CN111930112A - Intelligent vehicle path tracking control method and system based on MPC - Google Patents
Intelligent vehicle path tracking control method and system based on MPC Download PDFInfo
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Abstract
The invention discloses an intelligent vehicle path tracking control method and system based on MPC, which comprises the steps of establishing a three-degree-of-freedom dynamic model according to an entity intelligent vehicle structure and analyzing the three-degree-of-freedom dynamic model to obtain a geometric constraint condition meeting the vehicle motion driving; constructing a target function by using a multi-target optimization strategy, and converting the nonlinear dynamic model into a linear model to obtain a state space equation; constructing a predictive control model based on the state space equation, and performing optimal solution by combining the objective function and the constraint condition to generate an optimal control sequence; and inputting the optimal control sequence into the intelligent vehicle to control the vehicle to track the target track. The invention improves the running stability of the vehicle, meets the control requirement and stability of the existing intelligent vehicle, achieves stronger constraint processing capacity and has higher feasibility and stability.
Description
Technical Field
The invention relates to the technical field of intelligent vehicle control, in particular to an intelligent vehicle path tracking control method and system based on MPC.
Background
In the driving process, a driver knows the road condition through visual feedback and controls the driving direction, in the actual driving process, the driver is interfered by a plurality of external factors and has instability, the defects of the traditional vehicle driving mode are increasingly prominent, the frequent occurrence of traffic accidents is caused, aiming at the current existing traffic safety problems, the occurrence of an intelligent vehicle gives people hopes of effectively solving the problems, the intelligent vehicle senses the surrounding environment through a sensing device arranged on the vehicle, the artificial intelligence technology is utilized to simulate the driving habits of human beings and the coping mode of handling emergency accidents, the defect that the psychological pressure of human beings influences the behavior capacity under extreme conditions is avoided, the vehicle has the capability of autonomous driving, and the driving of the vehicle becomes safe and reliable.
The path tracking control means that an intelligent vehicle can follow a path obtained by a path planning layer and enables the vehicle to safely and stably run on a road surface, and the main purpose of the path tracking control is to output corresponding control variables such as wheel braking force, front wheel rotation angle and the like according to the constraints of kinematics and dynamics of the intelligent vehicle. At present, a plurality of control algorithms are applied, including a synovial membrane control algorithm, a PID control algorithm and a neural network control algorithm, but the algorithms have high dependence degree on parameters and environment, when a vehicle runs in an actual environment, the vehicle is interfered by road smoothness and wind power, so that the vehicle cannot be well adapted to path tracking in a new state, and when an intelligent vehicle runs, the intelligent vehicle is also constrained by kinematics and dynamics, and the algorithms cannot be used for processing the constraints.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides an intelligent vehicle path tracking control method and system based on MPC, which can solve the problem that the existing control algorithm is easily influenced by environmental parameters and constraints and cannot perform prediction control on the intelligent vehicle running track well.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of establishing a three-degree-of-freedom dynamic model according to an entity intelligent vehicle structure and analyzing the three-degree-of-freedom dynamic model to obtain a geometric constraint condition meeting the vehicle motion driving; constructing a target function by using a multi-target optimization strategy, and converting the nonlinear dynamic model into a linear model to obtain a state space equation; constructing a predictive control model based on the state space equation, and performing optimal solution by combining the objective function and the constraint condition to generate an optimal control sequence; and inputting the optimal control sequence into the intelligent vehicle to control the vehicle to track the target track.
