Automobile electro-hydraulic composite ABS layered optimization control method

Abstract

The invention relates to an automobile electro-hydraulic composite ABS layered optimization control method. The method comprises the steps that a vehicle dynamics model is established; determining an optimization variable of the LQR controller by adopting an improved Tianying algorithm to obtain an optimized LQR controller; the total braking torque of the automobile is calculated based on the optimized LQR controller; and the total braking torque is distributed to a motor system and a hydraulic system through a fuzzy controller. According to the method, the range of the eagle search space is enlarged and the search precision is improved by adopting the improved eagle algorithm, so that the LQR controller in the upper layer is optimized, the total braking torque required under different working conditions can be calculated more quickly and more accurately, and the adaptive capacity, robustness and calculation efficiency of the system are improved; the adaptive capacity of the electro-hydraulic composite braking system to different road conditions can be enhanced, braking force distribution can be effectively optimized, the slip rate better fits the ideal value, and therefore the braking performance of a vehicle is further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automobile electro-hydraulic composite anti-lock brake control, in particular to an automobile electro-hydraulic composite ABS hierarchical optimization control method. BACKGROUND

[0002] The anti-lock brake system (ABS) of an automobile is a widely used active safety technology, which can effectively avoid the locking phenomenon of the automobile during braking by monitoring and adjusting the slip rate of the wheels in real time. When the locking phenomenon of the wheels occurs, the friction force between the tire and the ground decreases sharply, causing the wheels to stop rotating, thereby reducing the braking performance of the automobile, and also causing the driver to lose control of the automobile, which can easily lead to vehicle loss of control and even serious accidents. The ABS can continuously adjust the braking force to ensure that the wheels are always within the ideal slip rate range, effectively improving the braking stability and maneuverability of the vehicle, and significantly reducing traffic accidents caused by loss of control during braking.

[0003] With the progress of electronic control and multi-sensor fusion technology, automobile active safety systems are developing towards higher efficiency and intelligence. The response speed of traditional hydraulic braking is relatively slow, and the control accuracy cannot meet the needs of high-performance vehicles. In addition, the traditional hydraulic ABS may not be able to effectively coordinate between multiple dynamic controls such as braking and steering under complex road conditions. SUMMARY

[0004] In order to overcome at least one deficiency in the prior art, the present application provides an automobile electro-hydraulic composite ABS hierarchical optimization control method.

[0005] In a first aspect, an automobile electro-hydraulic composite ABS hierarchical optimization control method is provided, comprising:

[0006] establishing a vehicle dynamics model;

[0007] determining the optimization variables of the LQR controller using the improved eagle algorithm to obtain an optimized LQR controller; the optimization variables include the weighted matrix of the state variables and the weighted matrix of the control variables; the improved eagle algorithm includes introducing an individual cooperative optimization strategy for population optimization in the expanded search stage of the eagle algorithm, and introducing a disturbance control optimization strategy to determine the global optimal position in the global optimal position determination stage of the eagle algorithm;

[0008] calculating the total braking torque of the automobile based on the optimized LQR controller;

[0009] distributing the total braking torque to the motor system and the hydraulic system through a fuzzy controller.

[0010] In one embodiment, an individual cooperation optimization strategy is introduced in the expanded search phase of the eagle algorithm to optimize the population, using the following formula:

[0011]

[0012] where t iter is the iteration number, X′1(t iter +1) is the solution at the t iter +1 iteration in the expanded search phase, X best (t iter ) is the position of the prey after the t iter iteration, T iter is the maximum iteration number, N is the number of candidate solutions, X i (t iter ) is the average position of the t iter iteration solution, and rand is a random number between 0 and 1.

[0013] In one embodiment, a disturbance control optimization strategy is introduced in the global optimal position determination phase of the eagle algorithm to determine the global optimal position, using the following formula:

[0014] X′ best (t iter )=Υ(X best (t iter ),ζ)

[0015]

[0016] where X′ best (t iter ) is the position of the prey after disturbance, Υ is a normal distribution, X best (t iter ) is the position of the prey after the t iter iteration, ζ is the radius parameter of the normal disturbance, T iter is the maximum iteration number, β1 and β2 are control parameters of the radius variation, ζ1<ζ2<ζ3, ζ1, ζ2, ζ3 are the values of the radius parameter under different conditions.

