Distributed driving electric vehicle path tracking control method

Abstract

The invention discloses a distributed driving electric vehicle path tracking control method, system and device, and relates to the technical field of vehicle chassis control. The method comprises the steps that vehicle parameter information is received, a vehicle dynamics model and a magic formula tire model are constructed according to the vehicle parameter information, and a vehicle-road system model used for trajectory tracking control is constructed based on the vehicle dynamics model and the magic formula tire model. According to the method, various uncertain disturbances in the vehicle driving process are comprehensively considered, a model predictive control framework based on minimum-maximum robustness is designed, and the robustness of a path tracking control system is enhanced; meanwhile, an online observer based on a minimum robust positive invariant set is designed, real-time and accurate obtaining of key parameters of the vehicle is achieved without increasing hardware cost, meanwhile, an active front wheel steering and torque vector cooperative control method is designed, and coordinated operation between a steering system and a four-wheel drive system is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of vehicle chassis control, and in particular to a path tracking control method for a distributed drive electric vehicle. Background Art

[0002] Thanks to the highly integrated design of wheel hub motors, distributed electric drive vehicles can independently control individual wheels, thereby significantly improving the vehicle's dynamic handling performance. However, due to the many uncertainties in the vehicle's driving road environment, the vehicle's internal state parameters, and the variable driving tasks, there are many interferences in the vehicle path tracking control system, which seriously affects the safety and stability of the vehicle. Therefore, establishing a robust distributed drive electric vehicle path tracking control architecture is of great significance to improving vehicle driving safety. In particular, considering that there may be functional overlap and control interference between the active steering and four-wheel torque control systems of distributed drive electric vehicles, the uncertainty and disturbance in the system may further amplify the problem. Summary of the invention

[0003] The purpose of the present invention is to provide a distributed drive electric vehicle path tracking control method, system and equipment, which can effectively solve various uncertain disturbances during vehicle driving, coordinate the operation between the steering system and the four-wheel drive system, and enhance the stability and tracking accuracy of the distributed drive electric vehicle under different driving conditions.

[0004] To achieve the above object, the present invention provides the following technical solution: a distributed drive electric vehicle path tracking control method, comprising the following steps:

[0005] Receiving vehicle parameter information, constructing a vehicle dynamics model and a magic formula tire model according to the vehicle parameter information, and constructing a vehicle-road system model for trajectory tracking control based on the vehicle dynamics model and the magic formula tire model, thereby constructing an LPV system equation;

[0006] According to the obtained LPV system equation, an online observer is designed based on the robust positive invariant set to obtain the vehicle center of mass sideslip angle data in real time, and the conservatism is reduced by updating the minimum robust positive invariant set online.

[0007] According to the designed online observer, a minimum-maximum robust predictive control model based on online observer is constructed by using linear matrix inequality.

[0008] A composite differential equation is constructed based on the vehicle dynamics equation and the magic formula tire model. The β-γ phase trajectory is drawn according to the composite differential equation, the predetermined longitudinal speed, the front wheel steering angle and the road friction coefficient. The stable point and the saddle point are calculated by searching for the equilibrium solution. The vehicle parameters under different working conditions are obtained by changing the vehicle longitudinal speed, the front wheel steering angle and the road friction coefficient. The boundary of the vehicle stability domain is obtained by parameter fitting.

[0009] An optimization objective function is designed. According to the expected additional yaw moment and the expected longitudinal vehicle speed, the optimal torque distribution strategy objective function is used to solve the four-wheel torque distribution of the four-wheel independent drive electric vehicle.

[0010] Furthermore, the vehicle parameter information includes front wheel steering angle, longitudinal speed, center of mass sideslip angle, yaw angular velocity, additional yaw moment and tire lateral force, and the vehicle parameter information is collected by a steering angle sensor, a gyroscope and a tire six-component force tester respectively.

[0011] Furthermore, the vehicle dynamics model and the magic formula tire model are constructed according to the vehicle parameter information, as follows: (31) Focusing on the trajectory tracking control of distributed drive electric vehicles, the vehicle dynamics equation is described as follows:

[0012]

[0013] Where m is the vehicle mass, I z is the yaw moment of inertia, l f and l r are the lengths from the vehicle's center of mass to the front and rear axles, v x is the longitudinal velocity of the vehicle, β and γ are the sideslip angle and yaw rate of the center of mass, δ f is the front wheel turning angle, ΔM z is the additional yaw moment, F yf and F yr , are the lateral forces of the front and rear wheels respectively;

[0014] (32) The magic formula tire model is constructed to characterize the longitudinal force and lateral force of the tire. The lateral force of the tire can be calculated by the following formula:

[0015]

[0016] Where α n , (n = f, r) is the tire slip angle, a 0 ~a 13 are the fitting parameters,

[0017]

[0018] BCD y =a 3sin[2arctan(F z / a 4 )],E y =a 6 F z +a 7

[0019] S hy =a 8 F z +a 9 +a 10 χ,S vy =a 11 F z χ+a 12 F z +a 13 .

[0020] Perform offline linearization for each slip angle:

[0021] F yf =K f α f , F yr =K r α r (3)

[0022] In the formula, K n , n = f, r is the equivalent tire cornering stiffness;

[0023] Introducing the adjustment factor ε f and ε r , so K n It is expressed as:

[0024] K f =ε f C f , K r =ε r C r (4)

[0025] Where C f and C r are the cornering stiffness values ​​of the front and rear wheels in the approximate linear region, respectively;

[0026] (33) A deviation tracking model is constructed based on the kinematic relationship between the vehicle and the road, specifically:

[0027]

[0028] Where ρ r is the road curvature, e y and are the lateral tracking deviation and heading angle deviation, l p is the preview distance;

[0029] Combining formula (1) to formula (5), the vehicle-road system model for trajectory tracking control is obtained as follows:

[0030]

[0031] In the formula is the state vector, u=[δ f ΔM z ] T is the control input, ω=ρ r is the system disturbance, H 2×1 It is a constant matrix introduced to describe the unknown disturbances in the measurement output process.

