A multi-objective control method and system for automobile suspension based on fuzzy-takagi-sugeno coordination under braking safety

Abstract

This invention relates to the field of automotive suspension control technology, and more specifically, to a multi-objective control method and system for automotive suspension based on fuzzy extension coordination, considering braking safety. This invention acquires vehicle driving information and calculates the output force F of the active suspension actuator based on this information. F is calculated jointly based on the output force u′ under a comfort control strategy, the output force U under a safety control strategy, weighting coefficients ξ, and fuzzy coefficients k1 and k2. This invention combines the advantages of LQR control and sliding mode control, ensuring smooth vehicle ride and reducing suspension impact. Considering braking safety, this invention uses extension control to dynamically coordinate LQR control and sliding mode control, further improving the performance of the suspension control system under braking conditions. Based on fuzzy control, this invention introduces fuzzy coefficients, which effectively reduces abrupt changes caused by weighting coefficients, enabling smooth control switching.

Description

Technical Field

[0001] This invention relates to the field of automotive suspension control technology, and more specifically, to a multi-objective control method and system for automotive suspension based on fuzzy extension coordination, taking into account braking safety. Background Technology

[0002] The automotive suspension is a vibration isolation system that reduces vibrations caused by road surface excitation, playing a crucial role in the vehicle's ride comfort and safety. Currently, active control of automotive suspensions often employs sliding mode control and fuzzy PID control. However, due to the complexity of road and driving conditions, a single control method is insufficient to satisfy the trade-off between suspension comfort and safety.

[0003] Although existing technologies also employ multi-objective control methods, for example: Chinese invention patent CN110722950A uses upper-level extension switching control to assign damping weights to lower-level controllers; Chinese invention patent CN107825930A combines hybrid control and fuzzy control methods, using the vibration velocity and acceleration of the vehicle body and wheels as inputs, and obtains the optimal damper damping control force through joint control of fuzzy control and hybrid control; Chinese invention patent CN114879476A establishes a variable universe fuzzy controller for nonlinear damping, and establishes a fractional-order PIλDu controller using the front-end output as input.

[0004] However, the above patents have two shortcomings: 1. The balance between the safety control and comfort control of the suspension under different road surfaces and driving conditions needs to be improved, especially the safety requirements under braking conditions are not fully considered; 2. The smoothness is not high when coordinating and switching between the safety control and comfort control of the suspension. Summary of the Invention

[0005] Based on this, it is necessary to address the two shortcomings of the existing technology mentioned above by providing a multi-objective control method and system for automobile suspension based on fuzzy extension coordination, taking into account braking safety.

[0006] This invention is achieved using the following technical solution:

[0007] In a first aspect, the present invention discloses a multi-objective control method for automobile suspension based on fuzzy extension coordination, considering braking safety, which is applied to blind spot scenarios.

[0008] The multi-objective control method for vehicle suspension based on fuzzy extension coordination, considering braking safety, includes the following steps:

[0009] Acquire vehicle driving information; wherein, driving information includes: road disturbance input W, vehicle braking deceleration a, and longitudinal distance x between the vehicle and the obstacle target point; the obstacle target point is the endpoint of the obstacle that is furthest from the vehicle along the vehicle's forward view;

[0010] Calculate the output force F of the active suspension actuator based on the driving information;

[0011] Where F = k1k2ξu′ + (1 - k1k2ξ)U;

[0012] In the formula, k1 and k2 represent fuzzy coefficients obtained based on fuzzy rules; ξ represents weighting coefficients; u′ represents the output force under the comfort control strategy calculated by LQR control based on the suspension model; and U represents the output force under the safety control strategy calculated by sliding mode control based on the suspension model.

[0013] This method for multi-objective control of vehicle suspension based on fuzzy extension coordination, considering braking safety, implements the method or process according to embodiments of this disclosure.

[0014] Secondly, the present invention discloses a multi-objective control system for vehicle suspension based on fuzzy extension coordination considering braking safety, which uses the multi-objective control method for vehicle suspension based on fuzzy extension coordination considering braking safety disclosed in the first aspect.

[0015] Considering braking safety, a multi-objective control system for automotive suspension based on fuzzy extension coordination includes: a driving information acquisition module and an output force calculation module.

[0016] The driving information acquisition module is used to acquire the vehicle's driving information. The output force calculation module is used to calculate force F.

[0017] This describes a method or process for implementing a multi-objective control system for automotive suspension based on fuzzy extension coordination, taking into account braking safety, according to embodiments of the present disclosure.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. This invention employs a multi-objective control method to control the output force of the active suspension actuator. This multi-objective control method effectively combines the advantages of LQR control and sliding mode control, and integrates the advantages of both to ensure smooth vehicle driving and reduce suspension impact, thereby optimizing the overall performance of the suspension.

