Interchange intelligent motorcade sliding mode control method considering disturbance and actuator saturation

Abstract

The invention discloses an interchange intelligent motorcade sliding mode control method considering disturbance and actuator saturation. The method comprises the following steps: respectively constructing three-order dynamic models considering disturbance and actuator saturation; establishing an intelligent network connection vehicle following model considering the expected speed of the road section in the interchange area, and combining a vehicle position tracking error and a speed error to construct an integral sliding mode surface and generate a coupling sliding mode surface; constructing a non-linear disturbance observer on the basis of the three-order dynamic model considering disturbance to generate a disturbance observation value; constructing an auxiliary dynamic system on the basis of the three-order dynamic model considering the saturation of the actuator to generate an auxiliary signal; in order to realize a motorcade control target, a motorcade controller is designed in combination with a coupling sliding mode surface, a disturbance observation value and an auxiliary signal to control the motion state of vehicles in the intelligent network connection motorcade, so that the driving of the intelligent network connection motorcade in an interchange area conforms to a traffic flow theory, and the consistency of the positions and the speeds of the vehicles in the motorcade and the stability of a motorcade string are ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent networked vehicles and wireless communications, and in particular to a sliding mode control method for an intelligent fleet of an interconnected interchange taking disturbance and actuator saturation into consideration. Background Art

[0002] With the advancement of the new generation of scientific and technological revolution and industrial transformation, intelligent networked vehicles have gradually become the strategic direction of the development of the global vehicle industry. It integrates advanced on-board sensors, controllers, actuators and other devices, and combines modern communication and network technologies to achieve intelligent information exchange and sharing between vehicles, vehicles and roads, vehicles and people, and vehicles and the cloud.

[0003] However, when the intelligent connected vehicle platoon is driving in the interchange area, there will inevitably be external disturbances such as uneven road surface and wind, as well as actuator saturation, which may make the system unstable and deteriorate the control performance. In addition, the existing intelligent connected vehicle fleet controller does not take into account the traffic flow characteristics of the platoon in the interchange area, which makes the platoon prone to negative vehicle spacing and negative speed. Therefore, designing a control strategy that can ensure that the intelligent connected vehicle fleet drives in the interchange area in accordance with the traffic flow characteristics while taking into account external disturbances and actuator saturation is crucial to ensure the safety and stability of the vehicle platoon.

[0004] By reviewing relevant patents and papers, it is found that most of the current research on intelligent networked fleet control in interchange areas only proposes corresponding feedback control strategies from the perspective of traffic flow theory, without considering the influence of external disturbances and actuator saturation, or proposes various control algorithms in the control scenarios of curve and slope fleets, but does not take into account the following interaction between vehicles in the interchange area, resulting in some phenomena that do not conform to the characteristics of traffic flow. For example, the patent with publication number CN113721634B discloses a fleet finite time cooperative control method based on backstepping method considering actuator saturation. This method uses backstepping method combined with finite time stability theory to design a controller, ensuring the stability of the system in the presence of saturation problems and external interference. However, this existing solution does not consider the following interaction between vehicles, which easily leads to negative vehicle spacing and negative speed, which does not conform to traffic flow theory. In addition, this existing solution does not clearly target the interchange area, and is difficult to apply to the driving scenario of the interchange fleet.

[0005] Therefore, how to design an intelligent connected fleet controller that takes into account the vehicle-following interaction behavior, external disturbances and actuator saturation in the interchange area is a technical problem that needs to be solved urgently. Summary of the invention

[0006] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is: how to provide a sliding mode control method for an intelligent fleet in an interchange taking into account disturbances and actuator saturation, by designing a fleet controller that takes into account the following interaction behavior of vehicles in the interchange area, external disturbances and actuator saturation, so that the driving of the intelligent connected fleet in the interchange area complies with traffic flow theory, keeps the fleet string stable, and can also take into account the influence of disturbances and actuator saturation, ensuring that the vehicle spacing, speed, etc. of the fleet converge over time.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] The sliding mode control method for intelligent fleet of interchanges considering disturbances and actuator saturation includes:

[0009] S1: Construct a third-order dynamic model considering disturbances for intelligent connected vehicle fleets;

[0010] S2: Construct a third-order dynamic model considering actuator saturation for intelligent connected vehicle fleets;

[0011] S3: Establish an intelligent connected vehicle following model that takes into account the expected speed of the interchange area section;

[0012] S4: Based on the intelligent connected vehicle following model, the integral sliding surface is constructed by combining the vehicle position tracking error and speed error, and the coupled sliding surface is further generated;

[0013] S5: Based on the third-order dynamics model considering disturbances, a nonlinear disturbance observer is constructed to generate disturbance observations;

[0014] S6: Construct an auxiliary dynamic system based on the third-order dynamic model considering actuator saturation to generate auxiliary signals;

[0015] S7: To achieve the convoy control goal, a convoy controller is designed by combining the coupled sliding surface, disturbance observations and auxiliary signals.

[0016] S8: The movement state of the vehicles in the intelligent connected fleet is controlled by the fleet controller, so that the driving of the intelligent connected fleet in the interchange area complies with the traffic flow theory, and the consistency of the position and speed of the vehicles in the fleet and the stability of the fleet are guaranteed.