[0032] Furthermore, the LPV system equation is constructed as follows:

[0033] (41) All time-varying terms in formula (6) are linearly stripped off to define the variable parameter σ of the LPV system: 1 ~σ 7 :

[0034]

[0035] where σ 1 , σ 3 and σ 5 The maximum and minimum values ​​of are reached synchronously and are related to σ 7 Reaching the maximum and minimum values ​​is just the opposite. Similarly, σ 2 , σ 4 and σ 6 The maximum and minimum values ​​of are also reached synchronously;

[0036] (42) Redefine the following four LPV system variable parameters λ 1 ~λ 4 for:

[0037] λ 1 =[σ 1min , σ 2min , σ 3min , σ 4min , σ 5min , σ 6min , σ 7max ],

[0038] λ 2 =[σ 1max , σ 2min , σ 3max , σ 4min , σ 5max , σ 6min , σ 7min ],

[0039] λ 3 =[σ 1min , σ 2max , σ 3min , σ 4max , σ 5min , σ 6max , σ 7max ],

[0040] λ 4 =[σ 1max , σ 2max , σ 3max , σ 4max , σ 5max , σ 6max , σ 7min ], (7)

[0041] Using variable parameter lambda 1 ~λ 4 The linear terms and nonlinear terms of the coefficient matrix of formula (6) are stripped off to obtain the following coefficient matrix:

[0042]

[0043] in:

[0044]

[0045] Combining formula (6) and formula (8), the zero-order hold method is used to discretize the model and obtain the discretized LPV system model:

[0046] x(k+1)=E(η(k))x(k)+F(η(k))u(k)+G(η(k))ω(k)

[0047] y(k)=Cx(k)+Hw(k) (9)

[0048] Among them, T s is the sampling period,

[0049]

[0050] Weight coefficient η j (k)≥0, (j=1, 2, 3, 4) satisfies And can be defined as:

[0051]

[0052] Where x(k), u(k), ω(k) and y(k) are the discrete forms of the state vector, control input, system disturbance, and output vector, respectively. is the coefficient matrix of the discretized vehicle-road system model in formula (6), E(η(k)), F(η(k)), G(η(k)) is the coefficient matrix of the LPV model, η j (k) is the weight coefficient.

[0053] Furthermore, according to the obtained LPV system equation, an online observer is designed based on the robust positive invariant set to obtain the vehicle center of mass sideslip angle data in real time, and the conservatism is reduced by updating the minimum robust positive invariant set online, as follows:

[0054] (51) Online observer performs real-time state estimation:

[0055] x h (k+1)=E(η(k))x h (k)+F(η(k))u(k)+L(η(k))(y(k)-Cx h (k)) (10)

[0056] In the formula, x h is the estimated state, is the observer gain, where L q ,q∈[1,2,…,q m ] are the independent observed gains of the subsystems to be solved;

[0057] So the state observation error is defined as x e (k) = x(k) - x h (k); Combining Formula 9 and Formula 10, the dynamic equation of the observation error system can be obtained as follows:

[0058] x e (k+1)=(E(η(k))-L(η(k))C)x e (k)+(G(η(k))-L(η(k))H)w(k) (11)

[0059] Define the quadratic performance function of the state observation Where P e is a symmetric positive definite matrix; according to the quadratic bounded theory, if the following inequality holds, the observation error will always be in the robust positive definite

[0060] In the variable set:

[0061]

[0062] For the observation system formula (11), it is assumed that the system disturbance is bounded, that is, Assume there is a matrix P e , R q ,q∈[1,2,…,q m], and a positive scalar θ∈(0,1), then the following optimization problem is solvable:

[0063]

[0064] Then formula (12) always holds true, and the gain of the online observer is given by Calculated, and then further calculated the minimum robust positive invariant set of formula (11) Where θ, Q w is a given positive definite scalar, q∈[1, 2, …, q m ] is the label of each fuzzy subsystem;

[0065] (52) Robust positive invariant set online update:

[0066] (52.1) Introducing a known scalar Make Established, of which is obtained by solving the optimization problem of formula (13); according to You can get:

[0067]

[0068] definition As an upper bound, since θ∈(0,1), when When That is, it can force the observation error to gradually decrease; when When Then the observation error will converge to the robust positive invariant set

[0069] Therefore, the set for When , the robust positive invariant set of the system observation error;

[0070] (52.2) Accordingly, It can be used to limit the allowable boundary of observation error at time k+1;

[0071] Find the minimum robust positive invariant set at time k+1 The details are as follows:

[0072] The observation error bound at time k is in is a known scalar, the optimal observation error bound at time k+1 By solving the following optimization problem:

[0073]

[0074] In the formula, κ 1 ≥0 and κ2 ≥0 are two scalars to be solved, Λ 22 , Λ 32 , Λ 33 is a matrix element.

[0075] Furthermore, according to the designed online observer, a robust Min-Max predictive control model based on the online observer is constructed using linear matrix inequality, as follows:

[0076] (61) For the LPV system formula (9), after introducing the observer formula (10), the predicted control input u(i|k) at time k is given by the following equation, where i ≥ 0 is the prediction length:

[0077]

[0078] Where N q (k), q=1,…,q m is the control gain at time k to be determined;

[0079] (62) Building an augmented system Combining formula (9), formula (11) and formula (14), the closed-loop prediction model of the augmented system is obtained as follows:

[0080] ξ(i+1|k)=∏(i,k)ξ(i|k)+Ξ(i,k)w(k+i),i≥0 (17)

[0081] in:

[0082]

[0083] For the augmented system formula (17), a robust predictive control model based on minimum-maximum control is constructed at each time k, where

[0084]

[0085] Where γ is the performance index, the matrix S = diag{S 1 , S 2}, where S 1 >0,S 2 >0, T>0 and U>0 weight matrices, Ω represents the set of uncertain systems, is the maximum control input based on actual physical constraints or for safety considerations, represents the maximum allowed system state, and Φ is a general constant matrix.