As a preferable solution of the MPC-based intelligent vehicle path tracking control method of the present invention, wherein: establishing the three-degree-of-freedom dynamic model, including establishing an xoy coordinate system according to the structure of the entity intelligent vehicle, and defining a direction which is vertical to an axle and points to the advancing direction of the vehicle as an x-axis and a direction which starts from the mass center of the vehicle and is parallel to the axle as a y-axis; according to the kinematic constraints of the front and the rear of the vehicle, the following equation is obtained,
wherein (X)r,Yr): axle center coordinate of vehicle rear axle (X)f,Yf): the axle center coordinate of the front axle of the vehicle,f: the turning angle of the front wheels of the vehicle,
a vehicle yaw angle; the derivation is performed based on geometric relationships to obtain a vehicle dynamics model, as follows,
wherein, l: vehicle wheelbase, Vr: vehicle front axle center speed, Vf: vehicle rear axle center speed; based on the vehicle dynamics model, a coordinate system oxyz is used as a vehicle coordinate system, a coordinate origin is defined at the position of a vehicle mass center, the real-time advancing direction of the vehicle is used as the positive direction of an x axis, a y axis is vertical to a longitudinal axis of the vehicle, and a z axis meets the right-hand rule; respectively calculating stress equations of the vehicle mass center along the x axis, the y axis and the z axis according to a Newton second law; respectively solving the slip angle, the slip rate, the road surface adhesion coefficient and the vertical load of the vehicle tire to finally obtain the nonlinear dynamic model,
wherein m: vehicle mass, a: distance of centroid to front axis, b: the distance of the center of mass to the rear axis,
the velocity in the x-direction of the center of mass,
the velocity in the y-direction of the center of mass,
the yaw rate is set to a value corresponding to the yaw rate,
the acceleration in the x-direction of the center of mass,
acceleration of the mass center in the y-direction, Iz: moment of inertia of vehicle about z-axis, CcfAnd CcrCornering stiffness, C, of the front and rear wheels of the vehicle, respectivelylfAnd ClrLongitudinal stiffness, s, of the front and rear wheels of the vehicle, respectivelyfAnd srRespectively the slip rates of the front and rear wheels.
As a preferable solution of the MPC-based intelligent vehicle path tracking control method of the present invention, wherein: analyzing the nonlinear dynamical model further comprises defining that the driving road surface of the vehicle is flat and has no vertical motion; defining the suspension device and the vehicle to be rigid, neglecting the effect of the coupling between the suspension and the tire on path tracking; considering the cornering characteristics of said tyre and neglecting the coupling relationship between the longitudinal and transverse directions of said tyre; disregarding the left and right shifts of load and disregarding the shifts of load of the front and rear axles when said vehicle is traveling at low speed; ignoring the effect of the vehicle longitudinal and transverse aerodynamics.
As a preferable solution of the MPC-based intelligent vehicle path tracking control method of the present invention, wherein: the constraint conditions comprise kinematic constraints and dynamic constraints; including, centroid slip angle constraints, vehicle adhesion condition constraints, and tire slip angle constraints.
As a preferable solution of the MPC-based intelligent vehicle path tracking control method of the present invention, wherein: constructing the objective function includes establishing the objective function based on the state quantity deviation and the control quantity optimization of the vehicle, as follows,
wherein, eta: output quantity, ηref: output reference, Δ u: control increment, Np: predicting time domain, Nc: control time domain, ρ: weight coefficient, of: constraint factor, χ: state quantity, u: and controlling quantity, Q and R are weight matrixes.
As a preferable solution of the MPC-based intelligent vehicle path tracking control method of the present invention, wherein: the state space equation comprises, from the nonlinear dynamical model, obtaining the state space equation as follows,
ηdyn＝hdyn(ξdyn)
wherein the content of the first and second substances,state quantity, select udyn＝fFor controlling the quantity, selecting
Is the output quantity.
As a preferable solution of the MPC-based intelligent vehicle path tracking control method of the present invention, wherein: the construction of the predictive control model comprises the steps of utilizing the state space equation to approximately linearize the nonlinear dynamical model to obtain the state quantity after the control quantity is applied; carrying out Taylor expansion calculation and discretization processing on the state quantity, and further obtaining a new state constraint equation based on vehicle front wheel steering angle control constraint; and determining the relation between variables according to the state constraint equation, and constructing the predictive control model by using the output quantity at the future moment.
As a preferable solution of the MPC-based intelligent vehicle path tracking control method of the present invention, wherein: the predictive control model specifically includes that the state constraint equation is as follows,
wherein the content of the first and second substances,
m: number of control amounts in model, n: the number of state quantities in the model; when predictedDomain is NpControl time domain as NcThe state quantity in the prediction time domain in the prediction control is obtained as follows,
the output is as follows,
the predictive control model is described as follows,
Y(t)＝Ψtξ(t|t)+ΘtΔU(t)
wherein, At: jacobian matrix of f versus ξ, Bt: jacobian matrix of f versus u, Ψt: controlling the state quantity, theta, of the time domaint: a control increment in the control time domain.
