Control method and device for vehicle following in lane keeping

Abstract

The present invention discloses a control method and device for vehicle platooning under lane keeping. The method comprises: constructing a platoon of vehicles on a road with slope and curvature; establishing a disturbed following control system for the platoon based on cooperative adaptive cruise control and lane keeping control, combined with the longitudinal and lateral dynamics of the vehicles; establishing a state feedback controller with feedforward compensation based on the acquired acceleration information of the leading vehicle and incorporating it into the disturbed following control system to obtain a platoon following model after feedforward compensation; processing nonlinear terms in the platoon following model and introducing a nonlinear saturation function to establish a saturation-constrained uncertain following vehicle prediction model; and using model predictive control technology to implement rolling optimization of performance targets based on the uncertain following vehicle prediction model to achieve longitudinal following and lateral lane keeping control of the vehicles. The present invention combines the longitudinal and lateral dynamics of the vehicles and lane keeping with cooperative adaptive cruise control on complex roads with slope and curvature to achieve safer and more stable platooning. By adopting model predictive control technology, the accuracy and timeliness of vehicle platoon following are improved.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to a control method, device, electronic device and computer-readable storage medium for vehicles following a platoon under lane keeping conditions. Background Art

[0002] With the rapid development of society and the economy, the automotive industry has flourished, bringing convenience to people's transportation lives. However, the sharp increase in motor vehicle ownership has also brought a series of problems that cannot be ignored, such as traffic congestion, traffic accidents, energy consumption, and environmental pollution. Urban traffic jams are common, causing cars to repeatedly start and stop, which also prevents fuel from being fully burned. This not only increases fuel consumption but also produces harmful substances. Frequent traffic accidents are often caused by improper driver operation. Therefore, there is an urgent need to develop intelligent driving technology to improve driving safety and reduce traffic accident rates, while also strengthening the control and planning of road vehicles to reduce traffic congestion.

[0003] Cooperative Adaptive Cruise Control (CACC) has emerged as an effective technology due to its significant potential for improving traffic throughput, safety, and fuel economy. While ensuring safety, several vehicles on the road are organized into a platoon. Using onboard sensors and wireless vehicle-to-vehicle (V2V) communication technology, the following vehicles in the platoon are controlled to track the speed of the leading vehicle while maintaining a safe distance between them. This improves road utilization, reduces traffic congestion, and achieves energy conservation and environmental protection. Traditional platoon cooperative adaptive cruise control designs are based on simple, flat, straight road scenarios. Consequently, they suffer from low modeling capabilities and rely solely on longitudinal control to achieve platooning. This makes them difficult to adapt to complex road conditions, resulting in poor control immunity and a tendency for vehicles to stray from designated lanes. Furthermore, the neglect of the inherent actuator saturation limitations in automotive systems reduces the control performance of the following vehicles. Summary of the Invention

[0004] In order to address the above-mentioned deficiencies in the prior art, the present invention provides a control method, device, electronic device and computer-readable storage medium for vehicles following a platoon under lane keeping.

[0005] The first object of the present invention is to provide a method for controlling vehicles traveling in a platoon while keeping lane.

[0006] A second object of the present invention is to provide a control device for vehicles following a convoy under lane keeping conditions.

[0007] A third object of the present invention is to provide an electronic device.

[0008] A fourth object of the present invention is to provide a computer-readable storage medium.

[0009] The first object of the present invention can be achieved by adopting the following technical solutions:

[0010] A method for controlling a vehicle following a platoon in lane keeping mode, the method comprising:

[0011] Constructing platoons of vehicles on roads with slopes and curvature; establishing a disturbed following control system for platoons based on coordinated adaptive cruise control and lane keeping control, combined with the longitudinal and lateral dynamics of the vehicles;

[0012] Based on the acquired acceleration information of the preceding vehicle, a state feedback controller with feedforward compensation is established and incorporated into the disturbed following control system to obtain a platoon following model with feedforward compensation.

[0013] Based on the platoon following model, the Lipschitz condition is used to transform the nonlinear terms into the quadratic product of linear terms. At the same time, a nonlinear saturation function is introduced to obtain a saturation-constrained uncertain following vehicle prediction model.

[0014] Based on the uncertain following vehicle prediction model, the model predictive control technology is used to optimize the performance target in a rolling manner, and the feedback control gain is updated in real time to achieve longitudinal following and lateral lane keeping control of the vehicle.

[0015] Furthermore, the method of using the model predictive control technology to perform rolling optimization of the performance target based on the uncertain following vehicle prediction model, updating the feedback control gain in real time, and solving the problem to achieve longitudinal following and lateral lane keeping control of the vehicle includes:

[0016] Based on the uncertain following vehicle prediction model, the controlled output z(k)=[y(k)(Gσ(u(k))) T ] T , the predictive control algorithm is designed to solve the following minimum-maximum robust performance objective:

[0017]

[0018] In the formula, the infinite time domain performance index y(k) is the output value of the system at the kth sampling moment, u(k) is the state feedback control at the kth sampling moment, z(k+i|k) is the predicted output value of the system at the kth sampling moment in the future k+i moment, G is the state feedback control weight matrix, σ(·) is the unit saturation function, that is, σ(u r (k))=sign(u r (k))min{1,|u r (k)|},u r (k) is the state feedback control corresponding to the dimension r; for non-unit saturation constraints, the weight matrix G is replaced based on Unitization processing, U=diag(u max ), diag(·) represents the diagonal block matrix, u max Enter a saturation constraint for maximum state feedback control.

