Spiral road intelligent motorcade control method based on dynamic surface sliding mode and variable time interval

Abstract

The invention discloses a spiral road intelligent motorcade control method based on a dynamic surface sliding mode and a variable time interval. The spiral road intelligent motorcade control method comprises the steps of defining a communication topology of an intelligent network connection motorcade; according to the geometrical characteristics of the spiral road, constructing a spiral road vehicle following model for the intelligent network connection vehicle team; constructing a variable time interval strategy enabling the distance of the intelligent network connection motorcade to adaptively change along with the vehicle speed; and based on the communication topology of the intelligent network connection vehicle team, constructing a variable time interval dynamic surface sliding mode queue controller on the spiral road for the intelligent network connection vehicle team in combination with a spiral road vehicle following model and a variable time interval strategy, and controlling the motion state of vehicles in the intelligent network connection vehicle team. According to the method, the influence of road factors on vehicle driving is considered by constructing the spiral road vehicle following model, so that intelligent vehicle control in a spiral road traffic scene is more targeted; the distance problem of the vehicles at different speeds is considered by constructing a variable time interval strategy, so that the distance of the vehicle queue can be adaptively changed along with the vehicle speeds.

Description

Technical Field

[0001] The present invention relates to the field of intelligent networked vehicle control, and in particular to a spiral road intelligent vehicle fleet control method based on dynamic surface sliding mode and variable time interval. Background Art

[0002] With the booming development of intelligent transportation systems, intelligent connected vehicles have received widespread attention. With its powerful wireless communication capabilities, vehicle wireless communication technology enables information exchange between vehicles and between vehicles and transportation infrastructure, allowing vehicles to instantly share key elements such as location information, speed data, and real-time traffic conditions. With the help of a column controller, an intelligent connected fleet can achieve precise real-time collaborative control under external interference, allowing the vehicle column to travel stably. The core goal of column control is to ensure that all vehicles can maintain the preset ideal speed and maintain appropriate vehicle spacing to optimize the driving experience. Vehicle platooning not only improves the traffic efficiency of vehicles, but also effectively reduces the risk of traffic accidents. In addition, it has shown an important positive role in alleviating urban traffic congestion, reducing fuel consumption, and reducing harmful gas emissions, laying a solid foundation for building a greener, more efficient, and safer future transportation system.

[0003] The road traffic environment has a significant and far-reaching impact on the driving state of vehicles, especially when facing complex and special road conditions such as spiral roads, the curvature change and slope fluctuation of the road become the key factors affecting the driving state of vehicles. Therefore, in order to ensure the driving safety and traffic efficiency of the intelligent connected fleet on spiral roads, it is necessary to design a vehicle queue controller based on spiral roads to ensure that the vehicles maintain the desired speed and vehicle-to-vehicle distance.

[0004] In current research, most of them only consider the longitudinal control of vehicles on flat roads, and pay less attention to the impact of complex road environments, such as special road conditions such as curves, ramps and spiral roads. The patent with publication number CN116610119A designs a platoon control method for unmanned vehicles based on a new time-varying spacing strategy. This method makes the initial spacing error between each vehicle zero, solving the problem in the prior art that the non-zero initial spacing error may cause the queue to be unstable, thus affecting the stability of the entire system. However, this method does not consider the impact of road factors on vehicle driving. The patent with publication number CN116859952A designs a longitudinal composite control method and system for a fleet based on a second-order continuous sliding mode. This method can ensure the finite time stability of the estimated error, use a continuous control signal, and avoid the jitter problem caused by the discontinuous control signal of the prior art solution, thereby improving the performance of the fleet longitudinal control system. However, this method does not consider the spacing problem of vehicles at different speeds. Summary of the invention

[0005] In view of the deficiencies of the above-mentioned prior art, the technical problem to be solved by the present invention is: how to provide a spiral road intelligent vehicle fleet control method based on dynamic surface sliding mode and variable time spacing, by constructing a spiral road vehicle following model to consider the impact of road factors on vehicle driving, so that the intelligent car control in the spiral road traffic scenario is more targeted; by constructing a variable time spacing strategy to consider the spacing problem of vehicles at different speeds, the spacing of vehicle queues can be adaptively changed with the vehicle speed, thereby improving the stability, safety and traffic efficiency of the intelligent networked vehicle fleet control in the spiral road traffic scenario.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] The intelligent convoy control method for spiral roads based on dynamic surface sliding mode and variable time interval includes:

[0008] S1: Define the communication topology of the intelligent connected vehicle fleet;

[0009] S2: Based on the geometric characteristics of spiral roads, a spiral road vehicle following model is constructed for the intelligent connected fleet;

[0010] S3: Construct a variable headway strategy that allows the spacing of the intelligent connected vehicle fleet to change adaptively with vehicle speed;

[0011] S4: Based on the communication topology of the intelligent connected fleet, combined with the spiral road vehicle following model and the variable headway strategy, a variable headway dynamic surface sliding mode platoon controller is constructed for the intelligent connected fleet on the spiral road.

[0012] S5: Control the motion state of vehicles in the intelligent connected vehicle fleet through a variable time-distance dynamic surface sliding mode queue controller.

[0013] Preferably, in step S1, a plurality of intelligent networked vehicles are defined on the spiral road to form an intelligent networked vehicle fleet; the front vehicle of the intelligent networked vehicle fleet is a pilot vehicle, which applies a reference behavior to the vehicles following behind; the vehicles following behind track according to the driving status of the front vehicle;

[0014] Through the directed graph G N = {V N ,E N ,A}Model the communication topology of the intelligent connected vehicle fleet,V N =1,2,...,N is the set of vehicles, E N ∈V N ×V N is a collection of communication links, and the information between vehicles is represented by the adjacency matrix Description, if there is a communication link between vehicle i and vehicle j, then a ij =1, otherwise 0;

[0015] Define the state connection diagonal matrix between the leading vehicle and the following vehicle When the information of the pilot vehicle can be directly used for the i-th vehicle, a i0 =1, otherwise 0.

[0016] Preferably, in step S2, the formula of the spiral road vehicle following model is expressed as:

[0017]

[0018] Where: f i (t) represents the vehicle following model on a spiral road; ρφ i , Respectively represent the position, velocity and acceleration of the i-th vehicle at time t; H(ρΔφ i (t)) represents the velocity function; ρΔφ i (t) = ρφ i+1 (t)-ρφ i (t) represents the distance between the i-th vehicle and the i+1-th vehicle; represents the speed difference between the i-th vehicle and the i+1-th vehicle; represents the sensitivity coefficient; τ represents the time delay; λ is the speed difference feedback coefficient;

[0019] Among them, the best speed function is:

[0020]

[0021] Where: ξ is a constant coefficient (0<ξ<1); + represents downhill conditions; - represents uphill conditions; p s (θ) represents the expected interval between vehicles on the spiral road; d i,i-1 represents the expected inter-vehicle distance between vehicle i and its preceding vehicle i-1. Different spacing strategies will produce different expected inter-vehicle distances. β is a constant coefficient, which is usually 1. g represents the acceleration of gravity. θ represents the angle of the spiral road. μ2 represents the lateral friction coefficient of the spiral road. ρ represents the radius of curvature of the spiral road.

[0022] Preferably, in step S3, the variable time interval strategy means that the distance between vehicles in the intelligent networked fleet can vary with the speed of the vehicles, with a low distance at low speed and a high distance at high speed;

[0023] In the variable headway strategy, the expected distance D between vehicle i and the preceding vehicle j is i,j It is expressed as:

[0024] D i,j =d i,i-1 +d i-1,i-2 +...+dj+1,j ;

[0025]

[0026] Where: d i,i-1 represents the inter-vehicle distance between vehicle i and its preceding vehicle i-1; h1 and h2 are the parameters to be designed for the spacing strategy; s is a positive constant representing the minimum expected distance between two adjacent vehicles when they are stationary.

[0027] Preferably, in step S4, a kinematic model is constructed for the intelligent connected vehicle fleet:

[0028] Kinematic model of the pilot vehicle:

[0029]

[0030] Kinematic model of the following vehicle:

[0031]

[0032] Where: p i (t), v i (t) represents the position and speed of the i-th vehicle respectively; m represents the vehicle mass; u i (t) represents the control input of the desired driving or braking force, i.e., the vehicle controller; f i (t) represents the spiral road vehicle following model, which is used to simulate the kinematics of the vehicle.