[0017] Preferably, in step S1, the third-order kinetic model considering disturbance is expressed as:

[0018]

[0019] Where: a i (t) represents the acceleration of the i-th vehicle; v i (t) and p i(t) represents the speed and position of the i-th vehicle at time t; u i (t) represents the actuator input of the i-th vehicle; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; c i Indicates the air resistance coefficient; f i Indicates the mechanical resistance coefficient; D i (t) represents the total disturbance of the vehicle.

[0020] Preferably, in step S2, the third-order dynamic model considering actuator saturation is expressed as:

[0021]

[0022] Where: a i (t) represents the acceleration of the i-th vehicle; v i (t) represents the speed of the i-th vehicle at time t; u i (t) represents the actuator input of the i-th vehicle; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; c i Indicates the air resistance coefficient; f i Indicates the mechanical resistance coefficient; D i (t) represents the total disturbance of the vehicle; u maxi and u mini They represent the upper and lower bounds of the known actuators respectively.

[0023] Preferably, in step S3, the intelligent connected vehicle following model is expressed as:

[0024]

[0025] PΔω i (t) = Pω i+1 (t)-Pω i (t);

[0026] P=r b / cosθ;

[0027]

[0028] Where: represents the acceleration of the i-th vehicle at time t; φ represents the arc length of the interchange area; r b represents the radius of the interchange road curve; θ represents the inclination angle of the interchange road; P represents the curvature radius of the interchange area; Pω i (t) represents the speed of the i-th vehicle at time t; α represents the sensitivity coefficient; v grepresents the expected speed information of the road section; λ represents the speed difference feedback coefficient; PΔφ i (t) represents the distance between the i-th vehicle and the i+1-th vehicle; PΔω i (t) represents the speed difference between the i-th vehicle and the i+1-th vehicle; γ represents the reaction coefficient; G(PΔφ i (t)) represents the optimal speed function of the i-th vehicle at time t; ω max represents the maximum angular velocity of the vehicle, + represents the downhill situation, - represents the uphill situation; χ represents the longitudinal friction coefficient; g s Indicates the safety distance when there is no angle; β=1; v g,max Indicates the maximum increase or decrease speed in the interchange area.

[0029] Preferably, in step S4, in order to handle the large transient problem caused by the non-zero initial vehicle position tracking error, the corrected vehicle position tracking error is expressed as:

[0030] Ξ i (t) = e i (t)-π i (t);

[0031] e i (t) = d i (t)-d ei (t)

[0032]

[0033] d i (t) = p i－1 (t)-p i (t)-L;

[0034] d ei (t) = r + hv i (t);

[0035] Where: i (t) represents the corrected vehicle position tracking error; e i (t) represents the uncorrected vehicle position tracking error; d i (t) represents the actual distance between two consecutive vehicles; L represents the length of the vehicle; d ei (t) represents the desired distance between vehicles with a fixed headway; r represents the safe distance when the vehicle is stationary; h represents the headway; v i (t) and p i (t) represents the speed and position of the i-th vehicle at time t.

[0036] Preferably, in step S4, the integral sliding surface is expressed as:

[0037]

[0038] Φ i (t) = α[G(PΔφ i (t))-Pω i (t)]+λPΔω i (t);

[0039] Γ i (t) = γ(v g -Pω i (t));

[0040] Where: s i (t) represents the integral sliding surface; β1 and β2 are positive constants; Pω i (t) represents the speed of the i-th vehicle at time t; G(PΔφ i (t)) represents the optimal speed function of the i-th vehicle at time t; λ represents the speed difference feedback coefficient; PΔω i (t) represents the speed difference between the i-th vehicle and the i+1-th vehicle; γ represents the reaction coefficient; v g Indicates the expected speed information of the road section;

[0041] The coupled sliding surface is expressed as:

[0042]

[0043] Where: S i (t) represents the coupled sliding surface; q represents the positive constant to be designed; N represents the total number of vehicles.

[0044] Preferably, in step S5, the nonlinear disturbance observer is expressed as:

[0045]

[0046] Where: L i (x) represents the unknown function to be designed; w i (t) represents the state of the nonlinear disturbance observer; Denotes the disturbance D i The observed value of (t); u i (t) represents the actuator input of the i-th vehicle; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; c i Indicates the air resistance coefficient; f i Represents the mechanical resistance coefficient; a i (t) represents the acceleration of the i-th vehicle; v i (t) represents the speed of the i-th vehicle at time t.

[0047] Preferably, in step S6, the auxiliary dynamic system is expressed as:

[0048]

[0049] Δu i (t) = sat(u i (t))-u i (t);

[0050]

[0051] Where: i (t) represents the auxiliary signal; k4 represents the design parameter; h represents the headway; q represents the normal number to be designed; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; u i (t) represents the actuator input of the i-th vehicle; u maxi and u mini They represent the upper and lower bounds of the known actuators respectively.

