Vehicle active empennage attack angle control method based on fuzzy logic

Abstract

The invention discloses a vehicle active empennage attack angle control method based on fuzzy logic. The design of double active controllers is adopted, the input of a main fuzzy controller is vehicle speed and acceleration request values, the output of the main fuzzy controller is an active empennage attack angle, the main fuzzy controller is processed through a membership function, a fuzzy rule is formulated, and the fuzzy quantity output by the main fuzzy controller is obtained through fuzzy reasoning; the output of the subordinate fuzzy controller is empennage attack angle correction, and the input of the subordinate fuzzy controller is the actual and reference difference and the variation of the difference between the front and rear wheel slip rates of the vehicle; formulating a fuzzy rule of a subordinate fuzzy controller, and obtaining a fuzzy quantity output by the subordinate fuzzy controller through fuzzy reasoning; the output of the master-slave fuzzy controller is defuzzified by adopting a gravity center method to obtain the accurate quantity of the master-slave fuzzy controller, finally, the attack angle of the active empennage can be obtained by combining the output of the master-slave fuzzy controller, and the slip rate of each tire and the vehicle speed are measured as feedback.

Description

Technical Field

[0001] The present invention relates to the field of vehicles, and in particular to a method for controlling the attack angle of an active tail wing of a vehicle based on fuzzy logic. Background Art

[0002] In addition to the friction from the ground, the vehicle is also affected by the force of the air during driving. Especially in the process of high-speed driving, the air force cannot be ignored and has an important impact on the vehicle's driving. In order to improve the impact of air force on vehicle movement, increase favorable air force and reduce unfavorable air force, vehicle aerodynamics came into being and is committed to optimizing the aerodynamic performance of the vehicle. In addition to the optimization design of the aerodynamic layout of the whole vehicle, the aerodynamic components of the vehicle have become a major focus of vehicle aerodynamic research in recent years. Among them, the vehicle tail wing is one of the most widely used components. The reasonable use of the tail wing can effectively improve the vehicle's handling performance and driving experience. However, the fixed tail wing cannot be adjusted with the change of the vehicle's state. The active tail wing with a variable angle of attack can adjust the state of the tail wing according to different driving conditions, thereby achieving different aerodynamic characteristics under different conditions and increasing the stability and safety performance of the car. Summary of the invention

[0003] In order to enable the vehicle to achieve the air performance state that best suits the current driving state and improve the performance of the vehicle, the present invention proposes a method for controlling the angle of attack of the vehicle's active rear wing based on fuzzy logic, which can control the angle of attack of the active rear wing according to the vehicle's operating state and the driver's operation during the driving process of the vehicle.

[0004] A method for controlling the angle of attack of an active tail wing of a vehicle based on fuzzy logic comprises the following steps:

[0005] Step 1: Obtain the vehicle speed V and the vehicle's expected acceleration Acc exp is the input quantity, and the main fuzzy controller performs fuzzification on the input quantity to obtain the membership degree of the fuzzy subset of the input quantity; among them, the expected acceleration Acc of the vehicle exp Accelerator pedal opening Pd Gas and the brake pedal opening Pd Brake The specific calculation method is as follows:

[0006] Acc exp = Pd Gas -Pd Brake (1)

[0007] Among them, the accelerator pedal opening Pd Gas and brake pedal opening Pd Brake The value range of is [0,1];

[0008] Step 2: Obtain the fuzzy value of the active tail wing attack angle A according to the control rules of the main fuzzy controller and the fuzzy reasoning method;

[0009] Step 3: Use the deviation E and error change E of the tire slip rate difference c is the input quantity, and the membership degree of the fuzzy subset of the input quantity is obtained by fuzzifying the input quantity through the fuzzy controller; among them, the tire slip rate difference S F&R is the difference between the front and rear tire slip rates, and the specific calculation formula is as follows:

[0010] S F&R =max{S RL , S RR}- max { S FL ,F FR} (2)

[0011] Where S RL ,S RR is the slip rate of the left and right rear wheels, S FL ,F FR is the slip rate of the left and right front wheels;

[0012] Step 4: Obtain the fuzzy value of the angle of attack correction value A0 according to the control rule of the fuzzy controller and the fuzzy reasoning method;

[0013] Step 5: Defuzzify the fuzzy quantities of the master fuzzy controller and the slave fuzzy controller respectively to obtain the final accurate output value;

[0014] Step 6: Sum the outputs of the master fuzzy controller and the slave fuzzy controller after defuzzification to obtain the attack angle control quantity of the active tail wing;

[0015] Step 7: Measure the slip rate S of each tire RL , S RR , S FL 、F FR And the vehicle speed V is used as feedback and returns to step one and step three.

