CN114265310A - Vehicle robust performance-guaranteeing coupling control method under unreliable data transmission environment - Google Patents
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Abstract
The invention discloses a vehicle robust performance-guaranteeing coupling control method under an unreliable data transmission environment. The design method of the robust performance-guaranteeing controller comprises the following steps: the method comprises the steps of constructing an uncertain tracking dynamics model of a vehicle under a time-lag environment through a pre-aiming kinematics model and an uncertain vehicle dynamics model, designing the uncertain multi-cell tracking dynamics model of the vehicle based on a multi-cell theory, and finally designing a robust performance-preserving controller by adopting a linear matrix inequality. The method has the advantages that the problem of model mismatch caused by time-varying parameters, modeling errors and the like is considered, H-infinity performance protection indexes are introduced, the vehicle robust performance protection controller is established, vehicle longitudinal and transverse coupling motion control under an unreliable data transmission environment is realized, good interference rejection capability and working condition adaptability are realized, the motion tracking errors can be quickly eliminated, and the transverse stability of the vehicle is effectively guaranteed.
Description
Technical Field
The invention relates to the technical field of use of road vehicle driving control systems which are not related to control of a specific subsystem, in particular to a vehicle robust performance-guaranteeing coupling control method under an unreliable data transmission environment.
Background
In the actual work of the intelligent vehicle, due to the existence of uncertain factors such as the uncertainty of a sensor, the unreliability of a communication technology, the inherent characteristics of an on-vehicle transmission technology and the limitation of an embedded algorithm, for example, the change of the GPS signal intensity, the packet drop of the on-vehicle network information, the limitation of the CAN communication bandwidth, the poor timeliness of the on-vehicle algorithm and the like, the effectiveness of data and control commands is greatly weakened, so that the control signal oscillation is caused, and the applicability of the algorithm is reduced. Therefore, how to ensure the motion control precision of the intelligent vehicle under the unreliable data transmission condition is very important for ensuring the driving safety and improving the riding quality.
However, most of the current tracking control studies considering signal skew can be divided into two categories: one is robust control, the robustness of time-lag parameters and parameter perturbation is enhanced through a robust control theory, and multiple control targets cannot be reasonably configured, so that the method is difficult to balance the targets and is easy to consider the targets; the other type is predictive control, a state quantity of future time is predicted through a prediction model, the method can reasonably configure multiple control targets in a weighting mode, but due to the model-based method, unmodeled errors including time-varying time lag can greatly reduce the control precision of the method.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a robust performance coupling control method for a vehicle under an unreliable data transmission environment, so as to realize accurate control on an intelligent vehicle under the unreliable data transmission condition. The specific technical scheme is as follows:
in a first aspect, a robust performance-preserving controller design method for a vehicle in an unreliable data transmission environment is provided, which includes:
constructing a preview kinematics model and an uncertain vehicle dynamics model;
constructing an uncertain tracking dynamics model of the vehicle under a time-lag environment based on the preview kinematics model and the uncertain vehicle dynamics model;
establishing an uncertain multicellular tracking dynamic model of the vehicle under a time-lag environment based on the multicellular theory and the uncertain tracking dynamic model;
and designing a robust performance-preserving controller by adopting a linear matrix inequality according to the uncertain multicellular tracking dynamics model.
With reference to the first aspect, in a first implementable manner of the first aspect, the pre-aiming kinematics model in the Frenet-Serret coordinate system is constructed based on a small heading angle error assumption.
With reference to the first aspect, in a second implementable manner of the first aspect, an uncertain vehicle dynamics model of uncertain parameters bounded by taking tire cornering stiffness as a norm is established based on the three-degree-of-freedom vehicle dynamics model.
With reference to the first aspect, the first or second implementable manner of the first aspect, in a third implementable manner of the first aspect, the robust performance-preserving tracking controller is designed by a Lyapunov-Krasovskii functional including a second-order integral and a linear inequality method according to the uncertain multicellular tracking dynamics model.
In a second aspect, a vehicle robust performance-guaranteeing longitudinal-lateral coupling control method in an unreliable data transmission environment is provided, which includes:
the robust performance-guaranteeing controller of the vehicle under the time lag environment is constructed by adopting any one of the first aspect and the first to third realizable modes of the first aspect;
and vehicle control is carried out through a designed robust performance-guaranteeing controller.