[0052] Preferably, in step S7, the fleet control objectives include:

[0053] 1) Single-vehicle stability, that is, each following vehicle in the intelligent connected vehicle fleet can eventually reach the same speed and maintain the desired distance between each other;

[0054] 2) The platoon is stable, that is, the distance error between two consecutive vehicles in the intelligent connected platoon eventually decreases over time and satisfies the tracking error transfer function |G i (s)|=|E i+1 (s) / E i (s)|≤1, that is, the vehicle behavior conforms to the classical traffic flow theory; where E i (s) is e i The Laplace transform function of (t), e i (t)≥0.

[0055] Preferably, in step S7, the fleet controller is represented as:

[0056]

[0057]

[0058] Φ i (t) = α[G(PΔφ i (t))-Pω i (t)]+λPΔω i (t);

[0059] Γ i(t) = γ(v g -Pω i (t));

[0060]

[0061] Where: u i (t) represents the actuator input of the i-th vehicle; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; h represents the headway time; q represents the normal constant to be designed; represents the estimated value of the upper bound of the unknown aggregate interference σ; S i (t) represents the coupled sliding surface; λ1, λ2 and λ3 represent control parameters; χ i (t) represents auxiliary signal; Denotes the disturbance D i The observed value of (t); c i Indicates the air resistance coefficient; a i (t) represents the acceleration of the i-th vehicle; v i (t) represents the speed of the i-th vehicle at time t; f i represents the mechanical resistance coefficient; β2 is a positive constant; PΔω i (t) represents the speed difference between the i-th vehicle and the i+1-th vehicle; γ represents the reaction coefficient; Pω i (t) represents the speed of the i-th vehicle at time t; G(PΔφ i (t)) represents the optimal speed function of the i-th vehicle at time t; i (t) represents the corrected vehicle position tracking error; λ represents the speed difference feedback coefficient; v g represents the expected speed information of the road section; η represents a design constant greater than zero; o∈(0,1).

[0062] Compared with the prior art, the sliding mode control method for an intelligent fleet of interchanges taking into account disturbances and actuator saturation in the present invention has the following beneficial effects:

[0063] On the basis of constructing a third-order dynamic model considering disturbance, the present invention further constructs a nonlinear disturbance observer to generate disturbance observations. First, by constructing a third-order dynamic model considering disturbance, the dynamic behavior of the intelligent networked vehicle fleet in a complex environment can be more accurately described, including external disturbance factors such as uneven road surface and wind resistance, and the nonlinear disturbance observer can estimate and compensate for these disturbances in real time, thereby enhancing the system's resistance to external disturbances and improving the robustness of the intelligent networked vehicle fleet. Secondly, the disturbance observations generated by the disturbance observer are used in the design of the controller as part of the control input for compensation, which helps to reduce the control error caused by disturbance and improve the accuracy and stability of the vehicle fleet control. Finally, in the interchange area, the traffic environment is complex and changeable, and there are many disturbance factors. By constructing a model considering disturbance and a disturbance observer, the intelligent networked vehicle fleet can better adapt to this complex environment and ensure the safety and stability of the vehicle fleet during driving in the interchange area.

[0064] Based on the construction of a third-order dynamic model that takes actuator saturation into consideration, the present invention further constructs an auxiliary dynamic system to generate auxiliary signals. First, actuator saturation is a common problem in the control of intelligent connected vehicle fleets, especially in complex traffic environments. By constructing a model that takes actuator saturation into consideration, it is possible to predict and avoid actuator overload and protect the actuator from damage. Secondly, the auxiliary signal generated by the auxiliary dynamic system can be used to adjust the output of the controller to ensure that good control effects can still be achieved when the actuator is saturated, which helps to optimize control performance and reduce control errors and instability caused by actuator saturation. Finally, actuator saturation may cause vehicle control failure or loss of control, posing a threat to the safety of intelligent connected vehicle fleets. By constructing a model that takes actuator saturation into consideration and an auxiliary dynamic system, the stability of the system in the interchange area can be effectively improved.

[0065] On the basis of constructing an intelligent connected vehicle following model that considers the expected speed of the road section in the interchange area, the present invention further constructs an integral sliding surface and generates a coupled sliding surface in combination with the tracking error of the vehicle position and speed. First, the following model that considers the expected speed of the road section in the interchange area can ensure that the intelligent connected vehicle fleet meets the actual traffic flow characteristics during driving, and reduce the occurrence of unreasonable phenomena such as negative vehicle spacing and negative speed. Secondly, the integral sliding surface and coupled sliding surface constructed in combination with the tracking error of the vehicle position and speed can achieve rapid and accurate tracking of the vehicle's motion state, improve control efficiency, and help the intelligent connected vehicle fleet maintain a stable driving state in a complex traffic environment. Finally, the integral sliding surface introduces a nonlinear function of vehicle following in the interchange area, and ensures that the fleet meets the string stability through the coupled sliding mode and transfer function, and at the same time, there is no negative vehicle spacing and speed, which is in line with the classical traffic flow theory.