[0017] In one embodiment, the improved eagle algorithm further includes population optimization in the reduced search phase using the following formula:

[0018] X′2(t iter +1)=X best (t iter )×Levy(D)+ε×(y′-x′)×rand

[0019] ε=X i (t iter )-X′R (t iter )

[0020] Among them, X′2(t iter +1) is the number t in the narrow search phase iter +1 iteration solution, X best (t iter ) is the tth iter The position of the prey after iterations, Levy (D) is the Levy flight function, ε is the mutual assistance, rand is a random number between [0,1], X i (t iter ) is the tth iter The average position of the iterative solution, X′ R (t iter ) is the position of the remaining individuals except the current individual, y′ is the component of the spiral path in the vertical direction of the search space, and x′ is the component of the spiral path in the horizontal direction of the search space.

[0021] In one embodiment, the LQR controller is expressed by the following formula:

[0022]

[0023] Where I is the cost function of the LQR controller, t0 is the start time of the control, and t f is the end time of control, X is the state variable, Q is the weighted matrix of the state variable, U is the control quantity, and R is the weighted matrix of the control quantity.

[0024] In one embodiment, the total braking torque of the vehicle is calculated based on the optimized LQR controller using the following formula:

[0025] T eff =K m T Emax

[0026]

[0027] Among them, T eff is the total braking torque of the car, K m is the effective torque ratio of the motor, T Emax is the maximum torque of the motor braking, T E is the motor braking torque, w E is the rated speed, P E is the rated power of the motor, and w is the motor speed.

[0028] In a second aspect, a stratified optimization control system for an automotive electro-hydraulic composite ABS is provided, comprising:

[0029] Model building module, used to build vehicle dynamics model;

[0030] an optimization module, configured to determine optimization variables of the LQR controller by using the improved algorithm of the grey wolf optimizer to obtain an optimized LQR controller; the optimization variables include a weighted matrix of state variables and a weighted matrix of control variables; the improved algorithm of the grey wolf optimizer includes introducing an individual cooperative optimization strategy for population optimization in an expanded search stage of the algorithm of the grey wolf optimizer and introducing a disturbance control optimization strategy to determine a global optimal position in a global optimal position determination stage of the algorithm of the grey wolf optimizer;

[0031] a calculation module, configured to calculate the total braking torque of the automobile based on the optimized LQR controller;

[0032] a distribution module, configured to distribute the total braking torque to the motor system and the hydraulic system by using a fuzzy controller.

[0033] Compared with the prior art, the application has the following beneficial effects:

[0034] 1. The automobile electro-hydraulic composite ABS hierarchical optimization control method can enhance the adaptability of the electro-hydraulic composite braking system to different road conditions, effectively optimize the braking force distribution, make the slip ratio more close to the ideal value, and further improve the braking performance of the vehicle.

[0035] 2. The algorithm of the grey wolf optimizer is improved, the disturbance control optimization strategy and the individual cooperative optimization strategy are adopted to form the improved algorithm of the grey wolf optimizer (DCICAO), the individual cooperative optimization strategy ensures that the algorithm can search in all directions, enhances the diversity of the population, and enhances the global search ability of the algorithm, the disturbance control optimization strategy performs disturbance according to the normal random distribution with adjustable variance, optimizes the global optimal solution of the algorithm of the grey wolf optimizer, and avoids the algorithm from falling into local optimization. The improved algorithm of the grey wolf optimizer increases the range of the search space of the grey wolf and improves the search precision, and then optimizes the LQR controller in the upper layer, so that the total braking torque required under different working conditions can be calculated faster and more accurately, and the self-adaptive ability, robustness and calculation efficiency of the system are improved. BRIEF DESCRIPTION OF DRAWINGS

[0036] The application can be better understood by referring to the description given below in conjunction with the accompanying drawings, which are incorporated in and form a part of the specification, and together with the detailed description, serve to explain the principles of the application. In the drawings:

[0037] Figure 1 A flow chart of the automobile electro-hydraulic composite ABS hierarchical optimization control method is shown.