As a preferable solution of the MPC-based intelligent vehicle path tracking control method of the present invention, wherein: the method also comprises the steps that the prediction control model compares an output measurement value with a model prediction value to obtain a prediction error of the prediction control model; correcting and iteratively optimizing the model predicted value by using the prediction error until an error-free predicted value is output; the prediction model carries out optimization solution according to the optimized predicted value by combining the objective function and the constraint condition to obtain the optimal control sequence in the control time domain; and inputting the optimal control sequence into the intelligent vehicle, controlling the steering driving of the vehicle according to the current control quantity, and completing the tracking of the target track.
As a preferable solution of the MPC-based intelligent vehicle path tracking control system of the present invention, wherein: the intelligent vehicle comprises a sensing module, a control module and a control module, wherein the sensing module is used for acquiring real-time data of an external environment and various power parameters when the intelligent vehicle runs; the decision module is connected with the sensing module and used for receiving and processing data information of the sensing module, the decision module comprises a calculation unit, a storage unit and an input/output management unit, the calculation unit is used for constructing the objective function, equation and model and calculating and processing various operational data to obtain a calculation result so as to generate a decision, the storage unit is used for storing the data information received and processed by the decision module and providing calling service for each module, and the input/output management unit is used for connecting data transmission and instruction feedback of each module and providing communication connection service for each module; the path planning and tracking module is used for planning and tracking the running path of the intelligent vehicle and comprises a planning unit and a tracking unit, wherein the planning unit is connected to the decision module and is used for planning a running route according to the existing real road condition information and the calculation result, and the tracking unit is used for receiving the planned path data of the planning unit and tracking the target track of the planned path data; the main control module is connected with the sensing module, the decision-making module and the path planning and tracking module and used for issuing control instructions to control the operation of each module, the main control module comprises a control body and a prediction unit, the control body is used for controlling the target track of the intelligent vehicle to run, and the prediction unit is used for predicting whether the intelligent vehicle runs according to the target track.
The invention has the beneficial effects that: according to the invention, the predictive control model is constructed through the three-degree-of-freedom dynamic model to obtain the optimized objective function, and the vehicle dynamic constraint is added to improve the driving stability of the vehicle.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is a schematic flow chart of an MPC based intelligent vehicle path tracking control method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a vehicle kinematics model of an MPC based intelligent vehicle path tracking control method according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of a predictive control model framework of an MPC based intelligent vehicle path tracking control method according to a first embodiment of the present invention;
FIG. 4 is a schematic diagram of a model predictive trajectory tracking control process of an MPC based intelligent vehicle path tracking control method according to a first embodiment of the present invention;
FIG. 5 is a schematic diagram of a path tracking test output curve of the MPC based intelligent vehicle path tracking control method according to the first embodiment of the present invention;
FIG. 6 is a schematic diagram of a path tracking test output curve of a conventional method for an MPC based intelligent vehicle path tracking control method according to a first embodiment of the present invention;
fig. 7 is a schematic block diagram of an intelligent MPC-based vehicle path tracking control system according to a second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 6, a first embodiment of the present invention provides an MPC-based intelligent vehicle path tracking control method, including:
s1: and establishing a three-degree-of-freedom dynamic model according to the structure of the entity intelligent vehicle and analyzing the three-degree-of-freedom dynamic model to obtain a geometric constraint condition meeting the vehicle motion driving. It should be noted that, with reference to fig. 2, a three-degree-of-freedom dynamic model is established, which includes:
establishing an xoy coordinate system according to an entity intelligent vehicle structure, and defining a direction which is vertical to an axle and points to the advancing direction of the vehicle as an x axis and a direction which starts from the mass center of the vehicle and is parallel to the axle as a y axis;