[0066] The present invention uses the fleet control target, coupled sliding surface, disturbance observation value and auxiliary signal to design a fleet controller to control the motion state of vehicles in the intelligent networked fleet, so that the driving of the intelligent networked fleet in the interchange area conforms to the traffic flow theory, and ensures the consistency of the position and speed of the vehicles in the fleet and the stability of the fleet. First, the fleet controller designed by combining the coupled sliding surface, disturbance observation value and auxiliary signal can realize the precise control of the intelligent networked fleet, which helps to ensure that the fleet maintains a stable queue configuration and speed during the driving process in the interchange area. Secondly, by establishing a nonlinear disturbance observer and a saturation model to overcome the influence of adverse factors, the Lyapunov method is used to prove that the controller guarantees the closed-loop stability of the system, and finally achieves the consistency of vehicle speed and vehicle spacing. Finally, due to the introduction of the nonlinear function of vehicle following in the interchange area in the integral sliding surface, the coupling sliding mode and transfer function are used to ensure that the fleet satisfies the stability of the fleet, and no negative vehicle spacing and speed appear, so that the driving of the intelligent networked fleet in the interchange area conforms to the traffic flow theory. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0068] Figure 1 The logic block diagram of the sliding mode control method for the intelligent fleet of interchanges considering disturbances and actuator saturation;

[0069] Figure 2 Schematic diagram of the intelligent connected vehicle platoon driving in the interchange area. DETAILED DESCRIPTION

[0070] 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.

[0071] The following is a further detailed description through specific implementation methods:

[0072] Example:

[0073] This embodiment discloses a sliding mode control method for an intelligent fleet of interchanges taking into account disturbances and actuator saturation.

[0074] like Figure 1 and Figure 2 As shown in FIG, the sliding mode control method for an intelligent fleet of interchanges considering disturbances and actuator saturation includes:

[0075] S1: Construct a third-order dynamic model considering disturbances for intelligent connected vehicle fleets;

[0076] S2: Construct a third-order dynamic model considering actuator saturation for intelligent connected vehicle fleets;

[0077] S3: Establish an intelligent connected vehicle following model that takes into account the expected speed of the interchange area section;

[0078] S4: Based on the intelligent connected vehicle following model, the integral sliding surface is constructed by combining the vehicle position tracking error and speed error, and the coupled sliding surface is further generated;

[0079] S5: Based on the third-order dynamics model considering disturbances, a nonlinear disturbance observer is constructed to generate disturbance observations;

[0080] S6: Construct an auxiliary dynamic system based on the third-order dynamic model considering actuator saturation to generate auxiliary signals;

[0081] S7: To achieve the convoy control goal, a convoy controller is designed by combining the coupled sliding surface, disturbance observations and auxiliary signals.

[0082] S8: The fleet controller controls the motion state of the vehicles in the intelligent networked fleet, so that the driving of the intelligent networked fleet in the interchange area complies with the traffic flow theory, and ensures the consistency of the position and speed of the vehicles in the fleet and the stability of the fleet. At the same time, it can also take into account the influence of disturbances and actuator saturation to ensure that the distance between vehicles and speed of the fleet converges over time.

[0083] On the basis of constructing a third-order dynamic model considering disturbance, the present invention further constructs a nonlinear disturbance observer to generate disturbance observations. First, by constructing a third-order dynamic model considering disturbance, the dynamic behavior of the intelligent networked vehicle fleet in a complex environment can be more accurately described, including external disturbance factors such as uneven road surface and wind resistance, and the nonlinear disturbance observer can estimate and compensate for these disturbances in real time, thereby enhancing the system's resistance to external disturbances and improving the robustness of the intelligent networked vehicle fleet. Secondly, the disturbance observations generated by the disturbance observer are used in the design of the controller as part of the control input for compensation, which helps to reduce the control error caused by disturbance and improve the accuracy and stability of the vehicle fleet control. Finally, in the interchange area, the traffic environment is complex and changeable, and there are many disturbance factors. By constructing a model considering disturbance and a disturbance observer, the intelligent networked vehicle fleet can better adapt to this complex environment and ensure the safety and stability of the vehicle fleet during driving in the interchange area.

[0084] Based on the construction of a third-order dynamic model that takes actuator saturation into consideration, the present invention further constructs an auxiliary dynamic system to generate auxiliary signals. First, actuator saturation is a common problem in the control of intelligent connected vehicle fleets, especially in complex traffic environments. By constructing a model that takes actuator saturation into consideration, it is possible to predict and avoid actuator overload and protect the actuator from damage. Secondly, the auxiliary signal generated by the auxiliary dynamic system can be used to adjust the output of the controller to ensure that good control effects can still be achieved when the actuator is saturated, which helps to optimize control performance and reduce control errors and instability caused by actuator saturation. Finally, actuator saturation may cause vehicle control failure or loss of control, posing a threat to the safety of intelligent connected vehicle fleets. By constructing a model that takes actuator saturation into consideration and an auxiliary dynamic system, the stability of the system in the interchange area can be effectively improved.