[0020] 2. This invention takes into account the safety of braking conditions and uses extension control to dynamically coordinate LQR control and sliding mode control. It can match appropriate weighting coefficients according to driving conditions to improve the performance of the suspension control system.

[0021] 3. This invention is based on fuzzy control. By introducing fuzzy coefficients, it improves the situation where the weighted coefficient jumps in the extension output cause sudden changes in the output force of multiple targets. It can effectively reduce the sudden changes caused by the weighted coefficients, so that the control can achieve smooth switching. Attached Figure Description

[0022] Figure 1 This is a simplified flowchart of the multi-objective control method for vehicle suspension based on fuzzy extension coordination under the consideration of braking safety in Embodiment 1 of the present invention;

[0023] Figure 2 for Figure 1 A schematic diagram illustrating the application of a multi-objective control method in a blind zone scenario;

[0024] Figure 3 for Figure 2 A graph showing the relationship between the optimal safe speed of a vehicle and time.

[0025] Figure 4 for Figure 2 A graph showing the relationship between vehicle braking deceleration and time.

[0026] Figure 5 This is a diagram of the suspension model constructed in Embodiment 1 of the present invention;

[0027] Figure 6 This is a distribution diagram of classical domains, extensional domains, and non-domains in Embodiment 1 of the present invention;

[0028] Figure 7 This is a graph showing the relationship between k1 and time in Embodiment 1 of the present invention;

[0029] Figure 8 This is a graph showing the relationship between k2 and time in Embodiment 1 of the present invention;

[0030] Figure 9 Comparison of the relationship between k1k2ξ, ξ and time in Embodiment 1 of the present invention;

[0031] Figure 10 This is a comparison diagram of the vertical acceleration of the vehicle body under two control methods implemented in Embodiment 1 of the present invention;

[0032] Figure 11 This is a comparison diagram of the vehicle pitch acceleration changes under two control methods implemented in Embodiment 1 of the present invention;

[0033] Figure 12 This is a comparison diagram of the forward stroke changes in two control methods implemented in Embodiment 1 of the present invention;

[0034] Figure 13 This is a comparison diagram of the changes in the rear travel distance when implementing two control methods in Embodiment 1 of the present invention;

[0035] Figure 14This is a comparison diagram of the forward deformation changes when implementing two control methods in Embodiment 1 of the present invention;

[0036] Figure 15 This is a comparison diagram of the post-deformation changes after implementing two control methods in Embodiment 1 of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] It should be noted that when a component is said to be "installed on" another component, it can be directly on the other component or it may be in a component that is centered on it. When a component is said to be "set on" another component, it can be directly set on the other component or it may also be in a component that is centered on it. When a component is said to be "fixed to" another component, it can be directly fixed to the other component or it may also be in a component that is centered on it.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0040] Example 1

[0041] Please see Figure 1 This is a schematic diagram of the multi-objective control method for vehicle suspension based on fuzzy extension coordination, which considers braking safety, disclosed in Embodiment 1.

[0042] It should be noted that this multi-objective control method is applied in blind zone scenarios. See [link / reference] Figure 2 This demonstrates a vehicle-pedestrian kinematics model for blind spot scenarios:

[0043] Before encountering a pedestrian in the blind spot, the vehicle speed should be as high as possible while avoiding collision with the pedestrian, taking into account both safety and passage time; considering that pedestrians may accelerate and run forward to avoid the vehicle after spotting it, the vehicle should give priority to pedestrians.

[0044] Based on the above two driving principles, the movement of a vehicle within a blind spot can be divided into two stages:

[0045] (1) In stage 1, the vehicle does not encounter pedestrians in the blind spot and maintains a high speed.

[0046] (2) In stage 2, the vehicle senses the pedestrian, applies emergency braking, and finally stops before the pedestrian crosses the trajectory line.

[0047] Therefore, the relative relationship between pedestrians and vehicles is:

[0048]

[0049] In the formula, v x Indicates the vehicle's speed, a x represents the vehicle's braking deceleration, s represents the longitudinal distance between the pedestrian and the vehicle, and t represents the time elapsed from when the vehicle enters the blind spot until it detects the pedestrian.

