CN117518779A - Parameter-adaptive intelligent patrol car high-precision track tracking control method - Google Patents
Parameter-adaptive intelligent patrol car high-precision track tracking control method Download PDFInfo
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Abstract
A parameter self-adaptive intelligent patrol car high-precision track tracking control method comprises the steps of establishing a track changing track function of a controlled vehicle through seven times of polynomials, acquiring an optimal track changing track according to the track changing track function, then establishing a track tracking error model of the controlled vehicle, introducing an LQR control algorithm to transversely adjust the controlled vehicle and introducing a PID controller to longitudinally control the controlled vehicle, wherein the PID controller introduces fuzzy control to adjust parameters of the controlled vehicle; finally, the PID controller outputs control signals for executing the throttle and the brake to execute track tracking control of the controlled vehicle and coordinate the stable lane change of the controlled vehicle.
Description
Technical Field
The invention relates to the technical field of patrol car track tracking, in particular to a parameter self-adaptive intelligent patrol car high-precision track tracking control method.
Background
The intelligent patrol car is used as an emerging technology in the fields of urban traffic management and safety, and has high dynamic property and diversity of urban road environments and high complex control requirements under the environments, and higher accuracy and stability of transverse and longitudinal control are required.
The problem of path specification of the patrol car is particularly complex, and the traditional path specification method generally adopts one algorithm of PID, LQR and MPC, so that the lateral and longitudinal control of the vehicle is not stable and accurate enough.
Disclosure of Invention
In order to solve the problem of poor stability and accuracy of transverse and longitudinal control of a vehicle in the prior art, the invention provides a parameter self-adaptive intelligent patrol car high-precision track tracking control method, which optimizes transverse and longitudinal control errors and improves flexibility and precision of a path track.
In order to achieve the above purpose, the invention adopts the following specific scheme: a parameter self-adaptive intelligent patrol car high-precision track tracking control method comprises the steps of establishing a track changing track function of a controlled vehicle through seven times of polynomials, acquiring an optimal track changing track according to the track changing track function, then establishing a track tracking error model of the controlled vehicle, introducing an LQR control algorithm to transversely adjust the controlled vehicle and introducing a PID controller to longitudinally control the controlled vehicle, wherein the PID controller introduces fuzzy control to adjust parameters of the controlled vehicle; finally, the PID controller outputs control signals for executing the throttle and the brake to execute track tracking control of the controlled vehicle, and coordinates smooth lane change of the controlled vehicle.
As an optimization scheme of the intelligent patrol car high-precision track tracking control method with the parameter self-adaption, the intelligent patrol car high-precision track tracking control method comprises the following steps: the method comprises the following steps:
s1, acquiring real-time state information of a controlled vehicle, establishing safety constraint conditions according to the real-time state information, establishing a track change track function of the controlled vehicle based on a seven-time polynomial, and acquiring optimal track information according to the track change track function;
s2, establishing a dynamics model of the controlled vehicle, and establishing a track tracking error model according to the track change track function and the dynamics model;
s3, optimizing the track tracking error according to a feedforward and feedback LQR control algorithm to obtain a front wheel steering angle of the controlled vehicle, and transversely adjusting the controlled vehicle;
s4, transmitting the real-time state information, the optimal track information and the optimized track tracking error to a PID controller, introducing fuzzy control to set parameters of the PID controller in real time, and finally outputting the parameters of the PID controller;
s5, outputting throttle and brake control signals according to the parameters of the PID, and executing track tracking control of the controlled vehicle.
As another optimization scheme of the intelligent patrol car high-precision track tracking control method with the parameter self-adaption, the intelligent patrol car high-precision track tracking control method is characterized in that: in the step S1, the real-time status information includes the current position, speed, acceleration and yaw angle of the controlled vehicle.
As another optimization scheme of the intelligent patrol car high-precision track tracking control method with the parameter self-adaption, the intelligent patrol car high-precision track tracking control method is characterized in that: in the step S1, the safety constraint condition is as follows:
wherein ω (t) 0 ) For the start point position ω (t p ) For the end point position, v (t 0 ) For the onset speed, v (t p ) For the end point speed, a (t 0 ) For starting point acceleration, a (t p ) For the end point acceleration, j (t 0 ) For the initial point acceleration change rate, j (t p ) Is the end point acceleration rate of change.