[0085] On the basis of constructing an intelligent connected vehicle following model that considers the expected speed of the road section in the interchange area, the present invention further constructs an integral sliding surface and generates a coupled sliding surface in combination with the tracking error of the vehicle position and speed. First, the following model that considers the expected speed of the road section in the interchange area can ensure that the intelligent connected vehicle fleet meets the actual traffic flow characteristics during driving, and reduce the occurrence of unreasonable phenomena such as negative vehicle spacing and negative speed. Secondly, the integral sliding surface and coupled sliding surface constructed in combination with the tracking error of the vehicle position and speed can achieve rapid and accurate tracking of the vehicle's motion state, improve control efficiency, and help the intelligent connected vehicle fleet maintain a stable driving state in a complex traffic environment. Finally, the integral sliding surface introduces a nonlinear function of vehicle following in the interchange area, and ensures that the fleet meets the string stability through the coupled sliding mode and transfer function, and at the same time, there is no negative vehicle spacing and speed, which is in line with the classical traffic flow theory.

[0086] The present invention uses the fleet control target, coupled sliding surface, disturbance observation value and auxiliary signal to design a fleet controller to control the motion state of vehicles in the intelligent networked fleet, so that the driving of the intelligent networked fleet in the interchange area conforms to the traffic flow theory, and ensures the consistency of the position and speed of the vehicles in the fleet and the stability of the fleet. First, the fleet controller designed by combining the coupled sliding surface, disturbance observation value and auxiliary signal can realize the precise control of the intelligent networked fleet, which helps to ensure that the fleet maintains a stable queue configuration and speed during the driving process in the interchange area. Secondly, by establishing a nonlinear disturbance observer and a saturation model to overcome the influence of adverse factors, the Lyapunov method is used to prove that the controller guarantees the closed-loop stability of the system, and finally achieves the consistency of vehicle speed and vehicle spacing. Finally, due to the introduction of the nonlinear function of vehicle following in the interchange area in the integral sliding surface, the coupling sliding mode and transfer function are used to ensure that the fleet satisfies the stability of the fleet, and no negative vehicle spacing and speed appear, so that the driving of the intelligent networked fleet in the interchange area conforms to the traffic flow theory.

[0087] In order to better introduce the technical solution of the present invention, this embodiment is described through the following parts.

[0088] 1. Dynamic Model of Leading Vehicle

[0089] In this embodiment, the final controller is designed only for the following vehicles, and the behavior of the leading vehicle belongs to the initial trajectory. Therefore, the dynamic model of the leading vehicle in the intelligent connected vehicle platoon is defined as follows:

[0090]

[0091] Where a0(t) is the acceleration of the leading car; v0(t) and p0(t) are the speed and position of the leading car at time t.

[0092] 2. Third-order dynamic model considering disturbance

[0093] In this embodiment, the force condition of the intelligent networked vehicle during longitudinal motion is analyzed, and a third-order dynamic model considering disturbance is constructed and expressed as:

[0094]

[0095] Where: a i (t) represents the acceleration of the i-th vehicle; v i (t) and p i (t) represents the speed and position of the i-th vehicle at time t; u i (t) represents the actuator input of the i-th vehicle; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; c i Indicates the air resistance coefficient; fi Indicates the mechanical resistance coefficient; D i (t) represents the disturbance caused by modeling errors and external interference to the vehicle, i.e., the lumped disturbance; D i (t) is bounded and the upper bound σ is unknown, satisfying |D i (t)|<σ,σ>0.

[0096] 3. Third-order dynamic model considering actuator saturation

[0097] In this embodiment, the third-order dynamic model considering actuator saturation is expressed as:

[0098]

[0099] The saturated model is:

[0100]

[0101] Where: a i (t) represents the acceleration of the i-th vehicle; v i (t) represents the speed of the i-th vehicle at time t; u i (t) represents the actuator input of the i-th vehicle; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; c i Indicates the air resistance coefficient; f i Indicates the mechanical resistance coefficient; D i (t) represents the disturbance caused by modeling errors and external disturbances to the vehicle, i.e., the lumped disturbance; u maxi and u mini They represent the upper and lower bounds of the known actuators respectively.

[0102] 4. Intelligent Connected Vehicle Following Model

[0103] In this embodiment, the intelligent connected vehicle following model considering the expected speed of the interchange area section is expressed as:

[0104]

[0105]

[0106] PΔω i (t) = Pω i+1 (t)-Pω i (t);

[0107] P=r b / cosθ;

[0108]

[0109]

[0110] Where: represents the acceleration of the i-th vehicle at time t; φ represents the arc length of the interchange area; r b represents the radius of the interchange road curve; θ represents the inclination angle of the interchange road; P represents the curvature radius of the interchange area; α represents the sensitivity coefficient; v g represents the expected speed information of the road section; λ represents the speed difference feedback coefficient; PΔφ i (t) represents the distance between the i-th vehicle and the i+1-th vehicle; PΔω i (t) represents the speed difference between the i-th vehicle and the i+1-th vehicle; γ represents the reaction coefficient; G(PΔφ i (t)) represents the optimal speed function of the i-th vehicle at time t; ω max g represents the maximum angular velocity of the vehicle, + represents downhill, - represents uphill; s represents the safety distance when there is no angle; β is a constant, β = 1; χ represents the longitudinal friction coefficient; v g,max Indicates the maximum increase or decrease speed in the interchange area; for simplicity, v can be taken g,max = sinθ.