[0033] Preferably, in step S4, a variable time interval dynamic surface sliding mode queue controller is constructed by the following steps:

[0034] S401: Design a vehicle longitudinal controller u based on the kinematic model of the following vehicle i (t), satisfy:

[0035]

[0036] Where: D i,j represents the desired spacing strategy between vehicle i and the preceding vehicle j;

[0037] S402: Define the consistent tracking error of vehicle i, expressed as:

[0038]

[0039] Where: i,p (t) represents the error between the inter-vehicle distance between vehicle i and the preceding vehicle j and the pilot vehicle and the expected inter-vehicle distance; δ i,v (t) represents the speed error between vehicle i and the preceding vehicle j and the pilot vehicle; a ijand a i0 denote the elements of the adjacency matrix and the state connection diagonal matrix respectively; Indicates the number of nodes that can communicate with the current vehicle i;

[0040] S403: Considering the kinematics of the vehicle, external interference and consistent tracking error, deriving Formula 1;

[0041] Formula 1 is expressed as:

[0042]

[0043] Where: d i (t) indicates external interference;

[0044] S404: Let δ i,p (t) is x1,δ i,v (t) is x2, convert formula 1 into formula 2;

[0045] Formula 2 is expressed as:

[0046]

[0047] S405: Define position tracking error z1 = x1-x 1d , x 1d is the target trajectory, then Define the Lyapunov function: but:

[0048] Define virtual control error z2 = x2-α1, then

[0049] S406: Take α1 as Low pass filter The output of And the following conditions are met:

[0050]

[0051] Available The filtering error

[0052] S407: Considering the influence of position tracking error, virtual control error and filtering error, define the Lyapunov function:

[0053]

[0054] but

[0055] S408: Make the following deduction:

[0056] because:

[0057]

[0058] so:

[0059]

[0060] in:

[0061]

[0062] S409: Through the derivation of step S408, the following variable time interval dynamic surface sliding mode queue controller is obtained:

[0063]

[0064] Where: u i (t) represents the controller of vehicle i; m i represents the mass of vehicle i; a ij Indicates whether there is a communication link between vehicle i and vehicle j, a ij =1, indicating existence, a ij =1, indicating non-existence; v j represents the speed of vehicle j; f i (t) represents the spiral road vehicle following model, which is used to simulate the kinematics of the vehicle; η represents the proportional adjustment parameter; sgn is a sign function, if x>0, then sgn(x)=1; if x=0, sgn(x)=0; if x<0, then sgn(x)=-1; c2 is a design parameter, which is a positive constant greater than zero.

[0065] Compared with the prior art, the spiral road intelligent vehicle fleet control method based on dynamic surface sliding mode and variable time interval in the present invention has the following beneficial effects:

[0066] In the process of constructing a queue controller, the present invention constructs a spiral road vehicle following model according to the geometric features of the spiral road. First, the spiral road vehicle following model can fully consider the geometric features of the spiral road, such as the curvature and slope, and consider the influence of road factors on vehicle driving, so that it can more accurately simulate the driving state of the vehicle on the spiral road, making the intelligent car control in the spiral road traffic scene more targeted. Secondly, in the process of constructing a queue controller, the spiral road vehicle following model can be introduced to avoid possible driving deviations or dangerous situations, thereby improving the stability and safety of the intelligent networked vehicle fleet control in the spiral road traffic scene. Finally, based on the spiral road vehicle following model, it is possible to optimize the vehicle's driving speed and acceleration according to the actual situation of the spiral road, reduce unnecessary braking and acceleration, and thus improve the traffic efficiency of the intelligent networked vehicle fleet control.

[0067] In the process of constructing a queue controller, the present invention constructs a variable time spacing strategy that allows the spacing of the intelligent networked fleet to change adaptively with the vehicle speed. When driving at high speeds, by increasing the distance between vehicles, the risk of rear-end collisions caused by sudden deceleration of the vehicle in front can be reduced; when driving at low speeds or in traffic congestion, by reducing the distance between vehicles, the utilization rate of the road can be improved, and traffic congestion and waiting time can be reduced. Through the variable time spacing strategy, the present invention can adjust the distance between vehicles in real time according to changes in vehicle speed, taking into account the spacing problem of vehicles at different speeds, so that the spacing of vehicle queues can change adaptively with vehicle speed, thereby improving the safety and traffic efficiency of intelligent networked fleet control in spiral road traffic scenarios.