[0038] Figure 2The fitness curves of the method of the application and other algorithms in searching global optimal solution are shown, wherein (a) represents the fitness curve of a function with a search range of [-10, 10], a dimension of 30 and an optimal fitness of 0, (b) represents the fitness curve of a function with a search range of [-100, 100], a dimension of 30 and an optimal fitness of 0; (c) represents the fitness curve of a function with a search range of [-30, 30], a dimension of 30 and an optimal fitness of 0; (d) represents the fitness curve of a function with a search range of [-500, 500], a dimension of 30 and an optimal fitness of -12569.5; (e) represents the fitness curve of a function with a search range of [-1.28, 1.28], a dimension of 30 and an optimal fitness of 0.

[0039] Figure 3 The control curves of the LQR controller of the application applied in the hierarchical optimization control system of the automobile electro-hydraulic composite ABS are shown, wherein (a) is a comparison diagram of total braking torque on dry concrete road surface, (b) is a schematic diagram of braking torque of each wheel after optimization, (c)-(f) are respectively comparison diagrams of vehicle speed and four-wheel speed on dry concrete road surface, (g)-(j) are respectively comparison diagrams of four-wheel slip rate on dry concrete road surface, figure (k) is a schematic diagram of electro-hydraulic composite braking acceleration on dry concrete road surface, and figure (l) is a comparison diagram of braking distance on dry concrete road surface. DETAILED DESCRIPTION

[0040] In the following, exemplary embodiments of the application will be described with reference to the accompanying drawings. In the description of the embodiments, not all of the features of the actual embodiments are described in order to avoid obscuring the application with unnecessary detail. It should be appreciated that in the development of any such actual embodiment, numerous implementation-specific decisions can be made to achieve the developer's specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless result in an embodiment being implemented in accordance with the present application.

[0041] It should also be noted that, in the interests of clarity, not all of the detail of the application has been described herein, as such detail can obscure the inventive aspects of the application. It is therefore intended to include all such details in the scope of the application, as long as such details do not depart from the spirit of the application.

[0042] It is to be understood that the application is not limited to the embodiments described, which can be modified in several ways, which will become apparent as the application is better understood in light of the following description and accompanying drawings. In particular, the embodiments can be combined with each other, features can be replaced or borrowed from one embodiment to another, and one or more features can be omitted in an embodiment.

[0043] The application is directed to the problem that the traditional hydraulic ABS may not effectively realize the coordination between multiple dynamic controls such as braking and steering under complex road conditions, and considering that motor braking can make up for the slow response of traditional hydraulic braking, by integrating the motor and hydraulic mechanism to generate braking torque and adjust hydraulic pressure, the traditional hydraulic ABS cannot match the advantages of the application in response speed, control accuracy and environmental adaptability, and a layered optimization control method of automobile electro-hydraulic composite ABS is proposed.

[0044] Figure 1 The flow chart of the layered optimization control method of automobile electro-hydraulic composite ABS is shown, referring to Figure 1 , the method mainly includes the following steps:

[0045] Step S1, a vehicle dynamics model is established.

[0046] The vehicle dynamics model includes an ideal two-degree-of-freedom model, a seven-degree-of-freedom whole vehicle model, a tire model, a hydraulic braking system model and a wheel motor model.

[0047] By ignoring the influence of the suspension system and the steering system, ignoring the motion in the vertical, roll and pitch directions, and excluding the effects of air resistance and rolling resistance, an ideal two-degree-of-freedom model is obtained, and the formula is as follows:

[0048]

[0049] Wherein, γ is the yaw angular velocity; L is the wheelbase; k f is the front wheel cornering stiffness, k r is the rear wheel cornering stiffness; m is the vehicle weight; L f is the distance from the front axle to the center of mass, L r is the distance from the rear axle to the center of mass; v x is the vehicle longitudinal speed; I z is the moment of inertia around the Z axis; θ c is the front wheel steering angle.