[0111] 5. Tracking Error

[0112] In this embodiment, the platoon vehicle tracking error is defined as:

[0113] e i (t) = d i (t)-d ei (t);

[0114] d i (t) = p i－1 (t)-p i (t)-L;

[0115] d ei (t) = r + hv i (t);

[0116] To handle the large transient problem caused by the non-zero initial vehicle position tracking error, the corrected vehicle position tracking error is expressed as:

[0117] Ξ i (t) = e i (t)-π i (t);

[0118]

[0119] Where: i(t) represents the corrected vehicle position tracking error; e i (t) represents the uncorrected vehicle position tracking error; d i (t) represents the actual distance between two consecutive vehicles; L represents the length of the vehicle; d ei (t) represents the desired distance between vehicles with a fixed headway; r represents the safe distance when the vehicle is stationary; h represents the headway; v i (t) and p i (t) represents the speed and position of the i-th vehicle at time t.

[0120] According to the above formula, we can get: i (0) = e i (0), It can be concluded that

[0121] 6. Coupled Sliding Surface

[0122] In this embodiment, the integral sliding surface under the constraint of the car-following behavior in the interchange area is designed as:

[0123]

[0124] Φ i (t) = α[G(PΔφ i (t))-Pω i (t)]+λPΔω i (t);

[0125] Γ i (t) = γ(v g -Pω i (t));

[0126] Where: s i (t) represents the integral sliding surface; β1 and β2 are positive constants; Pω i (t) represents the speed of the i-th vehicle at time t; G(PΔφ i (t)) represents the optimal speed function of the i-th vehicle at time t; λ represents the speed difference feedback coefficient; PΔω i (t) represents the speed difference between the i-th vehicle and the i+1-th vehicle; γ represents the reaction coefficient; v g Indicates the expected speed information of the road section.

[0127] This embodiment introduces the constraint function of the following behavior in the interchange area into the sliding surface to ensure that the longitudinal following behavior of vehicles in the interchange area queue complies with the traffic flow theory, for example, to avoid negative inter-vehicle distance errors. In addition, in order to ensure the string stability of the longitudinal driving of the fleet in the interchange area, a coupled sliding surface is further introduced:

[0128]

[0129] Where: S i (t) represents the coupled sliding surface; q represents the positive constant to be designed; N represents the total number of vehicles. Since the N+1 vehicle does not exist,

[0130] 7. Nonlinear Disturbance Observer

[0131] In this embodiment, the nonlinear disturbance observer is expressed as:

[0132]

[0133] Where: L i (x) represents the unknown function to be designed; w i (t) represents the state of the nonlinear disturbance observer; Denotes the disturbance D i The observed value of (t); u i (t) represents the actuator input of the i-th vehicle; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; c i Indicates the air resistance coefficient; f i Represents the mechanical resistance coefficient; a i (t) represents the acceleration of the i-th vehicle; v i (t) represents the speed of the i-th vehicle at time t.

[0134] 8. Auxiliary dynamic system

[0135] In this embodiment, the auxiliary dynamic system is expressed as:

[0136]

[0137] Δu i (t) = sat(u i (t))-u i (t);

[0138] Where: i (t) represents the auxiliary signal; k4 represents the design parameter and is a positive real number; h represents the headway; q represents the normal number to be designed; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; u i (t) represents the actuator input of the i-th vehicle; u maxi and u mini They represent the upper and lower bounds of the known actuators respectively.

[0139] 9. Fleet Control Objectives

[0140] In this embodiment, the fleet control objectives include:

[0141] 1) Single-vehicle stability, that is, each following vehicle in the intelligent connected vehicle fleet can eventually reach the same speed and maintain the desired distance between each other;

[0142] 2) The platoon is stable, that is, the distance error between two consecutive vehicles in the intelligent connected platoon eventually decreases over time and satisfies the tracking error transfer function |G i (s)|=|E i+1 (s) / E i (s)|≤1, that is, the vehicle behavior conforms to the classical traffic flow theory; where E i (s) is e i The Laplace transform function of (t), e i (t)≥0.

[0143] For the convenience of analysis, this embodiment unifies some mathematical symbols:

[0144] Let PΔφ i (t) = d i (t), Pω i (t) = v i (t), PΔω i (t) = Δv i (t), Pω max =v max , g s (θ) = r;

[0145] but:

[0146]

[0147] Φ i (t) = α[G(d i (t))-v i (t)]+λΔv i (t)

[0148] Γ i (t) = γ(v0-v i (t)) (here the expected speed of the road section is taken as the leading vehicle speed).

[0149] 10. Fleet Controller

[0150] In this embodiment, the fleet controller is represented as:

[0151]

[0152] Where: u i (t) represents the actuator input of the i-th vehicle; m irepresents the mass of the i-th vehicle; τ i represents the engine time constant; h represents the headway time; q represents the normal constant to be designed; represents the estimated value of the upper bound of the unknown aggregate interference σ; S i (t) represents the coupled sliding surface; λ1, λ2 and λ3 represent control parameters; χ i (t) represents auxiliary signal; Denotes the disturbance D i The observed value of (t); c i Indicates the air resistance coefficient; a i (t) represents the acceleration of the i-th vehicle; v i (t) represents the speed of the i-th vehicle at time t; f i Represents the mechanical resistance coefficient; i (t) represents the corrected position tracking error; β2 is a positive constant; PΔω i (t) represents the speed difference between the i-th vehicle and the i+1-th vehicle; γ represents the reaction coefficient; Pω i (t) represents the speed of the i-th vehicle at time t; G(PΔφ i (t)) represents the optimal speed function of the i-th vehicle at time t; λ represents the speed difference feedback coefficient; v g represents the expected speed information of the road section; η represents a design constant greater than zero; o∈(0,1).

