Multi-vehicle cooperative control method and system considering time-varying directed communication topology

Abstract

The invention discloses a multi-vehicle cooperative control method and system considering time-varying directed communication topology, and belongs to the technical field of vehicle control. The method comprises the following steps: firstly, modeling an unreliable communication topological structure, and representing the unreliable communication topological structure by adopting a continuous time Markov chain in order to describe the randomness of the communication topology; secondly, respectively establishing longitudinal dynamic models for the following vehicle and the pilot vehicle; thirdly, designing a fully-distributed adaptive observer to obtain state estimation information of the pilot vehicle; and finally, constructing an optimization control problem by using the pilot vehicle state and the front vehicle state information observed by the self-vehicle observer, introducing constraint conditions including chord stability constraints, and solving the optimization problem by adopting a DMPC framework to obtain an optimal control quantity so as to realize tracking control of the pilot vehicle state.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle control, and in particular relates to a multi-vehicle cooperative control method and system considering time-varying directional communication topology. Background Art

[0002] With the acceleration of global urbanization, the rapid growth of the number of vehicles has led to increasingly serious problems such as traffic congestion and frequent traffic accidents, which has brought severe challenges to road traffic. In order to solve these problems, improve road capacity, enhance driving comfort and safety, and reduce energy consumption, multi-vehicle platoon collaborative control technology has emerged.

[0003] The core of multi-vehicle platoon cooperative control technology is to achieve the coordinated operation of each vehicle in the platoon through communication and collaboration between vehicles. This technology can improve the stability of traffic flow, reduce the number of stops, shorten the safe distance between vehicles, and thus improve the overall traffic efficiency. In addition, by optimizing the operation status of the platoon, it can also effectively reduce vehicle fuel consumption and reduce environmental pollution.

[0004] In the process of implementing multi-vehicle platoon cooperative control technology, time-varying communication topology is a key factor. As the relative position and speed of vehicles change continuously during driving, the communication conditions and communication quality also change over time, which causes the communication links between vehicles to change accordingly, forming a time-varying communication topology. This is an unreliable communication condition, and the switching of topology structures poses challenges to the information exchange between vehicles and the implementation of cooperative control strategies. Therefore, studying how to achieve effective platoon cooperative control under unreliable communication conditions has become an important research direction in this field.

[0005] Distributed Model Predictive Control (DMPC), as an advanced control strategy, is also playing an increasingly important role in this field. Model Predictive Control (MPC) is a model-based control strategy that predicts the future behavior of the system by optimizing the control input and can explicitly consider the control input and system constraints. DMPC combines the optimization capability of MPC with the flexibility of distributed control, making it ideal for dealing with complex dynamic systems and therefore suitable for studying multi-vehicle platoon systems. In platoon control, the state error of any vehicle in the platoon may affect other vehicles and may even be amplified downstream in the traffic flow. Therefore, an important issue in platoon control is to ensure the chord stability of the platoon, which means that the disturbance upstream of the traffic flow can be weakened downstream of the traffic flow. The advantages of DMPC make it suitable for solving the control problem of vehicle platoons, but existing research on the chord stability of vehicle platoons using DMPC is still limited.

[0006] Prior art 1:

[0007] The first technical solution considers the case of time-varying undirected communication topology and proposes a cyber-physical level safety control framework for the intelligent connected automated vehicle (CAV) fleet. First, at the network level, a continuous-time Markov chain is used to simulate the unreliable communication topology, where the communication topology is time-varying and it is difficult to obtain global information. Then, a fully distributed adaptive observer is designed for each follower vehicle to obtain the state information of the lead vehicle. Finally, at the physical level, an adaptive fault-tolerant controller is designed to track the observer state and ultimately achieve the consistent control goal.

[0008] Disadvantages of the first prior art: it adopts an undirected communication topology, which requires that the Laplace matrix of the queue system is symmetric, that is, the CAVs in the queue are interconnected in pairs. In practice, in order to ensure information security or when communication is limited, only one-way information transmission is allowed among the queue vehicles. At this time, the designed observer will not be able to meet the observation requirements, and the influence of the chord stability factor is not considered in the controller. The vehicle spacing error will increase with the increase of the size of the vehicle queue, which will have a great impact on the stability of the queue.