[0016] Preferably, in step 1, the method by which the main fuzzy controller performs fuzzy processing on the input quantity is:

[0017] Expected acceleration Acc exp The value range of is [-1,1], and the value range of fuzzy domain is [-1,1]; set the expected acceleration Acc exp The fuzzy subsets are {BL (decrease), BM (decrease), BS (decrease), 0, GS (decrease), GM (decrease), GL (decrease)}, Acc expThe membership function distribution of the fuzzy set is set as follows: the two subsets BL and GL use mixed Gaussian membership functions, and the specific parameters are as follows:

[0018] BL:y=gauss2mf(x,[1-1 0.1-0.85]);

[0019] GL:y=gauss2mf(x,[0.1 0.85 1 1]);

[0020] Where y=gauss2mf(x,[σ1c1σ2c2]) represents a double Gaussian mixture function; y is the output value, x is the input value, σ1 and c1 are the standard deviation and mean of the Gaussian function on the left, and σ2 and c2 are the standard deviation and mean of the Gaussian function on the right; other subsets use Gaussian membership functions, and the specific parameters are as follows:

[0021] BM:y=gaussmf(x,[0.06-0.5]);

[0022] BS:y=gaussmf(x,[0.06-0.25]);

[0023] 0:y=gaussmf(x,[0.05 0]);

[0024] GS:y=gaussmf(x,[0.06 0.25]);

[0025] GM:y=gaussmf(x,[0.06 0.5]);

[0026] Where y=gaussmf(x,[σc]) represents the Gaussian function, y is the output value, x is the input value, σ and c are the standard deviation and mean of the Gaussian function respectively.

[0027] Another input is the vehicle speed V, which ranges from [0km / h, 180km / h], the fuzzy domain ranges from [0, 180], and its fuzzy subsets are {VS (very slow), MS (medium slow), SS (slightly slow), M (moderate), SF (slightly fast), MF (medium fast), VF (very fast)};

[0028] The membership function distribution of V fuzzy set is set as follows: VS and VF use joint Gaussian membership function, and their parameters are

[0029] VS:y=gauss2mf(x,[6 0 8 30]);

[0030] VF:y=gauss2mf(x,[3 160 10 180]);

[0031] Other fuzzy subsets use Gaussian membership functions, and their parameters are as follows:

[0032] MS:y=gaussmf(x,[8 60]);

[0033] SS:y=gaussmf(x,[6 90]);

[0034] M:y=gaussmf(x,[5 110]);

[0035] SF:y=gaussmf(x,[4 130]);

[0036] MF:y=gaussmf(x,[4 148]);

[0037] Preferably, the control rule of the main fuzzy controller in step 2 is as follows:

[0038] According to the properties of the active tail, the output of the control strategy is determined to be the angle of attack of the tail. The angle of attack of the active tail is A, and its adjustable range is [-30°, 30°]. The value of the fuzzy domain is [-30, 30], and its fuzzy subset is set to {NL (negative large), NM (negative medium), NS (negative small), 0, PS (positive small), PM (positive medium), PL (positive large)}; According to the above settings and the control logic of the tail, the fuzzy control rules of the angle of attack of the active tail are shown in the table.

[0039] Table 1

[0040]

[0041] The membership function of the angle of attack A fuzzy set adopts Gaussian and joint Gaussian membership functions, and the distribution is set as follows: The two fuzzy subsets NL and PL adopt the joint Gaussian membership function, and their parameters are as follows:

[0042] NL:y=gauss2mf(x,[1-30 2-25]);

[0043] PL:y=gauss2mf(x,[2 25 1 30]);

[0044] The other subsets use Gaussian membership functions with the following parameters:

[0045] NM:y=gaussmf(x,[2.5-17]);

[0046] NS:y=gaussmf(x,[2-8]);

[0047] 0:y=gaussmf(x,[2 0]);

[0048] PS:y=gaussmf(x,[2 8]);

[0049] PM:y=gaussmf(x,[2.5 17]);

[0050] Preferably, the fuzzy inference process of the main fuzzy controller in step 2 adopts the minimum-maximum-center-of-gravity method, which is as follows:

[0051] First, the current detection information Acc exp Substitute the membership of V into rule table 1 respectively, and the membership function of the output fuzzy set corresponding to each rule can be obtained; the rule whose two inputs are not zero is triggered, and the smaller membership of the two inputs of the same rule that has been triggered is calculated, and the result of each rule is called the premise credibility of the rule; the result obtained by taking the intersection of the premise credibility of each rule and the membership function of the fuzzy set output by each rule is the fuzzy value finally output by each rule; finally, the fuzzy value output by all rules is calculated by taking the union, and the fuzzy value of the active tail angle of attack A output by the main fuzzy controller can be obtained;

[0052] Preferably, in step 3, the method of fuzzifying the input quantity from the fuzzy controller is:

[0053] The input E of the fuzzy controller is the difference S between the front and rear wheel slip rates of the vehicle. F&R The difference between the actual value and the expected value, where the expected value is 0, the fuzzy domain takes the value [-1,1], and its fuzzy subsets are set to {FL (front large), FM (front middle), FS (front small), 0, RS (back small), RM (back middle), RL (back large)}; the membership function of the E fuzzy subset is set as follows: the FL and RL subsets use a mixed Gaussian membership function, and the specific parameters are as follows:

[0054] FL:y=gauss2mf(x,[0.58-1 0.05-0.8]);