[0050] Based on the law of sines:

[0051]

[0052] In the formula,

[0053] Where d1 represents the lateral distance between the vehicle and the obstacle; d2 represents the longitudinal distance between the vehicle and the obstacle; l represents the longitudinal length of the blind spot; v p y represents the pedestrian's speed; y represents the lateral distance between the pedestrian and the vehicle; x represents the longitudinal distance between the vehicle and the obstacle target point; the obstacle target point is the endpoint of the obstacle that is furthest from the vehicle within the vehicle's forward view, i.e. Figure 2 Point c in the middle.

[0054] However, this did not take into account the size and changes in the blind spot area. Therefore, the blind spot area S and the maximum speed limit v of urban roads were used. max With minimum vehicle speed v min The optimal safe speed v for the blind spot is calculated by taking into account factors such as these.

[0055] in,

[0056] In the formula, α is the weight factor; This indicates the average vehicle speed at which a collision with a pedestrian is avoided in the blind spot.

[0057] In this embodiment 1, specific values ​​are taken for the blind spot scene parameters to obtain the relationship between the optimal safe vehicle speed v and time, as follows: Figure 3 Then, differentiating the optimal safe speed v, we obtain the relationship between braking deceleration a and time, as follows: Figure 4It can be seen that from 0 to 0.4 seconds, the vehicle is in stage 1, maintaining a high speed; emergency braking begins at 0.4 seconds. Specifically, from 0.4 to 1.15 seconds, the vehicle is in stage 2, where the braking deceleration a is greater than 4, which is the emergency braking stage; from 1.15 to 2.6 seconds, the vehicle is in stage 3, where the braking deceleration a is less than 4, which is the non-emergency braking stage.

[0058] Based on the above scenario, this multi-objective control method includes the following steps:

[0059] Acquire vehicle driving information; wherein, driving information includes: road disturbance input W, vehicle braking deceleration a, and longitudinal distance x between the vehicle and the obstacle target point; the obstacle target point is the endpoint of the obstacle that is furthest from the vehicle along the vehicle's forward view.

[0060] Calculate the output force F of the active suspension actuator based on the driving information;

[0061] Where F = k1k2ξu′ + (1 - k1k2ξ)U.

[0062] In the formula,

[0063] u′ represents the output force under the comfort control strategy calculated using LQR control based on the suspension model;

[0064] U represents the output force calculated using sliding mode control based on the suspension model under the safety control strategy; ξ represents the weighting coefficient.

[0065] k1 and k2 represent the fuzzy coefficients obtained based on fuzzy rules.

[0066] The calculation methods for the above parameters are explained below in order:

[0067] ① First, for u′ and U, they are both based on such Figure 5 The calculations are based on the suspension model.

[0068] For the suspension model, its state equation is:

[0069]

[0070] In the formula,

[0071] I sy This represents the pitch inertia of the vehicle body;

[0072] z sf Indicates the displacement of the front spring-loaded mass;

[0073] z wf Indicates the displacement of the unsprung mass;

[0074] z srIndicates the displacement of the rear spring-loaded mass;

[0075] z wr Indicates the displacement of the unsprung mass.

[0076] 'a' indicates the front wheelbase;

[0077] b indicates the rear wheelbase;

[0078] h represents the height of the vehicle's center of gravity;

[0079] m s Indicates the sprung mass;

[0080] m wf Indicates the unsprung mass;

[0081] m wr Indicates the unsprung mass;

[0082] K sf Indicates the stiffness of the front suspension springs;

[0083] K sr Indicates the stiffness of the rear suspension springs;

[0084] C sf Indicates the front suspension damping coefficient;

[0085] C sr This indicates the rear suspension damping coefficient;

[0086] k wf This indicates the equivalent spring stiffness of the front wheel;

[0087] k wr This indicates the equivalent spring stiffness of the rear wheel;

[0088] F f1 This indicates the friction force between the front wheels and the ground.

[0089] F z2 This indicates the ground friction force of the rear wheels;

[0090] F f Indicates the main force of the front suspension;

[0091] F r Indicates the main force of the rear suspension;

[0092] Indicates the vertical velocity of the vehicle body; For z s The first derivative; z s Indicates the vertical displacement of the vehicle body;

[0093] Indicates the vehicle body pitch rate; express The second derivative; Indicates the vehicle's pitch angle;

[0094] q f This indicates the road surface excitation at the front tire location;

[0095] q r This indicates the road surface excitation at the rear tire location;

[0096] This indicates the vertical acceleration of the vehicle body; For z s The second derivative;

[0097] This indicates the vehicle's pitch angle acceleration; express The second derivative of .