[0019] Furthermore, in order to reduce the impact of the uncertain disturbance w(k+i|k) and improve the robustness of the uncertain following vehicle prediction model, a hybrid H2 / H ∞ The performance requirements are as follows:

[0020] H ∞ Performance requirements: Under zero initial conditions, for a given scalar λ>0, we have:

[0021]

[0022] H2 performance requirement: There exists a scalar γ(k)>0, so:

[0023]

[0024] Furthermore, set the quadratic Lyapunov function V(x(k+i|k))=x T (k+i|k)P(k)x(k+i|k), Where P(k) is the Lyapunov matrix at the k-th sampling moment, and x(k+i|k) is the predicted value of the system state at the k+i-th moment in the future at the k-th sampling moment;

[0025] Considering the uncertain following vehicle prediction model at sampling time k, given the adjustable parameters ε1>0, ε2>0, 0<∈<1, and the robustness rejection level λ, if there is a matrix Y(k), Z(k) and scalars γ(k), ξ(k)>0 so that the following minimization problem is θ=1,2,…,Θ can be solved:

[0026]

[0027] Robust stability constraints:

[0028]

[0029] Feasibility constraints:

[0030]

[0031] Input saturation auxiliary constraints:

[0032]

[0033] Output constraints:

[0034]

[0035] In the formula, x(k|k)=x(k), x(k) is the state value of the system at the kth sampling moment, w θ is the vertex of the cell body where the uncertain perturbation is located, is the upper bound of the finite perturbation energy, γ(k) is the upper bound of the target performance, 0 is the zero matrix of the corresponding dimension, I is the identity matrix of the corresponding dimension, is the rth row of the matrix Z(k), A l 、D l is the vertex of the cell where the system matrix is ​​located, B is the constant system input matrix, C is the system output matrix, W is the Lipschitz matrix, y max is the absolute value of the maximum inter-vehicle distance error, P(k)=γ(k)Q -1 (k), Y(k)=F(k)Q(k), Z(k)=H(k)Q(k), H(k) is the auxiliary state feedback controller gain at the kth sampling moment, E j is a diagonal matrix with diagonal elements of 0 or 1,

[0036] According to F(k)=Y(k)Q -1 (k) Design state feedback control law u(k)=F(k)x(k), through σ(u r (k))=sign(u r (k))min{1,|u r (k)|}, r=1,2,…,n u and Two-step saturation range restoration processing, adding feedforward compensation action u f (k), the final result The control input signal at time k is transmitted to the starting and / or braking and front-wheel steering actuators that control vehicle behavior. The optimization problem is solved in a rolling manner at each sampling moment to update the controller gains, thereby realizing the anti-disturbance saturation predictive control of the vehicle-following platoon system.

[0037] Furthermore, the state feedback controller with feedforward compensation is:

[0038]

[0039] Where, is the control input value of the system at the kth sampling moment; u f(k) = L(k)d(k) is the feedforward action at the kth sampling moment to compensate for the known disturbance d(k) related to the acceleration of the preceding vehicle, and L(k) is the feedforward gain at the kth sampling moment; u(k) = F(k)x(k) is the state feedback control at the kth sampling moment, F(k) is the feedback control gain matrix at the kth sampling moment, and x(k) is the state value at the kth sampling moment.

[0040] Furthermore, for the platoon following model, the output constraint of the system is set as: |y(k)|≤y max , and y max <d safe ; Among them, y(k) is the output value of the system at the kth sampling moment, y max d safe are the absolute value of the maximum inter-vehicle distance error and the given safe distance respectively;

[0041] Taking into account the inherent physical limitations of the actuator, the saturation constraint of the feedback control input is set as: Feedback control input saturation constraint |u r (k)|≤u r,max ,r=1,…,n u , n u is the dimension of the input vector, is the maximum input saturation level corresponding to dimension r, and is the maximum feedforward action level corresponding to dimension r.

[0042] Furthermore, the moving vehicle platoon is such that the front vehicle of any two consecutively moving vehicles is regarded as the leading vehicle and the rear vehicle is regarded as the following vehicle;

[0043] The establishment process of the disturbed car following control system is as follows:

[0044] According to the obtained position p of the leading vehicle l ,speed and acceleration a l , and the position p and velocity v of the following vehicle x and desired torque And the given safe distance d safe , based on the following error Δp=p l -pd safe and The longitudinal tracking error system of the following vehicle is established as:

[0045]

[0046] in:

[0047]

[0048] w1=0.5C d ρ air A f (v x +v wind ) 2 -mgμ c

[0049] Where, and e ψ are the lateral velocity, yaw rate and yaw angle error of the following vehicle relative to the lane, C d is the drag coefficient, ρ air is the air density, A f is the frontal area, v wind is the positive component of wind speed, m is the mass of the following vehicle, g is the acceleration of gravity, μ c is the rolling resistance coefficient, d is the known disturbance;

[0050] The lateral tracking error system of the following vehicle is established as:

[0051]

[0052] in:

[0053]

[0054] Where, e y is the lateral displacement of the following vehicle relative to the lane, δ is the steering angle of the front wheels of the following vehicle, c f 、c r are the turning stiffness of the front and rear wheels of the following vehicle, l f 、l r are the distances from the center of gravity of the following vehicle to the front and rear wheels, I z is the yaw moment of inertia, ρ is the desired yaw rate of the following vehicle, and θ is the lateral slope of the lane;

[0055] Define state x = [χ T e T ∫Δpdt ∫e y dt] T , Based on the lateral tracking error system and longitudinal following error system of the following vehicle, the overall error system of the following vehicle is obtained as:

[0056]

[0057] in:

[0058] C1=[1 0],C2=[1 0 0 0],

[0059]

[0060] f c (x) = Γ(v x ,x)x,

[0061] Where,

[0062] Define longitudinal velocity ν = 1 / v x ,but

[0063] At sampling time t s The overall error system is transformed into a discrete-time nonlinear parametric error system:

[0064]

[0065] y(k)=Cx(k)

[0066] in:

[0067] B=t s B c , f(x(k))=t s Γ(ν(k),x(k))x(k),E=t s E c , A1=I8+t s A c (ν), D1=t s D c (ν), C=[1 0 1×7 ]

[0068] Where k is the sampling time, x(k), d(k), w(k), and y(k) are the state value, control input value, determinable disturbance value, uncertain disturbance value, and output value of the system at the kth sampling moment, respectively;

[0069] The discrete-time nonlinear parametric error system is the disturbed vehicle following control system.

[0070] The second object of the present invention can be achieved by adopting the following technical solutions:

[0071] A control device for vehicles following a platoon in lane keeping mode, the device comprising:

[0072] The first building block is used to construct a platoon of vehicles on roads with slopes and curvature. For these platoons, a disturbed following control system is established based on coordinated adaptive cruise control and lane keeping control, combining the longitudinal and lateral dynamics of the vehicles.

[0073] The second building block is used to establish a state feedback controller with feedforward compensation based on the acquired acceleration information of the leading vehicle and incorporate it into the disturbed vehicle following control system to obtain a platoon following model with feedforward compensation.

[0074] The third building block is used to transform the nonlinear terms into quadratic products of linear terms based on the platoon following model using the Lipschitz condition. At the same time, a nonlinear saturation function is introduced to obtain a saturation-constrained uncertain following vehicle prediction model.

[0075] The solution module is used to implement longitudinal following and lateral lane keeping control of the vehicle by using model predictive control technology to perform rolling optimization of performance targets based on an uncertain following vehicle prediction model, and to update feedback control gains in real time.

[0076] The third object of the present invention can be achieved by adopting the following technical solutions:

[0077] An electronic device includes a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the above-mentioned method for controlling vehicle following in a lane keeping mode is implemented.

[0078] The fourth object of the present invention can be achieved by adopting the following technical solutions:

[0079] A computer-readable storage medium stores a program, which, when executed by a processor, implements the above-mentioned control method for vehicles following a platoon under lane keeping.

[0080] The present invention has the following beneficial effects compared to the prior art:

[0081] 1. Compared to existing cooperative adaptive cruise control designs for platooning, which are mostly based on simple, flat road scenarios, this approach is easy to model and only requires longitudinal control to achieve effective multi-vehicle platooning. This invention considers complex roads with slopes and curvatures. The longitudinal and lateral stability of a vehicle is affected by road conditions and the nonlinear coupling between them. Therefore, it combines longitudinal and lateral dynamics, and lane keeping with cooperative adaptive cruise control, to achieve safer and more stable platooning.

[0082] 2. The present invention designs a state feedback controller structure with feedforward compensation based on the known acceleration information of the preceding vehicle. By introducing feedforward compensation, the deviation caused by the acceleration disturbance information of the preceding vehicle is corrected, thereby improving the following performance of the following vehicle. The present invention uses model predictive control technology to dynamically optimize the state feedback controller. By monitoring the actual state of the vehicle system in real time, calculating the model predictive control problem online, and updating the stable feedback controller gain at every moment, it can ensure the real-time dynamic decision-making of the controller and the timeliness of the fleet tracking adjustment, which is of great significance for improving the accuracy and speed of vehicle platoon following.

[0083] 3. In the design process of state feedback controller based on model predictive control framework, the present invention introduces hybrid H2 / H ∞ Performance indicators can reduce the impact of unknown interference caused by wind speed, road conditions, etc. on vehicle platooning, enhance the robustness and interference resistance of the platooning control system, improve the tracking accuracy of vehicle platooning, and enable the following vehicle to follow the leading vehicle more stably;

[0084] 4. In the design process of the state feedback controller based on the model predictive control framework, the present invention takes into account the inherent physical limitations of the vehicle control actuator. Through the convex representation processing technology of the saturation constraint, an auxiliary controller is introduced to solve the problems of saturation nonlinearity, low control accuracy, poor performance and even instability in a less conservative way, thereby improving the control performance of the following vehicle under saturation constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0086] Figure 1 This is a flow chart of a method for controlling a vehicle following a platoon in lane keeping according to embodiment 1 of the present invention;

[0087] Figure 2 This is a structural diagram of a multi-vehicle platoon under a leading vehicle following topology according to embodiment 1 of the present invention;

[0088] Figure 3 Schematic diagram of information transmission during lane keeping and following vehicle control in Example 1 of the present invention;

[0089] Figure 4 This is a schematic diagram of a vehicle driving on a road with a slope and curvature according to Example 1 of the present invention;

[0090] Figure 5 This is a structural diagram of a longitudinal and transverse coupling control model of a vehicle system according to embodiment 1 of the present invention;

[0091] Figure 6 This is an architecture diagram of a vehicle platoon control process combining feedforward compensation and feedback control according to embodiment 1 of the present invention;

[0092] Figure 7 This is a structural block diagram of a control device for vehicles following a platoon in lane keeping according to Example 2 of the present invention;

[0093] Figure 8 This is a structural block diagram of an electronic device according to embodiment 3 of the present invention. DETAILED DESCRIPTION

[0094] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. It should be understood that the specific embodiments described are only used to explain this application and are not used to limit this application.