[0055] RL:y=gauss2mf(x,[0.06 0.5 0.6 1]);

[0056] Other subsets use Gaussian membership functions, and the specific parameters are as follows:

[0057] FM:y=gaussmf(x,[0.08-0.5]);

[0058] FS:y=gaussmf(x,[0.05-0.2]);

[0059] 0:y=gaussmf(x,[0.04 0]);

[0060] RS:y=gaussmf(x,[0.05 0.2]);

[0061] RM:y=gaussmf(x,[0.08 0.5]);

[0062] From the input E of the fuzzy controller c is the difference in slip rate between the front and rear wheels of the vehicle, S F&R The change in the difference between the actual value and the expected value of , the fuzzy domain takes the value [-1,1], and its fuzzy subsets are set to {NL (negative large), NM (negative medium), NS (negative small), 0, PS (positive small), PM (positive medium), PL (positive large)}; the membership function distribution of the E fuzzy subset is set as follows: the NL and PL subsets use mixed Gaussian membership functions, and the specific parameters are as follows:

[0063] NL:y=gauss2mf(x,[0.6-1 0.06-0.5]);

[0064] PL:y=gauss2mf(x,[0.06 0.5 0.6 1]);

[0065] Other subsets use Gaussian membership functions, and the specific parameters are as follows:

[0066] VM:y=gaussmf(x,[0.05-0.3]);

[0067] VS:y=gaussmf(x,[0.04-0.15]);

[0068] 0:y=gaussmf(x,[0.04 0]);

[0069] PS:y=gaussmf(x,[0.04 0.15]);

[0070] PM:y=gaussmf(x,[0.05 0.3]);

[0071] Preferably, the control rules of the fuzzy controller in step 4 are as follows:

[0072] The output of the fuzzy control is the tail wing’s angle of attack correction A0, which has an adjustable range of [-10°, 20°]. The value of the fuzzy domain is [-10, 20]. Its fuzzy subset is set to {NL (negative large), NS (negative small), 0, PSS (positive small), PS (positive small), PM (positive middle), PL (positive large)}; Based on the above settings, the fuzzy rules of the fuzzy controller are summarized as shown in Table 2.

[0073] Table 2

[0074]

[0075] The membership function of the angle of attack correction A0 fuzzy set adopts Gaussian and joint Gaussian membership functions, and the distribution is set as follows: The two fuzzy subsets NL and PL adopt the joint Gaussian membership function, and their parameters are as follows:

[0076] NL:y=gauss2mf(x,[0.5-15 1.2-8]);

[0077] PL:y=gauss2mf(x,[1 18 0.5 20]);

[0078] The other subsets use Gaussian membership functions with the following parameters:

[0079] NS:y=gaussmf(x,[1-4]);

[0080] 0:y=gaussmf(x,[0.8 0]);

[0081] PSS:y=gaussmf(x,[1 4]);

[0082] PS:y=gaussmf(x,[1.2 8]);

[0083] PM:y=gaussmf(x,[1.5 13]);

[0084] Preferably, in step 4, the minimum-maximum-centroid method is used from the fuzzy reasoning process of the fuzzy controller, as follows:

[0085] First, the current input information E and E c Substitute the membership degrees of the two inputs of the same rule into the rule table 2 respectively, and the membership function of the output fuzzy set corresponding to each rule can be obtained; the rule whose two inputs are not zero is triggered, and the smaller membership degree of the two inputs of the same rule that has been triggered is calculated, and the result of each rule is called the premise credibility of the rule; the result obtained by taking the intersection of the premise credibility of each rule and the membership function of the fuzzy set output by each rule is the fuzzy value finally output by each rule; finally, the fuzzy value output by all rules is calculated by taking the union, and the fuzzy value of the angle of attack correction A0 output from the fuzzy controller at this time can be obtained;

[0086] Preferably, in step five, the defuzzification process is performed on the fuzzy quantities of the master fuzzy controller and the slave fuzzy controller using a centroid method.

[0087] Beneficial effects of the present invention:

[0088] The present invention provides a method for controlling the angle of attack of an active rear wing of a vehicle based on fuzzy logic, which enables the vehicle to achieve an air performance state that best matches the current driving state in different vehicle driving scenarios, thereby improving the performance of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 This is a structural diagram of the dual fuzzy controller proposed in the present invention;

[0090] Figure 2 The tail force analysis diagram made for the present invention;

[0091] Figure 3 Schematic diagram of fuzzy rules of the main fuzzy controller;

[0092] Figure 4 It is a fuzzy schematic diagram from fuzzy rules;

[0093] Figure 5 The input map for the brake;

[0094] Figure 6 for Figure 5 Speed ​​comparison diagram under brake input shown;

[0095] Figure 7 is the steering wheel angle input;

[0096] Figure 8 For vehicles in Figure 7 Driving trajectory diagram under steering wheel angle input. DETAILED DESCRIPTION

[0097] The proposed method for controlling the angle of attack of the active tail wing is further described and illustrated below in conjunction with the accompanying drawings.

