input variables of the right inverse model are the transverse velocity v and the first derivative of the transverse velocity
The output variable is a front wheel steering angle delta;
step 3) for the original model of the intelligent automobile
Analyzing and deducing to determine that the left inverse model accords with a left inverse condition, wherein input variables of the left inverse model are front wheel rotation angle delta, transverse speed v and a first derivative of the transverse speed
The output variable being yaw rate omegar；
Step 4) constructing an intelligent automobile right inverse model by using an ANFIS (1) and an integrator, wherein the ANFIS (1) is a 5-layer network, the number of input nodes is 2, the number of output nodes is 1, an error index is the mean square error RMSE of a sample, membership functions of input and output variables are all a gbell (bell-shaped) function, each input is a 15 membership function, and the type of the output function is linear; the parameter determination step of the ANFIS (1) comprises the following steps:
(A) step excitation signal delta of front wheel corner*Adding the data to the input end of the original intelligent automobile model;
(B) acquisition of an excitation signal delta*And a lateral velocity v;
(C) off-line solving of first derivative of obtained transverse velocity v signal
Formed training sample set
(D) Training the ANFIS (1) by using a hybrid algorithm, and determining each parameter of the ANFIS (1) when the root mean square error RMSE of the training sample and the root mean square error RMSE of the checking sample meet the control precision requirement;
step 5) constructing an intelligent automobile left inverse model by using an ANFIS (2) and a differentiator, wherein the ANFIS (2) is a 5-layer network, the number of input nodes is 3, the error index is the mean square error RMSE of a sample, membership functions of input and output variables are all gbell (bell-shaped) functions, each input is a 15-membership function, and the type of the output function is linear; the parameter determination step of the ANFIS (2) comprises the following steps:
(A) step excitation signal delta of front wheel corner*Adding the data to the input end of the original intelligent automobile model;
(B) acquisition of an excitation signal delta*Lateral velocity v and yaw rate ωr；
(C) Off-line solving of first derivative of obtained transverse velocity v signal
Formed training sample set
(D) Training the ANFIS (2) by using a hybrid algorithm, and determining each parameter of the ANFIS (2) when the root mean square error RMSE of the training sample and the root mean square error RMSE of the checking sample meet the control precision requirement;
step 6) reconstructing a first-order transfer function new model G(s) -s according to the intelligent automobile right inverse model and the intelligent automobile original model-1Designing a model prediction steering control module, which comprises the following specific steps:
(A) designing a steering decision sub-module, which comprises determining whether to steer or not according to the automobile running parameters sent by the external environment sensing module and the automobile parameter measuring module, and giving a given value of a steering control parameter of the model predictive control steering control sub-module when determining to steer;
(B) design steering control submodule including design predictionEquation, constraint condition and objective function, wherein the prediction equation is to design a prediction time domain N according to a new discretized reconstruction modelpControl time domain NcAnd the sampling time T, wherein the constraint conditions comprise front wheel corner delta constraint, longitudinal speed u constraint and yaw speed omegar(yaw-rate observations of the left-inverse model output) constraint, objective function including error e (k) information, control input
Information and error correction factor h.
The specific embodiment of the invention: the method comprises the steps of forming an external environment perception module by using a camera and a laser radar, forming an automobile parameter measurement module by using a GPS (global positioning system) navigation system and a steering wheel corner measurement sensor, compiling a model prediction steering control module, an intelligent automobile right inverse model and an intelligent automobile left inverse model by using MATLAB/Simulink, downloading the models to dSPACE, connecting the models to the intelligent automobile by using a dSPACE subsidiary interface, building and connecting a virtual instrument, and realizing the intelligent automobile model prediction steering controller based on model reconstruction.
The above-listed detailed description is only a specific description of a possible embodiment of the present invention, and they are not intended to limit the scope of the present invention, and equivalent embodiments or modifications made without departing from the technical spirit of the present invention should be included in the scope of the present invention.