[0086] Furthermore, the β-γ phase trajectory is drawn, the stable point and the saddle point are calculated by searching the equilibrium solution, and the boundary of the vehicle stability region is obtained by parameter fitting according to the vehicle parameters under different working conditions, as follows:

[0087] (71) According to the composite differential equation composed of formula (1) and formula (2), the β-γ phase trajectory is drawn according to the predetermined longitudinal speed, the front wheel steering angle and the road friction coefficient, and then the stable point and the saddle point are calculated by searching the equilibrium solution based on the phase trajectory bifurcation stability theory;

[0088] (72) By changing the vehicle driving conditions, the vehicle stability status and data under different conditions are obtained, and the following vehicle dynamic stability domain boundary is calculated by parameter fitting method:

[0089] -Ψ 1 ≤φ 1 β+ζ 1 γ≤Ψ 1

[0090] -Ψ 2 ≤φ 2 β+ζ 2 γ≤Ψ 2 (twenty three)

[0091] In the formula, φ 1 , 1 ,Ψ 1 ,φ 2 , 2 ,Ψ 2 are the fitting parameters;

[0092] Considering the physical constraints of the actual steering system and the four-wheel independent drive system, the limits of the control input are set as follows:

[0093]

[0094] In the formula, and They are the maximum front wheel steering angle and the maximum additional yaw moment respectively.

[0095] Furthermore, an optimization objective function is designed. According to the obtained expected additional yaw moment and the expected longitudinal vehicle speed, the optimal torque distribution strategy objective function is used to solve the problem, thereby optimizing the four-wheel torque distribution of the four-wheel independent drive electric vehicle, as follows:

[0096] (81) The objective function of the optimization problem is designed as:

[0097]

[0098] Where J is the performance index, represents the utilization rate of adhesion of a single wheel, τ = fl, fr, rl, rr is the number of each wheel; is a given scalar used to adjust the weight distribution;

[0099] (82) The optimization problem of formula (25) is transformed into a standard quadratic programming problem and solved using the built-in QP function in Matlab. The equality and inequality constraints of the QP problem are as follows:

[0100]

[0101] Where ΔM z is the additional yaw moment, F x , F y , F z are the longitudinal, lateral and vertical forces of the tire, τ = fl, fr, rl, rr represent different wheels, μ is the road adhesion coefficient, l f is the front wheelbase, T max is the maximum output torque of a single wheel, r ω is the equivalent wheel rolling radius, t ω It is the width of the left and right wheels of the vehicle.

[0102] According to a second aspect of the present invention, the present invention provides a distributed drive electric vehicle path tracking control system based on a vehicle stability domain, which is used to implement the above-mentioned distributed drive electric vehicle path tracking control method, comprising:

[0103] A model building module is used to receive vehicle parameter information, build a vehicle dynamics model and a magic formula tire model according to the vehicle parameter information, and build a vehicle-road system model for trajectory tracking control based on the vehicle dynamics model and the magic formula tire model, thereby building an LPV system equation;

[0104] The observer design module is used to design an online observer based on the obtained LPV system equation and the robust positive invariant set, so as to obtain the vehicle center of mass sideslip angle data in real time and reduce the conservatism by updating the minimum robust positive invariant set online;

[0105] A predictive control module is used to construct a minimum-maximum robust predictive control model based on an online observer using linear matrix inequality according to the designed online observer;

[0106] The stability domain boundary derivation module is used to form a compound differential equation based on the vehicle dynamics equation and the magic formula tire model, draw the bg phase trajectory according to the compound differential equation, the predetermined longitudinal speed, the front wheel steering angle and the road friction coefficient, calculate the stable point and the saddle point by searching for the equilibrium solution, and change the vehicle longitudinal speed, the front wheel steering angle and the road friction coefficient to obtain the vehicle parameters under different working conditions, and obtain the vehicle stability domain boundary by parameter fitting;

[0107] The optimization module is used to design the optimization objective function. According to the obtained expected additional yaw moment and the expected longitudinal vehicle speed, the optimal torque distribution strategy objective function is solved to optimize the four-wheel torque distribution of the four-wheel independent drive electric vehicle.

[0108] According to a third aspect of the present invention, the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned distributed drive electric vehicle path tracking control method is adopted.

[0109] The present invention has at least the following beneficial effects:

[0110] 1. The present invention comprehensively considers various uncertain disturbances in the vehicle driving process, designs a predictive control architecture based on the minimum-maximum robust model, and enhances the robustness of the path tracking control system; at the same time, it designs an online observer based on the minimum robust positive invariant set, which realizes the real-time and accurate acquisition of key vehicle parameters without increasing hardware costs.

[0111] 2. The present invention designs an active front-wheel steering and torque vector coordinated control method, realizes the coordinated operation between the steering system and the four-wheel drive system, enhances the operational stability and path tracking accuracy of distributed drive electric vehicles under different driving conditions, and improves the safety of vehicle road driving.

[0112] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] Figure 1 It is a schematic diagram of the process of the method of the present invention;

[0114] Figure 2 This is the distributed drive electric vehicle path tracking control architecture of the present invention. DETAILED DESCRIPTION

[0115] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0116] Embodiment 1:

[0117] See also Figure 1-Figure 2The present invention provides a technical solution: a distributed drive electric vehicle path tracking control method, comprising the following steps:

[0118] Step S1: Chassis cooperative control system modeling: receiving vehicle parameter information, constructing a vehicle dynamics model and a magic formula tire model according to the vehicle parameter information, and constructing a vehicle-road system model for trajectory tracking control based on the vehicle dynamics model and the magic formula tire model, thereby constructing the LPV system equation:

[0119] Step S11: Vehicle-road system modeling

[0120] Focusing on the trajectory tracking control of distributed drive electric vehicles, the vehicle dynamics equation is described as follows:

[0121]

[0122] Where m is the vehicle mass, I z is the yaw moment of inertia, l f and l r are the lengths from the vehicle's center of mass to the front and rear axles, v x is the longitudinal velocity of the vehicle, β and γ are the sideslip angle and yaw rate of the center of mass, δ f is the front wheel turning angle, ΔM z is the additional yaw moment, F yf and F yr They are the lateral forces of the front and rear wheels respectively;

[0123] The magic formula tire model is constructed to characterize the longitudinal force and lateral force of the tire. The lateral force of the tire can be calculated by the following formula:

[0124]

[0125] Where α n (n = f, r) is the tire slip angle, a 0 ~a 13 are the fitting parameters,

[0126]

[0127] BCD y =a 3 sin[2arctan(F z / a 4 )],E y =a 6 F z +a 7

[0128] S hy =a 8 F z +a9 +a 10 χ,S vy =a 11 F z χ+a 12 F z +a 13 .

