CN115616903A - Longitudinal man-machine layered cooperative control method considering multiple front vehicles under condition of uncertain parameters - Google Patents

Abstract

The invention discloses a longitudinal man-machine layered cooperative control method considering multiple front vehicles under the condition of uncertain parameters, which comprises the following steps: 1) Defining the configuration, the following task and the scene of the intelligent automobile; 2) Constructing a vehicle dynamics model under uncertain parameters; 3) Constructing a speed following control system considering multiple front vehicles under the condition of uncertain parameters; 4) Carrying out stability analysis on the vehicle speed following control system; 5) The invention designs an H-infinity controller, aims at the problems that the uncertainty of information of multiple front vehicles and vehicle dynamics parameters explored by few scholars at present influences the cooperative control of longitudinal car-following machines and the like, and utilizes a Lyapunov-Krasovski functional to carry out stability analysis on a vehicle speed following control system, so that the vehicles can be pre-controlled according to the information of the more-distant front vehicles and can be further finely controlled according to the information of the more-distant front vehicles, the longitudinal car-following performance of the vehicles is optimized, and the driving comfort is improved while the operation load of drivers is reduced.

Description

A Longitudinal Human-Machine Layered Cooperative Control Method Considering Multiple Front Vehicles Under Parameter Uncertainty

technical field

The invention belongs to the field of human-machine co-driving of smart cars, and relates to a longitudinal human-machine layered cooperative control method considering multiple front vehicles under uncertain parameters, and considers the uncertainty of vehicle dynamic parameters and the information of multiple front cars to improve smart cars The car-following performance is improved, and the safety and comfort of the car-following process are improved through man-machine collaboration.

Background technique

Due to the complexity of traffic problems, the reliability of sensing equipment, and price and cost factors, it is still difficult to achieve full-working autonomous driving in the short term. In the future, there will be a long period of time when people and systems will jointly participate in vehicle control. man-machine co-driving stage.

With the development of sensor technology and communication technology, vehicles can obtain unobstructed obstacle targets within a certain range around the vehicle through radar, and can also obtain targets that cannot be detected by vehicle sensors through vehicle-vehicle communication technology. Multiple information sources are used together. Vehicle motion control. For vehicle systems with large inertia, making full use of on-board sensors and wireless communication data can improve the control performance of the vehicle and adapt to the real road traffic environment.

Patent document CN111562739A discloses a kind of man-machine hybrid intelligent cooperative car following control method that keeps the driver in the loop. This method obtains the relative distance and relative speed between the two vehicles through the sensor, and then divides the longitudinal car following task and formulates the corresponding controller, and designs a human-machine collaborative car following control method. However, for real scenarios, relying on single-front vehicle information for control will be affected by factors such as the mechanical transmission efficiency and inertia delay of the vehicle transmission system, and the response time of the front vehicle, resulting in a lag in the vehicle perception-decision-control execution process. This method fails to give full play to the advantages of wireless communication technology in terms of long-distance sensing and not being blocked by obstacles, and fails to take into account the impact of vehicle dynamics parameter uncertainty on vehicle performance. In addition, this human-machine division of labor control method may cause conflicts between the control functions of the driver and the driving automation system, which will affect the driving experience and driving safety. Layer coordination control method.

Contents of the invention

The object of the present invention is to provide a kind of vertical man-machine layered cooperative control method considering many front vehicles under uncertain parameters, to solve the problems raised in the above-mentioned background technology.

In order to achieve the above object, the present invention provides the following technical solutions: a longitudinal man-machine layered cooperative control method considering many front vehicles under a kind of parameter uncertainty, the method may further comprise the steps:

Step 1) Limit smart car configuration, car-following tasks and scenarios;

Step 2) constructing the vehicle longitudinal dynamics model under uncertain parameters, specifically including the following sub-steps:

Step 2.1: The longitudinal dynamics of the vehicle include engine, transmission system and tire friction, etc. The expression of the vehicle dynamics model is as follows:

Step 2.2: Using linear feedback technology to linearize the above-mentioned nonlinear model, the nonlinear dynamic model after feedback linearization can be obtained as follows:

Among them, u(t) is the input signal after linear feedback.