[0068] The present invention is based on the communication topology of the intelligent networked vehicle fleet, combined with the spiral road vehicle following model and the variable time interval strategy to construct a variable time interval dynamic surface sliding mode queue controller on the spiral road, and control the motion state of the vehicles in the intelligent networked vehicle fleet. First, through the dynamic surface sliding mode control algorithm, the controller can adjust the driving state of each vehicle in the fleet in real time to ensure that the fleet maintains a stable driving trajectory and speed on the spiral road. Secondly, by utilizing the communication topology structure of the intelligent networked vehicle fleet, the controller can realize real-time information sharing and collaborative control between vehicles within the fleet, thereby improving the overall coordination ability and response speed of the fleet. Finally, combined with the spiral road vehicle following model and the variable time interval strategy, the controller can optimize the driving speed and acceleration of the fleet according to different road conditions and vehicle states, reduce unnecessary braking and acceleration, and improve the driving efficiency of the fleet. In addition, the controller can adapt to changes in the geometric characteristics of different spiral roads and traffic flow conditions, ensuring that the fleet can maintain a safe and efficient driving state in various complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to make the purpose, technical solution and advantages of the invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0070] Figure 1 The logical block diagram of the intelligent convoy control method on spiral roads based on dynamic surface sliding mode and variable time interval;

[0071] Figure 2 This is a schematic diagram of vehicle queue control;

[0072] Figure 3 Schematic diagram of a vehicle driving on a spiral road. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but only represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0074] The following is a further detailed description through specific implementation methods:

[0075] Example:

[0076] This embodiment discloses a spiral road intelligent vehicle fleet control method based on dynamic surface sliding mode and variable time interval.

[0077] like Figure 1 As shown, the spiral road intelligent convoy control method based on dynamic surface sliding mode and variable time interval includes:

[0078] S1: Define the communication topology of the intelligent connected vehicle fleet;

[0079] S2: Based on the geometric characteristics of spiral roads, a spiral road vehicle following model is constructed for the intelligent connected fleet;

[0080] S3: Construct a variable headway strategy that allows the spacing of the intelligent connected vehicle fleet to change adaptively with vehicle speed;

[0081] S4: Based on the communication topology of the intelligent connected fleet, combined with the spiral road vehicle following model and the variable headway strategy, a variable headway dynamic surface sliding mode platoon controller is constructed for the intelligent connected fleet on the spiral road.

[0082] S5: Control the motion state of vehicles in the intelligent connected vehicle fleet through a variable time-distance dynamic surface sliding mode queue controller.

[0083] In the process of constructing a queue controller, the present invention constructs a spiral road vehicle following model according to the geometric features of the spiral road. First, the spiral road vehicle following model can fully consider the geometric features of the spiral road, such as the curvature and slope, and consider the influence of road factors on vehicle driving, so that it can more accurately simulate the driving state of the vehicle on the spiral road, making the intelligent car control in the spiral road traffic scene more targeted. Secondly, in the process of constructing a queue controller, the spiral road vehicle following model can be introduced to avoid possible driving deviations or dangerous situations, thereby improving the stability and safety of the intelligent networked vehicle fleet control in the spiral road traffic scene. Finally, based on the spiral road vehicle following model, it is possible to optimize the vehicle's driving speed and acceleration according to the actual situation of the spiral road, reduce unnecessary braking and acceleration, and thus improve the traffic efficiency of the intelligent networked vehicle fleet control.