[0095] Example 1:

[0096] like Figure 1 As shown, the control method for vehicles following a platoon in lane keeping provided in this embodiment includes the following steps:

[0097] S101. Construct a platoon of moving vehicles in a road scenario with slope and curvature, establish a disturbed following control system based on cooperative adaptive cruise control and lane keeping control, and consider the inherent physical limitations of the actuators.

[0098] It is understandable that each vehicle is equipped with on-board sensors to collect in real time the relative physical quantities between the vehicle and the preceding vehicle (vehicle distance, relative speed) and the relative physical quantities between the vehicle and the lane (lateral displacement relative to the lane, yaw angle error, lateral speed and yaw rate).

[0099] The convoy adopts a fixed-distance following vehicle platoon geometry configuration, and the leading vehicle follows the communication topology as follows Figure 2 As shown in Figure 1, based on good network characteristics, the leading vehicle transmits acceleration information to the trailing vehicle through V2V communication technology, and the trailing vehicle implements control based on the known acceleration information of the leading vehicle. The information transmission process in the autonomous vehicle control system is as follows: Figure 3 shown.

[0100] In one embodiment, every two consecutive vehicles are combined into a two-vehicle model, with the front vehicle as the leading vehicle and the rear vehicle as the following vehicle. The multi-vehicle platoon is simplified into multiple two-vehicle marshaling systems. For the two-vehicle marshaling system, a prediction model for cooperative adaptive cruise control of the following vehicles is established. Consider a road with slope and curvature, such as Figure 4 As shown in Figure 2, the longitudinal and lateral stability of the vehicle are affected by the road conditions and the nonlinear coupling between them. l 、 and a l are the position, velocity and acceleration of the leading vehicle, and p, v x and are the position, speed and expected torque of the following vehicle respectively, and the given safe distance is d safe Based on the following error Δp=p l -pd safe 、 The longitudinal tracking error system of the following vehicle is established as:

[0101]

[0102] in:

[0103] d=-ma l , w1=0.5C d ρ air A f (v x +v wind ) 2 -mgμ c

[0104] Where, and e ψ are the lateral velocity, yaw rate and yaw angle error of the following vehicle relative to the lane, C d is the drag coefficient, ρ air is the air density, A f is the frontal area, v wind is the positive component of wind speed, m is the mass of the following vehicle, g is the acceleration due to gravity, μ c is the rolling resistance coefficient. l Sent from the leader vehicle to the following vehicles through the V2V network, so d is a known disturbance.

[0105] The lateral tracking error system of the following vehicle is:

[0106]

[0107] in:

[0108]

[0109] Where, e y is the lateral displacement of the following vehicle relative to the lane, δ is the steering angle of the front wheels of the following vehicle, c f / c r is the turning stiffness of the front / rear wheels of the following vehicle, l f / l r is the distance from the center of gravity of the vehicle to the front / rear wheels, I z is the yaw moment of inertia, is (R is the road radius) and θ is the lane lateral slope.

[0110] Define state x = [χ T e T ∫Δpdt ∫e y dt] T , It contains the integral terms ∫Δpdt and ∫e y dt to eliminate Δp and e y The steady-state error.

[0111] The disturbance model of the following vehicle cooperative adaptive cruise control under lane keeping is an overall error system composed of (1)-(2), as follows: Figure 5 As shown:

[0112]

[0113] in:

[0114] C1=[1 0],C2=[1 0 00],

[0115]

[0116] f c (x) = Γ(v x ,x)x,

[0117] Where,

[0118] Considering the highway driving environment, the longitudinal speed is non-zero and Define longitudinal velocity ν=1 / v x ,but At sampling time t s Under this condition, the system represented by formula (3) can be converted into a discrete-time nonlinear parameter-varying system:

[0119]

[0120] y(k)=Cx(k)(4)

[0121] in:

[0122] B=t s B c , f(x(k))=t s Γ(ν(k),x(k))x(k),E=t s E c , A1=I8+t s A c (ν), D1=t s D c (ν), C=[10 1×7 ].

[0123] Where k is the sampling time, x(k), d(k), w(k), and y(k) are the state value, control input value, determinable disturbance value, uncertain disturbance value, and output value of the discrete error system at the kth sampling moment, respectively.

[0124] The disturbed discrete system of the following vehicle cooperative adaptive cruise control under lane keeping expressed by formula (4) is the disturbed following vehicle control system.

[0125] Furthermore, for the disturbed car following control system, based on the safety requirement of reducing rear-end collisions between the vehicle and the preceding vehicle, an error range constraint (5) is set for the inter-vehicle distance error; based on the inherent physical limitations of the actuator, a saturation range (6) of the control input is set:

[0126] |y(k)|≤y max (5)

[0127]

[0128] Among them, y max is the absolute value of the maximum vehicle distance error, requiring y max <d safe ;n u is the dimension of the input vector, is the maximum input saturation level corresponding to dimension r.

[0129] S102: Establish a following vehicle state feedback controller with feedforward compensation, apply feedforward compensation to known leading vehicle acceleration information, and obtain a platoon following model after feedforward compensation.

[0130] Specifically, for the disturbed car following control system (Formula (4)), a state feedback controller with feedforward compensation is constructed as follows:

[0131]

[0132] Where u f (k) = L(k)d(k) is the feedforward action to compensate for the known disturbance d(k) related to the acceleration of the preceding vehicle. The feedforward gain is is the left pseudo-inverse matrix of matrix B, requiring rank([BE])=rank(B); u(k)=F(k)x(k) is the state feedback control, where F(k) is the feedback control gain matrix at the kth sampling moment.

