CN110539752A - Intelligent automobile multi-prediction-range model prediction trajectory tracking control method and system - Google Patents
Intelligent automobile multi-prediction-range model prediction trajectory tracking control method and system Download PDFInfo
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- CN110539752A CN110539752A CN201910559172.5A CN201910559172A CN110539752A CN 110539752 A CN110539752 A CN 110539752A CN 201910559172 A CN201910559172 A CN 201910559172A CN 110539752 A CN110539752 A CN 110539752A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/0097—Predicting future conditions
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0062—Adapting control system settings
- B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2556/00—Input parameters relating to data
- B60W2556/45—External transmission of data to or from the vehicle
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
- Y02T10/84—Data processing systems or methods, management, administration
Abstract
The invention discloses a method and a system for intelligent automobile multi-prediction range model prediction trajectory tracking control, wherein the method comprises information perception, path planning, multi-prediction range model prediction controller modeling and driving execution; the method comprises the following steps that environment perception collects road information in front of an intelligent automobile and the state of the intelligent automobile in real time, and relevant events are collected in real time and transmitted to a multi-prediction-range model prediction controller for calling; planning a path according to the information-sensed data to plan an expected path; the multi-prediction-range model prediction controller can realize prediction control under an emergency event; the driving execution drives an actuator that steers the vehicle in accordance with the front wheel steering angle value output by the multi-prediction range model predictive controller. According to the invention, the prediction time domain corresponding to the possible occurrence of the event, the corresponding constraint set and the cost function are called by the multi-prediction-range model prediction controller, so that the unmanned vehicle can change the tracking path in advance to avoid the occurrence of emergency, thereby improving the driving stability of the vehicle.
Description
Technical Field
the invention belongs to the field of intelligent automobile control, and particularly relates to a control method of an intelligent automobile under the condition of predicting an emergency.
background
the driving intelligence is deeply developed in the global range as the core technology of the automobile technology change. Most of existing model prediction control focuses on improvement of accuracy of path tracking, but when the unmanned vehicle runs on a real road condition, corresponding reaction is often made when an emergency occurs, so that the unmanned vehicle cannot accurately track an expected path, because longitudinal force of wheels approaches saturation under a high-speed running working condition, steering is performed under the condition that the emergency occurs, but tires cannot provide more lateral force, and therefore the vehicle is prone to instability under emergency.
Environmental awareness in combination with V2X technology enables wireless communication between the vehicle and the surrounding environment, thereby providing technical support for determining emergency events. The ultimate goal of unmanned driving is oriented, and the intelligent automobile control system is required to have accurate, efficient and reliable control capability under complex working conditions, so that the steering stability and the driving safety of the automobile are ensured. The traditional single prediction range algorithm cannot solve the control requirements and control functions of the vehicle in different emergencies.
Disclosure of Invention
A control method of an intelligent automobile under an emergency event predicts the possible event by adding a plurality of prediction ranges on the basis of the traditional model prediction control single prediction range, and keeps reacting to the emergency plan such as sudden ice and snow road surfaces or the crossing of pedestrians on roads and the like while tracking the expected path instead of the emergency event. Thereby improving the stability of model predictive control to foreseeable but difficult to determine occurrences of probabilistic events.
the traditional model prediction control is based on the road friction coefficient estimation or the prior friction coefficient of an environment sensing module, and the road friction coefficient in the real road condition is difficult to express by an accurate mathematical formula; the behavior of roadside pedestrians is also difficult to estimate and predict with probability. Therefore, the environment sensing module is adopted to sense general conditions of traffic road events by combining with the V2X technology, for example, information is provided to the controller when the ice and snow weather is sensed, and the controller calls a relevant control strategy including a corresponding prediction time domain and a constraint set, wherein the control strategy is related to the low adhesion condition which possibly occurs on the road surface in advance; or in case of sensing the pedestrian at the roadside, calling the prediction time domain and the constraint set corresponding to the controller in advance to make a previous adjustment for the possibility that the pedestrian crosses the road, and the like. The transverse and longitudinal forces of the tire are controlled within a limit range through pre-adjustment, so that the robustness of a control system and the stability of a vehicle are improved. The complex working conditions can occur singly or simultaneously and are treated together.
The invention has the beneficial effects that:
1. according to the multi-time-domain model prediction control idea provided by the invention, an operation mode adopted after an original emergency occurs is replaced by a mode of judging a possibly occurring event through an environment perception and V2X technical module in advance, and a prediction time domain, a corresponding constraint set and a cost function corresponding to the possibly occurring event are called for a multi-prediction-range model prediction controller, so that an unmanned vehicle changes a tracking path in advance to avoid the occurrence of the emergency, and the driving stability of the vehicle is improved.