[0153] 11. System and convoy stability

[0154] In this embodiment, after the movement state of the intelligent networked vehicle fleet in the system is controlled by the vehicle fleet controller:

[0155] 1) When 2k4-λ3-1>0, 2λ1-λ3-1>0 and L i When (x)>0, the system is stable.

[0156] To prove the stability of the fleet system, the following Lyapunov function of the fleet is constructed:

[0157]

[0158] in Represents the Lyapunov function for a bicycle.

[0159] The following expression is obtained through theoretical derivation:

[0160]

[0161] in, It can be further seen that V(t) is bounded in finite time, which also means that all signals in the closed-loop system are ultimately bounded.

[0162] 2) When 2k4-λ3-1>0, 2λ1-λ3-1>0 and L i When (x)>0 and q satisfies q∈(0,1], the fleet string in the entire interchange area is stable.

[0163] To prove the stability of the convoy, the error transfer function expression between adjacent vehicles is obtained as follows:

[0164]

[0165] Among them, M i (s) is the function Φ i (t)+Γ i (t) is the Laplace transform. Then we can get:

[0166]

[0167] Therefore, when q∈(0,1], |G i (s)|≤1, at this time, the longitudinal driving of the fleet in the interchange area scenario satisfies the string stability.

[0168] 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 sliding mode control method for an intelligent fleet of interchange vehicles considering disturbances and actuator saturation, characterized in that: include: S1: Construct a third-order dynamic model considering disturbances for intelligent connected vehicle fleets; S2: Construct a third-order dynamic model considering actuator saturation for intelligent connected vehicle fleets; S3: Establish an intelligent connected vehicle following model that takes into account the expected speed of the interchange area section; S4: Based on the intelligent connected vehicle following model, the integral sliding surface is constructed by combining the vehicle position tracking error and speed error, and the coupled sliding surface is further generated; S5: Based on the third-order dynamics model considering disturbances, a nonlinear disturbance observer is constructed to generate disturbance observations; S6: Construct an auxiliary dynamic system based on the third-order dynamic model considering actuator saturation to generate auxiliary signals; S7: To achieve the convoy control goal, a convoy controller is designed by combining the coupled sliding surface, disturbance observations and auxiliary signals. S8: The movement state of the vehicles in the intelligent connected fleet is controlled by the fleet controller, so that the driving of the intelligent connected fleet in the interchange area complies with the traffic flow theory, and the consistency of the position and speed of the vehicles in the fleet and the stability of the fleet are guaranteed.

2. The sliding mode control method for an intelligent fleet of interchanges considering disturbance and actuator saturation as claimed in claim 1, characterized in that: In step S1, the third-order dynamic model considering disturbance is expressed as: Where: a i (t) represents the acceleration of the i-th vehicle; v i (t) and p i (t) represents the speed and position of the i-th vehicle at time t; u i (t) represents the actuator input of the i-th vehicle; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; c i Indicates the air resistance coefficient; f i Indicates the mechanical resistance coefficient; D i (t) represents the total disturbance of the vehicle.

3. The sliding mode control method for an intelligent fleet of interchanges considering disturbances and actuator saturation as claimed in claim 1, characterized in that: In step S2, the third-order dynamic model considering actuator saturation is expressed as: Where: a i (t) represents the acceleration of the i-th vehicle; v i (t) represents the speed of the i-th vehicle at time t; u i (t) represents the actuator input of the i-th vehicle; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; c i Indicates the air resistance coefficient; f i Indicates the mechanical resistance coefficient; D i (t) represents the total disturbance of the vehicle; u maxi and u mini They represent the upper and lower bounds of the known actuators respectively.

4. The sliding mode control method for an intelligent fleet of interchanges considering disturbances and actuator saturation as claimed in claim 1, characterized in that: In step S3, the intelligent connected vehicle following model is expressed as: PΔω i (t)=Pω i+1 (t)-Pω i (t); P=r b / cosθ; Where: represents the acceleration of the i-th vehicle at time t; φ represents the arc length of the interchange area; r b represents the radius of the interchange road curve; θ represents the inclination angle of the interchange road; P represents the curvature radius of the interchange area; Pω i (t) represents the speed of the i-th vehicle at time t; α represents the sensitivity coefficient; v g represents the expected speed information of the road section; λ represents the speed difference feedback coefficient; PΔφ i (t) represents the distance between the i-th vehicle and the i+1-th vehicle; PΔω i (t) represents the speed difference between the i-th vehicle and the i+1-th vehicle; γ represents the reaction coefficient; G(PΔφ i (t)) represents the optimal speed function of the i-th vehicle at time t; ω max represents the maximum angular velocity of the vehicle, + represents the downhill situation, - represents the uphill situation; χ represents the longitudinal friction coefficient; g s Indicates the safety distance when there is no angle; β=1; v g,max Indicates the maximum increase or decrease speed in the interchange area.