[0133] Because Bu f (k) = -Ed(k), and the controller form described in (7) is substituted into formula (4), and the platoon following model after feedforward compensation is obtained:

[0134] x(k+1)=A ν x(k)+Bu(k)+f(x(k))+D ν w(k)

[0135] y(k)=Cx(k) (8)

[0136] For the platoon following model after feedforward compensation, the input saturation constraint limited by the actuator physics is reduced to:

[0137] |u r (k)|≤u r,max ,r=1,…,n u ,n u =2 (9)

[0138] in, and is the maximum feedforward action level corresponding to dimension r.

[0139] The output constraint of the system remains as formula (5).

[0140] S103. Process the nonlinear terms in the platoon following model, introduce a nonlinear saturation function description, implement a convex representation of the saturation state feedback controller, and establish a saturation-constrained uncertain following vehicle prediction model.

[0141] Specifically, for the platoon following model after feedforward compensation expressed in formula (8), the nonlinear term f(x(k)) is processed as follows:

[0142] because is the yaw rate, Therefore |x6|≤r max +vmax / R min , r max is the maximum yaw rate, v max is the maximum longitudinal velocity, R min is the minimum lane radius. Considering the most conservative case, we choose the Lipschitz constant matrix:

[0143]

[0144] Then the nonlinear function f(x(k)) in formula (8) satisfies the Lipschitz condition f T (x(k))f(x(k))≤x T (k)W T Wx(k).

[0145] For the platoon following model after feedforward compensation (Formula (8)) and its saturation input constraint (Formula (9)), a standard unit nonlinear saturation function σ(·) is introduced, namely σ(u r (k))=sign(u r (k))min{1,|u r (k)|}. For the actual system non-unit saturation constraint (Formula (9)), the system is normalized as follows:

[0146]

[0147] Among them, U=diag(u max ), diag(·) denotes a diagonal block matrix. For simplicity, the subscript ∧ is omitted, and the platoon following model with input saturation constraint is restated as:

[0148] x(k+1)=A ν x(k)+Bσ(u(k))+f(x(k))+D ν w(k)

[0149] y(k)=Cx(k) (12)

[0150] Formula (12) is a saturation-constrained uncertain following vehicle prediction model.

[0151] For the saturated state feedback control σ(u(k))=σ(F(k)x(k)), when When the auxiliary condition of saturation is satisfied, where H(k) is the auxiliary state feedback controller gain, the saturated input σ(F(k)x(k)) can be restated as the following convex representation:

[0152]

[0153] Where 0≤η j≤1, E j is a diagonal matrix with diagonal elements of 0 or 1, Represents a symmetric polyhedron represents the rth row of the matrix H(k).

[0154] S104. Based on the uncertain following vehicle prediction model, the predictive control problem is solved online; the robust performance index is optimized in a rolling manner, and the feedback controller gain is updated to achieve longitudinal following and lateral lane keeping control of the vehicle.

[0155] Specifically, under the combination of feedforward compensation and feedback control, the controlled process of the vehicle following the team is as follows: Figure 6 As shown in Figure 2, for the feedback control part, the model predictive control technology is used to roll-update the feedback control gain and optimize the robust performance.

[0156] Based on the saturation-constrained uncertain following vehicle prediction model (Formula (12)), the controlled output (performance vector) z(k) = [y(k)(Gσ(u(k)))] for control performance evaluation is constructed. T ] T , the predictive control algorithm is designed to solve the following minimum-maximum robust performance objective:

[0157]

[0158] Among them, the infinite time domain performance index is z(k+i|k) is the predicted value of the system controlled output (performance vector) at the k+i moment in the future under the k-th sampling moment, G is the state feedback control weight matrix, and for non-unit saturation constraints, the weight matrix G can be replaced based on Unitization processing.

[0159] In order to reduce the influence of the uncertain disturbance w(k+i|k) and improve the robustness of the saturation-constrained uncertain following vehicle prediction model, the following hybrid H2 / H ∞ Performance requirements for designing feedback controller gains and implementing disturbance rejection robust control:

[0160] 1)H ∞ Performance requirements: Under zero initial conditions, for a given scalar λ>0 (i.e., robust interference rejection level), we have:

[0161]

[0162] 2) H2 performance requirements: There exists a scalar γ(k)>0,

[0163]

[0164] Based on the stability method of Lyapunov function, the problem of robust predictive control is solved. Set the quadratic Lyapunov function V(x(k+i|k))=x T (k+i|k)P(k)x(k+i|k), Where P(k) is the Lyapunov matrix at the kth sampling time, and x(k+i|k) is the predicted value of the system state at the kth sampling time. At sampling time k, assume that for all the predicted values ​​of the system state x(k+i|k), the predicted values ​​of the feedback control input u(k+i|k), and the predicted values ​​of the uncertain disturbance w(k+i|k) at the kth sampling time, the function V(x(k+i|k)) satisfies the following input-state stability constraints:

[0165] ΔV(x(k+i|k))≤-||z(k+i|k)|| 2 +λ 2 ||w(k+i|k)|| 2 (17)

[0166] It is assumed that the uncertainty process disturbance w(k+i|k), i≥0, is limited to the convex cell Co{w 1 ,…,w θ ,…,w Θ} within, w θ is the cell body vertex, θ=1,2,…,Θ, Θ is the number of the cell body vertices, and the energy is limited The upper bound of energy.