according to the kinematic constraints of the front and the rear of the vehicle, the following equation is obtained,
wherein (X)r,Yr): axle center coordinate of vehicle rear axle (X)f,Yf): the axle center coordinate of the front axle of the vehicle,f: the turning angle of the front wheels of the vehicle,
a vehicle yaw angle;
vehicle rear axle center (X) based on vehicle kinematic analysisr,Yr) The speed of the beam is, as follows,
the geometry of the front and rear wheels of the vehicle is as follows,
the yaw rate of the vehicle is calculated, as follows,
the derivation is performed based on geometric relationships to obtain a vehicle dynamics model, as follows,
wherein, l: vehicle wheelbase, Vr: vehicle front axle center speed, Vf: vehicle rear axle center speed;
based on a vehicle dynamics model, a coordinate system oxyz is used as a vehicle coordinate system, a coordinate origin is defined at the position of a vehicle mass center, the real-time vehicle advancing direction is used as the positive direction of an x axis, a y axis is vertical to a longitudinal axis of a vehicle, and a z axis meets the right-hand rule;
respectively calculating stress equations of the vehicle mass center along an x axis, a y axis and a z axis according to a Newton second law;
respectively solving the slip angle, the slip rate, the road surface adhesion coefficient and the vertical load of the vehicle tire to finally obtain a nonlinear dynamic model, as follows,
wherein, α: tire slip angle, s: slip ratio, μ: coefficient of road surface adhesion, Fz: vertical loading;
the slip angle is as follows,
wherein v iscAnd vlTire lateral and longitudinal speeds, respectively;
the slip ratio is as follows,
wherein, ω ist: wheel angular velocity, r: the radius of the wheel;
wherein m: vehicle mass, a: distance of centroid to front axis, b: the distance of the center of mass to the rear axis,
the velocity in the x-direction of the center of mass,
the velocity in the y-direction of the center of mass,
the yaw rate is set to a value corresponding to the yaw rate,
the acceleration in the x-direction of the center of mass,
acceleration of the mass center in the y-direction, Iz: moment of inertia of vehicle about z-axis, CcfAnd CcrCornering stiffness, C, of the front and rear wheels of the vehicle, respectivelylfAnd ClrLongitudinal stiffness, s, of the front and rear wheels of the vehicle, respectivelyfAnd srRespectively the slip rates of the front and rear wheels.
Further, analyzing the nonlinear dynamical model further comprises:
defining that the running road surface of the vehicle is flat and has no vertical motion;
defining the suspension device and the vehicle to be rigid, and neglecting the influence of the coupling between the suspension and the tire on the path tracking;
considering the cornering characteristics of the tyre and neglecting the coupling relationship between the longitudinal and transverse directions of the tyre;
the left and right load transfer is not considered, and the load transfer of the front axle and the rear axle under the low-speed running of the vehicle is ignored;
the effect of the vehicle longitudinal and transverse aerodynamics is neglected.
Specifically, the constraint conditions include:
kinematic and kinetic constraints;
centroid slip angle constraint, vehicle attachment condition constraint, tire slip angle constraint.
S2: and constructing an objective function by using a multi-objective optimization strategy, and converting the nonlinear dynamic model into a linear model to obtain a state space equation. It should be noted that, constructing the objective function includes:
the objective function is established based on the state quantity deviation and the control quantity optimization of the vehicle, as follows,
wherein, eta: output quantity, ηref: output reference, Δ u: control increment, Np: predicting time domain, Nc: control time domain, ρ: weight coefficient, of: constraint factor, χ: state quantity, u: and controlling quantity, Q and R are weight matrixes.
Further, the state space equation comprises:
the state space equation is derived from the nonlinear dynamical model, as follows,
ηdyn＝hdyn(ξdyn)
wherein the content of the first and second substances,
state quantity, select udyn＝fFor controlling the quantity, selecting
Is the output quantity.
S3: and constructing a predictive control model based on a state space equation, and performing optimal solution by combining a target function and constraint conditions to generate an optimal control sequence. It should be further noted that, with reference to fig. 3 and 4, constructing the prediction control model includes:
utilizing a state space equation to approximate a linear processing nonlinear dynamical model to obtain a state quantity after applying a control quantity;
taylor expansion calculation and discretization processing are carried out on the state quantity, and a new state constraint equation is further obtained based on the vehicle front wheel steering angle control constraint;
and (4) defining the relation between variables according to a state constraint equation, and constructing a predictive control model by using the output quantity at the future moment.