As another optimization scheme of the intelligent patrol car high-precision track tracking control method with the parameter self-adaption, the intelligent patrol car high-precision track tracking control method is characterized in that: in the step S1, the track change track function includes a transverse track function and a longitudinal track function, where the transverse track function is:
w p for the planned expected displacement, w 0 To initiate displacement, t p To plan time, t is time
The comprehensive track function is as follows:
wherein a is a coefficient to be solved, b is a coefficient to be solved, x is transverse displacement, and y is longitudinal displacement.
As another optimization scheme of the intelligent patrol car high-precision track tracking control method with the parameter self-adaption, the intelligent patrol car high-precision track tracking control method is characterized in that: the step S2 comprises the following steps:
s21, a dynamics model of the controlled vehicle is as follows:
wherein,for acceleration->For yaw acceleration, C f For cornering stiffness of front tyre, C r For cornering stiffness of rear tyre, L a L is the distance from the center of mass of the controlled vehicle to the front axle b For the distance from the center of mass of the controlled vehicle to the rear axle, m is the mass of the controlled vehicle, v x For controlling the longitudinal speed of the vehicle, I z The moment of inertia of the vehicle, delta is the front tire slip angle;
order theThe above formula is:
wherein,the method is characterized in that the method is a state vector of a controlled vehicle, A is a state transition matrix, B is a control input matrix, and u is a control input vector;
s22, designing an objective function comprehensively considering the traceability evaluation index:
J＝e 2 rr+u 2 ；
s23, establishing a tracking error model according to the dynamics model and the objective function:
wherein e d In order to account for the lateral displacement error,is a lateral velocity error>For lateral acceleration error>For yaw error, +.>For yaw rate error, +.>For yaw acceleration error, +.>For course angular velocity, C f For cornering stiffness of front tyre, C r For cornering stiffness of rear tyre, L a L is the distance from the center of mass of the controlled vehicle to the front axle b For the distance from the center of mass of the controlled vehicle to the rear axle, m is the mass of the controlled vehicle, v x For controlling the longitudinal speed of the vehicle, I z The moment of inertia of the vehicle, δ is the front tire slip angle.
As another optimization scheme of the intelligent patrol car high-precision track tracking control method with the parameter self-adaption, the intelligent patrol car high-precision track tracking control method is characterized in that: in the step S3, the feedforward control amount is:
wherein K is feedback gain obtained by LQR control algorithm, L a L is the distance from the center of mass of the controlled vehicle to the front axle b K is the distance from the center of mass of the controlled vehicle to the rear axle 3 Control gain of state weight matrix, m is mass of controlled vehicle, v x C for controlling the longitudinal speed of the vehicle f For cornering stiffness of front tyre, C r Is the cornering stiffness of the rear tyre;
the feedback control amount is:
u k ＝-(R+B T P k+1 ) -1 B T p k+1 Ax k
wherein R is an input weight matrix, B T To control the transpose of the input matrix, P k+1 A state covariance matrix at time k+1, A is a state transition matrix, B is a control input matrix, and x k Is the state vector at time k.
As another optimization scheme of the intelligent patrol car high-precision track tracking control method with the parameter self-adaption, the intelligent patrol car high-precision track tracking control method is characterized in that: in the step S4, the feedback control amount after the fuzzy control is introduced is as follows:
the parameters of the fuzzy PID controller are as follows:
wherein k is p As a proportion parameter, k i Is an integral parameter, k d E (t) is the error of the moment t as a differential parameter;
the setting rules of the parameters are as follows:
when the error is larger, increase k p Value sum k d Value, decrease k i A value;
error-neutral time reduction of k d Value sum k p A value;
when the error is small, k is reduced p Value of increasing k i Values.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a parameter self-adaptive intelligent patrol car high-precision track tracking control method, which comprises the steps of establishing a track changing track function of a controlled vehicle through seven times of polynomials, acquiring an optimal track changing track according to the track changing track function, then establishing a track tracking error model of the controlled vehicle, introducing an LQR control algorithm to transversely adjust the controlled vehicle and a PID controller to longitudinally control the controlled vehicle, wherein the PID controller introduces fuzzy control to adjust parameters of the controlled vehicle, optimizing transverse and longitudinal control errors, and improving flexibility and precision of a path track.
Drawings
FIG. 1 is a trace tracking error model of the present invention;
FIG. 2 is a schematic diagram of the coordinated transverse and longitudinal control of the present invention;
FIG. 3 is a schematic diagram of the LQR algorithm;
FIG. 4 is a flow chart of parameter tuning;
FIG. 5 is a blurring of a PID controllerΔk in regular curved surface p Is a nonlinear correspondence of (a).