5. The sliding mode control method for an intelligent fleet of interchanges considering disturbances and actuator saturation as claimed in claim 4, characterized in that: In step S4, in order to deal with the large transient problem caused by the non-zero initial vehicle position tracking error, the corrected vehicle position tracking error is expressed as: X i (t)=e i (t)-π i (t); e i (t)=d i (t)-d ei (t) d i (t)=p i-1 (t)-p i (t)-L; d ei (t)=r+hv i (t); Where: i (t) represents the corrected vehicle position tracking error; e i (t) represents the uncorrected vehicle position tracking error; d i (t) represents the actual distance between two consecutive vehicles; L represents the length of the vehicle; d ei (t) represents the desired distance between vehicles with a fixed headway; r represents the safe distance when the vehicle is stationary; h represents the headway; v i (t) and p i (t) represents the speed and position of the i-th vehicle at time t.

6. The sliding mode control method for an intelligent fleet of interchanges considering disturbances and actuator saturation as claimed in claim 5, characterized in that: In step S4, the integral sliding surface is expressed as: F i (t)=α[G(PΔφ i (t))-Pω i (t)]+λPΔω i (t); Γ i (t)=)v g -Pω i (t)); Where: s i (t) represents the integral sliding surface; β1 and β2 are positive constants; Pω i (t) represents the speed of the i-th vehicle at time t; G(PΔφi i (t)) represents the optimal speed function of the i-th vehicle at time t; λ represents the speed difference feedback coefficient; PΔω i (t) represents the speed difference between the i-th vehicle and the i+1-th vehicle; γ represents the reaction coefficient; v g Indicates the expected speed information of the road section; The coupled sliding surface is expressed as: Where: S i (t) represents the coupled sliding surface; q represents the positive constant to be designed; N represents the total number of vehicles.

7. The sliding mode control method for an intelligent fleet of interchanges considering disturbances and actuator saturation as claimed in claim 1, characterized in that: In step S5, the nonlinear disturbance observer is expressed as: Where: L i (x) represents the unknown function to be designed; w i (t) represents the state of the nonlinear disturbance observer; Denotes the disturbance D i The observed value of (t); u i (t) represents the actuator input of the i-th vehicle; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; c i Indicates the air resistance coefficient; f i Represents the mechanical resistance coefficient; a i (t) represents the acceleration of the i-th vehicle; v i (t) represents the speed of the i-th vehicle at time t.

8. The sliding mode control method for an intelligent fleet of interchanges considering disturbances and actuator saturation as claimed in claim 1, characterized in that: In step S6, the auxiliary dynamic system is expressed as: Δu i (t)=sat(u i (t))-u i (t); Where: i (t) represents the auxiliary signal; k4 represents the design parameter; h represents the headway; q represents the normal number to be designed; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; u i (t) represents the actuator input of the i-th vehicle; u maxi and u mini They represent the upper and lower bounds of the known actuators respectively.

9. The sliding mode control method for an intelligent fleet of interchanges considering disturbances and actuator saturation as claimed in claim 1, characterized in that: In step S7, the fleet control objectives include: 1) Single-vehicle stability, that is, each following vehicle in the intelligent connected vehicle fleet can eventually reach the same speed and maintain the desired distance between each other; 2) The platoon is stable, that is, the distance error between two consecutive vehicles in the intelligent connected platoon eventually decreases over time and satisfies the tracking error transfer function |G i (s)|=|E i+1 (s) / E i (s)|≤1, that is, the vehicle behavior conforms to the classical traffic flow theory; where E i (s) is e i The Laplace transform function of (t), e i (t)≥0.

10. The sliding mode control method for an intelligent fleet of interchanges considering disturbances and actuator saturation as claimed in claim 1, characterized in that: In step S7, the fleet controller is represented as: F i (t)=α[G(PΔφ i (t))-Pωi i (t)]+λPΔω i (t); C i (t)=γ(v g -Pω i (t)); Where: u i (t) represents the actuator input of the i-th vehicle; m i represents the mass of the i-th vehicle; τ i represents the engine time constant; h represents the headway time; q represents the normal constant to be designed; represents the estimated value of the upper bound of the unknown aggregate interference σ; S i (t) represents the coupled sliding surface; λ1, λ2 and λ3 represent control parameters; χ i (t) represents auxiliary signal; Denotes the disturbance D i The observed value of (t); c i Indicates the air resistance coefficient; a i (t) represents the acceleration of the i-th vehicle; v i (t) represents the speed of the i-th vehicle at time t; f i represents the mechanical resistance coefficient; β2 is a positive constant; PΔω i (t) represents the speed difference between the i-th vehicle and the i+1-th vehicle; γ represents the reaction coefficient; Pω i (t) represents the speed of the i-th vehicle at time t; G(PΔφ i (t)) represents the optimal speed function of the i-th vehicle at time t; i (t) represents the corrected vehicle position tracking error; λ represents the speed difference feedback coefficient; v g Indicates the expected speed information of the road section; η represents a design constant greater than zero; o∈(0,1).

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