[0009] Prior art 2:

[0010] A hierarchical cooperative control scheme for heterogeneous connected vehicles is proposed. A hierarchical control framework consisting of an upper-level pilot vehicle state observer and a lower-level state tracking controller is used. The closed-loop stability with and without communication delay is considered, and a finite-time convergent observer is proposed to deal with chord stability under general communication topologies.

[0011] Disadvantages of the second existing technology: The solution of the second technology assumes that the communication link of the vehicle queue in the queue is valid, but in actual applications, the communication between vehicles may be disturbed, there is a possibility of communication link failure or communication packet loss, and the queue communication topology will also switch over time. In addition, in this solution, the adjustment of the control gain of the lower-level controller requires certain experience, and the robustness of the tracking control algorithm is difficult to guarantee. At the same time, in actual applications, the control input and state will be limited to a certain range, and the lower-level controller of this solution cannot handle the constraints of input and state.

[0012] Existing technology three:

[0013] The solution of Technology 3 addresses the problem of communication delay in vehicle formations. Under the communication topology of leader vehicle and follower vehicle, a constant spacing strategy is applied and an improved DMPC method is proposed to improve the control robustness of vehicle formations under communication delay. The reliability of queue control is guaranteed by introducing robust constraints and chord stability constraints in the optimization problem.

[0014] Disadvantages of the existing technology 3: The solution of technology 3 requires a specific communication topology to achieve formation cooperative control. In this structure, each following vehicle only communicates with the preceding vehicle and the pilot vehicle. This structure simplifies the communication mode and limits the flexibility and scalability of the system. In practical applications, due to unreliable communication and communication distance limitations, it may be impossible to maintain communication with the pilot vehicle, which will have a significant impact on the entire formation control.

[0015] Prior art four:

[0016] The solution of technology four constructs a queue system model that takes the Bernoulli packet loss model into consideration, and designs a distributed controller based on a directed spanning tree communication topology with the pilot vehicle as the root node.

[0017] Disadvantages of the existing technology four: The solution of technology four adopts the Bernoulli packet loss model to simulate the packet loss phenomenon in unreliable communication. This is a simplified random packet loss model, which assumes that the packet loss event of each data packet is independent, that is, whether the previous data packet is lost does not affect the packet loss probability of the next data packet. In actual applications, communication packet loss has the characteristics of continuity, and the packet loss rate will change over time. The Bernoulli packet loss model cannot simulate the unreliable communication topology well. Summary of the invention

[0018] The purpose of an embodiment of the present invention is to provide a multi-vehicle cooperative control method considering a time-varying directed communication topology, which constructs a vehicle platoon system by establishing an unreliable communication topology model and a vehicle longitudinal dynamics model, designs a fully distributed adaptive observer and a DMPC controller, and ensures the chord stability of the platoon by imposing constraints on the optimization problem, so as to realize the vehicle platoon cooperative control under the time-varying directed communication topology, thereby solving at least one technical problem involved in the background technology.

[0019] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0020] An embodiment of the present invention provides a multi-vehicle cooperative control system considering a time-varying directed communication topology, including a communication module, an observation module, a calculation optimization module, and an actuator module, wherein:

[0021] The communication module is used to establish an unreliable communication topology model based on a continuous-time Markov process to describe a randomly switched communication topology based on vehicle-to-vehicle communication technology, receive the state estimation of the pilot vehicle and the vehicle state information of the preceding vehicle from the neighboring vehicle, and send them to the observation module;

[0022] The observation module is used to establish a pilot vehicle longitudinal dynamics model for characterizing the dynamic characteristics of the pilot vehicle, and to design an adaptive observer for observing the state information of the pilot vehicle based on the dynamics model of the pilot vehicle and the unreliable communication topology model, and to generate an estimate of the state of the pilot vehicle by the ego vehicle, and send it to the calculation optimization module together with the vehicle state of the leading vehicle;

[0023] The calculation optimization module is used to establish a longitudinal dynamics model of the following vehicle for characterizing the dynamic characteristics of the following vehicle, and to design a DMPC tracking controller based on the longitudinal dynamics model of the following vehicle, the estimation of the state of the pilot vehicle by the ego vehicle, the state of the leading vehicle and the state of the ego vehicle, and to output the optimal expected acceleration obtained by solving the optimization problem to the actuator module;

[0024] The actuator module is used to convert the optimal expected acceleration into a direct control signal of the actuator, and at the same time monitor the vehicle state of the vehicle and output it to the calculation optimization module.