Step 2.3: According to the above formula, a linear model of vehicle dynamics can be established:

Step 2.4: The above model is established when the parameters are determined. However, the vehicle is a multi-degree-of-freedom dynamic system, and there are a large number of uncertain factors in its motion process, such as frictional resistance coefficient, air resistance coefficient and engine time constant. The change of these parameters will have a certain impact on the operation of the vehicle. Therefore, considering the uncertainty of the vehicle engine time constant Δμ, the vehicle dynamics model considering the uncertainty of the engine time constant can be obtained:

Step 2.5: Define the state variable X(t)=[x(t),v(t),a(t)] ^T , and obtain the vehicle third-order state-space model considering parameter uncertainty as follows:

in,

According to the assumption of uncertain parameter Δμ, we can get:

[ΔA ΔB]=DG(t)[H ₁ H ₂ ]

G(t)满足G^T(t)G(t)≤I；in,

G(t) satisfies G ^T (t)G(t)≤I;

Step 3) Construct a speed-following control system considering multiple vehicles in front under uncertain parameters, which specifically includes the following sub-steps:

Step 3.1: Define the error between the expected speed and the current speed of the main vehicle relative to the front vehicle during the driving process as:

e ₁ =v _des1 (t)-v _ego (t)

Among them, v _ego (t) and v _des1 (t) are respectively the speed of the main vehicle at time t and the expected speed of the main vehicle relative to the front vehicle.

Step 3.2: Define the error between the expected speed of the main vehicle relative to the vehicle in front and the speed of the main vehicle during the driving process as:

e ₂ =v _des2 (t)-v _ego (t)

Among them, v _des2 (t) is the expected speed of the host vehicle relative to the preceding vehicle at time t.

得到考虑多前车信息和 模型不确定性的车速跟随控制系统；Step 3.3: Define State Variables

A speed-following control system that considers the information of multiple preceding vehicles and the uncertainty of the model is obtained;

Step 3.4: The controller of the vehicle speed following control system is in the form of feedback control, and the expression is as follows:

It can be seen that the information of the vehicle in front comes from the on-board sensor, and the information of the vehicle in front comes from the vehicle-to-vehicle communication device, which is heterogeneous information. According to different sources of information, it can be decomposed into:

u(t)=Kx(t)=K _s x _s (t)+K _c x _c (t)

Step 3.5: Considering the communication delay τ and external disturbance ω(t), determine the expression of the vehicle speed following control system of multiple vehicles in front under the condition of uncertain parameters;

Step 4) Carry out stability analysis to vehicle speed following control system;

Step 5) Design the H-infinity controller. Specifically, the following sub-steps are included:

Step 5.1: Define performance metrics:

For a given H∞ disturbance attenuation level γ>0, for all ω(t)∈l ₂ [0,∞), it should satisfy that J<0 holds true.

Step 5.2: For the closed-loop vehicle speed following control system and the given constant γ>0, if there are symmetric positive definite matrices X and G, matrices Y and Z, and ε ₁ , ε ₂ , ε ₃ >0 such that the following linear matrix inequality

is established, where * represents the symmetry of the matrix, and there are:

Ξ ₄ ＝A ^* X+XA ^*T +B ^* Z+ZB ^*T +(ε ₁ +4ε ₃ )D ^* D ^*T +4ε ₂ B ^* B ^*T , then the γ- Suboptimal H∞ robust controllers. In addition, the gain of the H-infinity controller can be obtained as follows:

K _s =YX ^-1

K _c = ZX ⁻¹ .

As a preferred technical solution of the present invention, in the expression of the vehicle dynamics model in the step 2.1, x(t), v(t) are respectively the position and the speed of the vehicle at time t; m is the quality of the vehicle ; η is the transmission efficiency of the transmission; _r is the radius of the tire; T(t) is the torque; c _a is the air resistance coefficient; g is the acceleration of gravity; When the slope angle is small, it can be considered that cos(θ(x(t)))≈1, sin(θ(x(t)))=θ(x(t)); μ is the engine time constant ( Also known as inertia delay); T _des (t) is the desired moment.

As a preferred technical solution of the present invention, in the vehicle dynamics model of the engine time constant uncertainty in step 2.4, |Δμ|=g(t), s(t) is a Lebesgue-measurable continuous function, and satisfies g ² (t)≥Γ, Γ>0.

As a kind of preferred technical scheme of the present invention, in described step 3.3, consider the vehicle speed following control system of many front vehicle information and model uncertainty as:

in,

and have,

in,

As a kind of preferred technical scheme of the present invention, described step 3.5) in, the expression of the vehicle speed following control system of many front vehicles under the situation of parameter uncertainty is:

As a preferred technical solution of the present invention, for the step 4, the conditions for the asymptotic stability of the vehicle speed following control system are given below. Considering the closed-loop vehicle speed following control system without disturbance, if there are symmetric positive definite matrices P and N, the matrix K, such that the following matrix inequality satisfies

则车速跟随控制系统是渐近稳定的，其中：

Then the vehicle speed following control system is asymptotically stable, where:

Θ ₂₂ = -N.