With reference to the second aspect, in a first implementable manner of the second aspect, the vehicle control by the designed robust performance controller includes:
calculating, by the robust performance controller, a desired active yaw moment and a desired longitudinal driving force;
and distributing the driving force of each wheel by adopting an optimization method according to the expected active yaw moment and the expected longitudinal driving force.
With reference to the second aspect, in a second achievable form of the second aspect, the distributing the driving forces of the respective wheels in accordance with the desired active yaw moment and the desired longitudinal driving force includes:
setting an optimal distribution objective function and optimization constraints;
establishing a driving force optimal distribution model according to the optimal distribution target and the optimal constraint;
and distributing the driving force of each wheel according to the expected active yaw moment and the expected longitudinal driving force based on the driving force optimal distribution model.
Has the advantages that: the vehicle robust performance-preserving coupling control method under the unreliable data transmission environment considers the upstream and downstream time-lag influence existing in vehicle-mounted network data transmission, constructs an uncertain dynamics model including external disturbance, parameter perturbation, data transmission time lag and the like by combining a preview kinematics model and a vehicle dynamics model, and establishes a vehicle robust performance-preserving controller by adopting a linear matrix inequality on the basis of the uncertain dynamics model, wherein the vehicle robust performance-preserving controller has the characteristics of good interference resistance and working condition adaptability, capability of rapidly eliminating motion tracking errors, capability of effectively guaranteeing the lateral stability of a vehicle and the like. The vehicle robust performance-guaranteeing controller controls the vehicle, can effectively eliminate the tracking error and guarantee the running stability of the vehicle, and has stronger working condition adaptability.
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In order to more clearly illustrate the embodiments of the present invention, the drawings, which are required to be used in the embodiments, will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to actual scale.
FIG. 1 is a flow chart of a vehicle network data transmission process;
FIG. 2 is a flow chart of a method for designing a controller according to an embodiment of the present invention;
FIG. 3 is a schematic view of a preview kinematic model;
FIG. 4 is a schematic diagram of a planar three-degree-of-freedom dynamic model of a vehicle;
FIG. 5 is a flowchart of a vehicle control method according to an embodiment of the present invention;
FIG. 6 shows the simulation comparison effect of the vehicle trajectory tracking under the double-traverse working condition of the control method and the comparison control algorithm of the present invention;
FIG. 7 is a graph of the simulated comparison effect of the dynamic response of the vehicle under the double-traverse working condition of the control method and the comparison control algorithm of the present invention;
FIG. 8 is a graph of the control method and the comparison control algorithm of the present invention showing the simulated comparison effect output by the controller under the condition of double-shift line.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It should be understood that the technical solution of the embodiment of the present invention is mainly applied to vehicle control of a distributed independently driven intelligent electric vehicle in an unreliable data transmission environment. The vehicle network transmission process of this type of intelligent electric vehicle is shown in fig. 1.
It should be understood that in the technical solution of the embodiment of the present invention, the time lag in the on-board data transmission chain is divided into an upstream time lag and a downstream time lag by taking the ECU as a boundary, wherein the upstream time lag is a data delay from the sensor to the ECU, and the downstream time lag is a signal delay from the ECU to the vehicle actuator. The signal received by each node can be represented as:
xc(t)＝xs(t-ds2c)
xa(t)＝xc(t-dc2a)＝xs(t-ds2c-dc2a)；
xs、xc、xarespectively representing signals received by the sensor, the ECU and the actuator at the time t; ds2c、 ds2aUpstream skew and downstream skew, respectively. Because signals are finally transmitted to the actuator, the time lag in the transmission process is integrated into the generalized time lag d of the system in the embodiment of the inventiongI.e. byAnd controlling the time lag of the signals, thereby facilitating the design of the controller and the processing of the time lag signals.