[0098] Selecting system state variables System output System control variable U = [F f ,F r Road surface disturbance input The above state equation can then be rewritten as:

[0099]

[0100] In the formula, A represents the system state matrix; B represents the control input matrix; C and D represent the adaptive dimension matrices; G represents the excitation input matrix; U represents the system control variables; and W represents the road disturbance input.

[0101] in,

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] ② Based on the suspension model above, let's first look at u′, whose calculation method includes:

[0108] The LQR performance index function is constructed based on the suspension model. The LQR performance index function is as follows:

[0109]

[0110] In the formula, J represents the LQR performance index function; q1, q2, q3, q4, q5, and q6 represent weighting coefficients; and R represents the weighting coefficient.

[0111] J can be written in quadratic form:

[0112] In the formula, Q d R d N d For the appropriate dimension matrix: Q d =C T QC; R d =R+D T QD; N d =C T QD;

[0113] Q represents the weight coefficient matrix:

[0114] Since the performance of LQR control depends on the values ​​of q1, q2, q3, q4, q5, and q6, a genetic algorithm is used to optimize the weight coefficients under different constraints in order to obtain a comfort control output force u′ that is primarily for comfort control.

[0115] In other words, based on the genetic algorithm, the optimal weight coefficients under the constraints of the comfort control strategy are calculated and used as the weight coefficient group.

[0116] Specifically, the process of solving for the weight coefficient set is as follows:

[0117] The fitness function for constructing the genetic algorithm is as follows:

[0118]

[0119] In the formula, avb(x), the(x), swsf(x), swsr(x), dtdf(x), and dtdr(x) are respectively z sf -z wf z sr -z wr z wf -q f z wr -q r The root mean square value, avb pas the pas ,swsf pas swsr pas dtdf pas dtdr pas These represent the corresponding performance characteristics of the passive suspension.

[0120] The constraints are as follows:

[0121]

[0122] Among them, F fmax Indicates the upper limit of the active force of the front suspension; F rmax This indicates the upper limit of the active force of the rear suspension.

[0123] Under the above constraints, the weight coefficients corresponding to the minimum value of the fitness function are obtained, thus yielding the weight coefficient set.

[0124] Substitute the weighted coefficient set into the LQR performance index function and solve the LQR performance index function to obtain the optimal feedback gain matrix K under the comfort control strategy, and then obtain u′; where u′=-KX.

[0125] Specifically, the process of solving for K is as follows:

[0126] Based on the set of weighted coefficients, substituting back into J yields a set of Q. d R d N d Then put the group Q d , R, N d Substitute K into the Ricardi algebraic equation and solve for it.

[0127] The Ricardi algebraic equation is:

[0128]

[0129] L is the symmetric positive definite solution to be found. That is, based on the known Q... d R d N d First, solve for L, then solve for K.

[0130] Substitute the obtained K into Find u′; where, This represents the front axle active control force component of u′; This represents the rear axle active control force component of u′.

[0131] ③ Based on the suspension model above, let's look at U again. Its calculation method includes:

[0132] The front sliding surface s1 and the rear sliding surface s2 are constructed based on the suspension model.

[0133] Specifically, for S1, λ1 represents the front sliding surface coefficient, λ1 > 0; e1 represents the front dynamic deflection error of the suspension; It represents the first derivative of e1.

[0134] It should be noted that sliding mode control is primarily a control strategy for suspension safety, and its control objective is to keep the suspension dynamic deflection constant or to allow it to fluctuate slightly within a stable range. In this embodiment 1, the suspension dynamic deflection is set to a constant value, i.e., the desired dynamic deflection is taken as 0.

[0135] Therefore, e1 = (z wf -z sf )-0=z wf -z sf .

[0136] Similarly, for s2, λ2 represents the rear sliding surface coefficient, λ2>0; e2 represents the rear dynamic deflection error of the suspension; e2=(z wr -z sr )-0=z wr -z sr ; It represents the first derivative of e2.

[0137] Choosing the isotropic reaching law, we get: Indicates the first derivative of s1; ε1 and ε2 represent the first derivative of s2; ε1 and ε2 represent the gain coefficients, ε1 > 0 and ε2 > 0; sgn(.) represents the sign function.

[0138] Find the first derivative of s1 and its derivative with respect to s1. United, obtained in, This represents the front axle active control force component of U.

[0139] in,

[0140]

[0141] Find the first derivative of s² and its derivative with respect to s². United, obtained in, This represents the rear axle active control force component of U.

[0142] in,

[0143]

[0144] based on Get U;

[0145] ④ The coefficient of ξ, as an extension output, is created to reconcile the contradiction between vehicle comfort and safety: under non-emergency braking conditions, the suspension function is mainly for comfort; under emergency braking conditions, the suspension function is mainly for safety.