Claims (8)
1. The intelligent automobile model prediction steering controller based on model reconstruction is characterized by comprising an external environment sensing module, an automobile parameter measuring module, an intelligent automobile original model, an intelligent automobile right inverse model, an intelligent automobile left inverse model, a model prediction steering control module and a steering execution module;
the external environment sensing module is used for acquiring lane information, traffic signs, nearby vehicles and pedestrian information of automobile driving and transmitting the information to the model prediction steering control module;
the automobile parameter measuring module is used for acquiring longitudinal speed u, transverse speed v and front wheel corner delta information of automobile running and transmitting the information to the model prediction steering control module;
the intelligent automobile original model
The input variable of (1) is the front wheel steering angle delta, and the control variable is the yaw rate omegarAnd a lateral velocity v, the output variable being the lateral velocity v;
the intelligent automobile right inverse model is placed in the intelligent automobile original model in a series connection mode
On the left side of the model, the intelligent automobile model is reconstructed into a standard first-order transfer function new model G(s) ═ s-1The input variable of the new model is the first derivative of the lateral speed of the intelligent automobile
The output variable is the transverse speed v of the intelligent automobile;
the intelligent automobile left inverse model is placed on the right side of the intelligent automobile original model in a series connection mode to form an intelligent automobile yaw velocity observation model;
the model prediction steering control module comprises a steering decision sub-module and a steering control sub-module;
the steering decision sub-module is used for deciding whether to steer or not according to the automobile running parameters sent by the external environment sensing module and the automobile parameter measuring module, and giving a given value of a steering control parameter of the model predictive control steering control sub-module when deciding to steer;
the steering execution module drives the steering execution mechanism according to the control signal transmitted by the model prediction steering control module, so that the intelligent automobile can steer autonomously;
the intelligent automobile right inverse model is composed of an adaptive neural fuzzy inference system (ANFIS (1)) and integrators s-1Configured, said adaptive neuro-fuzzy inference system (ANFIS (1)) comprises two inputs and one output, said adaptive neural modelThe fuzzy inference system (ANFIS (1)) is used for obtaining the front wheel corner delta, the transverse speed v and the transverse speed first derivative in the operation process of the intelligent automobile original model
The input variables of the intelligent automobile right inverse model are the transverse speed v and the first derivative of the transverse speed
The output variable is a front wheel steering angle delta;
the intelligent automobile left inverse model is constructed by an adaptive neural fuzzy inference system (ANFIS (2)) and a differentiator s, the adaptive neural fuzzy inference system (ANFIS (2)) comprises three inputs and one output, and the adaptive neural fuzzy inference system (ANFIS (2)) is constructed according to an intelligent automobile original model
Front wheel corner delta and yaw velocity omega obtained in running processrLateral velocity v and first derivative of lateral velocity
The input variables of the intelligent automobile left inverse model are front wheel turning angle delta, transverse speed v and first derivative of the transverse speed
The output variable being yaw rate omegar。
2. The intelligent vehicle model predictive steering controller based on model reconstruction as claimed in claim 1, wherein the intelligent vehicle original model
Coupled models being two-degree-of-freedom models and containing non-linear parameters, modes thereofThe type expression is:
wherein the meaning of the parameters: automobile front wheel to mass center distance a, rear wheel to mass center distance b and front wheel side deflection rigidity k1Rear wheel side yaw stiffness k2Mass m and moment of inertia I of automobilezFront wheel turning angle delta, longitudinal speed u, lateral speed v, yaw rate omegar。
3. The intelligent automobile model prediction steering controller based on model reconstruction as claimed in claim 1, wherein the steering control sub-module is designed according to a reconstructed new model, and comprises a prediction equation, a constraint condition and an objective function; the prediction equation is used for predicting a time domain N through design according to a new discretized reconstruction modelpControl time domain NcAnd sampling time T; the constraint conditions comprise front wheel turning angle delta constraint, longitudinal speed u constraint and yaw speed omegarConstraining; the objective function includes error e (k) information, control input
Information and error correction factor h.
4. The intelligent automobile model predictive steering controller based on model reconstruction as claimed in claim 3, characterized in that the predictive equation is specifically designed as follows:
in the prediction equation, vp(k +1| k) is the prediction output,
is a control input at time k, h isError correction coefficient, v (k) is the output of the reconstructed new model of the intelligent automobile at the moment k, vm(k) Is a standard model G(s) ═ s-1Output at time k, e (k) ═ v (k) — vm(k) Is the error at time k, predicting the time domain NpControl time domain N20cThe sampling time T is 0.05 s;
the constraint conditions are specifically designed as follows:
front wheel steering angle constraint: delta is between 10 degrees and 10 degrees, delta is between 0.8 degrees and 0.8 degrees;
and (3) longitudinal speed constraint: u is more than 0 and less than or equal to 70 km/h;
and (3) restricting the yaw velocity: -5.0 °/s ≦ ωr≤5.0°/s；
The objective function is specifically designed as follows:
in the objective function, vp(k +1| k) is the prediction output, vr(k +1) inputting a reference trajectory,
h is an error correction coefficient, e (k) is equal to v (k) -vm(k) Is the error of k time, v (k) is the output of the reconstructed new model of the intelligent automobile at the k time, vm(k) Is a standard model G(s) ═ s-1Output at time k.