dg＝ds2c+dc2a。
Fig. 2 is a flow chart of a design method of a vehicle robust performance guarantee controller in an unreliable data transmission environment, the design method comprising:
step 3, establishing an uncertain multicellular body tracking dynamic model of the vehicle in a time-lag environment based on the multicellular body theory and the uncertain tracking dynamic model;
and 4, designing a robust performance-preserving controller by adopting a linear matrix inequality according to the uncertain multicellular tracking dynamics model. Specifically, the design method includes:
firstly, constructing a preview kinematics model and an uncertain vehicle dynamics model;
as shown in fig. 3, during actual driving activities, the driver finds a reasonable reference position according to the current driving condition, and evaluates the posture of the current vehicle according to the driving condition, and then adjusts the vehicle to approach the reference path. According to the geometric relation between the reference point and the actual position of the vehicle, the technical scheme of the embodiment of the invention can construct and obtain a preview kinematics model under a Frenet-Serret coordinate system based on the assumption of small course angle error, wherein the preview kinematics model specifically comprises the following steps:
wherein e isy、
Respectively a transverse position error and a course angle error; v. ofx、vyRespectively the longitudinal and transverse speeds of the vehicle; r is the vehicle yaw rate; dpThe pre-aiming distance is the front of the driver; rhopTo predict road curvature at the point of aim, d1、d2Both unmodeled and linearized errors.
In order to reduce the design difficulty of the controller when constructing the uncertain vehicle dynamics model, the embodiment of the invention adopts the three-degree-of-freedom simplified model to construct the uncertain vehicle dynamics model, and the three-degree-of-freedom simplified model can better reflect the motion characteristics of the vehicle, as shown in fig. 4, wherein the dynamic equilibrium equations in all directions are as follows:
m、Izrespectively the mass of the whole vehicle and the moment of inertia of the vehicle body around the z axis; lf、lrThe distances from the front and rear axles to the center of mass of the vehicle, respectively; delta MzAn active yaw moment; d3、d4、d5The method comprises the following steps of (1) obtaining unmodeled errors including road gradient resistance, road surface rolling friction resistance, longitudinal wind and the like and external disturbance; fxfl、Fxfr、Fxrl、 FxrrLongitudinal forces of the front left, front right, rear left and rear right tires, respectively, Fyfl、Fyfr、Fyrl、FyrrThe lateral forces of the front left, front right, rear left and rear right tires of the vehicle, axpIs the reference acceleration at the preview point.
Based on the small tire sidewall deviation angle and the linear tire assumption, the front and rear axial lateral tire forces can be linearized as:
Fyfl＝Fyfr＝Cfαf＝Cf[δf-(vy+lfr)/vx]
Fyrl＝Fyrr＝Crαr＝Cr(-vy+lrr)/vx；
wherein, Cf、CrRespectively the steering stiffness of the front and rear axle wheel tires; a isf、arRespectively, the side deflection angle, delta, of the front and rear axle wheelsfIs the corner of the front wheel.
When the vehicle is in a severe working condition, the tire is easy to enter a nonlinear area, so that the difference between the nominal steering stiffness calculated based on the linear tire model and an actual value is large. Therefore, in order to improve the control accuracy of the designed controller, in the embodiment of the present invention, the tire cornering stiffness may be regarded as a norm-bounded uncertain parameter, specifically expressed as:
Cf＝Cf0+λf△Cf
Cr＝Cr0+λr△Cr；
wherein, Cf0、Cr0Respectively are nominal values of the tire sidewall deflection stiffness of the front axle wheel and the rear axle wheel; lambda [ alpha ]f、λrPerturbation parameters of the tire sidewall deflection stiffness of the front axle wheel and the rear axle wheel respectively and satisfy lambdaf|＝|λr|＝|λ|≤1；△Cf、△CrRespectively calculating the tire side deflection stiffness shooting quantity of the front axle wheel and the tire side deflection stiffness shooting quantity of the rear axle wheel, and constructing an obtained uncertain vehicle dynamics model as follows:
wherein the content of the first and second substances,
is a controlled system state variable; u (t)3×1＝[ax/m,δfd,△Mz]TInputting variables for a controlled system;
outputting variables for the system;
w(k)5×1＝[axp+d3,d1,-vxρr+d2,d4,d5]and the system disturbance comprises unmodeled errors and external disturbance. Wherein is thatThe system matrixes are respectively as follows:
A＝A0+△A＝A0+DFE1
B1＝B10+△B1＝A0+DFE2
B2＝eye(5,5),
a044＝-2(Cf0+Cr0)/mvx,
a045＝-vx-2(Cf0lf-Cr0lr)/mvx,
a054＝2(Cr0lr-Cf0lf)/Izvx
e44＝2(△Cf+△Cr)/mvx,
e45＝-2(△Cflf-△Crlr)/mvx,
e54＝2(△Crlr-△Cflf)/Izvx,
then, the baseCombining the preview kinematics model and the uncertain vehicle dynamics model with the generalized time lag dgThe method comprises the following steps of (1) constructing an uncertain tracking dynamics model of a vehicle under a time-lag environment, specifically:
and then, establishing an uncertain multi-cell tracking dynamic model of the vehicle under a time-lag environment according to the multi-cell theory and the uncertain tracking dynamic model so as to deal with the situation that the longitudinal vehicle speed changes.