[0084] In the process of constructing a queue controller, the present invention constructs a variable time spacing strategy that allows the spacing of the intelligent networked fleet to change adaptively with the vehicle speed. When driving at high speeds, by increasing the distance between vehicles, the risk of rear-end collisions caused by sudden deceleration of the vehicle in front can be reduced; when driving at low speeds or in traffic congestion, by reducing the distance between vehicles, the utilization rate of the road can be improved, and traffic congestion and waiting time can be reduced. Through the variable time spacing strategy, the present invention can adjust the distance between vehicles in real time according to changes in vehicle speed, taking into account the spacing problem of vehicles at different speeds, so that the spacing of vehicle queues can change adaptively with vehicle speed, thereby improving the safety and traffic efficiency of intelligent networked fleet control in spiral road traffic scenarios.

[0085] The present invention is based on the communication topology of the intelligent networked vehicle fleet, combined with the spiral road vehicle following model and the variable time interval strategy to construct a variable time interval dynamic surface sliding mode queue controller on the spiral road, and control the motion state of the vehicles in the intelligent networked vehicle fleet. First, through the dynamic surface sliding mode control algorithm, the controller can adjust the driving state of each vehicle in the fleet in real time to ensure that the fleet maintains a stable driving trajectory and speed on the spiral road. Secondly, by utilizing the communication topology structure of the intelligent networked vehicle fleet, the controller can realize real-time information sharing and collaborative control between vehicles within the fleet, thereby improving the overall coordination ability and response speed of the fleet. Finally, combined with the spiral road vehicle following model and the variable time interval strategy, the controller can optimize the driving speed and acceleration of the fleet according to different road conditions and vehicle states, reduce unnecessary braking and acceleration, and improve the driving efficiency of the fleet. In addition, the controller can adapt to changes in the geometric characteristics of different spiral roads and traffic flow conditions, ensuring that the fleet can maintain a safe and efficient driving state in various complex environments.

[0086] In order to better introduce the technical solution of the present invention, this embodiment is described through the following parts.

[0087] 1. Communication topology modeling

[0088] In this embodiment, a spiral road is defined, where a plurality of intelligent connected vehicles form an intelligent connected vehicle fleet; the vehicle at the front of the intelligent connected vehicle fleet is a pilot vehicle, which applies a reference behavior to the vehicles following behind; the vehicles following behind track according to the driving status of the leading vehicle;

[0089] The communication status between vehicles can be represented by a communication topology diagram. N = {V N ,E N ,A}Model the communication topology of the intelligent connected vehicle fleet,V N =1,2,...,N is the set of vehicles, E N ∈V N ×V N is a collection of communication links, and the information between vehicles is represented by the adjacency matrix Description, if there is a communication link between vehicle i and vehicle j, then a ij =1, otherwise 0;

[0090] Define the state connection diagonal matrix between the leading vehicle and the following vehicle When the information of the pilot vehicle can be directly used for the i-th vehicle, a i0 =1, otherwise 0.

[0091] In subsequent research, the pilot-following communication topology (LPF) is adopted, that is, each vehicle can directly or indirectly obtain the driving status of the pilot vehicle and the preceding vehicle, as shown in the schematic diagram. Figure 2 shown.

[0092] 2. Vehicle Following Model on Spiral Road

[0093] like Figure 3 As shown, consider a vehicle with a total mass of m driving along a spiral road with an inclination angle of θ under the action of gravitational acceleration g. At this time, the gravity acting on the vehicle can be decomposed into a component force mgcosθ perpendicular to the road surface and a component force mgsinθ along the tangent of the road surface. In addition, due to the action of friction, the vehicle is also subjected to a centripetal force f=μ2mg cosθ pointing to the center of the circle, where μ2 represents the lateral friction coefficient of the spiral road. If the arc length from point P to point Q is φ, and the radius of the circle is r, the radius of curvature can be expressed as ρ=r / cos 2 θ, and the length of the spiral road is L = ρφ. Combined with the FVD model, the kinematic model of the vehicle on the spiral road can be obtained as follows:

[0094]

[0095] Where: fi (t) represents the vehicle following model on a spiral road; ρφ i , Respectively represent the position, velocity and acceleration of the i-th vehicle at time t; H(ρΔφ i (t)) represents the velocity function; ρΔφ i (t) = ρφ i+1 (t)-ρφ i (t) represents the distance between the i-th vehicle and the i+1-th vehicle; represents the speed difference between the i-th vehicle and the i+1-th vehicle; represents the sensitivity coefficient; τ represents the time delay; λ is the speed difference feedback coefficient;