[0129] Perform offline linearization for each slip angle:

[0130] F yf =K f α f , F yr =K r α r (3)

[0131] In the formula, K n , n = f, r is the equivalent tire cornering stiffness;

[0132] Introducing the adjustment factor ε f and ε r , so K n It is expressed as:

[0133] K f =ε f C f , K r =ε r C r (4)

[0134] Where C f and C r are the cornering stiffness values ​​of the front and rear wheels in the approximate linear region, respectively;

[0135] In order to realize trajectory tracking control, a deviation tracking model can be constructed according to the kinematic relationship between the vehicle and the road, specifically:

[0136]

[0137] Where ρ r is the road curvature, e y and are the lateral tracking deviation and heading angle deviation, l p is the preview distance;

[0138] Combining formula (1) to formula (5), the vehicle-road system model for trajectory tracking control is obtained as follows:

[0139]

[0140] In the formula is the state vector, u=[δ f ΔMz ] T is the control input, w = ρ r is the system disturbance, H 2×1 It is a constant matrix introduced to describe the unknown disturbance in the measurement output process;

[0141] Step S12: LPV system construction. The LPV method can be used to transform the nonlinear system into a linear system, thereby simplifying the calculation process, improving the solution efficiency, and ensuring real-time performance:

[0142] All time-varying terms in formula (6) are linearly stripped to define the variable parameter σ of the LPV system: 1 ~σ 7 :

[0143]

[0144] where σ 1 , σ 3 and σ 5 The maximum and minimum values ​​of are reached synchronously and are related to σ 7 Reaching the maximum and minimum values ​​is just the opposite. Similarly, σ 2 , σ 4 and σ 6 The maximum and minimum values ​​of are also reached synchronously;

[0145] Therefore, in order to reduce the number of polytope vertices, the following four LPV system variable parameters λ can be redefined: 1 ~λ 4 for:

[0146] λ 1 =[σ 1min , σ 2min , σ 3min , σ 4min , σ 5min , σ 6min , σ 7max ],

[0147] λ 2 =[σ 1max , σ 2min , σ 3max , σ 4min , σ 5max , σ 6min , σ 7min ],

[0148] λ 3 =[σ 1min , σ 2max , σ 3min , σ 4max , σ 5min , σ 6max , σ7max ],

[0149] λ 4 =[σ 1max , σ 2max , σ 3max , σ 4max , σ 5max , σ 6max , σ 7min ], (7)

[0150] Using variable parameter lambda 1 ~λ 4 The linear terms and nonlinear terms of the coefficient matrix of formula (6) are stripped off to obtain the following coefficient matrix:

[0151]

[0152] in,

[0153]

[0154] Combining formula (6) and formula (8), the zero-order hold method is used to discretize the model and obtain the discretized LPV system model:

[0155] x(k+1)=e(η(k))x(k)+F(η(k))u(k)+G(η(k))w(k)

[0156] y(k)=Cx(k)+Hw(k) (9)

[0157] Among them, T s is the sampling period,

[0158]

[0159] Weight coefficient η j (k)≥0, (j=1, 2, 3, 4) satisfies And can be defined as:

[0160]

[0161] Where x(k), u(k), w(k) and y(k) are the discrete forms of the state vector, control input, system disturbance, and output vector, respectively. is the coefficient matrix of the discretized vehicle-road system model in formula (6), E(η(k)), F(η(k)), G(η(k)) is the coefficient matrix of the LPV model, η j (k) is the weight coefficient;

[0162] S2. Robust observer design: Based on the obtained LPV system equation, an online observer is designed based on the robust positive invariant set to obtain the vehicle center of mass sideslip angle data in real time, and the conservatism is reduced by updating the minimum robust positive invariant set online:

[0163] Step S21: Offline design of robust observer

[0164] Considering that the vehicle's center of mass sideslip angle is difficult to obtain accurately, the following observer is designed for real-time state estimation:

[0165] x h (k+1)=e(η(k))x h (k)+F(η(k))u(k)+L(η(k))(y(k)-Cx h (k)) (10)

[0166] In the formula, x h is the estimated state, is the observer gain, where L q ,q∈[1,2,…,q m ] are the independent observed gains of the subsystems to be solved;

[0167] So the state observation error is defined as x e (k) = x(k) - x h (k); Combining formula (9) and formula (10), the dynamic equation of the observation error system can be obtained as follows:

[0168] x e (k+1)=(e(η(k))L(η(k))C)x e (k)+(G(η(k))-L(η(k))H)w(k) (11)

[0169] In order to obtain better observation results, it is necessary to limit the real-time observation error to an acceptable range. Considering the existence of disturbance terms in formula (11), the quadratic performance function of state observation can be defined as Where P e is a symmetric positive definite matrix, so according to the quadratic boundedness theory, if the following inequality holds, the observation error will always be in the robust positive invariant set:

[0170]

[0171] In order to make formula (12) valid, the following Theorem 1 is given to obtain the observer gain L q and the matrix P e :

[0172] Theorem 1: For the observation system formula 11, assuming that the system perturbation is bounded, that is If there exists a matrix P e , R q ,q∈[1,2,…,q m ], and a positive scalar θ∈(0,1), so that the following optimization problem is solvable:

[0173]