Specifically, in the actual driving process, a driver can adjust the driving speed of the vehicle according to the front road curvature and the road surface attachment condition, and particularly, a considerable transverse stability margin is reserved for the vehicle through deceleration under the limit working condition so as to ensure that the vehicle can stably reach a reference point. Therefore, it is desirable that the longitudinal vehicle speed should be considered as a time-varying parameter. In order to ensure the timeliness of the algorithm, the robust performance-guaranteeing state feedback gain needs to be calculated off-line in advance, so that the longitudinal vehicle speed in the model cannot be adjusted in real time according to the actual situation.
Therefore, in the technical scheme of the embodiment of the invention, the uncertain multicellular method is adopted to deal with the change situation of the longitudinal vehicle speed, and the method specifically comprises the following steps:
it is assumed that the longitudinal vehicle speed during driving will vary within a certain fixed range, i.e.
vxmin≤vx≤vxmax
(1/vx)min≤(1/vx)≤(1/vx)max；
Based on the multilocular theory, any vehicle speed in the range can be obtained by linear combination of the speed multilocular boundaries:
based on the linear combination of the velocity multicellular body boundaries and the uncertainty tracking dynamics model under the simultaneous time-lag environment, the uncertainty multicellular body tracking dynamics model of the vehicle under the time-lag environment can be established, and in the technical scheme of the embodiment of the invention, the uncertainty multicellular body tracking dynamics model is specifically as follows:
wherein, C0Is a convex multilocular body considering time-varying vehicle speed.
And finally, designing the robust performance-preserving controller by adopting a linear matrix inequality according to the uncertain multicellular tracking dynamics model.
Specifically, an objective function of the controller may be defined first, and in the technical solution of the embodiment of the present invention, the defined objective function is:
wherein, yref(t)＝[0 0 0]TFor system reference trajectories, Wy，WuRespectively an output quantity weight and a control quantity weight.
In order to enable a system to have better tracking performance, the technical scheme of the embodiment of the invention adopts a state feedback mode to design a vehicle transverse tracking controller, so that a control system of a vehicle is gradually stabilized under the condition that external disturbance, nonlinear time-varying tire characteristics and an unreliable data transmission chain exist to meet H ∞ and a performance guarantee index, and on the basis of a Lyapunov-Krasovski functional containing second-order integral, a robust performance guarantee controller is designed by adopting a linear matrix inequality according to an established uncertain multicellular tracking dynamics model, and the method specifically comprises the following steps of:
attenuation coefficient gamma for a given disturbance>0 and the upper bound of the generalized time lag of the system dgmaxPositive definite matrix Q if matrix Y is present1，Q2And a positive real number εiSuch that the following linear inequality holds:
then there is a state feedback gain K ═ YQ-1And a state feedback control rate u (t) YQ-1x (t) let the system meet 0 ≦ d in the presence of an energy-bounded disturbance wg≤dgmaxAnd the corresponding control index converges to V (x (t)).
Wherein θ is sys [ (A)0+B1K)Q1]，sys[·]Denotes +.TTranspose the diagonal elements in the matrix, and I is the unit matrix of the appropriate dimension.