[0050] By assuming that the non-critical factors such as air resistance, road slope and wheel rolling resistance are ignored, a seven-degree-of-freedom whole vehicle model is constructed, and the formula is as follows:

[0051]

[0052] F x = F xlr +F xrr +F xrf ·cosθ c +F xlf ·cosθ c -F ylf ·sinθ c -F yrr• sin θ c

[0053] F y = F ylf + F yrr + F ylf • cos θ c + F yrf • cos θ c + F xlf • sin θ c + F xrf • sin θ c

[0054]

[0055] F b = L f (F xlf • sin θ c + F ylf • cos θ c + F xrf • sin θ c + F yrf • cos θ c )

[0056]

[0057] where v y is the lateral velocity of the vehicle; F xlf , F xrf , F xlr , F xrr are the longitudinal forces of the front left wheel, the front right wheel, the rear left wheel and the rear right wheel respectively; F ylf , F yrf , F ylr , F yrr are the lateral forces of the front left wheel, the front right wheel, the rear left wheel and the rear right wheel respectively; A f is the front wheel track, A r is the rear wheel track. F x is the lateral force of the vehicle, F y is the longitudinal force of the vehicle.

[0058] According to the relationship between the road adhesion coefficient and the slip rate, the tire model can be derived, and the formula is as follows:

[0059]

[0060] where S T is the optimal slip rate, S is the slip rate; μ h is the peak adhesion coefficient; μ g is the longitudinal adhesion coefficient of the wheel when the slip rate is equal to 1; μ is the longitudinal adhesion coefficient.

[0061] Hydraulic ABS system controls the braking torque delivered to the wheels by adjusting the wheel cylinder pressure, hydraulic brake system model, which is expressed by the following formula:

[0062]

[0063] Where, J1 and J2 are the hydraulic brake mechanism related parameter values; P m is the master cylinder pressure; P w is the wheel cylinder pressure; B1 and B2 are the pressure increasing valve and pressure reducing valve switch state, where open is 1 and closed is 0; P r is the accumulator pressure; d1 and d2 are the throttle valve index. If it is in the pressure maintaining state, B1 = B2 = 0 at this time; if the slip rate is lower than the ideal value, the brake pressure needs to be increased, B1 = 1, B2 = 0 at this time; if the wheel is locked, the pressure needs to be reduced, B1 = 0, B2 = 1 at this time.

[0064] A three-phase permanent magnet synchronous motor is used, and a double closed loop vector control strategy is applied to build a wheel motor model, which is as follows:

[0065] The electromagnetic torque equation is:

[0066] T e = 1.5P n i q [i d (L d -L q )+ ξ f ]

[0067] The motor motion equation is:

[0068]

[0069] Where, T e is the electromagnetic torque; P n is the number of motor pole pairs; K e is the motor torque constant; I e is the motor moment of inertia; F e is the damping coefficient; T L is the motor load torque. i q is the stator current in the q-axis component, i d is the stator current in the d-axis component, L d is the inductance in the d-axis component, L q is the inductance in the q-axis component, ξ f is the permanent magnet flux linkage, ω s is the motor electrical angular velocity.

[0070] Step S2, the improved Aquila Optimizer algorithm is used to determine the optimization variables of the LQR controller to obtain an optimized LQR controller; the optimization variables include a weighted matrix of state variables and a weighted matrix of control variables; the improved Aquila Optimizer algorithm includes introducing an individual cooperative optimization strategy in the expanded search stage of the Aquila Optimizer algorithm to optimize the population, and introducing a disturbance control optimization strategy in the global optimal position determination stage of the Aquila Optimizer algorithm to determine the global optimal position.

[0071] Here, the Aquila Optimizer (AO) is a meta-heuristic optimization algorithm inspired by the hunting behavior of Aquila, which solves complex optimization problems by simulating four hunting strategies of Aquila. In order to calculate the control parameters of LQR more quickly and accurately, the Aquila Optimizer algorithm is improved.

[0072] The LQR controller is represented by the following formula:

[0073]

[0074] Where I is the cost function of the LQR controller, t0 is the start time of control, t f is the end time of control, X is the state variable, Q is the weighted matrix of the state variable, U is the control variable, and R is the weighted matrix of the control variable.

[0075] The size of Q and R determines the importance of the state variable X and the control variable U in the index. R is a positive definite matrix, while Q can be a semi-positive definite matrix. Usually, the selected weighted matrix is in the form of a diagonal matrix. The initial values of Q and R are as follows:

[0076]

[0077] Under the LQR control, the calculation of the total braking torque of the vehicle depends on the weight coefficient, and the weight coefficient should be adjusted according to the change of the working condition, so that the total braking torque changes. Q and R are optimized by the improved Aquila Optimizer algorithm. Q is 8-dimensional and R is 2-dimensional, and both are diagonal matrices, so let Q = diag (Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8) and R = diag (R1, R2), then the optimization variables are [Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, R1, R2].