[0098] like Figure 1 As shown, the present invention proposes a method for controlling the angle of attack of an active tail wing of a vehicle based on a fuzzy controller, comprising the following steps:

[0099] Step 1: Create Figure 2 The six-degree-of-freedom model of the aerodynamic body of the vehicle is used to obtain the six-component aerodynamic force model and force analysis of the tail wing on the vehicle; Figure 2 The forces and moments shown in are all caused by air action.

[0100] like Figure 2 As shown, in the established aerodynamic body six-degree-of-freedom model of the vehicle, the X direction is the vehicle's forward direction, the Y direction points to the right side of the vehicle, and the Z direction is vertically upward;

[0101] According to the influence of the active rear wing on the six aerodynamic forces, the aerodynamic force and aerodynamic moment of the active rear wing on the vehicle body in the X, Y, and Z directions are as follows:

[0102] F AeroX =-F Drag (1)

[0103] F AreoY =0 (2)

[0104] F AreoZ =-F Down (3)

[0105] M AreoX =0 (4)

[0106] M AreoY =-F Drag HkD Down l (5)

[0107] M AreoZ =0 (6)

[0108] Among them, F AeroX 、F AeroY 、F AeroZ are the aerodynamic forces in the X, Y, and Z directions, M AeroX 、M AeroY 、M AeroZ are the aerodynamic moments around X, Y, and Z, respectively, and F Down is the downforce generated by the active rear wing, F Drag is the air resistance generated by the active rear wing, h is the height distance from the active rear wing action point to the vehicle's center of mass, and l is the longitudinal distance from the active rear wing action point to the vehicle's center of mass.

[0109] Step 2: Input and output of the main fuzzy controller and fuzzification processing. The input of the main fuzzy controller is determined to be the vehicle speed and the expected acceleration of the vehicle, where the expected acceleration is calculated by the accelerator pedal and brake pedal opening formula (7), and the output is the tail wing attack angle. The input is fuzzified using the joint Gaussian membership function and the Gaussian membership function;

[0110] Acc exp =Pd Gas -Pd Brake (7)

[0111] Expected acceleration Acc exp The value range of is [-1,1], and the value range of fuzzy domain is [-1,1]; set the expected acceleration Acc exp The fuzzy subsets are {BL (decrease), BM (decrease), BS (decrease), 0, GS (decrease), GM (decrease), GL (decrease)}, Acc exp The membership function distribution of the fuzzy set is set as follows: the two subsets BL and GL use mixed Gaussian membership functions, and the specific parameters are as follows:

[0112] BL:y=gauss2mf(x,[1-1 0.1-0.85]);

[0113] GL:y=gauss2mf(x,[0.1 0.85 1 1]);

[0114] Where y=gauss2mf(x,[σ1c1σ2c2]) represents a double Gaussian mixture function; y is the output value, x is the input value, σ1 and c1 are the standard deviation and mean of the Gaussian function on the left, and σ2 and c2 are the standard deviation and mean of the Gaussian function on the right; other subsets use Gaussian membership functions, and the specific parameters are as follows:

[0115] BM:y=gaussmf(x,[0.06-0.5]);

[0116] BS:y=gaussmf(x,[0.06-0.25]);

[0117] 0:y=gaussmf(x,[0.05 0]);

[0118] GS:y=gaussmf(x,[0.06 0.25]);

[0119] GM:y=gaussmf(x,[0.06 0.5]);

[0120] Where y=gaussmf(x,[σc]) represents the Gaussian function, y is the output value, x is the input value, σ and c are the standard deviation and mean of the Gaussian function respectively.

[0121] Another input is the vehicle speed V, which ranges from [0km / h, 180km / h], the fuzzy domain ranges from [0, 180], and its fuzzy subsets are {VS (very slow), MS (medium slow), SS (slightly slow), M (moderate), SF (slightly fast), MF (medium fast), VF (very fast)};

[0122] The membership function distribution of V fuzzy set is set as follows: VS and VF use joint Gaussian membership function, and their parameters are

[0123] VS:y=gauss2mf(x,[6 0 8 30]);

[0124] VF:y=gauss2mf(x,[3 160 10 180]);

[0125] Other fuzzy subsets use Gaussian membership functions, and their parameters are as follows:

[0126] MS:y=gaussmf(x,[8 60]);

[0127] SS:y=gaussmf(x,[6 90]);

[0128] M:y=gaussmf(x,[5 110]);

[0129] SF:y=gaussmf(x,[4 130]);

[0130] MF:y=gaussmf(x,[4 148]);

[0131] Step 3: Obtain the fuzzy value of the active tail wing attack angle A according to the control rules of the main fuzzy controller and the fuzzy reasoning method;