[0096] Among them, the best speed function is:

[0097]

[0098] Where: ξ is a constant coefficient (0<ξ<1); + represents downhill conditions; - represents uphill conditions; p s (θ) represents the expected interval between vehicles on the spiral road; d i,i-1 represents the expected inter-vehicle distance between vehicle i and its preceding vehicle i-1. Different spacing strategies will produce different expected inter-vehicle distances. β is a constant coefficient, which is usually 1. g represents the acceleration of gravity. θ represents the angle of the spiral road. μ2 represents the lateral friction coefficient of the spiral road. ρ represents the radius of curvature of the spiral road.

[0099] 3. Variable Time Distance Strategy

[0100] The spacing strategy refers to the method of maintaining an appropriate distance between vehicles to ensure driving safety and improve traffic efficiency during the driving process. In this embodiment, a variable time spacing strategy is designed, that is, the vehicle spacing can change with the speed of the vehicle, and a low spacing is adopted at low speed and a high spacing is adopted at high speed.

[0101] In the variable headway strategy, the expected distance D between vehicle i and the preceding vehicle j is i,j It is expressed as:

[0102] D i,j =d i,i-1 +d i-1,i-2 +...+d j+1,j ;

[0103]

[0104] Where: d i,i-1represents the inter-vehicle distance between vehicle i and its preceding vehicle i-1; h1 and h2 are the parameters to be designed for the spacing strategy; s is a positive constant representing the minimum expected distance between two adjacent vehicles when they are stationary.

[0105] 4. Variable Time Distance Dynamic Surface Sliding Mode Queue Controller

[0106] In this embodiment, a kinematic model is constructed for the intelligent connected vehicle fleet:

[0107] Kinematic model of the pilot vehicle:

[0108]

[0109] Kinematic model of the following vehicle:

[0110]

[0111] Where: p i (t), v i (t) represents the position and speed of the i-th vehicle respectively; m represents the vehicle mass; u i (t) represents the control input of the desired driving or braking force, i.e., the vehicle controller; f i (t) represents the spiral road vehicle following model, which is used to simulate the kinematics of the vehicle.

[0112] Specifically, the variable time interval dynamic surface sliding mode queue controller is constructed through the following steps:

[0113] S401: In order to make the following vehicle experience the control input in equation (2), it can follow the leading vehicle that applies the reference behavior in equation (1). A vehicle longitudinal controller u is designed based on the kinematic model (2) of the following vehicle. i (t), satisfy:

[0114]

[0115] Where: D i,j represents the desired spacing strategy between vehicle i and the preceding vehicle j;

[0116] S402: Define the consistent tracking error of vehicle i, expressed as:

[0117]

[0118] Where: i,p (t) represents the error between the inter-vehicle distance between vehicle i and the preceding vehicle j and the pilot vehicle and the expected inter-vehicle distance; δ i,v (t) represents the speed error between vehicle i and the preceding vehicle j and the pilot vehicle; a ij and a i0denote the elements of the adjacency matrix and the state connection diagonal matrix respectively; Indicates the number of nodes that can communicate with the current vehicle i;

[0119] S403: Considering the kinematics of the vehicle, external interference (wind resistance and ground friction, etc.) and consistent tracking error, derive Formula 1;

[0120] Formula 1 is expressed as:

[0121]

[0122] Where: d i (t) indicates external interference;

[0123] S404: Let δ i,p (t) is x1,δ i,v (t) is x2, convert formula 1 into formula 2;

[0124] Formula 2 is expressed as:

[0125]

[0126] S405: Define position tracking error z1 = x1-x 1d , x 1d is the target trajectory, then Define the Lyapunov function: but:

[0127] Define virtual control error z2 = x2-α1, then

[0128] In this embodiment, position tracking refers to the control system that requires the output position x1(t) of the controlled object to follow the target reference signal x1(t). 1d (t) is a dynamic change. Virtual control is a design strategy that introduces intermediate variables to deal with the control problem of high-order nonlinear systems. The role of virtual control α1 is to make x2 track α1.