[0078] Step S3, the total braking torque of the vehicle is calculated based on the optimized LQR controller.

[0079] Specifically, a proportional coefficient K m is designed to represent the proportion of the effective braking torque output by the motor, and a fuzzy control method is used for adjustment. A double-input single-output structure is used, the inputs are SOC (state of charge or remaining capacity of the battery) and vehicle speed v, and the output is K m, all membership functions are in the form of triangular function. When SOC exceeds 0.9, the motor stops participating in braking, so the fuzzy domain of SOC is [0, 0.9]; the fuzzy domain of vehicle speed is set to [20, 120], that is, when the vehicle speed is lower than 20km / h, the motor also stops participating in braking. The fuzzy domain of output K m is set to [0, 1]. The fuzzy subsets of input and output are defined as {LB, LS, M, HS, HB}, which represent low, lower, medium, higher, and high, respectively. The gravity method is selected for defuzzification to obtain the effective torque ratio K m of the motor, and then the total braking torque of the vehicle is obtained using the following formula:

[0080] T eff =K n T Emax

[0081]

[0082] , wherein T eff is the total braking torque of the vehicle, K m is the effective torque ratio of the motor, T Emax is the maximum torque of the motor braking, T E is the motor braking torque, w E is the rated speed, P E is the rated power of the motor, and w is the motor speed.

[0083] Step S4, distributing the total braking torque to the motor system and the hydraulic system through the fuzzy controller.

[0084] In this embodiment, a hierarchical control strategy is constructed. In the upper layer control, the system calculates the total braking torque required by the vehicle through LQR; in the lower layer control, a torque distribution strategy is adopted to distribute the total braking torque calculated by the upper layer to the motor braking system and the hydraulic braking system, and then to accurately distribute the braking torque to each wheel.

[0085] In addition, the algorithm is improved, and a disturbance control optimization strategy and an individual cooperation optimization strategy are adopted to form an improved algorithm (DCICAO). The individual cooperation optimization strategy ensures that the algorithm can search in all directions, enhances the diversity of the population, and enhances the global search ability of the algorithm. The disturbance control optimization strategy performs disturbance according to the normal random distribution with adjustable variance, optimizes the global optimal solution of the algorithm, and avoids the algorithm from falling into local optimum. The improved algorithm increases the range of the search space of the algorithm and improves the search accuracy, and then optimizes the LQR controller in the upper layer, so that the total braking torque required under different working conditions can be calculated faster and more accurately, and the adaptive ability, robustness and calculation efficiency of the system are improved.

[0086] In one embodiment, the individual cooperative optimization strategy is introduced in the expanded search phase of the Eagle algorithm to optimize the population, and the following formula is used:

[0087]

[0088] where t iter is the iteration number, X′1(t iter +1) is the solution at the t iter +1th iteration in the expanded search phase, X best (t iter ) is the position of the prey after the t iter th iteration, T iter is the maximum iteration number, N is the number of candidate solutions, X i (t iter ) is the average position of the t iter th iteration solution, and rand is a random number between 0 and 1.

[0089] In one embodiment, the position update mainly depends on the positions of the elite individuals and a random individual, and the original Eagle algorithm may reduce the search efficiency. To solve this problem, the improved Eagle algorithm also includes the following formula for population optimization in the reduced search phase:

[0090] X′2(t iter +1)=X best (t iter )×Levy(D)+ε×(y′-x′)×rand

[0091] ε=X i (t iter )-X′ R (t iter )

[0092] where X′2(t iter +1) is the solution at the t iter +1th iteration in the reduced search phase, X best (t iter ) is the position of the prey after the t iter th iteration, Levy(D) is the Levy flight function, ε is the mutual aid quantity, reflecting the cooperative relationship characteristics of the current individual and other individuals in the population; y′ is the component of the spiral path in the vertical axis direction of the search space, x′ is the component of the spiral path in the horizontal axis direction of the search space. rand is a random number between 0 and 1, X i (t iter ) is the average position of the t iter th iteration solution, and X′ R (t iter ) is the position of the remaining individuals except the current individual.