[0132] The control rule design of the main fuzzy controller considers the main impact of the active rear wing on the vehicle's aerodynamics, which is to provide the vehicle with additional downforce and drag. The main factors affecting the size of downforce and drag are the rear wing angle of attack and vehicle speed. Considering that the drag provided by the rear wing can increase the acceleration during braking, but it will generate additional drag during the acceleration phase, thereby reducing the acceleration performance, the acceleration request value, that is, the difference between the throttle and the brake, is considered when formulating the fuzzy rules, so that when the driver brakes, the rear wing angle is increased to provide additional drag to assist braking, and when the driver accelerates, the rear wing angle is reduced to reduce the impact of additional drag on the acceleration process. The stability of a vehicle is extremely important when driving at high speeds, and downforce plays a vital role in the stability of the vehicle. Therefore, referring to the speed of the vehicle, when the speed increases, the angle of attack of the rear wing is appropriately increased to provide more additional downforce, enhance the vehicle's grip, and improve the vehicle's stability. The schematic diagram of the formulated main fuzzy control rules is shown below. Figure 3 As shown;

[0133] The angle of attack of the active tail wing is A, and its adjustable range is [-30°, 30°]. The value of the fuzzy domain is [-30, 30], and its fuzzy subset is set to {NL (negative large), NM (negative medium), NS (negative small), 0, PS (positive small), PM (positive medium), PL (positive large)}; According to the above settings and the control logic of the tail wing, the fuzzy control rules of the angle of attack of the active tail wing are shown in the table.

[0134] Table 1

[0135]

[0136] The membership function of the angle of attack A fuzzy set adopts Gaussian and joint Gaussian membership functions, and the distribution is set as follows: The two fuzzy subsets NL and PL adopt the joint Gaussian membership function, and their parameters are as follows:

[0137] NL:y=gauss2mf(x,[1-30 2-25]);

[0138] PL:y=gauss2mf(x,[2 25 1 30]);

[0139] The other subsets use Gaussian membership functions with the following parameters:

[0140] NM:y=gaussmf(x,[2.5-17]);

[0141] NS:y=gaussmf(x,[2-8]);

[0142] 0:y=gaussmf(x,[2 0]);

[0143] PS:y=gaussmf(x,[2 8]);

[0144] PM:y=gaussmf(x,[2.5 17]).

[0145] The fuzzy reasoning process of the main fuzzy controller adopts the minimum-maximum-center-of-gravity method, which is as follows:

[0146] First, the current detection information Acc exp Substitute the membership of V and V into Table 1 respectively, and the membership function of the output fuzzy set corresponding to each rule can be obtained; the rule whose two inputs are not zero is triggered, and the smaller membership of the two inputs of the same rule that has been triggered is calculated, and the result of each rule is called the premise credibility of the rule; the result obtained by taking the intersection of the premise credibility of each rule and the membership function of the fuzzy set output by each rule is the fuzzy value finally output by each rule; finally, the fuzzy values ​​output by all rules are calculated by taking the union, and the fuzzy value of the active tail angle of attack A output by the main fuzzy controller can be obtained.

[0147] Step 4: Use the deviation E and error change E of the tire slip rate difference c is the input quantity, which is fuzzified by the fuzzy controller; among them, the tire slip rate difference S F&R is the difference between the front and rear tire slip rates, and the specific calculation formula is as follows:

[0148] S F&R =max{S RL , S RR}- max { S FL ,F FR} (8)

[0149] Where S RL ,S RR is the slip rate of the left and right rear wheels, S FL ,FFR is the slip rate of the left and right front wheels;

[0150] The input E of the fuzzy controller is the difference S between the front and rear wheel slip rates of the vehicle. F&R The difference between the actual value and the expected value of , the fuzzy domain takes the value [-1,1], and its fuzzy subsets are set to {FL (front large), FM (front middle), FS (front small), 0, RS (back small), RM (back middle), RL (back large)}; the membership function of the E fuzzy subset is set as follows: the FL and RL subsets use mixed Gaussian membership functions, and the specific parameters are as follows:

[0151] FL:y=gauss2mf(x,[0.58-1 0.05-0.8]);

[0152] RL:y=gauss2mf(x,[0.06 0.5 0.6 1]);

[0153] Other subsets use Gaussian membership functions, and the specific parameters are as follows:

[0154] FM:y=gaussmf(x,[0.08-0.5]);

[0155] FS:y=gaussmf(x,[0.05-0.2]);

[0156] 0:y=gaussmf(x,[0.04 0]);

[0157] RS:y=gaussmf(x,[0.05 0.2]);

[0158] RM:y=gaussmf(x,[0.08 0.5]);

[0159] From the input E of the fuzzy controller c is the difference in slip rate between the front and rear wheels of the vehicle, S F&R The change in the difference between the actual value and the expected value of , the fuzzy domain takes the value [-1,1], and its fuzzy subsets are set to {NL (negative large), NM (negative medium), NS (negative small), 0, PS (positive small), PM (positive medium), PL (positive large)}; the membership function distribution of the E fuzzy subset is set as follows: the NL and PL subsets use mixed Gaussian membership functions, and the specific parameters are as follows:

[0160] NL:y=gauss2mf(x,[0.6-1 0.06-0.5]);

[0161] PL:y=gauss2mf(x,[0.06 0.5 0.6 1]);

[0162] Other subsets use Gaussian membership functions, and the specific parameters are as follows:

[0163] VM:y=gaussmf(x,[0.05-0.3]);

[0164] VS:y=gaussmf(x,[0.04-0.15]);

[0165] 0:y=gaussmf(x,[0.04 0]);

[0166] PS:y=gaussmf(x,[0.04 0.15]);

[0167] PM:y=gaussmf(x,[0.05 0.3]);

[0168] Step 5: Obtain the fuzzy value of the angle of attack correction value A0 according to the control rule of the fuzzy controller and the fuzzy reasoning method;

[0169] The fuzzy control rules are formulated by using the force analysis of the tail wing and the slip rate properties of the vehicle.