FIG. 6 shows Δk in the fuzzy rule surface of the PID controller i Is a nonlinear correspondence of (a).
FIG. 7 shows Δk in the fuzzy rule surface of the PID controller d Is a nonlinear correspondence of (a).
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to specific embodiments, and the portions of the present invention that are not specifically described and disclosed in the following embodiments should be understood as the prior art that is or should be known to those skilled in the art.
Examples
A parameter self-adaptive intelligent patrol car high-precision track tracking control method establishes a track change track function of a controlled car through seven times of polynomials, obtains an optimal track change track according to the track change track function, and realizes efficient and accurate track tracking of the intelligent patrol car during track change. Then, a track tracking error model of the controlled vehicle is established, an LQR control algorithm is introduced to transversely adjust the controlled vehicle, a PID controller is used for longitudinally controlling the controlled vehicle, displacement errors of the controlled vehicle are adjusted, accumulated errors in track tracking of the controlled vehicle are effectively eliminated, the controlled vehicle is ensured to run according to planned displacement, the stable form of track tracking is improved, and the vehicle can change tracks according to an optimal track. The PID controller adjusts parameters by introducing fuzzy control, so that the system is better suitable for dynamic parameter changes in different environments, and the performance of the PID controller under complex road conditions is improved. Finally, the PID controller outputs control signals for executing the throttle and the brake to execute track tracking control of the controlled vehicle, and coordinates smooth lane change of the controlled vehicle.
Specifically, the method comprises the following steps:
s1, acquiring real-time state information of a controlled vehicle by using a radar and a sensing device, wherein the state information comprises current displacement, speed, acceleration, yaw angle and the like of the controlled vehicle, and establishing a safety constraint condition according to the real-time state information, in the embodiment, setting a lane change starting point of the controlled vehicle as t=0 and a lane change end point as t=p, and the transverse constraint conditions of the lane change starting point and the lane change end point displacement, the speed v (t), the acceleration a (t) and the acceleration change rate j (t) are as follows:
wherein ω (t) 0 ) For the start point position ω (t p ) For the end point position, v (t 0 ) For the onset speed, v (t p ) For the end point speed, a (t 0 ) For starting point acceleration, a (t p ) For the end point acceleration, j (t 0 ) For the initial point acceleration change rate, j (t p ) Is the end point acceleration rate of change.
And constructing a seven-time polynomial as an interface of track planning of the controlled vehicle, analyzing four state values of displacement, speed, acceleration and acceleration change rate of the controlled vehicle, establishing a track change track function of the controlled vehicle based on the seven-time polynomial, and acquiring optimal track information according to the track change track function. The lane-change track function comprises a transverse track function and a longitudinal track function, wherein:
ω(t)＝a 0 +a 1 t+a 2 t 2 +a 3 t 3 +a 4 t 4 +a 5 t 5 +a 6 t 6 +a 7 t 7
wherein t is time, a i (i=1,..7) is the coefficient to be solved, ω (t) is the transverse trajectory function;
and (3) solving a parallel vertical expression of first-order, second-order and third-order derivatives of omega (t) to obtain a transverse track function as follows:
wherein w is p For the planned expected displacement, w 0 To initiate displacement, t p For planning time, t is time;
the comprehensive track function is as follows:
wherein a is a coefficient to be solved, b is a coefficient to be solved, x is lateral displacement, and y is lateral displacement.