[0093] In nature, individuals of the same species capture prey through division of labor to maintain the continuation of the population, and when the ith individual obtains the position information of all other individuals, it can obtain sufficient hunting benefits. However, the positions of individuals in the population are complex, and therefore the interaction between individuals often only obtains partial benefits, which cannot guarantee that they can all benefit.

[0094] In an embodiment, a disturbance control optimization strategy is introduced, and disturbance is performed according to a variance-adjustable normal random distribution to obtain a new global optimal position. Meanwhile, the hawk population learns towards the updated global optimal position. The disturbance control optimization strategy is introduced in the global optimal position determination stage of the hawk algorithm to determine the global optimal position, and the following formula is used:

[0095] X′ best (t iter )=Υ(X best (t iter ),ζ)

[0096]

[0097] wherein X′ best (t iter ) is the position of the prey after disturbance, Y is a normal distribution, X best (t iter ) is the position of the prey after the t iter th iteration, ζ is a radius parameter of normal disturbance, T iter is the maximum number of iterations, β1 and β2 are control parameters of the radius change, β1<β2, ζ1<ζ2<ζ3, ζ1, ζ2, ζ3 are values of the radius parameter in different cases.

[0098] In order to further verify the effectiveness of the method of the application, experimental analysis is performed, and the following experimental results are given:

[0099] Figure 2 The fitness curves of the method of the application and other algorithms in searching for global optimal solutions are shown, wherein (a) represents a fitness curve of a function with a search range of [-10, 10], a dimension of 30, and an optimal fitness of 0, (b) represents a fitness curve of a function with a search range of [-100, 100], a dimension of 30, and an optimal fitness of 0, (c) represents a fitness curve of a function with a search range of [-30, 30], a dimension of 30, and an optimal fitness of 0, (d) represents a fitness curve of a function with a search range of [-500, 500], a dimension of 30, and an optimal fitness of -12569.5, and (e) represents a fitness curve of a function with a search range of [-1.28, 1.28], a dimension of 30, and an optimal fitness of 0.

[0100] By Figure 2 It can be seen that the method of the application is superior to the comparative algorithm in convergence speed and convergence accuracy, verifying the effectiveness of the optimization strategy, which can effectively improve the performance of the controller after adding the controller.

[0101] Figure 3 The control curve of the LQR controller of the application applied in the hierarchical optimization control system of the automobile electro-hydraulic composite ABS is shown, wherein (a) is a comparison diagram of total braking torque on dry concrete pavement, (b) is a schematic diagram of braking torque of each wheel after optimization, (c)-(f) are comparison diagrams of vehicle speed and four-wheel speed on dry concrete pavement, (g)-(j) are comparison diagrams of four-wheel slip rate on dry concrete pavement, figure (k) is a schematic diagram of electro-hydraulic composite braking acceleration on dry concrete pavement, and figure (l) is a comparison diagram of braking distance on dry concrete pavement.

[0102] Based on the same inventive concept as the hierarchical optimization control method of the automobile electro-hydraulic composite ABS, the embodiment also provides a corresponding hierarchical optimization control system of the automobile electro-hydraulic composite ABS, which comprises:

[0103] A model establishing module is configured to establish a vehicle dynamics model.

[0104] An optimization module is configured to determine optimization variables of the LQR controller by using the improved eagle algorithm to obtain an optimized LQR controller; the optimization variables include a weighted matrix of state variables and a weighted matrix of control variables; the improved eagle algorithm includes introducing an individual cooperative optimization strategy for population optimization in the expansion search stage of the eagle algorithm, and introducing a disturbance control optimization strategy to determine the global optimal position in the global optimal position determination stage of the eagle algorithm.

[0105] A calculation module is configured to calculate the total braking torque of the automobile based on the optimized LQR controller.

[0106] A distribution module is configured to distribute the total braking torque to the motor system and the hydraulic system through a fuzzy controller.

[0107] The hierarchical optimization control system of the automobile electro-hydraulic composite ABS of the embodiment has the same inventive concept as the hierarchical optimization control method of the automobile electro-hydraulic composite ABS described above, so the specific embodiments of the device can be seen in the embodiment part of the hierarchical optimization control method of the automobile electro-hydraulic composite ABS described above, and the technical effects thereof correspond to the technical effects of the above method, which will not be repeated here.