[0025] Optionally, the communication topology of random switching is represented by the random switching graph indicates that, and The communication topology representing the vehicle platoon will randomly switch between l different graphs if and only if σ(t) = p∈{1,2,…,l}, Through the transfer rate matrix Define an infinitesimal generator of σ(t). For any positive scalar Δt, when Δt→0, it is defined as follows:

[0026]

[0027] In the formula, μ pq is the transition rate from state p to state q, if p≠q then μ pq ≥0, if p=q then μ pp =-∑ p≠q μ pq ,o(Δt) represents the infinitesimal value of Δt, satisfying

[0028] Optionally, the following vehicle longitudinal dynamics model is expressed by the following formula:

[0029]

[0030] In the formula, p i (t), v i (t) and a i (t) represent the position, velocity and acceleration of the following vehicle i respectively; η T,i and r w,i Respectively represent mechanical efficiency and tire radius; T i (t) and T des,i (t) represent the actual and expected driving or braking torque of the following vehicle i, respectively; m i and g represent the mass of the following vehicle and the gravity constant respectively; C A,i and f r,i Respectively refer to the lumped air resistance coefficient and the rolling resistance coefficient; τ i represents the inertial time lag following the longitudinal dynamics of the vehicle and satisfies

[0031] Using feedback linearization techniques for linearization, the desired driving or braking torque is:

[0032]

[0033] In the formula, is the new control input; thus, a third-order linear model of the following vehicle is established:

[0034]

[0035] Right now:

[0036]

[0037] In the formula, A i is the system matrix, B iis the input matrix, and:

[0038]

[0039] Optionally, the longitudinal dynamics model of the pilot vehicle is expressed by the following formula:

[0040]

[0041] In the formula,

[0042] Optionally, the adaptive observer is expressed by the following formula:

[0043]

[0044] In the formula, is the observation error, and is defined is the observed value of the observer, Estimating the position of the pilot vehicle;

[0045] Estimate speed for the pilot vehicle; is the estimated acceleration of the pilot car; x0(t) is the state of the pilot car; φ i (t) is the relative observation error, defined as:

[0046]

[0047] make where ρ i (0)≥1,g i (ρ i ) is ρ i is a nonlinear increasing nonnegative function of .

[0048] The present invention also provides a multi-vehicle cooperative control method considering time-varying directional communication topology based on the multi-vehicle cooperative control system considering time-varying directional communication topology, comprising the following steps:

[0049] Step 1: Initialize at time t = 0 and set the initial states x0(0) and x i (0), i∈{1,2,…,N} and the observer initial value

[0050] Step 2: Initialize the assumed trajectory of the following vehicle and set the assumed estimated state trajectory Assuming the control input trajectory Assume state trajectory Generated by the following formula:

[0051]

[0052] Step 3: At t>0, broadcast the estimated state of the ego vehicle to the pilot vehicle Assume state trajectories for neighboring vehicles and the vehicle itself To the following car;

[0053] Step 4: Receive the estimated state of the pilot vehicle from neighboring vehicles and the front vehicle’s assumed state trajectory

[0054]

[0055] Step 5: Generate the estimated state trajectory of the ego vehicle to the pilot vehicle

[0056]

[0057] Step 6: Solve the optimization problem Generate optimal control input trajectory sequence and the optimal state trajectory

[0058] Step 7: Extract the optimal control input trajectory sequence The first element of applied to a control actuator module;

[0059] Step 8: Update the assumed control input trajectory and the assumed state trajectory of the ego vehicle Updated by the following formula:

[0060]

[0061] Step 9: Update the ego vehicle observer state according to the update law designed for the adaptive observer

[0062]

[0063] Step 10: Determine whether the platoon driving task is completed. If it is completed, end the algorithm process; otherwise, return to step 3.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. Compared with the prior art 1, the adaptive observer designed in the present invention can be applied to the unidirectional random switching communication topology with the pilot vehicle as the root node, further relaxing the restrictions on the communication topology conditions, and can achieve accurate estimation of the state of the pilot vehicle without the information of the following vehicle, and can achieve chord stability of the vehicle queue through constraint conditions.