[0170] The main function of the slave fuzzy controller is to fine-tune the attack angle of the active rear wing according to the actual situation of the difference in the slip rate between the front and rear wheels of the vehicle after the main controller has completed the setting of the attack angle of the rear wing. From the mechanical analysis of step one, it can be concluded that the downforce of the rear wing can change the overall downforce of the vehicle and generate a torque on the vehicle, which can affect the downforce distribution of the front and rear wheels. The difference in the expected front and rear wheel slip rate should be 0. When the rear wheel slip rate is too large, it means that the downforce of the rear wheel is insufficient. At this time, the angle of the rear wing is increased to increase the downforce of the rear wheel. When the front wheel slip rate is too large, it means that the downforce of the front wheel is insufficient. At this time, the angle of the rear wing is reduced so that the front wheel can obtain more downforce distribution.

[0171] The output of the slave fuzzy control is the tail angle correction value A0, whose adjustable range is [-10°, 20°], and the value of the fuzzy domain is [-10, 20]. Its fuzzy subset is set to {NL (negative large), NS (negative small), 0, PSS (positive small), PS (positive small), PM (positive medium), PL (positive large)}; According to the above settings, the fuzzy rules of the slave fuzzy controller are summarized as shown in Table 2. The schematic diagram of the completed slave fuzzy control rules is shown in Figure 4 As shown;

[0172] Table 2

[0173]

[0174] The membership function of the angle of attack correction A0 fuzzy set adopts Gaussian and joint Gaussian membership functions, and the distribution is set as follows: The two fuzzy subsets NL and PL adopt the joint Gaussian membership function, and their parameters are as follows:

[0175] NL:y=gauss2mf(x,[0.5-15 1.2-8]);

[0176] PL:y=gauss2mf(x,[1 18 0.5 20]);

[0177] The other subsets use Gaussian membership functions with the following parameters:

[0178] NS:y=gaussmf(x,[1-4]);

[0179] 0:y=gaussmf(x,[0.8 0]);

[0180] PSS:y=gaussmf(x,[1 4]);

[0181] PS:y=gaussmf(x,[1.2 8]);

[0182] PM:y=gaussmf(x,[1.5 13]);

[0183] The minimum-maximum-center-of-gravity method is used from the fuzzy reasoning process of the fuzzy controller, as follows:

[0184] First, the current input information E and E c Substitute the membership degrees of the two inputs of the same rule that have been triggered into Rule Table 2 respectively, and the membership function of the output fuzzy set corresponding to each rule can be obtained; the rule whose two inputs are not zero is triggered, and the smaller membership degree of the two inputs of the same rule that has been triggered is calculated, and the result of each rule is called the premise credibility of the rule; the result of taking the intersection of the premise credibility of each rule and the membership function of the fuzzy set output by each rule is the fuzzy value finally output by each rule; finally, the fuzzy values ​​output by all rules are calculated by taking the union, and the fuzzy value of the angle of attack correction A0 output from the fuzzy controller at this time can be obtained.

[0185] Step 6: Defuzzify the fuzzy quantities of the master fuzzy controller and the slave fuzzy controller respectively;

[0186] Since the centroid method has a smoother output inference control, that is, corresponding to a slight change in the input signal, the final output of its inference will generally change to a certain extent, therefore, the centroid method is preferably used for defuzzification. The centroid method takes the centroid of the area enclosed by the fuzzy membership function curve and the horizontal coordinate as the final output value of the fuzzy inference, that is, the output is:

[0187]

[0188] Where v0 is the output result of defuzzification, u(v) is the membership function, and v is the corresponding output value.

[0189] Step 7: Sum the defuzzified outputs of the main fuzzy controller and the slave fuzzy controller to obtain the angle of attack of the active tail.

[0190] Step 8: Measure the slip rate S of each tire RL , S RR , S FL 、F FR And the vehicle speed V is used as feedback and returns to step 2 and step 4.

[0191] The simulation experiment data of the technical solution provided by the present invention are given below.

[0192] The simulation environment of this experiment is a joint simulation platform built using CarSim and Simulink software. Different vehicle driving conditions are designed to verify the effectiveness of the proposed active tail controller.

[0193] Figure 6 As shown in the simulation vehicle braking process, the active rear wing obviously brakes to zero in a shorter time and has better braking performance. Figure 8 The figure shows the trajectory of the simulated vehicle under the condition of fixed steering wheel angle. It is obvious that the vehicle with active rear wing has a smaller turning radius and better performance. It can be seen that the control method of active rear wing based on fuzzy control proposed by the present invention can improve vehicle performance.