2. The dynamic piecewise affine model comprehensively considering the transverse and longitudinal forces effectively improves the precision of the model and solves the problem that the unmanned vehicle has larger control deviation under complex working conditions.
Drawings
FIG. 1 is a logic block diagram of a control module;
FIG. 2 is a multiple prediction horizon block diagram;
FIG. 3 is a block diagram of a multiple prediction horizon cost function;
FIG. 4 is a context aware flow diagram;
Detailed Description
The invention will be further explained with reference to the drawings.
as shown in fig. 1, the composition of the system of the present invention includes the following:
sensing module
The method comprises the steps of collecting front road information, yaw angular velocity gamma, vehicle speed Vx and mass center slip angle beta of the intelligent vehicle in real time. The traffic road information such as ice and snow weather, roadside pedestrians or front vehicle sudden stop and other road condition related events are collected in real time by combining a V2X short-distance sensing technology and transmitted to a control module for the controller to judge and call in advance; the mating relationship with other modules is shown in fig. 4.
path planning module
planning an expected path according to the data transmitted by the sensing module, wherein the planned path does not comprise the calculation of complex working conditions;
Dynamics modeling module
In order to adapt to the precision requirement under the complex working condition, single lateral dynamics cannot be met, so that a piecewise affine dynamics model of transverse and longitudinal forces is established. Meanwhile, considering the transverse force and the longitudinal force, the nonlinear transverse and longitudinal force model is subjected to piecewise affine processing, and the actual relation curve of the tire slip angle alpha i and the tire lateral force Fyi is divided into three sections to be linearly represented
Wherein Ci1 and Ci2 are cornering stiffnesses of the piecewise affine expression for the i-th tire lateral force; fi1 and fi2 are constant terms of the piecewise affine expression of the lateral force of the ith wheel; α pi1, α pi2 being a segmentation point;
Dividing the actual relation curve of the tire longitudinal slip ratio ki and the tire longitudinal force Fxi into three sections to be linearly represented
wherein Ki1 and Ki2 are the longitudinal stiffness of the piecewise affine expression of the ith tire longitudinal force; gi1 and gi2 are constant terms of the piecewise affine expression of the i-th wheel lateral force; kpi1, kpi2 are segmentation points;
The resulting kinetic model was as follows:
The derivative of the longitudinal path of the automobile is the reciprocal of the lateral error of the distance from the mass center of the automobile to the path, kappa is the curvature of the road, delta psi is the error of the heading angle, Vx, Vy, beta and r are the longitudinal speed, the transverse speed, the mass center lateral deviation angle and the yaw angular speed of the automobile respectively, and Fxf, Fxr, Fyf, Fyr, m, a, b and Iz. Fxf and Fxr are longitudinal forces of the front wheel and the rear wheel respectively, Fyf and Fyr are lateral forces of the front wheel and the rear wheel respectively, m is the mass of a vehicle body, and a, b and IZ are the front-rear wheel base and the rotational inertia around the Z axis respectively.
control module
after the dynamic model is established, a multi-time domain model predictive control method is used for control. Converting the dynamic model established in step3 into a state space form and discretizing the model by using a first-order Euler method to obtain the model
x＝Ax+Bu+d
The state quantity x consists of (VxVyr delta ψ e), the transverse and longitudinal dynamic characteristics are comprehensively considered, A, B is a state matrix, and d is an affine item matrix;
as shown in fig. 2, where xn is similar to conventional deterministic model predictive control, while xd1, … xdn represent various contingency plans, corresponding to different constraints and cost functions, as shown in fig. 3. The method has the advantages that only the cost function corresponding to the xn is calculated and output when no emergency occurs, only the corresponding emergency plan is calculated under the emergency, one emergency can be used when one emergency occurs, and the calculation can be carried out when multiple types of emergency possibly occur at the same time, so that the calculation burden is reduced; an, Adn, Bn, Bdn, dn, ddn. An and Adn are state quantity matrixes, Bn and Bdn are control quantity matrixes, and dn and ddn are affine item matrixes.
The performance index is established as follows
Wherein Np and Nc are prediction and control time domains respectively, Q, R is a corresponding weight matrix set respectively, ρ and ε are a weight coefficient set and a relaxation factor set respectively, xk is a state variable, and Δ uk is a control increment.
the system constraints are:
wherein
when k is 0, the initial control amount of each prediction range at time k is respectively set.
Constraint set H, G is used to constrain emergency events that may occur. It remains at U0 to trace the originally intended path while maintaining emergency operation for the emergency complex event. Hn and Gn are constraint sets under no emergency, and Hn1, Gn1, Hn2, Gn2, … … and the like are different constraint sets under different events. Such as
V＝Vα+br
And the alpha r and sat are the slip angles when the rear wheels are saturated and are used as constraint sets of Hn.
The following formula is used as the constraint set for Gn.