[0066] 2. Compared with the second prior art, the adaptive observer designed in the present invention has stronger robustness and can be applied to the situation of randomly switching communication topology. At the same time, the designed DMPC controller is an optimal control algorithm that can explicitly handle constraints and obtain the optimal control quantity.

[0067] 3. Compared with the prior art 3, the present invention can be applied to multiple communication topologies, thereby enhancing the flexibility and scalability of the queue system.

[0068] 4. Compared with the prior art 4, the present invention adopts Markov chain to simulate unreliable communication situations including communication packet loss, and can adapt to the working conditions where the network conditions are constantly changing and the packet loss is continuous.

[0069] 5. The present invention uses a continuous-time Markov chain to simulate unreliable communication topology, which can adapt to the working conditions where network conditions are constantly changing and packet loss is continuous. Compared with the method of modeling communication packet loss as a Bernoulli model, this method also regards communication packet loss as a communication topology structure, which can be included in a unified research framework for research, thereby enhancing the flexibility and scalability of the queue system.

[0070] 6. The fully distributed adaptive observer designed in the present invention is suitable for time-varying directed communication topology. Compared with the time-varying undirected communication topology, it reduces the amount of information required, relaxes the restrictions on communication topology conditions, and can accurately estimate the state of the lead vehicle without the information of the following vehicle, thereby enhancing the robustness of the system under unreliable communication conditions.

[0071] 7. The present invention designs a DMPC controller, which uses the information of the upper observer and the information of the preceding vehicle to construct the cost function and constraint conditions. Compared with the non-optimized control algorithm, it can explicitly handle the constraints and obtain the optimal control quantity, thereby improving the control accuracy and efficiency. Moreover, by adding the chord stability constraint, it can ensure that the spacing error in the queue system will not be amplified in the process of propagating downstream along the queue, thereby improving the stability and safety of the queue. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0073] Figure 1 A schematic diagram of the framework of a multi-vehicle cooperative control system considering a time-varying directional communication topology provided by the present invention;

[0074] Figure 2A possible communication topology diagram under unreliable communication conditions provided by the present invention;

[0075] Figure 3 A schematic flow chart of a multi-vehicle cooperative control method considering a time-varying directional communication topology provided by the present invention. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] The terms "first", "second", etc. in the specification and claims of the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0078] See also Figure 1 As shown, an embodiment of the present invention provides a multi-vehicle cooperative control system considering a time-varying directional communication topology, including a communication module, an observation module, a calculation optimization module and an actuator module.

[0079] The communication module is used to establish an unreliable communication topology model based on a continuous-time Markov process to describe a randomly switched communication topology based on vehicle-to-vehicle communication technology, receive the state estimation of the pilot vehicle and the vehicle state information of the preceding vehicle from the neighboring vehicle, and send them to the observation module.

[0080] It should be further explained that the communication topology of the queue system is the information interaction relationship between CAVs in the queue, which can be represented by graph theory. Graphs can be divided into two types: directed graphs and undirected graphs. Two nodes in a directed graph can only transmit information in one direction, while nodes in an undirected graph can exchange information in two directions. Indicates that, is a point set, representing the set of vehicles in the platoon system, is an edge set, indicating that there is one-way information transmission between vehicles in the queue system, and N is the total number of following vehicles in the queue system.

[0081] In addition, if there is a The tree subgraph of all nodes is called Contains a directed spanning tree. Furthermore, if the directed spanning tree has the pilot car as the initial node, it is called It contains a directed spanning tree with the pilot vehicle as the root node. Assuming that the following vehicles i∈{1,2,…N}, define the adjacency matrix When car i can receive information from car j, a ij =1, otherwise a ij = 0. Assume there is no self-loop, that is, a ii = 0. Define the traction matrix When the i car can receive the information of the pilot car, ω i0 =1, otherwise ω i0 = 0. Define the Laplacian matrix When i=j, When i≠j, l ij =-a ij . Define the information flow matrix The present invention only relies on unidirectional information to realize queue cooperative control, which is an asymmetric communication topology structure. The Laplace matrix of this type of communication topology is also asymmetric.