[0174] Then formula (12) always holds true, and the gain of the online observer is given by Calculated, and then further calculated the minimum robust positive invariant set of formula (11) Where θ, Q w is a given positive definite scalar, q∈[1, 2, …, q m ] is the label of each fuzzy subsystem;

[0175] Step S22: Online update of the estimated error set

[0176] pass Then formula (12) always holds true, and the gain of the online observer is given by Calculated, and then further calculated the minimum robust positive invariant set of formula (11) Where θ, Q w is a given positive definite scalar,

[0177] q∈[1, 2, …, q m ] is the label of each fuzzy subsystem (Formula 14) As an upper bound, since θ∈(0,1), when When That is, it can force the observation error to gradually decrease; when When It shows that the observation error will converge to the robust positive invariant set Therefore, we know that the set for When , the robust positive invariant set of the system observation error, correspondingly, It can be used to limit the allowable boundary of observation error at time k+1;

[0178] In order to obtain better observation results, one possible way is to obtain the minimum robust positive invariant set of observation errors at each moment. Therefore, the following Theorem 2 is given to find the minimum robust positive invariant set at moment k+1:

[0179] Theorem 2: For the observation error bound at time k in is a known scalar. Considering all possible observation errors, parameter uncertainties, and bounded input disturbances in the observation system at time k, the optimal observation error bound at time k+1 is It can be obtained by solving the following optimization problem:

[0180]

[0181] In the formula, κ 1 ≥0 and κ 2 ≥0 are two scalars to be solved, Λ 22 , Λ 32 , Λ 33 is a matrix element;

[0182] S3. Robust model predictive control construction: According to the designed online observer, the minimum-maximum robust predictive control model based on the online observer is constructed using linear matrix inequality:

[0183] For the LPV system formula 9, after introducing the observer formula 10, the predicted control input u(i|k) at time k can be given by the following equation, where i ≥ 0 is the prediction length,

[0184]

[0185] Where N q (k), q=1,…,q m is the control gain at time k to be determined. In order to avoid the adverse effect of the estimated action on the optimization process of the robust model predictive control system, an augmented system can be constructed. Therefore, combining Formula 9, Formula 11 and Formula 14, the closed-loop prediction model of the augmented system is obtained as follows:

[0186] ξ(i+1|k)=Π(i,k)ξ(i|k)+Ξ(i,k)ω(k+i), i≥0 (17)

[0187] in:

[0188]

[0189] For the augmented system formula (17), a robust predictive control model based on minimum-maximum control is constructed at each time k, where

[0190]

[0191] Where Υ is the performance index, the matrix S = diag{S 1 , S 2}, where S 1 >0,S 2 >0, T>0 and U>0 weight matrices, Ω represents the set of uncertain systems, is the maximum control input based on actual physical constraints or for safety considerations, represents the maximum allowed system state, Φ is a general constant matrix;

[0192] Thus, we can see that formula 19 represents the state constraint of the augmented system, formula 20 is the quadratic bounded condition that satisfies the performance index, formula 21 is the input constraint, and formula 22 represents the state constraint of formula 9 of the original system. Therefore, the optimization problem is to solve the optimization problem with uncertainty and multiple states and input constraints at each time k so that i obtains the minimum value;

[0193] S4. Construction of vehicle stability domain: Based on the vehicle dynamics equation and the magic formula tire model, a compound differential equation is constructed. The phase trajectory is drawn according to the compound differential equation, the predetermined longitudinal speed, the front wheel steering angle and the road friction coefficient. The stable point and saddle point are calculated by searching for the equilibrium solution, and the vehicle parameters under different working conditions are obtained by changing the vehicle longitudinal speed, the front wheel steering angle and the road friction coefficient. The boundary of the vehicle stability domain is obtained by parameter fitting:

[0194] According to the composite differential equation composed of Formula 1 and Formula 2, the β-γ phase trajectory can be drawn according to the predetermined longitudinal speed, front wheel steering angle and road friction coefficient. Then, based on the phase trajectory bifurcation stability theory, the stable point and saddle point can be calculated by searching for the equilibrium solution. By changing the vehicle driving conditions, the vehicle stability state and data under different conditions can be obtained. Through the parameter fitting method, the following vehicle dynamic stability domain boundary can be obtained:

[0195] -Ψ 1 ≤φ 1 β+ζ 1 γ≤Ψ 1

[0196] -Ψ 2 ≤φ 2 β+ζ 2 γ≤Ψ 2 (twenty three)

[0197] In the formula, φ 1 , 1 , ψ 1 ,φ 2 , 2 ,Ψ 2 are the fitting parameters;

[0198] Considering the physical constraints of the actual steering system and the four-wheel independent drive system, the limits of the control input are set as follows:

[0199]

[0200] In the formula, and They are the maximum front wheel steering angle and the maximum additional yaw moment;

[0201] S5. Optimal distribution of four-wheel torque: Design an optimization objective function, and solve the objective function of the optimal torque distribution strategy based on the expected additional yaw moment and the expected longitudinal vehicle speed, so as to optimize the four-wheel torque distribution of the four-wheel independent drive electric vehicle:

[0202] In order to further ensure the stability of the vehicle, the optimal torque distribution strategy is used to optimize the four-wheel torque distribution of the four-wheel independent drive electric vehicle. In addition to considering the total adhesion coefficient utilization rate of the four wheels as small as possible, in order to make the vehicle run more smoothly, the average deviation of the adhesion coefficient utilization rate between different wheels is also considered. Therefore, the objective function of the optimization problem is designed as follows:

[0203]

[0204] Where J is the performance index, represents the utilization rate of adhesion of a single wheel, τ = fl, fr, rl, rr is the number of each wheel; is a given scalar used to adjust the weight distribution;

[0205] The optimization problem of formula (25) can be transformed into a standard quadratic programming problem, which can be solved using the built-in QP function in Matlab. The equality and inequality constraints of the QP problem are as follows:

[0206]

[0207] Where ΔM z is the additional yaw moment, F x , F y , F z are the longitudinal, lateral and vertical forces of the tire, τ = fl, fr, rl, rr represent different wheels, μ is the road adhesion coefficient, l f is the front wheelbase, T max is the maximum output torque of a single wheel, r ω is the equivalent wheel rolling radius, t ω It is the width of the left and right wheels of the vehicle.