Further, the prediction control model specifically includes:
the state constraint equation is as follows,
wherein the content of the first and second substances,
m: number of control amounts in model, n: the number of state quantities in the model;
when the prediction time domain is NpControl time domain as NcThen, the state quantity in the prediction time domain in the prediction control is obtained, as follows,
the output is as follows,
the model of predictive control is as follows,
Y(t)＝Ψtξ(t|t)+ΘtΔU(t)
wherein, At: jacobian matrix of f versus ξ, Bt: jacobian matrix of f versus u, Ψt: controlling the state quantity, theta, of the time domaint: a control increment in the control time domain.
Specifically, the method comprises the following steps:
according to the formula, the state quantity and the output quantity in the prediction time domain are obtained through calculation according to the known current state quantity and the control increment in the control time domain, and therefore model prediction control is achieved.
S4: and inputting the optimal control sequence into the intelligent vehicle to control the vehicle to track the target track. What should be further described in this step is:
the prediction control model compares the output measurement value with the model prediction value to obtain the prediction error of the prediction control model;
correcting and iteratively optimizing the model predicted value by using the prediction error until the error-free predicted value is output;
the prediction model carries out optimization solution by combining a target function and constraint conditions according to the optimized predicted value to obtain an optimal control sequence in a control time domain;
and inputting the optimal control sequence into the intelligent vehicle, controlling the steering driving of the vehicle according to the current control quantity, and completing the tracking of the target track.
In general, since the nonlinear constraints such as the control input of the model executed by the intelligent vehicle in high-speed motion, the slip caused by the friction between the tire and the ground, and the roll caused by the lateral acceleration are more severe than those in low-speed motion, in addition to the control quantity constraint and the control increment constraint, the dynamic constraints including the centroid slip angle constraint and the vehicle attachment condition constraint are added, as follows:
(1) centroid slip angle constraint
The centroid slip angle has a large influence on the stability of the vehicle, so the centroid slip angle must be restricted within a reasonable range, on the road surface with a high adhesion coefficient, the centroid slip angle can reach a limit value of plus or minus 12 degrees, on the road surface with a low adhesion coefficient, the limit value is only plus or minus 2 degrees, as the vehicle generally runs normally on the road surface, the limit value is difficult to reach, the embodiment is selected as follows,
(2) vehicle attachment condition constraints
The dynamic performance of an automobile is restricted not only by the driving force but also by the adhesion condition of the tire to the road surface, so that it is necessary to consider the restriction of the adhesion condition of the vehicle, and the acceleration of the vehicle is restricted by the adhesion force of the road surface when the vehicle is driven at a constant speed in the longitudinal direction, as follows,
|ay|≤μg
wherein, ayIs the lateral acceleration;
when the road adhesion condition is good, the constraint condition is loose, the riding comfort is affected by too large lateral acceleration, but the calculation and solving failure of the controller is also caused by too narrow constraint condition, then the embodiment selects to set the road adhesion coefficient as soft constraint, as follows,
ay,min-≤ay≤ay,max+
wherein, ay,minAnd ay,maxRespectively the minimum value and the maximum value of the lateral acceleration;
(3) tire cornering angle restraint
The tire side deflection angle and the lateral force are in a linear relation within the range of +/-5 degrees according to the tire side deflection characteristic, and because the established vehicle dynamic model is under the assumption of a small angle, stricter constraint limitation is carried out on the tire side deflection angle, as follows,
-2.5°≤α≤2.5°
optimization based on the predictive control model using the established objective function and constraints is performed, as follows,
wherein, yhcAnd yscRespectively hard and soft constrained outputs, yhc,minAnd yhc,maxFor hard constraint limits, ysc,minAnd ysc,maxIs a soft constraint limit;
the above equation is solved in each control period to obtain the control increment and the constraint factor in the control time domain, as follows,
inputting the first element of the control sequence into the smart vehicle as an actual control increment, obtaining the following formula,
and when the operation of the prediction control model enters the next period, repeating the calculation to complete the tracking of the target track.
Preferably, in order to better verify and explain the technical effects adopted in the method of the present invention, the embodiment selects the conventional method for learning vehicle path tracking and predicting by a support vector machine to perform a comparison test with the method of the present invention, and compares the test results by means of scientific demonstration to verify the real effects of the method of the present invention; in order to verify that the method has higher operation efficiency, higher control stability, lower cost and stronger practicability compared with the traditional method, the traditional method and the method of the invention are adopted to respectively carry out real-time measurement comparison on the path tracking control of a certain intelligent vehicle.