[0167] Since the perturbation energy is limited, V(x(∞|k))=0 when predicting the infinite future time, the condition of updating over time is introduced. By adding both ends of formula (17) from i = 0 to i = ∞, we can get the objective function J ∞ An upper bound for (k):

[0168]

[0169] Assume any future time Add both ends of formula (17) from i=0 to have:

[0170]

[0171] Get the robust control invariant set of the predicted state at sampling time k That is, based on the current sampling time k, the state of the uncertain following vehicle prediction model (Formula (12)) at any time in the future is maintained within the set Ω(P(k),γ(k)). Therefore, for the condition updated over time To ensure that it holds true at the next real sampling moment, it is necessary to ensure This holds true for any value of the perturbation w(k|k), establishing the recursive feasibility of the prediction algorithm. That is, if the algorithm has a solution at any moment, it must also be feasible at the next moment, thereby ensuring the continuous implementation of the prediction algorithm.

[0172] The model predictive control method for disturbance rejection saturation can be restated as a minimization control optimization problem: Consider the uncertain following vehicle prediction model at sampling time k, given the adjustable parameters ε1>0, ε2>0, 0<∈<1, and the robust disturbance rejection level λ>0, if there exists a matrix Y(k), Z(k) and scalars γ(k)>0, ξ(k)>0 make the following minimization problem θ=1,2,…,Θ can be solved:

[0173]

[0174] Robust stability constraints:

[0175]

[0176] Feasibility constraints:

[0177]

[0178] Input saturation auxiliary constraints:

[0179]

[0180] Output constraints:

[0181]

[0182] Where x(k|k)=x(k), 0 is a zero matrix of appropriate dimension, I is an identity matrix of appropriate dimension, is the rth row of the matrix Z(k), P(k)=γ(k)Q -1 (k), Y(k)=F(k)Q(k), Z(k)=H(k)Q(k), Then according to F(k)=Y(k)Q -1 (k) Design state feedback control law u(k)=F(k)x(k), through σ(u r (k))=sign(u r (k))min{1,|u r (k)|}, r=1,2,…,n u and Two-step saturation range restoration process, adding the feedforward compensation action u f (k), and finally the income The control input signal at time k is transmitted to the starting and / or braking and front-wheel steering actuators that control vehicle behavior. The optimization problem is solved in a rolling manner at each sampling moment to update the controller gains, thereby realizing the anti-disturbance saturation predictive control of the vehicle-following platoon system.

[0183] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be completed by instructing related hardware through a program, and the corresponding program may be stored in a computer-readable storage medium.

[0184] It should be noted that although the method operations of the above embodiments are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed to achieve the desired results. Rather, the depicted steps may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into a single step, and / or a single step may be broken down into multiple steps.

[0185] Example 2:

[0186] like Figure 7 As shown, this embodiment provides a control device for vehicles following a platoon in lane keeping mode, the device comprising a first building module 701, a second building module 702, a third building module 703, and a solution module 704, wherein:

[0187] The first building module 701 is used to build a moving vehicle platoon on a road with slope and curvature. For the moving vehicle platoon, a disturbed following control system is established based on coordinated adaptive cruise control and lane keeping control, combined with the longitudinal and lateral dynamics of the vehicles.

[0188] The second building module 702 is used to establish a state feedback controller with feedforward compensation based on the acquired leading vehicle acceleration information and incorporate it into the disturbed vehicle following control system to obtain a platoon following model with feedforward compensation;

[0189] The third building block 703 is used to convert the nonlinear terms into quadratic products of linear terms using the Lipschitz condition based on the platoon following model, and introduce a nonlinear saturation function to obtain a saturation-constrained uncertain following vehicle prediction model;

[0190] The solution module 704 is used to implement longitudinal following and lateral lane keeping control of the vehicle by using model predictive control technology to perform rolling optimization of the performance target based on the uncertain following vehicle prediction model, and to update the feedback control gain in real time.

[0191] The specific implementation of each module in this embodiment can be found in the above-mentioned embodiment 1, and will not be described one by one here; it should be noted that the device provided in this embodiment is only illustrated by the division of the above-mentioned functional modules. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0192] Example 3:

[0193] This embodiment provides an electronic device, which may be a computer, such as Figure 8 As shown, a processor 802, a memory, an input device 803, a display 804, and a network interface 805 are connected via a system bus 801. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium 806 and an internal memory 807. The non-volatile storage medium 806 stores an operating system, a computer program, and a database. The internal memory 807 provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. When the processor 802 executes the computer program stored in the memory, the control method for following a vehicle in a lane keeping mode of the above-mentioned embodiment 1 is implemented, namely: constructing a vehicle platoon under a slope and curvature road condition; for the vehicle platoon, based on Based on the coordinated adaptive cruise control and lane keeping control, the longitudinal and lateral dynamics of the vehicle are combined to establish a disturbed following control system. Based on the acquired acceleration information of the leading vehicle, a state feedback controller with feedforward compensation is established and incorporated into the disturbed following control system to obtain a platoon following model after feedforward compensation. Based on the platoon following model, the Lipschitz condition is used to transform the nonlinear terms into quadratic products of linear terms, and a nonlinear saturation function is introduced to obtain a saturation-constrained uncertain following vehicle prediction model. Based on the uncertain following vehicle prediction model, model predictive control technology is used to rollingly optimize the performance target, and the feedback control gain is updated in real time to achieve a solution to achieve longitudinal following and lateral lane keeping control of the vehicle.