[0208] See also Figure 2 It can be seen that the real-time vehicle center of mass sideslip angle can be obtained by a robust observer based on a robust positive invariant set. Based on this, an observer-based robust model predictive control is proposed. The observer gain of the online operation is obtained by offline calculation, and the minimum allowable observation error can be obtained by online refreshing. Then, the minimum-maximum robust model predictive optimization problem is constructed. According to the robust positive invariant set theory, the state constraints based on phase plane stability and the input saturation constraints caused by physical characteristics are handled. Finally, the optimal torque distribution is performed to achieve additional yaw moment intervention and longitudinal velocity tracking.

[0209] In summary, the present invention comprehensively considers various uncertain disturbances in the vehicle driving process, designs a predictive control architecture based on the minimum-maximum robust model, and enhances the robustness of the path tracking control system; at the same time, an online observer based on the minimum robust positive invariant set is designed to achieve real-time and accurate acquisition of key vehicle parameters without increasing hardware costs; in addition, the present invention designs an active front-wheel steering and torque vector collaborative control method to achieve coordinated operation between the steering system and the four-wheel drive system, enhances the operational stability and path tracking accuracy of distributed drive electric vehicles under different driving conditions, and improves the safety of vehicle road driving.

[0210] Embodiment 2:

[0211] This embodiment provides a distributed drive electric vehicle path tracking control system based on a vehicle stability domain, which is used to implement the distributed drive electric vehicle path tracking control method described in the first embodiment, including:

[0212] A model building module is used to receive vehicle parameter information, build a vehicle dynamics model and a magic formula tire model according to the vehicle parameter information, and build a vehicle-road system model for trajectory tracking control based on the vehicle dynamics model and the magic formula tire model, thereby building an LPV system equation;

[0213] The observer design module is used to design an online observer based on the obtained LPV system equation and the robust positive invariant set, so as to obtain the vehicle center of mass sideslip angle data in real time and reduce the conservatism by updating the minimum robust positive invariant set online;

[0214] A predictive control module is used to construct a minimum-maximum robust predictive control model based on an online observer using linear matrix inequality according to the designed online observer;

[0215] The stability domain boundary derivation module is used to form a compound differential equation based on the vehicle dynamics equation and the magic formula tire model, draw a phase trajectory according to the compound differential equation, the predetermined longitudinal speed, the front wheel steering angle and the road friction coefficient, calculate the stability point and the saddle point by searching for the equilibrium solution, and change the vehicle longitudinal speed, the front wheel steering angle and the road friction coefficient to obtain vehicle parameters under different working conditions, and obtain the vehicle stability domain boundary by parameter fitting;

[0216] The optimization module is used to design the optimization objective function. According to the obtained expected additional yaw moment and the expected longitudinal vehicle speed, the optimal torque distribution strategy objective function is solved to optimize the four-wheel torque distribution of the four-wheel independent drive electric vehicle.

[0217] Specifically, the above-mentioned model construction module, observer design module, predictive control module, stable domain boundary derivation module and optimization module can be embedded in a computer processing system. The computer calls the above-mentioned modules to complete the task of enhancing vehicle driving tracking control according to the above-mentioned distributed drive electric vehicle path tracking control method; the above-mentioned model construction module, observer design module, predictive control module, stable domain boundary derivation module and optimization module can perform operations according to the specific steps given in the distributed drive electric vehicle path tracking control method.

[0218] It should be noted that it should be understood that the division of the various modules of the above system is only the division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated, and these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. For example, the model building module can be a separately established processing element, or it can be integrated in a chip of the above-mentioned device. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and called and executed by a processing element of the above-mentioned device. The functions of the above signal processing module, and the implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed by the hardware integrated logic circuit in the processor element or the instructions in the form of software.

[0219] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more digital singnal processors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processors that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0220] Embodiment three:

[0221] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned distributed drive electric vehicle path tracking control method is adopted.

[0222] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device can also include input and output devices, network access devices and buses.

[0223] Furthermore, the processor may adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be adopted. The general-purpose processor may adopt a microprocessor or any conventional processor, etc., and the present application does not impose any restrictions on this.

[0224] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants 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.

[0225] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can also be a centered element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be a centered element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are only for illustrative purposes and are not intended to be the only implementation method.

[0226] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0227] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

Claims

1. A distributed drive electric vehicle path tracking control method, characterized in that: The following steps are involved: Receiving vehicle parameter information, constructing a vehicle dynamics model and a magic formula tire model according to the vehicle parameter information, and constructing a vehicle-road system model for trajectory tracking control based on the vehicle dynamics model and the magic formula tire model, thereby constructing an LPV system equation; According to the obtained LPV system equation, an online observer is designed based on the robust positive invariant set to obtain the vehicle center of mass sideslip angle data in real time, and the conservatism is reduced by updating the minimum robust positive invariant set online. According to the designed online observer, a minimum-maximum robust predictive control model based on online observer is constructed by using linear matrix inequality. A composite differential equation is constructed based on the vehicle dynamics equation and the magic formula tire model. The β-γ phase trajectory is drawn according to the composite differential equation, the predetermined longitudinal speed, the front wheel steering angle and the road friction coefficient. The stable point and the saddle point are calculated by searching for the equilibrium solution. The vehicle parameters under different working conditions are obtained by changing the vehicle longitudinal speed, the front wheel steering angle and the road friction coefficient. The boundary of the vehicle stability domain is obtained by parameter fitting. An optimization objective function is designed. According to the expected additional yaw moment and the expected longitudinal vehicle speed, the optimal torque distribution strategy objective function is used to solve the four-wheel torque distribution of the four-wheel independent drive electric vehicle.