[0129] By defining the position tracking error z1 = x1-x 1d and virtual control error z2 = x2-α1, gradually decomposing the system control problem. These error variables correspond to the dynamic surface of the system. The concept of dynamic surface here refers to the control target (for example, x1→x 1d ) and the intermediate target (e.g. x2→α1), that is, the system needs to be stabilized on these "dynamic surfaces". The goal of sliding mode control is to make the system state "slide" to the target surface (sliding surface) and remain on it. The sliding surface in this embodiment is s=z2. The dynamic surface sliding mode avoids high-order differential solutions by introducing a low-pass filter.

[0130] S406: Take α1 as Low pass filter The output of And the following conditions are met:

[0131]

[0132] Available The filtering error

[0133] S407: Considering the influence of position tracking error, virtual control error and filtering error, define the Lyapunov function:

[0134]

[0135] but

[0136] S408: Make the following deduction:

[0137] because:

[0138]

[0139] so:

[0140]

[0141] in:

[0142]

[0143] S409: Through the derivation of step S408, the following variable time interval dynamic surface sliding mode queue controller is obtained:

[0144]

[0145] Where: u i (t) represents the controller of vehicle i; m i represents the mass of vehicle i; a ij Indicates whether there is a communication link between vehicle i and vehicle j, a ij =1, indicating existence, a ij =1, indicating non-existence; v j represents the speed of vehicle j; f i (t) represents the spiral road vehicle following model, which is used to simulate the kinematics of the vehicle; η represents the proportional adjustment parameter, whose value is greater than the external disturbance d i(t); sgn is a sign function, if x>0, then sgn(x)=1; if x=0, sgn(x)=0; if x<0, then sgn(x)=-1; c2 is a design parameter, which is a positive constant greater than zero.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A spiral road intelligent fleet control method based on dynamic surface sliding mode and variable time interval, characterized in that: include: S1: Define the communication topology of the intelligent connected vehicle fleet; S2: Based on the geometric characteristics of spiral roads, a spiral road vehicle following model is constructed for the intelligent connected fleet; S3: Construct a variable headway strategy that allows the spacing of the intelligent connected vehicle fleet to change adaptively with vehicle speed; S4: Based on the communication topology of the intelligent connected fleet, combined with the spiral road vehicle following model and the variable headway strategy, a variable headway dynamic surface sliding mode platoon controller is constructed for the intelligent connected fleet on the spiral road. S5: Control the motion state of vehicles in the intelligent connected vehicle fleet through a variable time-distance dynamic surface sliding mode queue controller.

2. The method for controlling a spiral road intelligent vehicle fleet based on a dynamic surface sliding mode and variable time interval according to claim 1, characterized in that: In step S1, a plurality of intelligent networked vehicles on a spiral road are defined to form an intelligent networked vehicle fleet; the vehicle at the front of the intelligent networked vehicle fleet is a pilot vehicle, which imposes a reference behavior on the vehicles following behind; The following vehicle tracks the driving status of the leading vehicle; Through the directed graph G N = {V N , E N ,A} Model the communication topology of the intelligent connected vehicle fleet,V N =1, 2, ..., N is the set of vehicles, E N ∈V N ×V N is a collection of communication links, and the information between vehicles is represented by the adjacency matrix Description, if there is a communication link between vehicle i and vehicle j, then a ij =1, otherwise 0; Define the state connection diagonal matrix between the leading vehicle and the following vehicle When the information of the pilot vehicle can be directly used for the i-th vehicle, a i0 =1, otherwise 0.

3. The spiral road intelligent vehicle fleet control method based on dynamic surface sliding mode and variable time interval according to claim 1, characterized in that: In step S2, the formula of the spiral road vehicle following model is expressed as: Where: f i (t) represents the vehicle following model on a spiral road; ρφ i , Respectively represent the position, velocity and acceleration of the i-th vehicle at time t; H(ρΔφ i (t)) represents the velocity function; ρΔφ i (t) = ρφ i+1 (t)-ρφ i (t) represents the distance between the i-th vehicle and the i+1-th vehicle; represents the speed difference between the i-th vehicle and the i+1-th vehicle; represents the sensitivity coefficient; τ represents the time delay; λ is the speed difference feedback coefficient; Among them, the best speed function is: Where: ξ is a constant coefficient (0<ξ<1); + represents downhill conditions; - represents uphill conditions; p s (θ) represents the expected interval between vehicles on the spiral road; d i，i-1 represents the expected inter-vehicle distance between vehicle i and its preceding vehicle i-1. Different spacing strategies will produce different expected inter-vehicle distances. β is a constant coefficient, which is usually 1. g represents the acceleration of gravity. θ represents the angle of the spiral road. μ2 represents the lateral friction coefficient of the spiral road. ρ represents the radius of curvature of the spiral road.