And (3) testing conditions are as follows: (1) in the traditional method, a support vector machine is adopted to learn vehicle characteristics so as to establish a vehicle support vector machine model, and the vehicle path tracking is controlled based on the model and the FPGA heterogeneous acceleration is carried out;
(2) the method adopts an entity vehicle structure to establish a dynamic model to obtain constraint conditions, and optimizes and solves the established predictive control model to generate an optimal control sequence to control the vehicle path tracking;
(3) building a Simulink/Matlab and Carsim combined simulation platform, referring to a B-class Hatchback front wheel steering drive vehicle type, and adopting a double-wire shifting working condition as a test working condition;
(4) the road surface adhesion coefficient mu is 0.85, the longitudinal speeds of 30km/h, 60km/h and 90km/h are respectively set, and the same dynamic parameters are input for active steering control.
Referring to fig. 5, which is a curve correspondingly output by path tracking according to the method of the present invention, referring to fig. 6, which is a curve correspondingly output by path tracking according to the conventional method, according to the schematic diagrams of fig. 5 and 6, it can be intuitively seen that when the method of the present invention tracks an expected path at 30km/h, the tracking deviation at a straight line section is small, when entering a double-lane-shifting steering section, the position deviation gradually increases, but the position deviation is relatively small as seen from the whole, and the goodness of fit between an actual position and the expected path is high, whereas the curve output by the conventional method tends to be more tortuous, extremely not smooth, the position deviation is large, and the goodness of fit between the actual position and the expected path is; under the simulation speed of 60km/h, the tracking effect of the method is better than the simulation effects of 30km/h and 90km/h, the method accords with the actual driving speed condition, and the traditional method also accords with the actual driving speed condition, but the effect is not ideal compared with the curve trend output by the method; when the vehicle speed is 90km/h, the position deviation of the vehicle output by the method in the turning process is larger than the deviation of the two speeds of 30km/h and 60km/h, but the vehicle can still meet the actual requirement, while the curve output by the traditional method is obviously lower than the curve deviation output by the method, and the actual requirement cannot be met; therefore, under the same control parameters, the vehicle controlled by the method has good path tracking performance under different speeds, and the method has strong robustness to the speed and stability of predictive control.
Example 2
Referring to fig. 7, a second embodiment of the present invention, which is different from the first embodiment, provides an MPC-based intelligent vehicle path tracking control system, including:
and the sensing module 100 is used for acquiring real-time data of an external environment and various power parameters when the intelligent vehicle runs.
The decision module 200 is connected to the sensing module 100 and configured to receive and process data information of the sensing module 100, the decision module 200 includes a calculation unit 201, a storage unit 202, and an input/output management unit 203, the calculation unit 201 is configured to construct an objective function, an equation, and a model, calculate and process various operation data, and obtain a calculation result to generate a decision, the storage unit 202 is configured to store data information received and processed by the decision module 200, and provide a call service for each module, and the input/output management unit 203 is configured to connect data transmission and instruction feedback of each module, and provide a communication connection service for each module.
The path planning and tracking module 300 is used for planning and tracking a driving path of an intelligent vehicle, and includes a planning unit 301 and a tracking unit 302, the planning unit 301 is connected to the decision module 200 and is used for planning a driving route according to the current real road condition information and the calculation result, and the tracking unit 302 is used for receiving the planned path data of the planning unit 301 and tracking the target trajectory thereof.
The main control module 400 is connected to the sensing module 100, the decision module 200 and the path planning and tracking module 300, and is configured to issue a control instruction to control operations of the modules, where the main control module 400 includes a control unit 401 and a prediction unit 402, the control unit 401 is configured to control a target trajectory of the intelligent vehicle to run, and the prediction unit 402 is configured to predict whether the intelligent vehicle runs according to the target trajectory.