2. The distributed drive electric vehicle path tracking control method according to claim 1, characterized in that: The vehicle parameter information includes front wheel steering angle, longitudinal speed, center of mass sideslip angle, yaw angular velocity, additional yaw moment and tire lateral force, and the vehicle parameter information is collected by a steering angle sensor, a gyroscope and a tire six-component force tester respectively.

3. The distributed drive electric vehicle path tracking control method according to claim 1, characterized in that: The vehicle dynamics model and the magic formula tire model are constructed according to the vehicle parameter information, as follows: (31) Focusing on the trajectory tracking control of distributed drive electric vehicles, the vehicle dynamics equation is described as follows: Where m is the vehicle mass, l z is the yaw moment of inertia, l f and l r are the lengths from the vehicle's center of mass to the front and rear axles, v x is the longitudinal velocity of the vehicle, β and γ are the sideslip angle and yaw rate of the center of mass, respectively, and δ f It's the front wheel Angle, ΔM z is the additional yaw moment, F yf and F yr They are the lateral forces of the front and rear wheels respectively; (32) The magic formula tire model is constructed to characterize the longitudinal force and lateral force of the tire. The lateral force of the tire can be calculated by the following formula: Where α n , (n = f, r) is the tire slip angle, a0 ~ a 13 are the fitting parameters, <h2 style=";text-align:left;direction:ltr">BCD<h2 style=";text-align:left;direction:ltr"> y <h2 style=";text-align:left;direction:ltr"> = a3sin[2arctan(F<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> / E4)],E<h2 style=";text-align:left;direction:ltr"> y <h2 style=";text-align:left;direction:ltr"> =a6F<h2 style=";text-align:left;direction:ltr"> z <h2 style=";text-align:left;direction:ltr"> +a7 S hy =a8F z +a9+a 10 χ,S vy =a 11 F z χ+a 12 F z +a 13 . Perform offline linearization for each slip angle: F yf =K f a f ,F yr =K r a r (3) In the formula, K n , n = f, r is the equivalent tire cornering stiffness; Introducing the adjustment factor ε f and ε r , so K n It is expressed as: K f =e f C f ,K r =e r C r (14) Where C f and C r are the cornering stiffness values ​​of the front and rear wheels in the approximate linear region, respectively; (33) A deviation tracking model is constructed based on the kinematic relationship between the vehicle and the road, specifically: Where ρ r is the road curvature, e y and are the lateral tracking deviation and heading angle deviation, l p is the preview distance; Combining formula (1) to formula (5), the vehicle-road system model for trajectory tracking control is obtained as follows: y(t)=Cx(t)+Hw(t) (6) In the formula is the state vector, u=[δ f ΔM z ] T is the control input, w=ρ r is the system disturbance, H 2×1 It is a constant matrix introduced to describe the unknown disturbances in the measurement output process.

4. The distributed drive electric vehicle path tracking control method according to claim 3 is characterized in that: Construct the LPV system equation as follows: (41) All time-varying terms in formula (6) are linearly stripped off to define the variable parameters σ1~σ7 of the LPV system: The maximum and minimum values ​​of σ1, σ3 and σ5 are reached synchronously, which is exactly opposite to the maximum and minimum values ​​of σ7. Similarly, the maximum and minimum values ​​of σ2, σ4 and σ6 are also reached synchronously. (42) Redefine the following four LPV system variable parameters λ1~λ4 as: λ1=[σ 1min ,s 2min ,s 3min ,s 4min ,s 5min ,s 6min ,s 7max ], λ2=[σ 1max ,s 2min ,s 3max ,s 4min ,s 5max ,s 6min ,s 7min ], λ3=[σ 1min ,s 2max ,s 3min ,s 4max ,s 5min ,s 6max ,s 7max ], λ4=[σ 1max ,s 2max ,s 3max ,s 4max ,s 5max ,s 6max ,s 7min ], (7) The linear and nonlinear terms of the coefficient matrix of formula (6) are stripped using variable parameters λ1~λ4 to obtain the following coefficient matrix: in: Combining formula (6) and formula (8), the zero-order hold method is used to discretize the model and obtain the discretized LPV system model: x(k+1)=E(η(k))x(k)+F(η(k))u(k)+G(η(k))w(k) y(k)=Cx(k)+Hw(k) (9) Among them, T s is the sampling period, Weight coefficient η j (k)≥0, (j=1, 2, 3, 4) satisfies And can be defined as: Where x(k), u(k), w(k) and y(k) are the discrete forms of the state vector, control input, system disturbance, and output vector, respectively. is the coefficient matrix of the discretized vehicle-road system model in formula (6), E(η(k)), F(η(k)), G(η(k)) is the coefficient matrix of the LPV model, η j (k) is the weight coefficient.

5. The distributed drive electric vehicle path tracking control method according to claim 4, characterized in that: According to the obtained LPV system equation, an online observer is designed based on the robust positive invariant set to obtain the vehicle center of mass sideslip angle data in real time, and the conservatism is reduced by updating the minimum robust positive invariant set online, as follows: (51) Online observer performs real-time state estimation: x h (k+1)=E(η(k))x h (k)+F(η(k))u(k)+L(η(k))(y(k)-Cx h (k)) (10) In the formula, x h is the estimated state, is the observer gain, where L q ,q∈[1,2,…,q m ] are the independent observed gains of the subsystems to be solved; So the state observation error is defined as x e (k) = x(k) - x h (k); Combining Formula 9 and Formula 10, the dynamic equation of the observation error system can be obtained as follows: x e (k+1)=(E(η(k))-L(η(k))C)x e (k)+(G(η(k))-L(η(k))H)w(k) (11) Define the quadratic performance function of the state observation Where P e is a symmetric positive definite matrix; According to the quadratic boundedness theory, if the following inequality holds, the observation error will always be in the robust positive invariant set: For the observation system formula (11), it is assumed that the system disturbance is bounded, that is, Assume there is a matrix P e , R q ,q∈{1,2,…,q m ], and a positive scalar θ∈(0,1), then the following optimization problem is solvable: Then formula (12) always holds true, and the gain of the online observer is L q =P e -1 R q Calculated, and then further calculated the minimum robust positive invariant set of formula (11) Where θ, Q w is a given positive definite scalar, q∈{1, 2, …, q m ] is the label of each fuzzy subsystem; (52) Robust positive invariant set online update: (52.1) Introducing a known scalar Make Established, of which is obtained by solving the optimization problem of formula (13); according to You can get: definition As an upper bound, since θ∈(0,1), when When That is, it can force the observation error to gradually decrease; when When Then the observation error will converge to the robust positive invariant set Therefore, the set for When , the robust positive invariant set of the system observation error; (52.2) Accordingly, It can be used to limit the allowable boundary of observation error at time k+1; Find the minimum robust positive invariant set at time k+1 The details are as follows: The observation error bound at time k is in is a known scalar, the optimal observation error bound at time k+1 By solving the following optimization problem: In the formula, κ1≥0 and κ2≥0 are two scalars to be solved, Λ 22 , Λ 32 , Λ 33 is a matrix element.