S2, establishing a dynamics model of the controlled vehicle, and establishing a track tracking error model according to the track change track function and the dynamics model; specific:
s21, in order to reduce the calculated amount, assuming that the controlled vehicle runs on a road surface without gradient, neglecting longitudinal and transverse coupling of tire force and load transfer of front and rear axles, disregarding left and right load transfer, neglecting influence of transverse and longitudinal aerodynamics on yaw characteristics of the controlled vehicle, and establishing a dynamics model of the controlled vehicle:
wherein,for acceleration->For yaw acceleration, C f For cornering stiffness of front tyre, C r For cornering stiffness of rear tyre, L a L is the distance from the center of mass of the controlled vehicle to the front axle b For the distance from the center of mass of the controlled vehicle to the rear axle, m is the mass of the controlled vehicle, v x For controlling the longitudinal speed of the vehicle, I z The moment of inertia of the vehicle, delta is the front tire slip angle;
order theThe above formula is:
wherein,is the state vector of the controlled vehicle, A is the shapeThe state transition matrix, B is a control input matrix, and u is a control input vector;
s22, designing an objective function comprehensively considering the traceability evaluation index:
J＝e 2 rr +u 2 ；
s23, establishing a tracking error model according to the dynamics model and the objective function, wherein the objective is to select a proper control quantity u, namely solving the objective function J=e 2 rr +u 2 At the position ofMinimum under conditions. The error between the current actual position and the planned track point, finding the closest point between the planned discrete track point and the actual position, and referring to the point as a matching point, carrying out error analysis on the actual position and the position of the matching point, taking the transverse error, the change rate of the transverse error, the course angle and the change rate of the course angle error as state quantities, and constructing an error state space equation, namely, a tracking error model is as follows:
wherein e d In order to account for the lateral displacement error,is a lateral velocity error>For lateral acceleration error>For yaw error, +.>For yaw rate error, +.>For yaw acceleration error, +.>For course angular velocity, C f For cornering stiffness of front tyre, C r For cornering stiffness of rear tyre, L a L is the distance from the center of mass of the controlled vehicle to the front axle b For the distance from the center of mass of the controlled vehicle to the rear axle, m is the mass of the controlled vehicle, v x For controlling the longitudinal speed of the vehicle, I z The moment of inertia of the vehicle, δ is the front tire slip angle.
S3, according to FIG. 2, dividing a lane change strategy of a control vehicle into three parts of planning, control and a model, firstly utilizing seven-time polynomial curve fitting to obtain an optimal lane change track, inputting planned track displacement and planning speed to a controller, wherein a longitudinal fuzzy double PID controller adjusts according to errors of actual and planning speeds, and the obtained speed errors and acceleration errors output driving/braking force to the model through an accelerator brake calibration table; the transverse controller with feedforward and feedback outputs the front wheel rotation angle to the motor model after the planned displacement error is adjusted, so as to control the acceleration or braking of the vehicle in real time, and v in the figure y Is the lateral speed of the controlled vehicle.
Firstly, a feedforward and feedback LQR control algorithm is designed, the track tracking error is optimized to obtain the front wheel steering angle of the controlled vehicle, and the controlled vehicle is transversely adjusted. In this embodiment, the feedforward+feedback LQR control algorithm adjusts the steering input according to the error of the discrete trajectory points and the true position:
wherein K is feedback gain, err and obtained by LQR control algorithmAnd cannot be zero at the same time, i.e. the final steady state error cannot remain zero at all times.
Adding feedforward control to find a proper feedforward control quantity, so that the error is as zero as possible:
let err be zero, the resulting weight control is:
wherein K is feedback gain obtained by LQR control algorithm, L a L is the distance from the center of mass of the controlled vehicle to the front axle b K is the distance from the center of mass of the controlled vehicle to the rear axle 3 Control gain of state weight matrix, m is mass of controlled vehicle, v x C for controlling the longitudinal speed of the vehicle f For cornering stiffness of front tyre, C r Is the cornering stiffness of the rear tyre.
According to the forward Euler methodDiscretizing to obtain->Finally, the feedback control amount is obtained as follows:
u k ＝-(R+B T P k+1 B) -1 B T p k+1 Ax k
wherein R is an input weight matrix, B T To control the transpose of the input matrix, P k+1 A state covariance matrix at time k+1, A is a state transition matrix, B is a control input matrix, and x k Is the state vector at time k.
Specifically, as shown in FIG. 4, the vehicle is controlled by its own parameters (specifically, as shown in Table 1), and the longitudinal speed v x Obtaining A, B parameters, and obtaining a feedback gain K by setting reasonable Q and R parameters and an LQR control algorithm; then, the error err and the road curvature k between the current state and the planning state are obtained through the state of the controlled vehicle, the planning speed and the current longitudinal speed; finally, the feedforward control quantity delta is obtained through the feedback gain, the current longitudinal speed, k and err f Further, a feedback control amount u is obtained.