[0194] Example 4:

[0195] This embodiment provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the control method for vehicle following in lane keeping according to the above-mentioned embodiment 1, namely: constructing a platoon of moving vehicles on a road with slope and curvature; establishing a disturbed following control system for the platoon of moving vehicles based on coordinated adaptive cruise control and lane keeping control, combined with the longitudinal and lateral dynamics of the vehicle; establishing a state feedback controller with feedforward compensation based on acquired acceleration information of a leading vehicle and adding it to the disturbed following control system to obtain a platoon following model after feedforward compensation; based on the platoon following model, converting nonlinear terms into quadratic products of linear terms using Lipschitz conditions, and introducing a nonlinear saturation function to obtain a saturation-constrained uncertain following vehicle prediction model; based on the uncertain following vehicle prediction model, using model predictive control technology to rollingly optimize performance targets, updating feedback control gains in real time, and solving to achieve longitudinal following and lateral lane keeping control of the vehicle.

[0196] It should be noted that the computer-readable storage medium of the present embodiment may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0197] In summary, the present invention constructs a platoon of vehicles on roads with slopes and curvature. For the platoon, based on cooperative adaptive cruise control and lane keeping control, a disturbance-following control system is established, combining the longitudinal and lateral dynamics of the vehicles and taking into account inherent actuator physical limitations. A following vehicle state feedback controller structure with feedforward compensation is established, applying feedforward compensation to the known acceleration information of the leading vehicle. Nonlinear terms in the platoon-following system are addressed by introducing a nonlinear saturation function to describe the input constraints, achieving a convex representation of the saturated state feedback controller, and establishing a saturation-constrained uncertain following vehicle prediction model. A rolling optimization robust performance indicator is used to update the feedback controller gain, achieving longitudinal following and lateral lane keeping control of the vehicles. By coupling longitudinal and lateral dynamics, a driving process system is established based on complex road conditions, combining lane keeping with cooperative adaptive control, achieving more realistic and stable platoon driving. By considering actuator saturation and robust disturbance rejection, a robust predictive control algorithm with online decision-making is implemented, which is of great significance for improving the accuracy, speed, and safety of vehicle platoon following.

[0198] The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes based on the technical solution and inventive concept of the present invention within the scope disclosed by the present invention, which falls within the scope of protection of the present invention.

Claims

1. A method for controlling vehicles following a platoon in lane keeping mode, characterized in that: The method comprises: Constructing platoons of vehicles on roads with slopes and curvature; establishing a disturbed following control system for platoons based on coordinated adaptive cruise control and lane keeping control, combined with the longitudinal and lateral dynamics of the vehicles; Based on the acquired acceleration information of the preceding vehicle, a state feedback controller with feedforward compensation is established and incorporated into the disturbed following control system to obtain a platoon following model with feedforward compensation. Based on the platoon following model, the Lipschitz condition is used to transform the nonlinear terms into the quadratic product of linear terms. At the same time, a nonlinear saturation function is introduced to obtain a saturation-constrained uncertain following vehicle prediction model. Based on the uncertain following vehicle prediction model, the model predictive control technology is used to optimize the performance target in a rolling manner, and the feedback control gain is updated in real time to achieve longitudinal following and lateral lane keeping control of the vehicle.

2. The control method according to claim 1, characterized in that: The method uses a model predictive control technology to optimize the performance target in a rolling manner based on an uncertain following vehicle prediction model, updates the feedback control gain in real time, and solves the problem to achieve longitudinal following and lateral lane keeping control of the vehicle, including: Based on the uncertain following vehicle prediction model, the controlled output z(k)=[y(k) (Gσ(u(k))) T ] T , the predictive control algorithm is designed to solve the following minimum-maximum robust performance objective: In the formula, the infinite time domain performance index y(k) is the output value of the system at the kth sampling moment, u(k) is the state feedback control at the kth sampling moment, z(k+i|k) is the predicted output value of the system at the kth sampling moment in the future k+i moment, G is the state feedback control weight matrix, σ(·) is the unit saturation function, that is, σ(u r (k))=sign(u r (k))min{1,|u r (k)|},u r (k) is the state feedback control corresponding to the dimension r; for non-unit saturation constraints, the weight matrix G is replaced based on Unitization processing, U=diag(u max ), diag(·) represents the diagonal block matrix, u max Enter a saturation constraint for maximum state feedback control.

3. The control method according to claim 2, characterized in that: In order to reduce the impact of uncertain disturbance w(k+i|k) and improve the robustness of the uncertain following vehicle prediction model, a hybrid H2 / H ∞ The performance requirements are as follows: H ∞ Performance requirements: Under zero initial conditions, for a given scalar λ>0, we have: H2 performance requirement: There exists a scalar γ(k)>0, so:

4. The control method according to claim 3, characterized in that: Set the quadratic Lyapunov function V(x(k+i|k))=x T (k+i|k)P(k)x(k+i|k), Where P(k) is the Lyapunov matrix at the k-th sampling moment, and x(k+i|k) is the predicted value of the system state at the k+i-th moment in the future at the k-th sampling moment; Considering the uncertain following vehicle prediction model at sampling time k, given the adjustable parameters ε1>0, ε2>0, 0<∈<1, and the robustness rejection level λ, if there is a matrix Y(k), Z(k) and scalars γ(k), ξ(k)>0 so that the following minimization problem is θ=1,2,…,Θ can be solved: Robust stability constraints: Q(k)≥ξ(k)I Feasibility constraints: Input saturation auxiliary constraints: Output constraints: In the formula, x(k|k)=x(k), x(k) is the state value of the system at the kth sampling moment, w θ is the vertex of the cell body where the uncertain perturbation is located, is the upper bound of the finite perturbation energy, γ(k) is the upper bound of the target performance, 0 is the zero matrix of the corresponding dimension, I is the identity matrix of the corresponding dimension, is the rth row of the matrix Z(k), A l 、D l is the vertex of the cell where the system matrix is ​​located, B is the constant system input matrix, C is the system output matrix, W is the Lipschitz matrix, y max is the absolute value of the maximum inter-vehicle distance error, P(k)=γ(k)Q -1 (k), Y(k)=F(k)Q(k), Z(k)=H(k)Q(k), H(k) is the auxiliary state feedback controller gain at the kth sampling moment, E j is a diagonal matrix with diagonal elements of 0 or 1, According to F(k)=Y(k)Q -1 (k) Design state feedback control law u(k)=F(k)x(k), through σ(u r (k))=sign(u r (k))min{1,|u r (k)|}, r=1,2,…,n u and Two-step saturation range restoration processing, adding feedforward compensation action u f (k), the final result The control input signal at time k is transmitted to the starting and / or braking and front-wheel steering actuators that control vehicle behavior. The optimization problem is solved in a rolling manner at each sampling moment to update the controller gains, thereby realizing the anti-disturbance saturation predictive control of the vehicle-following platoon system.