Wherein the sum is a set of lateral error boundaries that are a function of path distance along the obstacle avoidance and remaining on the road; approximately the vehicle equivalent width.
and (3) responding to different events, adjusting values in the constraint such as alpha R and sat, and if low-temperature ice and snow weather forecast is detected to have low ice and snow attached road surfaces at corners, adjusting a weight matrix Q to punish a transverse error and a course error so that the bending rate is reduced and the road surfaces can enter the curve more easily, and punishing a front wheel steering angle rate by a weight matrix R to maintain the stability of the vehicle so that the final result obtained by the controller is different, thereby avoiding the instability condition.
h and G respectively adjust the yaw rate, the centroid slip angle and the environmental constraint so as to continuously track the original expected path after the event is ended. When detecting that pedestrians are likely to cross the road at the roadside, adjusting the weight matrix Q to punish longitudinal speed, transverse errors and course errors so that the speed of the vehicle is reduced and the vehicle is slightly inclined to drive away from the pedestrians, and correspondingly adjusting R, H and G similarly to the road surface encountering ice and snow. If a plurality of events are detected simultaneously, output results under a single event are calculated simultaneously and finally weighted and summed to obtain a final result. The desired path is always tracked if no emergency has occurred.
execution module
the control execution module drives the control execution mechanism according to the front wheel steering angle value output by the control module, executes the autonomous steering of the unmanned automobile, and returns the automobile body state information to the environment perception module and the dynamics modeling module so that the unmanned automobile tracks the expected track.
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 (11)
1. the intelligent automobile multi-prediction range model prediction trajectory tracking control method is characterized by comprising the following steps:
information perception, path planning, modeling of a multi-prediction-range model prediction controller and driving execution;
The information perception real-time acquisition system comprises an information perception real-time acquisition module, a central processing module, a prediction range model prediction controller and a central processing module, wherein the information perception real-time acquisition module is used for acquiring front road information, yaw velocity gamma, vehicle speed Vx and mass center side slip angle beta of an intelligent vehicle, collecting relevant events in real time and transmitting the relevant events to the central processing module prediction controller for the controller to judge and;
The path planning plans an expected path according to the information-sensed data, and the planned path does not include the calculation of complex working conditions;
the multi-prediction-range model prediction controller can realize prediction control under an emergency event;
The driving execution drives an actuator that steers the vehicle in accordance with the front wheel steering angle value output by the multi-prediction range model predictive controller.
2. The intelligent automobile multi-prediction range model prediction trajectory tracking control method according to claim 1, wherein the multi-prediction range model prediction controller modeling comprises dynamic modeling.
3. The intelligent automobile multi-prediction-range model prediction trajectory tracking control method according to claim 2, characterized in that the basis of the dynamic modeling is a piecewise affine dynamic model of transverse and longitudinal forces; the piecewise affine dynamic model of the transverse and longitudinal force is as follows:
Dividing the actual relation curve of the tire slip angle and the tire lateral force into three sections to be linearly represented
Wherein Ci1 and Ci2 are cornering stiffnesses of the piecewise affine expression for the i-th tire lateral force; fi1 and fi2 are constant terms of the piecewise affine expression of the lateral force of the ith wheel; α pi1, α pi2 being a segmentation point;
dividing the actual relation curve of the longitudinal slip rate and the longitudinal force of the tire into three sections to be linearly represented
Wherein Ki1 and Ki2 are the longitudinal stiffness of the piecewise affine expression of the ith tire longitudinal force; gi1 and gi2 are constant terms of the piecewise affine expression of the i-th wheel lateral force; kpi1, kpi2 are segmentation points.
4. The intelligent automobile multi-prediction-range model prediction trajectory tracking control method according to claim 3, characterized in that the expression of the dynamic modeling is as follows:
the derivative of the longitudinal path of the automobile is the reciprocal of the lateral error of the distance from the mass center of the automobile to the path, kappa is the curvature of the road, delta psi is the error of the heading angle, Vx, Vy, beta and r are the longitudinal speed and the transverse speed of the automobile, the mass center lateral deviation angle and the yaw angular speed respectively.
5. the intelligent automobile multi-prediction range model prediction trajectory tracking control method according to claim 4, characterized in that the modeling method of the multi-prediction range model prediction controller comprises discretization of a dynamic model:
Converting the dynamic model into a state space form and discretizing the model by using a first-order Euler method
x＝Ax+Bu+d
the state quantity x consists of (Vx Vy r delta ψ e), the transverse and longitudinal dynamic characteristics are comprehensively considered, A, B is a state matrix, and d is an affine term matrix;
6. The intelligent automobile multi-prediction range model prediction trajectory tracking control method according to claim 4, wherein the modeling method of the multi-prediction range model prediction controller further comprises the following steps:
establishing a performance index:
Wherein Np and Nc are prediction and control time domains respectively, Q, R is a corresponding weight matrix set respectively, ρ and ε are a weight coefficient set and a relaxation factor set respectively, xk is a state variable, and Δ uk is a control increment.