[0108] The above merely provides the various embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for optimizing the stratification control of an automotive electro-hydraulic composite ABS, characterized in that: include: Establish vehicle dynamics model; The improved Sky Eagle algorithm is used to determine the optimization variables of the LQR controller and the optimized LQR controller is obtained. The optimization variables include a weighted matrix of state variables and a weighted matrix of control variables; the improved Sky Eagle algorithm includes introducing an individual collaborative optimization strategy to perform population optimization in the expanded search phase of the Sky Eagle algorithm, and introducing a disturbance control optimization strategy to determine the global optimal position in the global optimal position determination phase of the Sky Eagle algorithm; Calculating the total braking torque of the vehicle based on the optimized LQR controller; The total braking torque is distributed to the motor system and the hydraulic system through a fuzzy controller.

2. The method according to claim 1, wherein in, In the expanded search phase of the Sky Eagle algorithm, an individual collaborative optimization strategy is introduced for population optimization, using the following formula: Among them, t iter is the number of iterations, X1′(t iter +1) is the tth iter +1 iteration solution, X best (t iter ) is the tth iter The position of the prey after iterations, T iter is the maximum number of iterations, N is the number of candidate solutions, X i (t iter ) is the tth iter The average position of the iterative solution, rand is a random number between [0,1].

3. The method according to claim 1, wherein in, In the global optimal position determination stage of the Sky Eagle algorithm, a disturbance control optimization strategy is introduced to determine the global optimal position, using the following formula: X′ best (t iter )=Y(X best (t iter ),g) Among them, X′ best (t iter ) is the position of the prey after disturbance, Υ is the normal distribution, X best (t iter ) is the tth iter The position of the prey after iterations, ζ is the radius parameter of the normal perturbation, T iter is the maximum number of iterations, β1 and β2 are the control parameters of radius change, ζ1<ζ2<ζ3, ζ1, ζ2, ζ3 are the values ​​of radius parameters in different situations.

4. The method according to claim 1, wherein The improved Sky Eagle algorithm also includes using the following formula to perform population optimization in the narrow search phase: X′2(t iter +1)=X best (t iter )×Levy(D)+ε×(y′-x′)×rand ε=X i (t iter )-X′ R (t iter ) Among them, X2′(t iter +1) is the number t in the narrow search phase iter +1 iteration solution, X best (t iter ) is the tth iter The position of the prey after iterations, Levy (D) is the Levy flight function, ε is the mutual assistance, rand is a random number between [0,1], X i (t iter ) is the tth iter The average position of the iterative solution, X′ R (t iter ) is the position of the remaining individuals except the current individual, y′ is the component of the spiral path in the vertical direction of the search space, and x′ is the component of the spiral path in the horizontal direction of the search space.

5. The method according to claim 1, wherein The LQR controller is expressed by the following formula: Where I is the cost function of the LQR controller, t0 is the start time of the control, and t f is the end time of control, X is the state variable, Q is the weighting matrix of the state variable, U is the control quantity, and R is the weighting matrix of the control quantity.

6. The method according to claim 1, wherein in, The total braking torque of the vehicle is calculated based on the optimized LQR controller using the following formula: T eff =K m T Emax Among them, T eff is the total braking torque of the car, K n is the effective torque ratio of the motor, T Emax is the maximum torque of the motor braking, T E is the motor braking torque, w E is the rated speed, P E is the rated power of the motor, and w is the motor speed.

7. An automotive electro-hydraulic composite ABS layered optimization control system, characterized in that: include: Model building module, used to build vehicle dynamics model; an optimization module for determining optimization variables of the LQR controller using an improved Sky Eagle algorithm to obtain an optimized LQR controller; the optimization variables include a weighted matrix of state variables and a weighted matrix of control variables; the improved Sky Eagle algorithm includes introducing an individual collaborative optimization strategy to perform population optimization during the expanded search phase of the Sky Eagle algorithm, and introducing a disturbance control optimization strategy to determine the global optimal position during the global optimal position determination phase of the Sky Eagle algorithm; a calculation module, configured to calculate a total braking torque of a vehicle based on the optimized LQR controller; The distribution module is used to distribute the total braking torque to the motor system and the hydraulic system through a fuzzy controller.

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