[0082] The application of V2V communication can improve the road safety of the vehicle platoon system and reduce fuel consumption. However, in actual applications, V2V communication may encounter interference from various factors, such as channel fading, communication congestion, etc., which may make V2V communication unreliable. This unreliability may cause communication packet loss and switching of communication topology, posing a potential threat to the safety of the vehicle platoon system. In order to describe the unreliability and randomness of actual communication, a continuous-time Markov model is used to characterize the time-varying communication topology. Considering the simplicity and uniformity of modeling, the present invention also models communication packet loss as a communication topology type with partial link failure.

[0083] The communication topology between CAVs is a random switching graph. express,

[0084] The communication topology representing the vehicle platoon will randomly switch between l different graphs if and only if σ(t) = p∈{1,2,…,l}, Here is directed. Through the transfer rate matrix Define an infinitesimal generator of σ(σ), for any positive scalar Δt, when Δt→0, it is defined as follows:

[0085]

[0086] Where η pq is the transition rate from state p to state q, if p≠q then η pq ≥0; if p=q then μ pp =-∑ p≠q μ pq ,o(Δt) represents the infinitesimal value of Δt, satisfying

[0087] The unreliable communication topology model assumes that the switching process of the communication topology is controlled by a continuous-time Markov process {σ(t), t≥0}, where the transition rate matrix μ is ergodic. This means that no matter what the initial state is, the process will eventually visit all its possible states with the same probability, and the long-term statistical characteristics are stable.

[0088] For a continuous-time Markov process, there exists a probability vector π = [π1,π2,…π l ] T ,satisfy And π q ≥0,n=1,2,…,l. It describes the probability of the process reaching various states after a long period of operation. This probability vector is fixed, that is, the steady-state distribution. No matter what state the process starts from, it will eventually tend to this probability distribution.

[0089] In the current research on vehicle platoon control, the most commonly used unidirectional communication topologies are predecessor following (PF) and predecessor-leader following (PLF). Considering the unreliable communication situation, some links will fail, that is, communication packet loss will occur, such as Figure 2 As shown, it is a schematic diagram of the time-varying communication topology under unreliable communication considered by the present invention. The switching of these four communication topology diagrams is modeled as a continuous-time Markov process, and each diagram is a state of the process.

[0090] The observation module is used to establish a longitudinal dynamic model of the pilot vehicle for characterizing the dynamic characteristics of the pilot vehicle, and to design an adaptive observer for observing the state information of the pilot vehicle based on the dynamic model of the pilot vehicle and the unreliable communication topology model, generate the self-vehicle's estimate of the pilot vehicle's state, and send it to the calculation optimization module together with the vehicle state of the leading vehicle.

[0091] The computational optimization module is used to establish a longitudinal dynamics model of the following vehicle for characterizing the dynamic characteristics of the following vehicle, and to design a DMPC tracking controller based on the longitudinal dynamics model of the following vehicle, the estimation of the state of the pilot vehicle by the ego vehicle, the state of the leading vehicle and the state of the ego vehicle, and to output the optimal expected acceleration obtained by solving the optimization problem to the actuator module.

[0092] It should be further explained that for the longitudinal dynamics modeling of the pilot vehicle and the following vehicle, the present invention considers a vehicle platoon consisting of CAVs, which consists of a pilot vehicle and N following vehicles, with labels 0, 1, ..., N. All CAVs are coupled into a platoon system through interrelated sensors and V2V communication networks, and each CAV uses the vehicle status information broadcast by the adjacent CAVs and the estimated pilot vehicle information to adjust its own speed and acceleration in real time. The control goal of the vehicle platoon system is to adjust the vehicle spacing based on a certain spacing strategy and make the speed and acceleration errors with the pilot vehicle zero.

[0093] The present invention considers the longitudinal driving process of a vehicle formation with heterogeneous parameters in a highway scenario, and makes the following assumptions and simplifications to the vehicle dynamics model:

[0094] 1) Only the longitudinal vehicle motion is considered. The vehicle travels in a straight direction and the motion in other directions is ignored. That is, there is no vertical bumping and no lateral deviation motion.

[0095] 2) The vehicle is considered as a bilaterally symmetrical rigid body, and the load transfer effect of the suspension system is ignored;

[0096] 3) Assuming the vehicle is driving on a smooth and good road, the tires will not slip longitudinally;

[0097] 4) Assume that the input and output characteristics of the vehicle's power system can be described by a first-order inertia link.