6. The distributed drive electric vehicle path tracking control method according to claim 5, characterized in that: According to the designed online observer, a robust Min-Max predictive control model based on the online observer is constructed using linear matrix inequality, as follows: (61) For the LPV system formula (9), after introducing the observer formula (10), the predicted control input u(i|k) at time k is given by the following equation, where i ≥ 0 is the prediction length: Where N q (k), q=1,…,q m is the control gain at time k to be determined; (62) Building an augmented system Combining formula (9), formula (11) and formula (14), the closed-loop prediction model of the augmented system is obtained as follows: ξ(i+1|k)=Π(i,k)ξ(i|k)+Ξ(i,k)w(k+i),i≥0 (17) in: For the augmented system formula (17), a robust predictive control model based on minimum-maximum control is constructed at each time k, where Where γ is the performance index, the matrix S = diag{S1,S2}, where S1>0, S2>0, T>0 and U>0 are weight matrices, Ω represents the set of uncertain systems, is the maximum control input based on actual physical constraints or for safety considerations, represents the maximum allowed system state, and Φ is a general constant matrix.

7. The distributed drive electric vehicle path tracking control method according to claim 6, characterized in that: The β-γ phase trajectory is drawn, the stable point and the saddle point are calculated by searching the equilibrium solution, and the vehicle stability region boundary is obtained by parameter fitting according to the vehicle parameters under different working conditions, as follows: (71) According to the composite differential equation composed of formula (1) and formula (2), the β-γ phase trajectory is drawn according to the predetermined longitudinal speed, the front wheel steering angle and the road friction coefficient, and then the stable point and the saddle point are calculated by searching the equilibrium solution based on the phase trajectory bifurcation stability theory; (72) By changing the vehicle driving conditions, the vehicle stability status and data under different conditions are obtained, and the following vehicle dynamic stability domain boundary is calculated by parameter fitting method: -Ψ1≤φ1β+ζ1γ≤Ψ1 -Ψ2≤φ2β+ζ2γ≤Ψ2 (23) Where φ1, ζ1, Ψ1, φ2, ζ2, Ψ2 are fitting parameters; Considering the physical constraints of the actual steering system and the four-wheel independent drive system, the limits of the control input are set as follows: In the formula, and They are the maximum front wheel steering angle and the maximum additional yaw moment respectively.

8. The distributed drive electric vehicle path tracking control method according to claim 7, characterized in that: The optimization objective function is designed. According to the expected additional yaw moment and the expected longitudinal vehicle speed, the optimal torque distribution strategy objective function is used to solve the problem, so as to optimize the four-wheel torque distribution of the four-wheel independent drive electric vehicle. The details are as follows: (81) The objective function of the optimization problem is designed as: Where J is the performance index, represents the utilization rate of adhesion of a single wheel, τ = fl, fr, rl, rr is the number of each wheel; is a given scalar used to adjust the weight distribution; (82) The optimization problem of formula (25) is transformed into a standard quadratic programming problem and solved using the built-in QP function in Matlab. The equality and inequality constraints of the QP problem are as follows: Where ΔM z is the additional yaw moment, F x , F y , F z are the longitudinal, lateral and vertical forces of the tire, τ = fl, fr, rl, rr represent different wheels, μ is the road adhesion coefficient, l f is the front wheelbase, T max is the maximum output torque of a single wheel, r ω is the equivalent wheel rolling radius, t ω It is the width of the left and right wheels of the vehicle.

9. A distributed drive electric vehicle path tracking control system based on vehicle stability domain, used to implement the distributed drive electric vehicle path tracking control method according to any one of claims 1 to 8, characterized in that: include: A model building module is used to receive vehicle parameter information, build a vehicle dynamics model and a magic formula tire model according to the vehicle parameter information, and build a vehicle-road system model for trajectory tracking control based on the vehicle dynamics model and the magic formula tire model, thereby building an LPV system equation; The observer design module is used to design an online observer based on the obtained LPV system equation and the robust positive invariant set, so as to obtain the vehicle center of mass sideslip angle data in real time and reduce the conservatism by updating the minimum robust positive invariant set online; A predictive control module is used to construct a minimum-maximum robust predictive control model based on an online observer using linear matrix inequality according to the designed online observer; The stability domain boundary derivation module is used to form a compound differential equation based on the vehicle dynamics equation and the magic formula tire model, draw the bg phase trajectory according to the compound differential equation, the predetermined longitudinal speed, the front wheel steering angle and the road friction coefficient, calculate the stable point and the saddle point by searching for the equilibrium solution, and change the vehicle longitudinal speed, the front wheel steering angle and the road friction coefficient to obtain the vehicle parameters under different working conditions, and obtain the vehicle stability domain boundary by parameter fitting; The optimization module is used to design the optimization objective function. According to the obtained expected additional yaw moment and the expected longitudinal vehicle speed, the optimal torque distribution strategy objective function is solved to optimize the four-wheel torque distribution of the four-wheel independent drive electric vehicle.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the path tracking control method of a distributed drive electric vehicle according to any one of claims 1 to 8 is adopted.

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