[0146] The methods for calculating ξ include:

[0147] Determine the current state of the vehicle's active suspension based on driving information;

[0148] For details, please refer to Figure 6 The vehicle's active suspension is divided into three states: classic domain, extension domain, and non-domain. These correspond to three suspension modes: comfort as the primary function, a balance between comfort and safety, and safety as the primary function. The classic domain is rectangular region one, with its four endpoints (0,0), (a1,0), (a1,1 / x2), and (0,1 / x2). The extension domain is the inverted L-shaped region obtained by removing rectangular region one from rectangular region two; the four endpoints of rectangular region two are (0,0), (a2,0), (a2,1 / x1), and (0,1 / x1). Here, a1, a2, x1, and x2 represent preset thresholds; a1 < a2, x1 > x2. The non-domain is the region outside rectangular region two.

[0149] Generally, the values ​​of a1, a2, x1, and x2 are in the following ranges: 1.9 ≤ a1 ≤ 2.1; 3.8 ≤ a2 ≤ 4; 30 ≤ x1 ≤ 32; 8 ≤ x2 ≤ 9. In this Example 1, a1 is 2; a2 is 4; x1 is 30; and x2 is 8.

[0150] Therefore, based on the driving information, we can determine which area we are in, i.e., the current state.

[0151] If the vehicle's active suspension is in the classic domain, ξ = 1, the purpose is to improve the vehicle's comfort;

[0152] If the vehicle's active suspension is in the extrinsic domain, ξ=K(S), the vehicle's deceleration increases, the control difficulty increases, and comfort and safety need to be balanced.

[0153] If the vehicle's active suspension is in a non-domain, ξ = 0, a control strategy is adopted to improve its safety.

[0154] Where K(S) is the correlation function,

[0155] In the formula, P0 is the current characteristic of the vehicle's active suspension; O represents the ideal state origin; P1 represents the intersection of line OP0 and the boundary of the classical domain; P2 represents the intersection of line OP0 and the boundary of the extension domain; d(P0,<O,P1> ) represents the extensibility distance from P0 to the classical domain; d(P0,<O,P2> ) represents the extension distance from P0 to the extension domain.

[0156] Additionally, for d(P0,<O,P1> ), d(P0,<O,P2> The specific calculation formula is as follows:

[0157]

[0158] In the formula, i = 1, 2; P1m is the midpoint of line segment OP1, and P2m is the midpoint of line segment OP2.

[0159] ⑤ For k1 and k2, it is to perform fuzzy optimization on the extension output and reduce the control jumps between the different domains mentioned above.

[0160] The basic design idea of ​​k1 and k2 is as follows: when ξ jumps between 0 and 1, F jumps between u′ and U. At this time, k1 and k2 change in coordination to make the jump smoother and reduce the jump amplitude. Specifically, when ξ jumps from 1 to 0, the value of k1 changes slowly from large to small, while k2 remains at a relatively large value; when ξ jumps from 0 to 1, the value of k1 increases rapidly from small to large, while k2 first changes rapidly to its minimum value and then increases slowly.

[0161] Specifically, the calculation methods for k1 and k2 are as follows:

[0162] Fuzzy quantization is applied to 1 / x, making the universe of discourse L of 1 / x larger. 1 / X Range: -1 to 0;

[0163] The methods for fuzzy quantization of 1 / x include:

[0164] By blurring 1 / x, we obtain the blurred quantity 1 / X; where,

[0165]

[0166] Performing field processing on 1 / X yields L. 1 / X ;in,

[0167]

[0168] In the formula, a′ and b′ represent the boundary coefficients of the universe of discourse, where a′>1 and b′>1.

[0169] Fuzzy quantization is applied to a to make the universe of discourse L of a larger. A It is between 0 and 1;

[0170] The methods for fuzzy quantization of 'a' include:

[0171] By performing fuzzy processing on 'a', we obtain the fuzzy quantity A; where,

[0172]

[0173] Perform field processing on A to obtain L A ;in,

[0174]

[0175] In the formula, m represents the boundary coefficient of the universe of discourse, and m > 1.

[0176] Then, L 1 / XThe data is fuzzified into 9 fuzzy subsets, including {S1,S2,MS1,MS2,Z,MB1,MB2,B1,B2}; where S1,S2,MS1,MS2,Z,MB1,MB2,B1,B2 increase sequentially.

[0177] L A The data is fuzzified into 9 fuzzy subsets, including {S1′,S2′,MS1′,MS2′,Z′,MB1′,MB2′,B1′,B2′}; where S1′,S2′,MS1′,MS2′,Z′,MB1′,MB2′,B1′,B2′ increase sequentially.