By solving the linear inequality feasibility optimization problem, the state feedback gains K corresponding to the vertexes of the convex multilocular body can be obtained respectivelyiBy means of linear scheduling, canAnd obtaining the final control gain of the system, thereby calculating the ideal control rate u (t) in the maximum generalized time lag upper bound, specifically:
the vehicle robust performance-guaranteeing longitudinal-transverse coupling control method under the unreliable data transmission environment shown in fig. 5 comprises the following steps:
constructing a robust performance-guaranteeing controller of the vehicle under a time-lag environment by adopting the design method of the robust performance-guaranteeing controller of the vehicle;
and vehicle control is carried out through a designed robust performance-guaranteeing controller.
Specifically, first, the design method in the technical solution of the embodiment of the present invention may be adopted to establish a robust performance-preserving controller for a vehicle in a time-lag environment. Then, vehicle state and environment information are collected through sensors, an expected active yaw moment and an expected longitudinal driving force are calculated by the ECU through a constructed robust performance-maintaining controller according to the collected wheel state data and the collected environment information, and an actuator control signal is determined according to the expected active yaw moment and the expected longitudinal driving force and sent to an actuator so as to control the vehicle.
In this embodiment, optionally, the distributing the driving forces of the respective wheels according to the desired active yaw moment and the desired longitudinal driving force comprises:
setting an optimal distribution objective function and optimization constraints;
establishing a driving force optimal distribution model according to the optimal distribution target and the optimal constraint;
and calculating the driving force of each wheel according to the expected active yaw moment and the expected longitudinal driving force based on the driving force optimal distribution model.
Specifically, after the desired active yaw moment and the desired longitudinal driving force are calculated by the designed controller, the driving forces of the respective wheels of the vehicle can be reasonably distributed by using an optimization method. To reduce energy consumption, the optimal allocation objective function may be set as:
Ja＝FTHF；
wherein H is a weight matrix; f ═ FxflFxfrFxrlFxrr]TTo optimize the variables.
In addition, the driving force of each wheel is also required to satisfy the requirements for longitudinal and lateral stability control, and the optimization constraints can be set to:
△Mz＝b1Fxfl+b2Fxfr-ls(Fxrl-Fxrr)
Fxtotal＝cosδf(Fxfl+Fxfr)+Fxrl+Fxrr；
b1＝-lscosδf+lfsinδf
b2＝lssinδf+lfcosδf；
wherein lsIs the length of the half shaft of the vehicle.
The optimal driving force distribution model can be obtained through the simultaneous equations, and the driving force of each wheel can be calculated by adopting the optimal driving force distribution model according to the expected active yaw moment and the expected longitudinal driving force.
The effectiveness of the technical scheme of the embodiment of the invention is verified by adopting a Simulink-Carsim combined simulation platform, an estimation-control algorithm is built in the Simulink, an E-class vehicle is selected as an algorithm verification model in Carsim, main vehicle parameters are shown in the following table, the lateral deflection stiffness uptake of the uncertain tire is 20% of a nominal value, and the maximum generalized time lag of the system is 0.04 s.
An H-infinity state feedback controller and a performance-preserving state feedback controller are introduced which are derived from a time-lag dependent stability condition. The guaranteed performance state feedback controller does not consider the H ∞ performance index, and the H ∞ state feedback controller does not consider the guaranteed performance control index. In this set of conditions, the vehicle will perform a double lane maneuver test on low adhesion roads. In the simulation process, the vehicle runs at 100km/h, and the working condition can simulate the continuous collision avoidance of the vehicle in rainy and snowy weather. Wherein, the H ∞ state feedback controller is C4, the performance-preserving state feedback controller is C5, and the technical solution of the embodiment of the present invention is C3.
Fig. 6 shows the tracking effect of the comparative condition, in addition, C3 shows more excellent control performance, and can quickly eliminate the tracking error, the tracking quality of C4 and C5 is poor, and a large overshoot error occurs in each lane change process, and the control accuracy of C3 is effectively guaranteed under the severe condition because the adverse effect caused by the mismatch of the kinematic model is weakened by the H ∞ performance index.