[0098] When the assumptions are met, the simplified vehicle longitudinal dynamics model is analyzed. According to Newton's second law, the longitudinal dynamics model of the following vehicle i can be obtained:

[0099]

[0100] In the formula, p i (t), v i (t) and a i (t) represent the position, velocity and acceleration of the following vehicle i respectively; η T,i and r w,i Respectively represent mechanical efficiency and tire radius; T i (t) and T des,i (t) represents the actual and expected driving or braking torque of the following vehicle i; m iand g represent the mass of the following vehicle and the gravity constant respectively; C A,i and f r,i Respectively refer to the lumped air resistance coefficient and the rolling resistance coefficient; τ i represents the inertial time lag of the following vehicle longitudinal dynamics, satisfying

[0101] In order to facilitate the analysis of the control system, the common feedback linearization technology is used to linearize the nonlinear equations, and the desired driving or braking torque becomes:

[0102]

[0103] In the formula, is the new control input. Thus, the nonlinear model is transformed into a third-order linear model:

[0104]

[0105] This formula can be rewritten as:

[0106]

[0107] In the formula, A i is the system matrix, B i is the input matrix, and:

[0108]

[0109] The dynamic model of the virtual pilot car is expressed as in The status of the virtual pilot car can only be received by some of the following vehicles through V2V communication.

[0110] According to the established communication model and dynamic model, the goal of queue control can be expressed as follows:

[0111]

[0112] Where, d i0 represents the distance between the following vehicle i and the leading vehicle, d i0 =i·d0, d0 is the expected distance between the front and rear vehicles, represents the mathematical expectation, and ||·|| represents the Euclidean norm.

[0113] The adaptive observer includes an upper-layer fully distributed adaptive observer and a lower-layer distributed model predictive controller.

[0114] For the upper fully distributed adaptive observer, considering that the following vehicles downstream of the vehicle queue cannot directly obtain the information of the pilot vehicle, they can only estimate it through the observation information of the neighboring vehicles. Therefore, a fully distributed adaptive observer is designed at the upper network level to directly obtain the state of the pilot vehicle or indirectly estimate it from the observation information of the neighboring vehicles in the network. The upper fully distributed observer is designed as follows:

[0115]

[0116] in, is the observation error, and is defined is the observed value of the observer, Estimate the position of the pilot car, Estimate the speed for the pilot car, is the estimated acceleration of the pilot car, x0(t) is the state of the pilot car, φ i (t) is the relative observation error, defined as:

[0117]

[0118]

[0119] make where ρ i (0)≥1,g i (ρ i ) is ρ i is a nonlinear increasing nonnegative function of .

[0120] Through the update law of the observer, the observed state can be converged to the state of the pilot vehicle: Right now:

[0121]

[0122] For the lower-level distributed model predictive controller, the core idea of ​​DMPC is to decompose the entire system into multiple subsystems, each of which has its own MPC controller, and then connect the controllers in these subsystems through a communication protocol to achieve control of the entire system. In a platoon system, each following vehicle is a subsystem. MPC is an optimal control method, and optimal control usually requires finding a control law or control trajectory sequence that can optimize the objective function within the allowed control set. The core idea of ​​MPC is to construct an optimization problem based on the established system model, the system's control objectives, and the system's constraints, and solve an optimization problem in the prediction time domain at each moment to obtain the optimal control trajectory sequence, and then apply the first item of the control trajectory sequence to the current system. At the next moment, the optimization process rolls forward and continuously iterates to generate new control trajectory sequences and state trajectories.

[0123] Defining the sampling time The observation values ​​of the pilot vehicle obtained by the upper observer are discretized and sent to the lower controller. The DMPC controller design optimization problem of the discrete system is used to discretize the follower vehicle dynamics model:

[0124]

[0125] In the formula, is the discretized system matrix, is the discretized input matrix, which is defined as:

[0126]

[0127] In the prediction domain N p Within, three types of trajectories are defined:

[0128] 1) and The predicted state trajectory and the predicted control input trajectory are the quantities to be optimized in the optimization problem.

[0129] 2) and The optimal state trajectory and the optimal control input trajectory are the optimal solutions obtained by solving the local optimization problem.

[0130] 3) and The assumed state trajectory and the assumed control input trajectory are the trajectories generated by the optimal trajectory.