Technical effect and advantage of the present invention:

The present invention proposes a longitudinal man-machine layered cooperative control method considering multiple vehicles in front under uncertain parameters. To deal with issues such as the influence of cooperative control, the Lyapunov-Krasovskii functional is used to analyze the stability of the vehicle speed following control system, and on this basis, a γ-suboptimal H _∞ robust controller is designed. Information pre-control, and further fine-grained control can be carried out based on the information of the vehicle in front, which optimizes the performance of the vehicle's longitudinal follow-up and improves driving comfort while reducing the driver's operating load;

The present invention proposes a longitudinal human-machine hierarchical cooperative control method considering multiple front vehicles under uncertain parameters, which can provide a reference for the design of control strategies in the field of automatic vehicle human-machine co-driving.

Description of drawings

Fig. 1 is a schematic diagram of the overall flow of the present invention.

detailed description

The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

As shown in Fig. 1, the present invention provides a longitudinal man-machine hierarchical cooperative control method considering multiple front vehicles under uncertain parameters, a longitudinal human-machine hierarchical cooperative control method considering multiple front vehicles under uncertain parameters, Step 1) Limit smart car configuration, car-following tasks and scenarios;

Step 2) constructing the vehicle longitudinal dynamics model under uncertain parameters, specifically including the following sub-steps:

Step 2.1: The longitudinal dynamics of the vehicle include engine, transmission system and tire friction, etc. The expression of the vehicle dynamics model is as follows:

Step 2.2: Using linear feedback technology to linearize the above nonlinear model, the nonlinear dynamic model after feedback linearization can be obtained as follows:

Among them, u(t) is the input signal after linear feedback.

Step 2.3: According to the above formula, a linear model of vehicle dynamics can be established:

Step 2.4: The above model is established when the parameters are determined. However, the vehicle is a multi-degree-of-freedom dynamic system, and there are a large number of uncertain factors in its motion process, such as frictional resistance coefficient, air resistance coefficient and engine time constant. The change of these parameters will have a certain impact on the operation of the vehicle. Therefore, considering the uncertainty of the vehicle engine time constant Δμ, the vehicle dynamics model considering the uncertainty of the engine time constant can be obtained:

Step 2.5: Define the state variable X(t)=[x(t),v(t),a(t)] ^T , and obtain the vehicle third-order state-space model considering parameter uncertainty as follows:

in,

According to the assumption of uncertain parameter Δμ, we can get:

[ΔA ΔB]=DG(t)[H ₁ H ₂ ]

G(t) 满足G^T(t)G(t)≤I；in,

G(t) satisfies G ^T (t)G(t)≤I;

Step 3) Construct a speed-following control system considering multiple vehicles in front under uncertain parameters, which specifically includes the following sub-steps:

Step 3.1: Define the error between the expected speed and the current speed of the main vehicle relative to the front vehicle during the driving process as:

e ₁ =v _des1 (t)-v _ego (t)

Among them, v _ego (t) and v _des1 (t) are respectively the speed of the main vehicle at time t and the expected speed of the main vehicle relative to the front vehicle.

Step 3.2: Define the error between the expected speed of the main vehicle relative to the vehicle in front and the speed of the main vehicle during the driving process as:

e ₂ =v _des2 (t)-v _ego (t)

Among them, v _des2 (t) is the expected speed of the host vehicle relative to the preceding vehicle at time t.

得到考虑多前车 信息和模型不确定性的车速跟随控制系统；Step 3.3: Define State Variables

A speed-following control system that considers the information of multiple preceding vehicles and model uncertainty is obtained;

Step 3.4: The controller of the vehicle speed following control system is in the form of feedback control, and the expression is as follows:

It can be seen that the information of the vehicle in front comes from the on-board sensor, and the information of the vehicle in front comes from the vehicle-to-vehicle communication device, which is heterogeneous information. According to different sources of information, it can be decomposed into:

u(t)=Kx(t)=K _s x _s (t)+K _c x _c (t)

Step 3.5: Considering the communication delay τ and external disturbance ω(t), determine the expression of the vehicle speed following control system of multiple vehicles in front under the condition of uncertain parameters;

Step 4) Carry out stability analysis to vehicle speed following control system;

Step 5) Design the H-infinity controller. Specifically, the following sub-steps are included:

Step 5.1: Define performance metrics:

For a given H∞ disturbance attenuation level γ>0, for all ω(t)∈l ₂ [0,∞), it should satisfy that J<0 holds true.