The vehicle lateral dynamic response is shown in fig. 7. As can be seen from the figure, the mass center side offset angle and the yaw rate response of the vehicle under the control of C3 are smaller overall, and the curve is smoother, which shows that the dynamic stability can be still better ensured by C3 under the limit condition. In contrast, the fluctuations of C4 and C5 are more severe, and a larger peak occurs during each lane change, indicating that the risk of vehicle instability is greater, and at this time, the tire force is close to the boundary of the attachment ellipse, so that in order to avoid vehicle instability caused by tire lateral force saturation, the requirement for tire longitudinal force is reduced, and the vehicle has a larger lateral stability margin, which is also the reason for larger speed tracking error.
FIG. 8 is a vehicle control input. As can be seen from the graph, the fluctuation of the control amount of C3 floats less, and the output is significantly smaller than the other two methods, which indicates that C3 can significantly improve the turning portability and the steering stability of the vehicle. In conclusion, the simulation result of the comparison working condition shows that the control strategy provided by the invention can effectively eliminate the tracking error and ensure the driving stability of the vehicle, and has stronger working condition adaptability.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may be modified, or some or all of the technical features may be equivalently replaced; the modifications or substitutions do not depart from the spirit of the technical solutions of the embodiments of the present invention, and the technical solutions of the embodiments of the present invention are intended to be covered by the claims and the specification.
Claims (7)
1. A design method of a vehicle robust performance-guaranteeing controller in an unreliable data transmission environment is characterized by comprising the following steps:
constructing a preview kinematics model and an uncertain vehicle dynamics model;
constructing an uncertain tracking dynamics model of the vehicle under a time-lag environment based on the preview kinematics model and the uncertain vehicle dynamics model;
establishing an uncertain multicellular body tracking dynamic model of the vehicle under a time-lag environment based on the multicellular body theory and the uncertain tracking dynamic model;
and designing a robust performance-preserving controller by adopting a linear matrix inequality according to the uncertain multicellular tracking dynamics model.
2. The design method of the vehicle robust performance-guaranteeing controller according to claim 1, wherein a pre-aiming kinematic model in a Frenet-Serret coordinate system is constructed based on a small heading angle error assumption.
3. The design method of the vehicle robust performance-preserving controller according to claim 1, wherein an uncertain vehicle dynamic model of uncertain parameters bounded by a norm of tire cornering stiffness is established based on the three-degree-of-freedom vehicle dynamic model.
4. The design method of the vehicle robust performance-guaranteeing controller according to any one of claims 1 to 3, wherein the robust performance-guaranteeing tracking controller is designed by a Lyapunov-Krasovski functional containing second-order integral and a linear inequality method according to the uncertain multicellular tracking dynamics model.
5. A vehicle robust performance-guaranteeing longitudinal and transverse coupling control method under an unreliable data transmission environment is characterized by comprising the following steps:
constructing a robust performance-guaranteeing controller of the vehicle under a time-lag environment by adopting the design method of the vehicle robust performance-guaranteeing controller as claimed in any one of claims 1 to 4;
and vehicle control is carried out through a designed robust performance-guaranteeing controller.
6. The design method of the vehicle robust performance guarantee controller according to claim 5, wherein the vehicle control through the designed robust performance guarantee controller comprises the following steps:
calculating, by the robust performance controller, a desired active yaw moment and a desired longitudinal driving force;
and distributing the driving force of each wheel by adopting an optimization method according to the expected active yaw moment and the expected longitudinal driving force.
7. The vehicle robust performance controller design method according to claim 6, wherein distributing the driving force of each wheel includes:
setting an optimal distribution objective function and optimization constraints;
establishing a driving force optimal distribution model according to the optimal distribution target and the optimal constraint;
and distributing the driving force of each wheel according to the expected active yaw moment and the expected longitudinal driving force based on the driving force optimal distribution model.
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| CN113176733A (en) * | 2021-04-27 | 2021-07-27 | 广东工业大学 | Autonomous vehicle path tracking and stability control method based on switching control |
| CN113386781A (en) * | 2021-05-24 | 2021-09-14 | 江苏大学 | Intelligent vehicle trajectory tracking control method based on data-driven vehicle dynamics model |
| CN113619563A (en) * | 2021-09-06 | 2021-11-09 | 厦门大学 | Intelligent electric vehicle transverse control system and method based on man-machine sharing |
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