[0131] The goal of the lower tracking controller is to converge the state of the following vehicle to the state of the leader vehicle estimated by the upper observer: Right now:

[0132]

[0133] The open-loop optimization problem is defined as

[0134]

[0135] Constraints:

[0136]

[0137] Here the cost function is defined as:

[0138]

[0139] and They are respectively and The generated estimated states of the self-car and the front car for the pilot car, the initial state Depend on renew.

[0140] When the state of each following vehicle’s fully distributed adaptive observer reaches the same state as the leading vehicle have:

[0141]

[0142] In the formula, R i 、F i , S i , G i They are all positive definite diagonal matrices, representing the weights of corresponding items; represents the penalty for the control input; represents the penalty for deviation from the previous moment’s assumed trajectory; represents the penalty for deviation from the state trajectory of the preceding vehicle; represents the penalty for the deviation of the ego vehicle's estimate of the pilot vehicle's state.

[0143] At the same time, for the constraints, is the initial state constraint, To follow the vehicle dynamics model constraints, is the control input trajectory constraint, where u lb is the minimum control quantity, u ub is the maximum control amount, is the terminal constraint, let The string stability constraint can be written as:

[0144]

[0145] In the formula, β i ∈(0,1], γ=[1,0,0];||·|| ∞ Indicates l ∞ norm; The purpose of the chord stability constraint is to ensure that spacing errors in the queue system are not amplified as they propagate downstream along the queue.

[0146] The actuator module is used to convert the optimal expected acceleration into a direct control signal of the actuator, and at the same time monitor the vehicle state of the vehicle and output it to the calculation optimization module.

[0147] Recombination Figure 3 As shown, the present invention also provides a multi-vehicle cooperative control method considering time-varying directional communication topology based on the multi-vehicle cooperative control system considering time-varying directional communication topology, comprising the following steps:

[0148] Step 1: Initialize at time t = 0 and set the initial states x0(0) and x i (0), i∈{1,2,…,N} and the observer initial value

[0149] Step 2: Initialize the assumed trajectory of the following vehicle and set the assumed estimated state trajectory Assuming the control input trajectory Assume state trajectory Generated by the following formula:

[0150]

[0151] Step 3: At t>0, broadcast the estimated state of the ego vehicle to the pilot vehicle Assume state trajectories for neighboring vehicles and the vehicle itself To the following car;

[0152] Step 4: Receive the estimated state of the pilot vehicle from neighboring vehicles and the front vehicle’s assumed state trajectory

[0153]

[0154] Step 5: Generate the estimated state trajectory of the ego vehicle to the pilot vehicle

[0155]

[0156] Step 6: Solve the optimization problem Generate optimal control input trajectory sequence and the optimal state trajectory

[0157] Step 7: Extract the optimal control input trajectory sequence The first element of applied to a control actuator module;

[0158] Step 8: Update the assumed control input trajectory and the assumed state trajectory of the ego vehicle Updated by the following formula:

[0159]

[0160] Step 9: Update the ego vehicle observer state according to the update law designed for the adaptive observer

[0161]

[0162] Step 10: Determine whether the platoon driving task is completed. If it is completed, end the algorithm process; otherwise, return to step 3.

[0163] Through the multi-vehicle cooperative control method considering the time-varying directed communication topology provided by the present invention, the vehicle platoon control target can be achieved, and the states of all following vehicles can converge to the desired state in the mean square:

[0164]

[0165] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0166] In addition, it should be noted that the scope of the methods and systems in the embodiments of the present invention is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

[0167] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A multi-vehicle cooperative control system considering time-varying directed communication topology, characterized in that: It includes communication module, observation module, calculation optimization module and actuator module, among which: The communication module is used to establish an unreliable communication topology model based on a continuous-time Markov process to describe a randomly switched communication topology based on vehicle-to-vehicle communication technology, receive the state estimation of the pilot vehicle and the vehicle state information of the preceding vehicle from the neighboring vehicle, and send them to the observation module; The observation module is used to establish a pilot vehicle longitudinal dynamics model for characterizing the dynamic characteristics of the pilot vehicle, and to design an adaptive observer for observing the state information of the pilot vehicle based on the dynamics model of the pilot vehicle and the unreliable communication topology model, and to generate an estimate of the state of the pilot vehicle by the ego vehicle, and send it to the calculation optimization module together with the vehicle state of the leading vehicle; The calculation optimization module is used to establish a longitudinal dynamics model of the following vehicle for characterizing the dynamic characteristics of the following vehicle, and to design a DMPC tracking controller based on the longitudinal dynamics model of the following vehicle, the estimation of the state of the pilot vehicle by the ego vehicle, the state of the leading vehicle and the state of the ego vehicle, and to output the optimal expected acceleration obtained by solving the optimization problem to the actuator module; The actuator module is used to convert the optimal expected acceleration into a direct control signal of the actuator, and at the same time monitor the vehicle state of the vehicle and output it to the calculation optimization module.