Step 5.2: For the closed-loop vehicle speed following control system and the given constant γ>0, if there are symmetric positive definite matrices X and G, matrices Y and Z, and ε ₁ , ε ₂ , ε ₃ >0 such that the following linear matrix inequality

is established, where * represents the symmetry of the matrix, and there are:

Ξ ₄ ＝A ^* X+XA ^*T +B ^* Z+ZB ^*T +(ε ₁ +4ε ₃ )D ^* D ^*T +4ε ₂ B ^* B ^*T , then the γ- Suboptimal H∞ robust controller. In addition, the gain of the H-infinity controller can be obtained as follows:

K _s =YX ^-1

K _c = ZX ⁻¹ .

As a preferred embodiment of the present invention, in the expression of the vehicle dynamics model of described step 2.1, x(t), v(t) are respectively the position and the speed of the vehicle at time t; m is the quality of the vehicle ; η is the transmission efficiency of the transmission; r is the radius of the tire; T(t) is the torque; c _a is the air resistance coefficient; g is the acceleration of gravity; c _r is the rolling damping coefficient; When the slope angle is small, it can be considered that cos(θ(x(t)))≈1, sin(θ(x(t)))=θ(x(t)); μ is the engine time constant ( Also known as inertia time delay); T _des (t) is the desired moment;

Further, in the vehicle dynamics model of engine time constant uncertainty in step 2.4, |Δμ|=g(t), s(t) is a Lebesgue-measurable continuous function, and satisfies g ² (t)≥Γ ,Γ>0.

As a kind of preferred embodiment of the present invention, in described step 3.3, consider the vehicle speed following control system of many front vehicle information and model uncertainty as:

in,

and have,

in,

Further, in said step 3.5), the expression of the vehicle speed following control system of many front vehicles under the uncertain situation of parameters is:

As another preferred embodiment of the present invention, for the step 4, the conditions for the asymptotic stability of the vehicle speed following control system are given below, considering the closed-loop vehicle speed following control system without disturbance, if there are symmetric positive definite matrices P and N, Matrix K such that the following matrix inequality satisfies

则车速跟随控制系统是渐近稳定的，其中：

Then the vehicle speed following control system is asymptotically stable, where:

Θ ₂₂ = -N .

To sum up, the present invention is a longitudinal man-machine layered cooperative control method considering multiple vehicles in front under uncertain parameters. In order to deal with issues such as the influence of man-machine cooperative control of vehicle following, the Lyapunov-Krasovskii functional is used to analyze the stability of the vehicle speed following control system, and on this basis, a γ-suboptimal H _∞ robust controller is designed. Pre-control is carried out based on the information of the vehicle far ahead, and further fine-grained control can be carried out based on the information of the vehicle in front, which optimizes the performance of the vehicle's longitudinal follow-up and improves driving comfort while reducing the driver's operating load. A longitudinal human-machine layered cooperative control method considering multiple front vehicles under uncertain parameters proposed by the present invention can provide a reference for the design of control strategies in the field of automatic vehicle human-machine co-driving.

Although the embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications and substitutions can be made to these embodiments without departing from the principle and spirit of the present invention. and modifications, the scope of the invention is defined by the appended claims and their equivalents.

Claims (6)

1. A longitudinal man-machine layered cooperative control method considering multiple front vehicles under the condition of uncertain parameters is characterized by comprising the following steps:

step 1) limiting intelligent automobile configuration, automobile following tasks and scenes;

step 2) constructing a vehicle longitudinal dynamics model under uncertain parameters, and specifically comprising the following substeps:

step 2.1: the longitudinal dynamics of the vehicle include engine, transmission system and tire friction, etc., and the expression of the vehicle dynamics model is as follows:

step 2.2: the nonlinear model is linearized by using a linear feedback technology, and the following nonlinear dynamical model after feedback linearization can be obtained:

where u (t) is the input signal after linear feedback.

Step 2.3: a linear model of vehicle dynamics can be built according to the above equation:

step 2.4: the above model is established under the condition of parameter determination, however, the vehicle is a multi-degree-of-freedom dynamic system, and there are a lot of uncertain factors such as friction resistance coefficient, air resistance coefficient and engine time constant in the motion process, and the change of these parameters will have a certain influence on the vehicle operation, therefore, the following vehicle dynamic model considering the uncertainty of the engine time constant can be obtained by considering the uncertainty Δ μ of the vehicle engine time constant:

step 2.5: defining a state variable X (t) = [ X (t), v (t), a (t)] ^T Obtaining a vehicle three-order state space model considering parameter uncertainty as follows:

wherein,

from the assumption of the uncertainty parameter Δ μ, one can obtain:

[ΔA ΔB]＝DG(t)[H ₁ H ₂ ]

wherein,

g (t) satisfies G ^T (t)G(t)≤I；

Step 3) constructing a speed following control system considering multiple front vehicles under the condition of uncertain parameters, and specifically comprising the following substeps:

step 3.1: defining the error magnitude between the expected speed and the current speed of the host vehicle relative to the front vehicle during the driving process as follows:

e ₁ ＝v _des1 (t)-v _ego (t)

wherein v is _ego (t)、v _des1 (t) the velocity of the host vehicle and the desired velocity of the host vehicle relative to the preceding vehicle, respectively, at time t.