TABLE 1 self parameters of controlled vehicle
| Parameter name | Numerical value |
| Whole vehicle quality (kg) | 1870 |
| Wheel tread (mm) | 1640 |
| Wheelbase (mm) | 2910 |
| Vehicle body size (mm) | 4975*1910*1495 |
| Distance (mm) of centroid to front axle | 1015 |
S4, the real-time state information, the optimal track information and the track tracking error after optimization are designed and transmitted to the PID controller, the parameters of the PID controller are set in real time by introducing fuzzy control, and finally, the parameters of the PID controller are output. In this embodiment, the PID controller includes a displacement PID controller and a velocity PID controller, and the velocity PID controller introduces fuzzy control. The longitudinal displacement error and the longitudinal speed error are used as the input of the PID controller, specifically, as shown in fig. 5, the expected displacement and the actual displacement are input into the position PID controller to obtain the errors of the expected displacement and the actual displacement, the expected speed, the actual speed and the errors are input into the speed PID controller, the change rate of the speed error is increased by introducing fuzzy control, and the speed PID parameter is adjusted to obtain the speed error.
FIG. 6 is a graph of a fuzzy rule surface, corresponding to Δk, respectively p 、Δk i And Deltak d The parameters of the fuzzy speed PID controller are as follows:
wherein k is p As a proportion parameter, k i Is an integral parameter, k d Is a differential parameter;
the feedback control amount after the fuzzy control is introduced is as follows:
wherein k is p As a proportion parameter, k i Is an integral parameter, k d E (t) is the error at time t, which is a derivative parameter.
When the controlled vehicle runs at low speed, the proportion parameter k is gradually adjusted p Ensuring that the vehicle can realize accurate tracking on the planned lane change track, and moderately reducing k if the controlled vehicle deviates or is difficult to effectively return p Is a numerical value of (2); according to the amplitude of each adjustment of the controlled vehicle, the differential parameter k is adjusted d Maintaining the system in a better damping state; feedback control amount based on accumulated error, fine tuning integral parameter k i Further improving the stability and precision of the control system. The specific regulation rules of the regulation parameters are as follows:
when the error control amount is large, k is increased p The value accelerates the response of the system; increasing k to prevent system tuning from vibrating and overshooting too quickly d A value; reducing k i Values to ensure steady state error.
Error control amount is medium, k is reduced for controlling steady state error and controlling overshoot d A value; avoid too fast speed and reduce k in proper amount p Values.
When the error is small, k is reduced p Value of increasing k i Values to ensure its steady state characteristics.
S5, outputting throttle and brake control signals according to parameters of the PID, and executing track tracking control of the controlled vehicle, namely, inputting speed errors and acceleration errors into a calibration table of the throttle and the brake so as to control front wheel rotation angles and driving braking force of the vehicle.
The displacement and front wheel steering angle error are controlled by a feed-forward and feedback LQR control algorithm in the transverse direction, the error of expected displacement and actual displacement is adjusted by a longitudinal position PID controller, the track changing track of the vehicle is adjusted in real time, the speed error and the acceleration error are considered by a fuzzy speed PID controller, the longitudinal speed is ensured to be matched with the transverse movement in a coordinated manner, and the integral transverse and longitudinal cooperative control is realized. The method ensures the control precision and is a simple and feasible complex environment channel-changing driving control scheme.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A parameter self-adaptive intelligent patrol car high-precision track tracking control method is characterized in that: establishing a track changing function of the controlled vehicle through a seven-time polynomial, acquiring an optimal track changing track according to the track changing function, then establishing a track tracking error model of the controlled vehicle, introducing an LQR control algorithm to transversely adjust the controlled vehicle and a PID controller to longitudinally control the controlled vehicle, wherein the PID controller introduces fuzzy control to set parameters of the controlled vehicle; finally, the PID controller outputs control signals for executing the throttle and the brake to execute track tracking control of the controlled vehicle, and coordinates smooth lane change of the controlled vehicle.
2. The intelligent patrol car high-precision track tracking control method with parameter self-adaption as claimed in claim 1, comprising the following steps:
s1, acquiring real-time state information of a controlled vehicle, establishing safety constraint conditions according to the real-time state information, establishing a track change track function of the controlled vehicle based on a seven-time polynomial, and acquiring optimal track information according to the track change track function;
s2, establishing a dynamics model of the controlled vehicle, and establishing a track tracking error model according to the track change track function and the dynamics model;
s3, optimizing the track tracking error according to a feedforward and feedback LQR control algorithm to obtain a front wheel steering angle of the controlled vehicle, and transversely adjusting the controlled vehicle;
s4, transmitting the real-time state information, the optimal track information and the optimized track tracking error to a PID controller, introducing fuzzy control to set parameters of the PID controller in real time, and finally outputting the parameters of the PID controller;
s5, outputting throttle and brake control signals according to the parameters of the PID, and executing track tracking control of the controlled vehicle.