5. The control method according to claim 1, characterized in that: The state feedback controller with feedforward compensation is: Where, is the control input value of the system at the kth sampling moment; u f (k) = L(k)d(k) is the feedforward action at the kth sampling moment to compensate for the known disturbance d(k) related to the acceleration of the preceding vehicle, and L(k) is the feedforward gain at the kth sampling moment; u(k) = F(k)x(k) is the state feedback control at the kth sampling moment, F(k) is the feedback control gain matrix at the kth sampling moment, and x(k) is the state value at the kth sampling moment.

6. The control method according to claim 1, characterized in that: For the platoon following model, the output constraint of the system is set as: |y(k)|≤y max , and y max <d safe ; Among them, y(k) is the output value of the system at the kth sampling moment, y max d safe are the absolute value of the maximum inter-vehicle distance error and the given safe distance respectively; Taking into account the inherent physical limitations of the actuator, the saturation constraint of the feedback control input is set as: Feedback control input saturation constraint |u r (k)|≤u r,max ,r=1,…,n u , n u is the dimension of the input vector, is the maximum input saturation level corresponding to dimension r, and is the maximum feedforward action level corresponding to dimension r.

7. The control method according to any one of claims 1 to 6, characterized in that: The said moving vehicle platoon is to take the leading vehicle of any two consecutive moving vehicles as the leading vehicle and the trailing vehicle as the following vehicle; The establishment process of the disturbed car following control system is as follows: According to the obtained position p of the leading vehicle l ,speed and acceleration a l , and the position p and velocity v of the following vehicle x and desired torque And the given safe distance d safe , based on the following error Δp=p l -pd safe and The longitudinal tracking error system of the following vehicle is established as: in: d-ma l , w1=0.5C d ρ air AND f (in x +v wind ) 2 -mgμ c Where, and e ψ are the lateral velocity, yaw rate and yaw angle error of the following vehicle relative to the lane, C d is the drag coefficient, ρ air is the air density, A f is the frontal area, v wind is the positive component of wind speed, m is the mass of the following vehicle, g is the acceleration of gravity, μ c is the rolling resistance coefficient, d is the known disturbance; The lateral tracking error system of the following vehicle is established as: in: Where, e y is the lateral displacement of the following vehicle relative to the lane, δ is the steering angle of the front wheels of the following vehicle, c f 、c r are the turning stiffness of the front and rear wheels of the following vehicle, l f 、l r are the distances from the center of gravity of the following vehicle to the front and rear wheels, I z is the yaw moment of inertia, ρ is the desired yaw rate of the following vehicle, and θ is the lateral slope of the lane; Define state x = [χ T e T ∫Δpdt ∫e y dt] T , Based on the lateral tracking error system and longitudinal following error system of the following vehicle, the overall error system of the following vehicle is obtained as: in: C1=[1 0],C2=[1 0 0 0], Where, Define longitudinal velocity ν = 1 / v x ,but At sampling time t s The overall error system is transformed into a discrete-time nonlinear parametric error system: y(k)=Cx(k) in: B=t s B c ,f(x(k))=t s Γ(ν(k),x(k))x(k),E=t s E c , A1=I8+t s A c (ν), D1=t s D c (ν), C=[1 0 1×7 ] Where k is the sampling time, x(k), d(k), w(k), and y(k) are the state value, control input value, determinable disturbance value, uncertain disturbance value, and output value of the system at the kth sampling moment, respectively; The discrete-time nonlinear parametric error system is the disturbed vehicle following control system.

8. A control device for vehicles following a convoy under lane keeping, characterized in that: The device comprises: The first building block is used to construct a platoon of vehicles on roads with slopes and curvature. For these platoons, a disturbed following control system is established based on coordinated adaptive cruise control and lane keeping control, combining the longitudinal and lateral dynamics of the vehicles. The second building block is used to establish a state feedback controller with feedforward compensation based on the acquired acceleration information of the leading vehicle and incorporate it into the disturbed vehicle following control system to obtain a platoon following model with feedforward compensation. The third building block is used to transform the nonlinear terms into quadratic products of linear terms based on the platoon following model using the Lipschitz condition. At the same time, a nonlinear saturation function is introduced to obtain a saturation-constrained uncertain following vehicle prediction model. The solution module is used to implement longitudinal following and lateral lane keeping control of the vehicle by using model predictive control technology to perform rolling optimization of performance targets based on an uncertain following vehicle prediction model, and to update feedback control gains in real time.

9. An electronic device comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the control method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the control method according to any one of claims 1 to 7 is implemented.

Images