[0178] Define the same fuzzy subsets for k1 and k2, including {S1″,S2″,MS1″,MS2″,Z″,MB1″,MB2″,B1″,B2″}; where S1″,S2″,MS1″,MS2″,Z″,MB1″,MB2″,B1″,B2″ increase sequentially.

[0179] Select k1 and k2 according to the following fuzzy rules:

[0180] When 1 / x is S1, and a is successively S1′, S2′, MS1′, MS2′, Z′, MB1′, MB2′, B1′, B2′, then k1 is successively B2″, B2″, B2″, B2″, B2″, B2″, S1″, S1″, and k2 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″;

[0181] When 1 / x is S2, and a is successively S1′, S2′, MS1′, MS2′, Z′, MB1′, MB2′, B1′, B2′, then k1 is successively B2″, B2″, B2″, B2″, B2″, B2″, S1″, S1″, and k2 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″;

[0182] When 1 / x is MS1, and a is successively S1′, S2′, MS1′, MS2′, Z′, MB1′, MB2′, B1′, B2′, then k1 is successively S1″, S1″, S1″, S1″, S1″, S1″, S1″, S1″, S1″, and k2 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″;

[0183] When 1 / x is MS2, and a is successively S1′, S2′, MS1′, MS2′, Z′, MB1′, MB2′, B1′, B2′, then k1 is successively S1″, B2″, B2″, B2″, B2″, B2″, S1″, S1″, and k2 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″;

[0184] When 1 / x is Z, and a is successively S1′, S2′, MS1′, MS2′, Z′, MB1′, MB2′, B1′, B2′, then k1 is successively B2″, B2″, B2″, B2″, B2″, B2″, S1″, S1″, and k2 is successively B2″, Z″, S1″, S1″, S1″, S1″, S1″, S1″, S1″, S1″;

[0185] When 1 / x is MB1, and a is successively S1′, S2′, MS1′, MS2′, Z′, MB1′, MB2′, B1′, B2′, then k1 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, and k2 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, MB2″;

[0186] When 1 / x is MB2, and a is successively S1′, S2′, MS1′, MS2′, Z′, MB1′, MB2′, B1′, B2′, then k1 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, and k2 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″;

[0187] When 1 / x is B1, and a is successively S1′, S2′, MS1′, MS2′, Z′, MB1′, MB2′, B1′, B2′, then k1 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, and k2 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″;

[0188] When 1 / x is B2, and a is successively S1′, S2′, MS1′, MS2′, Z′, MB1′, MB2′, B1′, B2′, then k1 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, and k2 is successively B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″, B2″.

[0189] See Figure 7, Figure 8 This displays graphs showing the changes of k1 and k2 over time, consistent with the design principles of k1 and k2. Additionally, a comparison is made between ξ and k1k2ξ individually; see [link / reference]. Figure 9 It can be seen that after introducing k1 and k2, the change range of k1k2ξ is reduced compared with ξ, and the change of k1k2ξ is smoother, which helps to reduce the impact of sudden changes and improve the overall performance of the vehicle.

[0190] In summary, F can be calculated based on the parameter calculation methods described above.

[0191] This embodiment 1 also examines the suspension performance comparison between the above-mentioned multi-objective control method (i.e., the fuzzy extension shown in the figure) and the method using only extension control (i.e., the extension shown in the figure) in the above-mentioned blind spot scenario. The results are shown in [reference]. Figures 10-15 .

[0192] Depend on Figure 10 , Figure 11 It can be seen that, compared with the method using only extension control, this multi-objective control method can effectively reduce the vehicle's vertical acceleration and pitch acceleration. From Figure 12 , Figure 13 It can be seen that multi-objective control methods, compared to methods using extension control alone, can effectively reduce the forward and backward travel of the vehicle. Figure 14 , Figure 15 It can be seen that multi-objective control methods can effectively reduce forward and backward dynamic deformation compared to methods that only use extension control.

[0193] In summary, this multi-objective control method can achieve smooth control of active suspension in blind spot scenarios, while also balancing safety control and comfort control.

[0194] Example 2

[0195] This embodiment 2 discloses a multi-objective control system for vehicle suspension based on fuzzy extension coordination considering braking safety, which uses the multi-objective control method for vehicle suspension based on fuzzy extension coordination considering braking safety from embodiment 1.

[0196] Considering braking safety, a multi-objective control system for automotive suspension based on fuzzy extension coordination includes: a driving information acquisition module and an output force calculation module.