Step 3.2: defining the error between the expected speed of the host vehicle relative to the front vehicle and the speed of the host vehicle during running as follows:

e ₂ ＝v _des2 (t)-v _ego (t)

wherein v is _des2 (t) the desired velocity of the host vehicle relative to the preceding vehicle at time t, respectively.

Step 3.3: defining state variables

Obtaining a vehicle speed following control system considering information of multiple front vehicles and model uncertainty;

step 3.4: the controller of the vehicle speed following control system is in a feedback control form, and the expression is as follows:

it can be seen that the information of the front vehicle is derived from the vehicle-mounted sensor, the information of the front vehicle is derived from the vehicle-to-vehicle communication device, and the information is heterogeneous information, and can be decomposed into:

u(t)＝Kx(t)＝K _s x _s (t)+K _c x _c (t)

step 3.5: considering communication time delay tau and external disturbance omega (t), determining an expression of a vehicle speed following control system of a plurality of front vehicles under the condition of uncertain parameters;

step 4) carrying out stability analysis on the vehicle speed following control system;

and 5) designing the H-definition controller. The method specifically comprises the following substeps:

step 5.1: defining a performance index:

for a given H ∞ disturbance attenuation level γ > 0, for all ω (t) ∈ l ₂ [0, ∞), it should be satisfied that J < 0 is always true.

Step 5.2: for closed loop vehicle speed following control system and methodConstant gamma > 0, positive definite matrices X and G if symmetric, matrices Y and Z, and epsilon ₁ ,ε ₂ ,ε ₃ > 0 such that the following linear matrix inequality

Where, denotes the symmetry of the matrix, and has:

Ξ ₄ ＝A ^* X+XA ^*T +B ^* Z+ZB ^*T +(ε ₁ +4ε ₃ )D ^* D ^*T +4ε ₂ B ^* B ^*T therefore, the gamma-suboptimal H infinity robust controller of the vehicle speed following control system can be obtained. In addition, the gain of the H-infinity controller can be obtained as follows:

K _s ＝YX ^-1

K _c ＝ZX ^-1 。

2. the longitudinal human-machine hierarchical cooperative control method considering multiple front vehicles under the condition of uncertain parameters according to claim 1 is characterized in that in the expression of the vehicle dynamics model of the step 2.1, x (t) and v (t) are the position and the speed of the vehicle at the time t respectively; m is the mass of the vehicle; eta is the transmission efficiency of the transmission device; r is the radius of the tire; t (T) is torque; c. C _a Is the air resistance coefficient; g is the acceleration of gravity; c. C _r Is the rolling damping coefficient; θ (x (t)) is the slope angle of the road, and when the slope angle is small, cos (θ (x (t))) = 1,sin (θ (x (t))) = θ (x (t))); μ is the engine time constant (also known as inertial time delay); t is _des (t) is the desired torque.

3. The longitudinal human-machine hierarchical cooperative control method considering multiple front vehicles under uncertain parameters of claim 1 is characterized in that in the vehicle dynamics model with uncertain engine time constants of step 2.4, | Δ μ | = g (t), s (t) is a Lebesgue-measurable continuous function and satisfies g ² (t)≥Γ,Γ＞0。

4. The longitudinal man-machine hierarchical cooperative control method considering multiple leading vehicles under uncertain parameters according to claim 1 is characterized in that in the step 3.3, a vehicle speed following control system considering multiple leading vehicle information and model uncertainty is as follows:

wherein,

and is provided with a plurality of groups of the materials,

wherein,

5. the longitudinal man-machine hierarchical cooperative control method considering multiple vehicles with uncertain parameters according to claim 1, wherein in the step 3.5), the expression of the vehicle speed following control system of the multiple vehicles with uncertain parameters is as follows:

6. the longitudinal man-machine layered cooperative control method according to claim 1, wherein for step 4, the condition that the vehicle speed following control system is asymptotically stable is given below, the closed-loop vehicle speed following control system without disturbance is considered, and if symmetric positive definite matrixes P and N, matrix K exist, the following matrix inequality satisfies the following condition

The vehicle speed following control system is asymptotically stable, wherein:

Θ ₂₂ ＝-N。

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