4. The method for controlling a spiral road intelligent vehicle fleet based on a dynamic surface sliding mode and variable time interval according to claim 1, characterized in that: In step S3, the variable time interval strategy means that the distance between vehicles in the intelligent networked fleet can change with the speed of the vehicles, and a low distance is adopted at low speed and a high distance is adopted at high speed; In the variable headway strategy, the expected distance D between vehicle i and the preceding vehicle j is i，j It is expressed as: D i，j =d i，i-1 +d i-1，i-2 +...+d j+1，j ; Where: d i，i-1 represents the inter-vehicle distance between vehicle i and its preceding vehicle i-1; h1 and h2 are the parameters to be designed for the spacing strategy; s is a positive constant, representing the minimum expected distance between two adjacent vehicles when they are stationary.

5. The spiral road intelligent vehicle fleet control method based on dynamic surface sliding mode and variable time interval as claimed in claim 1, characterized in that: In step S4, a kinematic model is constructed for the intelligent connected vehicle fleet: Kinematic model of the pilot vehicle: Kinematic model of the following vehicle: Where: p i (t), v i (t) represents the position and speed of the i-th vehicle respectively; m represents the vehicle mass; u i (t) represents the control input of the desired driving or braking force, i.e., the vehicle controller; f i (t) represents the spiral road vehicle following model, which is used to simulate the kinematics of the vehicle.

6. The method for controlling a spiral road intelligent vehicle fleet based on a dynamic surface sliding mode and variable time interval according to claim 5, characterized in that: In step S4, a variable time interval dynamic surface sliding mode queue controller is constructed by the following steps: S401: Design a vehicle longitudinal controller based on the kinematic model of the following vehicle satisfy: Where: D i，j represents the desired spacing strategy between vehicle i and the preceding vehicle j; S402: Define the consistent tracking error of vehicle i, expressed as: Where: i，p (t) represents the error between the inter-vehicle distance between vehicle i and the preceding vehicle j and the pilot vehicle and the expected inter-vehicle distance; δ i，v (t) represents the speed error between vehicle i and the preceding vehicle j and the pilot vehicle; a ij and a i0 denote the elements of the adjacency matrix and the state connection diagonal matrix respectively; Indicates the number of nodes that can communicate with the current vehicle i; S403: Considering the kinematics of the vehicle, external interference and consistent tracking error, deriving Formula 1; Formula 1 is expressed as: Where: d i (t) indicates external interference; S404: Let δ i，p (t) is x1,δ i，v (t) is x2, convert formula 1 into formula 2; Formula 2 is expressed as: S405: Define position tracking error z1 = x1-x 1d , x 1d is the target trajectory, then Define the Lyapunov function: but: Define virtual control error z2 = x2-α1, then S406: Take α1 as Low pass filter The output of And the following conditions are met: Available The filtering error S407: Considering the influence of position tracking error, virtual control error and filtering error, define the Lyapunov function: but S408: Make the following deduction: because: so: in: S409: Through the derivation of step S408, the following variable time interval dynamic surface sliding mode queue controller is obtained: Where: u i (t) represents the controller of vehicle i; m i represents the mass of vehicle i; a ij Indicates whether there is a communication link between vehicle i and vehicle j, a ij =1, indicating existence, a ij =1, indicating non-existence; v j represents the speed of vehicle j; f i (t) represents the spiral road vehicle following model, which is used to simulate the kinematics of the vehicle; η represents the proportional adjustment parameter; sgn is a sign function, if x>0, then sgn(x)=1; if x=0, sgn(x)=0; if x<0, then sgn(x)=-1; c2 is a design parameter, which is a positive constant greater than zero.

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