[0197] The driving information acquisition module is used to acquire the driving information of the target vehicle. The output force calculation module is used to calculate F.

[0198] This embodiment 2 also discloses a hub motor driven automobile, which uses the multi-objective control method for automobile suspension based on fuzzy extension coordination under the consideration of braking safety in embodiment 1.

[0199] Example 3

[0200] This embodiment 3 discloses a readable storage medium storing computer program instructions. When the computer program instructions are read and executed by a processor, the steps of the multi-objective control method for automobile suspension based on fuzzy extension coordination under the consideration of braking safety in embodiment 1 are performed.

[0201] When applying the method of Example 1, it can be applied in the form of software, such as a program designed to run independently on a computer-readable storage medium, which can be a USB flash drive or a USB security token, and designed to be a program that starts the entire method through an external trigger.

[0202] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0203] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A multi-objective control method for vehicle suspension based on fuzzy extension coordination under the consideration of braking safety, applied to blind spot scenarios, characterized in that, The multi-objective control method for vehicle suspension based on fuzzy extension coordination, considering braking safety, includes the following steps: Acquire vehicle driving information; wherein, the driving information includes: road disturbance input amount. W Vehicle braking deceleration a Longitudinal distance between the vehicle and the obstacle target point x The obstacle target point is the endpoint of the obstacle that is furthest from the vehicle within the vehicle's forward viewing angle. Calculate the output force of the active suspension actuator based on driving information. F ; in, ; In the formula, This represents the output force calculated using LQR control based on the suspension model under a comfort control strategy. U This represents the output force under the safety control strategy calculated using sliding mode control based on the suspension model. Indicates the weighting coefficient; k 1. k 2 represents the fuzzy coefficient obtained based on the fuzzy rules.

2. The multi-objective control method for vehicle suspension based on fuzzy extension coordination considering braking safety as described in claim 1, characterized in that, The expression for the suspension model is: ； In the formula, express X The first derivative; X Represents system state variables; ; z sf Indicates the displacement of the front spring-loaded mass. z wf Indicates the displacement of the unsprung mass. z sr Indicates the displacement of the rear spring-loaded mass. z wr Indicates the displacement of the unsprung mass. Indicates the vertical velocity of the vehicle body; for z s The first derivative; z s Indicates the vertical displacement of the vehicle body; Indicates the vehicle body pitch rate; express The first derivative; Indicates the vehicle's pitch angle; q f This indicates road surface excitation at the front tire location. q r This indicates the road surface excitation at the rear tire location; Y Indicates the system output quantity; ; This indicates the vertical acceleration of the vehicle body; for z s The second derivative; z s Indicates the vertical displacement of the vehicle body; This indicates the vehicle's pitch angle acceleration; express The second derivative; Indicates the vehicle's pitch angle; A Represents the system state matrix; B Represents the control input matrix; C, D Represents an appropriate dimension matrix; G Represents the excitation input matrix; W This indicates the amount of road surface disturbance input.

3. The multi-objective control method for vehicle suspension based on fuzzy extension coordination considering braking safety as described in claim 2, characterized in that, The calculation methods include: An LQR performance index function is constructed based on the suspension model; wherein, the LQR performance index function is: ； In the formula, J Represents the LQR performance index function; q 1. q 2. q 3 、q 4 、q 5 、q 6 represents the weighting coefficient; R Indicates the weighting coefficient; Based on the genetic algorithm, the optimal weight coefficients under the constraints of the comfort control strategy are calculated and used as the weight coefficient group. By substituting the weighted coefficient set into the LQR performance index function and solving the LQR performance index function, the optimal feedback gain matrix under the comfort control strategy is obtained. K And thus obtain ;in, .

4. The multi-objective control method for vehicle suspension based on fuzzy extension coordination considering braking safety as described in claim 2, characterized in that, U The calculation methods include: Construct the front sliding surface based on the suspension model. s 1. Back sliding surface s 2; among which, ; ; Indicates the front sliding surface coefficient; e 1 indicates the front dynamic deflection error of the suspension; ; express e The first derivative of 1; Indicates the back sliding surface coefficient; e 2 indicates the rear dynamic deflection error of the suspension; ; express e The first derivative of 2; Choosing the isotropic reaching law, we get: ; ; express s The first derivative of 1; express s The first derivative of 2; , Indicates the gain coefficient; Represents a symbolic function; right s 1. Find the first derivative and its parallel with the first derivative. United, obtained ;in, express U The front axle active control force component; right s 2. Find the first derivative and its parallel with the first derivative. United, obtained ;in, express U The rear axle active control force component; based on , get U ; .