It should be further noted that, the decision module 200 is mainly divided into three layers, including a control layer, an operation layer and a storage layer, where the control layer is a command control center of the decision module 200 and is composed of an instruction register IR, an instruction decoder ID and an operation controller OC, and the control layer can sequentially fetch each instruction from a memory according to a program pre-programmed by a user, place the instruction in the instruction register IR, analyze and determine the instruction in the instruction decoder, notify the operation controller OC to operate, and send a micro-operation control signal to a corresponding component according to a determined time sequence; the operation layer is the core of the decision module 200, can perform arithmetic operations (such as addition, subtraction, multiplication, division and addition operations) and logical operations (such as shift, logical test or two-value comparison), is connected to the control layer, and performs operation operations by receiving control signals of the control layer; the storage layer is a database of the decision module 200, which can store data (both pending and processed).
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. An intelligent vehicle path tracking control method based on MPC is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
establishing a three-degree-of-freedom dynamic model according to the structure of the entity intelligent vehicle and analyzing the three-degree-of-freedom dynamic model to obtain a geometric constraint condition meeting the vehicle motion driving;
constructing a target function by using a multi-target optimization strategy, and converting the nonlinear dynamic model into a linear model to obtain a state space equation;
constructing a predictive control model based on the state space equation, and performing optimal solution by combining the objective function and the constraint condition to generate an optimal control sequence;
and inputting the optimal control sequence into the intelligent vehicle to control the vehicle to track the target track.
2. The MPC-based intelligent vehicle path-tracking control method of claim 1, wherein: establishing the three-degree-of-freedom dynamic model, including,
establishing an xoy coordinate system according to the structure of the entity intelligent vehicle, and defining a direction which is vertical to an axle and points to the advancing direction of the vehicle as an x axis and a direction which starts from the center of mass of the vehicle and is parallel to the axle as a y axis;
according to the kinematic constraints of the front and the rear of the vehicle, the following equation is obtained,
wherein (X)r,Yr): axle center coordinate of vehicle rear axle (X)f,Yf): the axle center coordinate of the front axle of the vehicle,f: the turning angle of the front wheels of the vehicle,
a vehicle yaw angle;
the derivation is performed based on geometric relationships to obtain a vehicle dynamics model, as follows,
wherein, l: vehicle wheelbase, Vr: vehicle front axle center speed, Vf: vehicle rear axle center speed;
based on the vehicle dynamics model, a coordinate system oxyz is used as a vehicle coordinate system, a coordinate origin is defined at the position of a vehicle mass center, the real-time advancing direction of the vehicle is used as the positive direction of an x axis, a y axis is vertical to a longitudinal axis of the vehicle, and a z axis meets the right-hand rule;
respectively calculating stress equations of the vehicle mass center along the x axis, the y axis and the z axis according to a Newton second law;
respectively solving the slip angle, the slip rate, the road surface adhesion coefficient and the vertical load of the vehicle tire to finally obtain the nonlinear dynamic model,
wherein m: vehicle mass, a: distance of centroid to front axis, b: the distance of the center of mass to the rear axis,
the velocity in the x-direction of the center of mass,
the velocity in the y-direction of the center of mass,
the yaw rate is set to a value corresponding to the yaw rate,
the acceleration in the x-direction of the center of mass,
acceleration of the mass center in the y-direction, Iz: moment of inertia of vehicle about z-axis, CcfAnd CcrCornering stiffness, C, of the front and rear wheels of the vehicle, respectivelylfAnd ClrLongitudinal stiffness, s, of the front and rear wheels of the vehicle, respectivelyfAnd srRespectively the slip rates of the front and rear wheels.
3. The MPC-based intelligent vehicle path tracking control method of claim 1 or 2, wherein: analyzing the non-linear dynamical model further comprises,
defining the running road surface of the vehicle to be flat and free of vertical movement;
defining the suspension device and the vehicle to be rigid, neglecting the effect of the coupling between the suspension and the tire on path tracking;
considering the cornering characteristics of said tyre and neglecting the coupling relationship between the longitudinal and transverse directions of said tyre;
disregarding the left and right shifts of load and disregarding the shifts of load of the front and rear axles when said vehicle is traveling at low speed;
ignoring the effect of the vehicle longitudinal and transverse aerodynamics.