Claims

1. A method for controlling the angle of attack of an active rear wing of a vehicle based on fuzzy logic, comprising the following steps: Step 1: Obtain the vehicle speed V and the vehicle's expected acceleration Acc exp is the input quantity, and the input quantity is fuzzified by the main fuzzy controller to obtain the membership degree of the fuzzy subset of the input quantity; The expected acceleration of the vehicle Acc exp Accelerator pedal opening Pd Gas and the brake pedal opening Pd Brake The specific calculation method is as follows: Acc exp = Pd Gas - Pd Brake (1) Among them, the accelerator pedal opening Pd Gas and brake pedal opening Pd Brake The value range of is [0,1]; Step 2: Obtain the fuzzy value of the active tail wing attack angle A according to the control rules of the main fuzzy controller and the fuzzy reasoning method; Step 3: Use the deviation E and error change E of the tire slip rate difference c is the input quantity, and the membership degree of the fuzzy subset of the input quantity is obtained by fuzzifying the input quantity through the fuzzy controller; among them, the tire slip rate difference S F&R is the difference between the front and rear tire slip rates, and the specific calculation formula is as follows: S F&R =max{S RL , S RR }- max { S FL ,F FR } (2) Where S RL ,S RR is the slip rate of the left and right rear wheels, S FL ,F FR is the slip rate of the left and right front wheels; Step 4: Obtain the fuzzy value of the angle of attack correction value A0 according to the control rules of the fuzzy controller and the fuzzy reasoning method; Step 5: Defuzzify the fuzzy quantities of the master fuzzy controller and the slave fuzzy controller respectively to obtain the final accurate output value; Step 6: Sum the outputs of the master fuzzy controller and the slave fuzzy controller after defuzzification to obtain the attack angle control quantity of the active tail wing; Step 7: Measure the slip rate S of each tire RL , S RR , S FL 、F FR And the vehicle speed V is used as feedback and returns to step one and step three.

2. The method for controlling the angle of attack of an active rear wing of a vehicle based on fuzzy logic according to claim 1, characterized in that: In step 1, the method by which the main fuzzy controller performs fuzzy processing on the input quantity is: Expected acceleration Acc exp The value range of is [-1,1], and the value range of fuzzy domain is [-1,1]; set the expected acceleration Acc exp The fuzzy subsets are {BL (decrease), BM (decrease), BS (decrease), 0, GS (decrease), GM (decrease), GL (decrease)}, Acc exp The membership function distribution of the fuzzy set is set as follows: the two subsets BL and GL use mixed Gaussian membership functions, and the specific parameters are as follows: BL:y=gauss2mf(x,[1-1 0.1-0.85]); GL:y=gauss2mf(x,[0.1 0.85 1 1]); Where y=gauss2mf(x,[σ1c1σ2c2]) represents a double Gaussian mixture function; y is the output value, x is the input value, σ1 and c1 are the standard deviation and mean of the Gaussian function on the left, and σ2 and c2 are the standard deviation and mean of the Gaussian function on the right; other subsets use Gaussian membership functions, and the specific parameters are as follows: BM:y=gaussmf(x,[0.06-0.5]); BS:y=gaussmf(x,[0.06-0.25]); 0:y=gaussmf(x,[0.05 0]); GS:y=gaussmf(x,[0.06 0.25]); GM:y=gaussmf(x,[0.06 0.5]); Where y = gaussmf (x, [σc]) represents the Gaussian function, y is the output value, x is the input value, σ and c are the standard deviation and mean of the Gaussian function respectively; Another input is the vehicle speed V, which ranges from [0km / h, 180km / h], the fuzzy domain ranges from [0, 180], and its fuzzy subsets are {VS (very slow), MS (medium slow), SS (slightly slow), M (moderate), SF (slightly fast), MF (medium fast), VF (very fast)}; The membership function distribution of V fuzzy set is set as follows: VS and VF use joint Gaussian membership function, and their parameters are VS:y=gauss2mf(x,[6 0 8 30]); VF:y=gauss2mf(x,[3 160 10 180]); Other fuzzy subsets use Gaussian membership functions, and their parameters are as follows: MS:y=gaussmf(x,[8 60]); SS:y=gaussmf(x,[6 90]); M:y=gaussmf(x,[5 110]); SF:y=gaussmf(x,[4 130]); MF:y=gaussmf(x,[4 148]).

3. The method for controlling the angle of attack of an active rear wing of a vehicle based on fuzzy logic according to claim 2, characterized in that: The control rules of the main fuzzy controller in step 2 are as follows: According to the properties of the active tail, the output of the control strategy is determined to be the angle of attack of the tail; the angle of attack of the active tail is A, and its adjustable range is [-30°, 30°]. The value of the fuzzy domain is [-30, 30], and its fuzzy subset is set to {NL (negative large), NM (negative medium), NS (negative small), 0, PS (positive small), PM (positive medium), PL (positive large)}; According to the above settings and the control logic of the tail, the fuzzy control rules of the angle of attack of the active tail are shown in Table 1; Table 1 The membership function of the angle of attack A fuzzy set adopts Gaussian and joint Gaussian membership functions, and the distribution is set as follows: The two fuzzy subsets NL and PL adopt the joint Gaussian membership function, and their parameters are as follows: NL:y=gauss2mf(x,[1-30 2-25]); PL:y=gauss2mf(x,[2 25 1 30]); The other subsets use Gaussian membership functions with the following parameters: NM:y=gaussmf(x,[2.5-17]); NS:y=gaussmf(x,[2-8]); 0:y=gaussmf(x,[2 0]); PS:y=gaussmf(x,[2 8]); PM:y=gaussmf(x,[2.5 17]).