5. The multi-objective control method for vehicle suspension based on fuzzy extension coordination considering braking safety as described in claim 1, characterized in that, The calculation methods include: Determine the current state of the vehicle's active suspension based on driving information; If the vehicle's active suspension is in the classic domain If the vehicle's active suspension is in the extension domain, If the vehicle's active suspension is in a non-domain, ; in, For correlation functions, ; In the formula, P 0 indicates the current characteristic quantity of the vehicle's active suspension; O Represents the origin of the ideal state; P 1 represents a straight line OP The intersection of 0 and the boundary of the classical field; P 2 represents a straight line OP The intersection of 0 and the boundary of the extended domain; express P The extension distance from 0 to the classical domain; express P The extension distance from 0 to the extension domain.

6. The multi-objective control method for vehicle suspension based on fuzzy extension coordination considering braking safety as described in claim 5, characterized in that, The classical domain is a rectangular region 1, with its four endpoints being (0,0), (...). a 1,0), ( a 1,1 / x 2) (0,1 / x 2); The extended region is the inverted L-shaped region obtained by removing rectangular region one from rectangular region two; the four endpoints of rectangular region two are (0,0), (…). a 2,0), ( a 2,1 / x 1) (0,1 / x 1); in, a 1. a 2. x 1. x 2 indicates a preset threshold; a 1 < a 2, x 1 < x 2; The non-domain refers to the area outside the second rectangular region.

7. The multi-objective control method for vehicle suspension based on fuzzy extension coordination considering braking safety as described in claim 6, characterized in that, 1.9≤ a 1≤2.1；3.8≤ a 2≤4；30≤ x 1≤32；8≤ x 2≤9。 8. The multi-objective control method for vehicle suspension based on fuzzy extension coordination considering braking safety as described in claim 6, characterized in that, k 1. k The calculation method for 2 is as follows: For 1 / x Perform fuzzy quantization to make 1 / x domain of discourse It is between -1 and 0; right a Perform fuzzy quantization to make a domain of discourse It is between 0 and 1; Will The fuzzification is divided into 9 fuzzy subsets, including ; Will The fuzzification is divided into 9 fuzzy subsets, including ; right k 1. k 2. Define the same fuzzy subset, including ; The fuzzy rules include: When 1 / x for S 1, a In order ,but k 1 corresponds to the following in sequence , k 2 corresponds to the following in sequence: ; When 1 / x for S 2, a In order ,but k 1 corresponds to the following in sequence , k 2 corresponds to the following in sequence: ; When 1 / x For M S 1, a In order ,but k 1 corresponds to the following in sequence , k 2 corresponds to the following in sequence: ; When 1 / x For M S 2, a In order ,but k 1 corresponds to the following in sequence , k 2 corresponds to the following in sequence: ; When 1 / x For Z, a In order ,but k 1 corresponds to the following in sequence , k 2 corresponds to the following in sequence: ; When 1 / x For M B 1, a In order ,but k 1 corresponds to the following in sequence , k 2 corresponds to the following in sequence: ; When 1 / x For M B 2, a In order ,but k 1 corresponds to the following in sequence , k 2 corresponds to the following in sequence: ; When 1 / x for B 1, a In order ,but k 1 corresponds to the following in sequence , k 2 corresponds to the following in sequence: ; When 1 / x for B 2, a In order ,but k 1 corresponds to the following in sequence , k 2 corresponds to the following in sequence: .

9. The multi-objective control method for vehicle suspension based on fuzzy extension coordination considering braking safety as described in claim 8, characterized in that, For 1 / x Methods for performing fuzzy quantization include: For 1 / x Perform blurring to obtain the blur quantity 1 / X ;in, ； For 1 / X Perform domain processing to obtain ;in, ； In the formula, 、 Denotes the boundary coefficients of the universe of discourse. , ; right a Methods for performing fuzzy quantization include: right a Perform fuzzy processing to obtain the fuzzy quantity. A ;in, ； right A Perform domain processing to obtain ;in, ； In the formula, m Denotes the boundary coefficients of the universe of discourse. m >1.

10. A multi-objective control system for automotive suspension based on fuzzy extension coordination, considering braking safety, characterized in that, It uses the multi-objective control method for vehicle suspension based on fuzzy extension coordination, considering braking safety as described in any one of claims 1-9; The multi-objective control system for vehicle suspension based on fuzzy extension coordination, considering braking safety, includes: The driving information acquisition module is used to acquire the vehicle's driving information; as well as Output force calculation module, which is used to calculate F .

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