2. The multi-vehicle cooperative control system considering time-varying directional communication topology according to claim 1, characterized in that: The communication topology of random switching is given by the random switching graph indicates that, and The communication topology representing the vehicle platoon will randomly switch between l different graphs if and only if σ(t) = p∈{1,2,…,l}, Through the transfer rate matrix Define an infinitesimal generator of σ(t). For any positive scalar Δt, when Δt→0, it is defined as follows: In the formula, μ pq is the transition rate from state p to state q, if p≠q then μ pq ≥0, if p=q then μ pp =-∑ p≠q μ pq ,o(Δt) represents the infinitesimal value of Δt, satisfying 3. The multi-vehicle cooperative control system considering time-varying directional communication topology according to claim 2, characterized in that: The following vehicle longitudinal dynamics model is expressed as follows: In the formula, p i (t), v i (t) and a i (t) represent the position, velocity and acceleration of the following vehicle i respectively; η T,i and r w,i Respectively represent mechanical efficiency and tire radius; T i (t) and T des,i (t) represent the actual and expected driving or braking torque of the following vehicle i, respectively; m i and g represent the mass of the following vehicle and the gravity constant respectively; C A,i and f r,i Respectively refer to the lumped air resistance coefficient and the rolling resistance coefficient; τ i represents the inertial time lag following the longitudinal dynamics of the vehicle and satisfies Using feedback linearization techniques for linearization, the desired driving or braking torque is: In the formula, is the new control input; thus, a third-order linear model of the following vehicle is established: Right now: In the formula, A i is the system matrix, B i is the input matrix, and:

4. The multi-vehicle cooperative control system considering time-varying directional communication topology according to claim 3, characterized in that: The longitudinal dynamics model of the pilot vehicle is expressed as follows: In the formula, 5. The multi-vehicle cooperative control system considering time-varying directional communication topology according to claim 4, characterized in that: The adaptive observer is expressed as follows: In the formula, is the observation error, and is defined is the observed value of the observer, Estimating the position of the pilot vehicle; Estimate speed for the pilot vehicle; is the estimated acceleration of the pilot car; x0(t) is the state of the pilot car; φ i (t) is the relative observation error, defined as: make where ρ i (0)≥1,g i (ρ i ) is ρ i is a nonlinear increasing nonnegative function of .

6. A multi-vehicle cooperative control method considering time-varying directional communication topology based on the multi-vehicle cooperative control system considering time-varying directional communication topology according to any one of claims 1 to 5, characterized in that: The steps include: Step 1: Initialize at time t = 0 and set the initial states x0(0) and x i (0), i∈{1,2,…,N} and the observer initial value Step 2: Initialize the assumed trajectory of the following vehicle and set the assumed estimated state trajectory Assuming the control input trajectory Assume state trajectory Generated by the following formula: Step 3: At t>0, broadcast the estimated state of the ego vehicle to the pilot vehicle Assume state trajectories for neighboring vehicles and the self-vehicle To the following car; Step 4: Receive the estimated state of the pilot vehicle from neighboring vehicles and the front vehicle’s assumed state trajectory Step 5: Generate the estimated state trajectory of the ego vehicle to the pilot vehicle Step 6: Solve the optimization problem Generate optimal control input trajectory sequence and the optimal state trajectory Step 7: Extract the optimal control input trajectory sequence The first element of applied to a control actuator module; Step 8: Update the assumed control input trajectory and the assumed state trajectory of the ego vehicle Updated by the following formula: Step 9: Update the ego vehicle observer state according to the update law designed for the adaptive observer Step 10: Determine whether the platoon driving task is completed. If it is completed, end the algorithm process; otherwise, return to step 3.

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