4. The MPC-based intelligent vehicle path-tracking control method of claim 3, wherein: the constraint conditions comprise kinematic constraints and dynamic constraints;
including, centroid slip angle constraints, vehicle adhesion condition constraints, and tire slip angle constraints.
5. The MPC-based intelligent vehicle path tracking control method of claim 1 or 4, wherein: constructing the objective function includes the steps of,
the objective function is established based on the state quantity deviation and the control quantity optimization of the vehicle, as follows,
wherein, eta: output quantity, ηref: output reference, Δ u: control increment, Np: predicting time domain, Nc: control time domain, ρ: weight coefficient, of: constraint factor, χ: state quantity, u: and controlling quantity, Q and R are weight matrixes.
6. The MPC-based intelligent vehicle path-tracking control method of claim 5, wherein: the state-space equations comprise the equations of,
the state space equation is derived from the nonlinear dynamical model as follows,
ηdyn＝hdyn(ξdyn)
wherein the content of the first and second substances,
state quantity, select udyn＝fFor controlling the quantity, selecting
Is the output quantity.
7. The MPC-based intelligent vehicle path-tracking control method of claim 6, wherein: constructing the predictive control model includes, in part,
the nonlinear dynamical model is processed by approximate linearization by the state space equation to obtain the state quantity after the control quantity is applied;
carrying out Taylor expansion calculation and discretization processing on the state quantity, and further obtaining a state constraint equation based on vehicle front wheel steering angle control constraint;
and determining the relation between variables according to the state constraint equation, and constructing the predictive control model by using the output quantity at the future moment.
8. The MPC-based intelligent vehicle path-tracking control method of claim 7, wherein: the predictive control model may specifically include a model of,
the state constraint equation is as follows,
wherein the content of the first and second substances,
m: number of control amounts in model, n: the number of state quantities in the model;
when the prediction time domain is NpControl time domain as NcThe state quantity in the prediction time domain in the prediction control is obtained as follows,
the output is as follows,
the predictive control model is described as follows,
Y(t)＝Ψtξ(t|t)+ΘtΔU(t)
wherein, At: jacobian matrix of f versus ξ, Bt: jacobian matrix of f versus u, Ψt: controlling the state quantity, theta, of the time domaint: a control increment in the control time domain.
9. The MPC-based intelligent vehicle path-tracking control method of claim 8, wherein: also comprises the following steps of (1) preparing,
the prediction control model compares the output measurement value with a model prediction value to obtain a prediction error of the prediction control model;
correcting and iteratively optimizing the model predicted value by using the prediction error until an error-free predicted value is output;
the prediction model carries out optimization solution according to the optimized predicted value by combining the objective function and the constraint condition to obtain the optimal control sequence in the control time domain;
and inputting the optimal control sequence into the intelligent vehicle, controlling the steering driving of the vehicle according to the current control quantity, and completing the tracking of the target track.
10. An intelligent vehicle path tracking control system based on MPC is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the sensing module (100) is used for acquiring real-time data of an external environment and various power parameters when the intelligent vehicle runs;
the decision module (200) is connected to the sensing module (100) and used for receiving and processing data information of the sensing module (100), the decision module (200) comprises a calculation unit (201), a storage unit (202) and an input and output management unit (203), the calculation unit (201) is used for constructing the objective function, equation and model, calculating and processing various operation data to obtain a calculation result so as to generate a decision, the storage unit (202) is used for storing the data information received and processed by the decision module (200) and providing calling service for each module, and the input and output management unit (203) is used for connecting data transmission and instruction feedback of each module and providing communication connection service for each module;
the path planning and tracking module (300) is used for planning and tracking the driving path of the intelligent vehicle and comprises a planning unit (301) and a tracking unit (302), wherein the planning unit (301) is connected to the decision module (200) and is used for planning a driving route according to the existing real road condition information and the calculation result, and the tracking unit (302) is used for receiving the planned path data of the planning unit (301) and tracking a target track;
the main control module (400) is connected to the perception module (100), the decision module (200) and the path planning and tracking module (300) and is used for issuing control instructions to control the operation of the modules, the main control module (400) comprises a control body (401) and a prediction unit (402), the control body (401) is used for controlling the target track of the intelligent vehicle to run, and the prediction unit (402) is used for predicting whether the intelligent vehicle runs according to the target track.
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