7. The intelligent automobile multi-prediction range model prediction trajectory tracking control method according to claim 5, wherein the modeling method of the multi-prediction range model prediction controller further comprises the following steps:
Establishing system constraint:
Wherein
at the point where k is 0, the position of the first electrode,
Constraint set H, G is used to constrain emergency events that may occur; it remains to track the originally expected path at U0 while maintaining emergency operation for the emergency complex event; hn and Gn are constraint sets without emergencies, and Hn1, Gn1, Hn2 and Gn 2.
8. The intelligent automobile multi-prediction-range model prediction trajectory tracking control method according to claim 6, characterized in that the method for constraining the possible emergency events comprises the following steps: h and G respectively adjusting the yaw rate, the centroid slip angle and the environmental constraint to enable the original expected path to be continuously tracked after the event is ended; when an emergency is detected, punishment is made on the longitudinal speed, the transverse error and the course error by adjusting the weight matrix Q, so that the vehicle speed is reduced and the vehicle slightly deviates to the position far away from the emergency to run; if multiple events are detected simultaneously, output results under a single event are calculated simultaneously and finally weighted and summed to obtain a final result, and if no emergency occurs, an expected path is tracked all the time.
9. the intelligent automobile multi-prediction-range model prediction track following control method as claimed in claim 1, wherein after the autonomous steering is executed, the driving execution can return body state information to the environment perception part and the dynamic model so that the intelligent automobile tracks the expected track.
10. An intelligent automobile multi-prediction-range model prediction trajectory tracking control system is characterized by comprising a sensing module, a path planning module, a control module and an execution module;
The sensing module collects road information in front of the intelligent automobile, the yaw velocity gamma, the speed Vx and the mass center slip angle beta in real time. The traffic road information such as ice and snow weather, roadside pedestrians or front vehicle sudden stop and other road condition related events are collected in real time by combining a V2X short-distance sensing technology and transmitted to a control module for the controller to judge and call in advance;
the path planning module plans an expected path according to the data transmitted by the sensing module, and the planned path does not include the calculation of complex working conditions;
the control module: converting the established dynamic model into a state space form and discretizing the model by using a first-order Euler method
x＝Ax+Bu+d
The state quantity x consists of (Vx Vy r delta ψ e), the transverse and longitudinal dynamic characteristics are comprehensively considered, A, B is a state matrix, and d is an affine term matrix;
Establishing a performance index:
wherein Np and Nc are respectively prediction and control time domains, Q, R are respectively corresponding weight matrix sets, ρ and ε are respectively a weight coefficient set and a relaxation factor set, xk is a state variable, and Δ uk is a control increment;
establishing system constraint:
Wherein
At the point where k is 0, the position of the first electrode,
the constraint set H, G is used to constrain the possible emergency events, and still trace the original expected path at U0, while maintaining emergency operation for the emergency complex events, Hn and Gn are constraint sets without emergency events, Hn1, Gn 1; hn2, Gn2, etc. are different constraint sets corresponding to different events;
the execution module drives the control execution mechanism according to the front wheel steering angle value output by the control module, executes the autonomous steering of the unmanned automobile, and returns the automobile body state information to the environment perception module and the dynamics modeling module so that the unmanned automobile tracks the expected track.
11. The intelligent automobile multi-prediction-range model prediction trajectory tracking control system as claimed in claim 9, wherein the modeling of the dynamic model is a piecewise affine dynamic model based on transverse and longitudinal forces, and comprises:
Dividing the actual relation curve of the tire slip angle and the tire lateral force into three sections to be linearly represented
Wherein Ci1 and Ci2 are cornering stiffnesses of the piecewise affine expression for the i-th tire lateral force; fi1 and fi2 are constant terms of the piecewise affine expression of the lateral force of the ith wheel; α pi1, α pi2 being a segmentation point;
Dividing the actual relation curve of the longitudinal slip rate and the longitudinal force of the tire into three sections to be linearly represented
Wherein Ki1 and Ki2 are the longitudinal stiffness of the piecewise affine expression of the ith tire longitudinal force; gi1 and gi2 are constant terms of the piecewise affine expression of the i-th wheel lateral force; kpi1, kpi2 are segmentation points;
The kinetic model is as follows:
The derivative of the longitudinal path of the automobile is the reciprocal of the lateral error of the distance from the mass center of the automobile to the path, k is the curvature of the road, delta psi is the error of the heading angle, Vx, Vy, beta and r are the longitudinal speed and the transverse speed of the automobile, the mass center lateral deviation angle and the yaw angular speed respectively.
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