5. The intelligent automobile model prediction steering control method based on model reconstruction is characterized by comprising the following steps of:
step 1) simplifying and equivalence are carried out on an intelligent automobile to obtain a two-degree-of-freedom intelligent automobile original model, wherein an input variable is a front wheel corner delta, and an output variable is a transverse speed v;
step 2) determining an original model of the intelligent automobile
The input variable of the right inverse model is the transverse velocityDegree v and first derivative of lateral velocity
The output variable is a front wheel steering angle delta;
step 3) determining an original model of the intelligent automobile
The left inverse model of (2) has the input variables of the front wheel turning angle delta, the lateral velocity v and the first derivative of the lateral velocity
The output variable being yaw rate omegar；
Step 4), constructing an intelligent automobile right inverse model by using an adaptive neural fuzzy inference system ANFIS (1) and an integrator;
step 5), constructing an intelligent automobile left inverse model by using an adaptive neural fuzzy inference system ANFIS (2) and a differentiator;
step 6) reconstructing a first-order transfer function new model G(s) -s according to the intelligent automobile right inverse model and the intelligent automobile original model-1And designing a model prediction steering control module, and taking a yaw velocity observation value output by the intelligent automobile left-inverse model as a constraint condition of a steering control submodule.
6. The intelligent automobile model predictive steering control method based on model reconstruction as claimed in claim 5, wherein the parameter determination method of the ANFIS (1) in the step 4) is as follows: step excitation signal delta of front wheel corner*Adding the data to the input end of the original intelligent automobile model; acquisition of an excitation signal delta*And a lateral velocity v; off-line solving of first derivative of obtained transverse velocity v signal
Using formed training sample sets
And (3) training the ANFIS (1), and determining each parameter of the ANFIS (1) when the root mean square error of the training sample and the root mean square error of the checking sample meet the control precision requirement.
7. The intelligent automobile model predictive steering control method based on model reconstruction as claimed in claim 5, wherein the parameter determination method of the ANFIS (2) in the step 5) is as follows: step excitation signal delta of front wheel corner*Adding the data to the input end of the original intelligent automobile model; acquisition of an excitation signal delta*Lateral velocity v and yaw rate ωr(ii) a Off-line solving of first derivative of obtained transverse velocity v signal
Using formed training sample sets
And (3) training the ANFIS (2), and determining each parameter of the ANFIS (2) when the root mean square error of the training sample and the root mean square error of the checking sample meet the control precision requirement.
8. The intelligent automobile model predictive steering control method based on model reconstruction as claimed in claim 5, characterized in that the design method of the model predictive steering control module in the step 6) is as follows:
designing a steering decision submodule: the method comprises the steps of determining whether to steer or not according to automobile running parameters sent by an external environment sensing module and an automobile parameter measuring module, and giving a given value of a steering control parameter of a model predictive control steering control sub-module when the steering is determined to be steered;
designing a steering control submodule: designing a prediction equation, a constraint condition and an objective function; wherein, the prediction equation is to design a prediction time domain N according to a new discretized reconstruction modelpControl time domain NcAnd the sampling time T, wherein the constraint conditions comprise front wheel corner delta constraint, longitudinal speed u constraint and yaw speed omegarConstrainingThe objective function includes error e (k) information, control input
Information and error correction factor h;
the prediction equation is as follows:
in the prediction equation, vp(k +1| k) is the prediction output,
for the control input at the moment k, h is an error correction coefficient, v (k) is the output of the reconstructed new model of the intelligent automobile at the moment k, vm(k) Is a standard model G(s) ═ s-1Output at time k, e (k) ═ v (k) — vm(k) Is the error at time k, predicting the time domain NpControl time domain N20cThe sampling time T is 0.05 s;
the constraints are as follows:
front wheel steering angle constraint: delta is between 10 degrees and 10 degrees, delta is between 0.8 degrees and 0.8 degrees;
and (3) longitudinal speed constraint: u is more than 0 and less than or equal to 70 km/h;
and (3) restricting the yaw velocity: -5.0 °/s ≦ ωr≤5.0°/s；
The objective function is as follows:
in the objective function, vp(k +1| k) is the prediction output, vr(k +1) inputting a reference trajectory,
h is an error correction coefficient, e (k) is equal to v (k) -vm(k) Is the error of k time, v (k) is the reconstruction of a new model of the intelligent automobile at the k timeV output ofm(k) Is a standard model G(s) ═ s-1Output at time k.
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