3. The intelligent patrol car high-precision track tracking control method with self-adaptive parameters as claimed in claim 2, wherein the method is characterized in that: in the step S1, the real-time status information includes the current position, speed, acceleration and yaw angle of the controlled vehicle.
4. The intelligent patrol car high-precision track tracking control method with self-adaptive parameters as claimed in claim 1, wherein the method comprises the following steps: in the step S1, the safety constraint condition is as follows:
ω(t 0 )＝0 ω(t p )＝a；
wherein ω (t) 0 ) For the start point position ω (t p ) For the end point position, v (t 0 ) For the onset speed, v (t p ) For the end point speed, a (t 0 ) For starting point acceleration, a (t p ) For the end point acceleration, j (t 0 ) For the initial point acceleration change rate, j (t p ) Is the end point acceleration rate of change.
5. The intelligent patrol car high-precision track tracking control method with self-adaptive parameters as claimed in claim 1, wherein the method comprises the following steps: in the step S1, the track change track function includes a transverse track function and a longitudinal track function, where the transverse track function is:
wherein w is p For the planned expected displacement, w 0 To initiate displacement, t p For planning time, t is time;
the comprehensive track function is as follows:
wherein a is a coefficient to be solved, b is a coefficient to be solved, x is transverse displacement, and y is longitudinal displacement.
6. The intelligent patrol car high-precision track tracking control method with self-adaptive parameters as claimed in claim 1, wherein the method comprises the following steps: the step S2 comprises the following steps:
s21, a dynamics model of the controlled vehicle is as follows:
wherein,for acceleration->For yaw acceleration, C f For cornering stiffness of front tyre, C r For cornering stiffness of rear tyre, L a L is the distance from the center of mass of the controlled vehicle to the front axle b For the distance from the center of mass of the controlled vehicle to the rear axle, m is the mass of the controlled vehicle, v x For controlling the longitudinal speed of the vehicle, I z The moment of inertia of the vehicle, delta is the front tire slip angle;
order theu=δ, then the above formula is:
wherein,the method is characterized in that the method is a state vector of a controlled vehicle, A is a state transition matrix, B is a control input matrix, and u is a control input vector;
s22, designing an objective function comprehensively considering the traceability evaluation index:
J＝e 2 rr +u 2 ；
s23, establishing a tracking error model according to the dynamics model and the objective function:
wherein e d In order to account for the lateral displacement error,is a lateral velocity error>For lateral acceleration error>For yaw error, +.>For yaw rate error, +.>For yaw acceleration error, +.>For course angular velocity, C f For cornering stiffness of front tyre, C r For cornering stiffness of rear tyre, L a L is the distance from the center of mass of the controlled vehicle to the front axle b For the distance from the center of mass of the controlled vehicle to the rear axle, m is the mass of the controlled vehicle, v x For controlling the longitudinal speed of the vehicle, I z The moment of inertia of the vehicle, δ is the front tire slip angle.
7. The intelligent patrol car high-precision track tracking control method with self-adaptive parameters as claimed in claim 1, wherein the method comprises the following steps: in the step S3, the feedforward control amount is:
wherein K is feedback gain obtained by LQR control algorithm, L a L is the distance from the center of mass of the controlled vehicle to the front axle b Is a controlled vehicleDistance from vehicle centroid to rear axle, K 3 Control gain of state weight matrix, m is mass of controlled vehicle, v x C for controlling the longitudinal speed of the vehicle f For cornering stiffness of front tyre, C r Is the cornering stiffness of the rear tyre;
the feedback control amount is:
wherein R is an input weight matrix,to control the transpose of the input matrix, P k+1 A state covariance matrix at time k+1, A is a state transition matrix, B is a control input matrix, and x k Is the state vector at time k.
8. The intelligent patrol car high-precision track tracking control method with self-adaptive parameters as claimed in claim 1, wherein the method comprises the following steps: in the step S4, the feedback control amount after the fuzzy control is introduced is as follows:
the parameters of the fuzzy PID controller are as follows:
wherein k is p As a proportion parameter, k i Is an integral parameter, k d E (t) is the error of the moment t as a differential parameter;
the setting rules of the parameters are as follows:
when the error is larger, increase k p Value sum k d Value, decrease k i A value;
error-neutral time reduction of k d Value sum k p A value;
when the error is small, k is reduced p Value of increasing k i Values.
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