4. The method for controlling the angle of attack of an active rear wing of a vehicle based on fuzzy logic according to claim 3, characterized in that: The fuzzy reasoning process of the main fuzzy controller in step 2 adopts the minimum-maximum-center-of-gravity method, which is as follows: First, the current detection information Acc exp Substitute the membership of V and V into Table 1 respectively, and the membership function of the output fuzzy set corresponding to each rule can be obtained; the rule whose two inputs are not zero is triggered, and the smaller membership of the two inputs of the same rule that has been triggered is calculated, and the result of each rule is called the premise credibility of the rule; the result obtained by taking the intersection of the premise credibility of each rule and the membership function of the fuzzy set output by each rule is the fuzzy value finally output by each rule; finally, the fuzzy values ​​output by all rules are calculated by taking the union, and the fuzzy value of the active tail angle of attack A output by the main fuzzy controller can be obtained.

5. The method for controlling the angle of attack of an active rear wing of a vehicle based on fuzzy logic according to claim 4, characterized in that: The method of fuzzifying the input quantity from the fuzzy controller in step 3 is: The input E of the fuzzy controller is the difference S between the front and rear wheel slip rates of the vehicle. F&R The difference between the actual value and the expected value, where the expected value is 0, the fuzzy domain takes the value [-1,1], and its fuzzy subsets are set to {FL (front large), FM (front middle), FS (front small), 0, RS (back small), RM (back middle), RL (back large)}; the membership function of the E fuzzy subset is set as follows: the FL and RL subsets use a mixed Gaussian membership function, and the specific parameters are as follows: FL:y=gauss2mf(x,[0.58-1 0.05-0.8]); RL:y=gauss2mf(x,[0.06 0.5 0.6 1]); Other subsets use Gaussian membership functions, and the specific parameters are as follows: FM:y=gaussmf(x,[0.08-0.5]); FS:y=gaussmf(x,[0.05-0.2]); 0:y=gaussmf(x,[0.04 0]); RS:y=gaussmf(x,[0.05 0.2]); RM:y=gaussmf(x,[0.08 0.5]); From the input E of the fuzzy controller c is the difference in slip rate between the front and rear wheels of the vehicle, S F&R The change in the difference between the actual value and the expected value of , the fuzzy domain takes the value [-1,1], and its fuzzy subsets are set to {NL (negative large), NM (negative medium), NS (negative small), 0, PS (positive small), PM (positive medium), PL (positive large)}; the membership function distribution of the E fuzzy subset is set as follows: the NL and PL subsets use mixed Gaussian membership functions, and the specific parameters are as follows: NL:y=gauss2mf(x,[0.6-1 0.06-0.5]); PL:y=gauss2mf(x,[0.06 0.5 0.6 1]); Other subsets use Gaussian membership functions, and the specific parameters are as follows: VM:y=gaussmf(x,[0.05-0.3]); VS:y=gaussmf(x,[0.04-0.15]); 0:y=gaussmf(x,[0.04 0]); PS:y=gaussmf(x,[0.04 0.15]); PM:y=gaussmf(x,[0.05 0.3]).

6. The method for controlling the angle of attack of an active rear wing of a vehicle based on fuzzy logic according to claim 5, characterized in that: The control rules from the fuzzy controller in step 4 are as follows: The output of the fuzzy control is the tail angle correction A0, whose adjustable range is [-10°, 20°], and the value of the fuzzy domain is [-10, 20]. Its fuzzy subset is set to {NL (negative large), NS (negative small), 0, PSS (positive small), PS (positive small), PM (positive medium), PL (positive large)}; According to the above settings, the fuzzy rules of the fuzzy controller are summarized as shown in Table 2; Table 2 The membership function of the angle of attack correction A0 fuzzy set adopts Gaussian and joint Gaussian membership functions, and the distribution is set as follows: The two fuzzy subsets NL and PL adopt the joint Gaussian membership function, and their parameters are as follows: NL:y=gauss2mf(x,[0.5-15 1.2-8]); PL:y=gauss2mf(x,[1 18 0.5 20]); The other subsets use Gaussian membership functions with the following parameters: NS:y=gaussmf(x,[1-4]); 0:y=gaussmf(x,[0.8 0]); PSS:y=gaussmf(x,[1 4]); PS:y=gaussmf(x,[1.2 8]); PM:y=gaussmf(x,[1.5 13]).

7. The method for controlling the angle of attack of an active rear wing of a vehicle based on fuzzy logic according to claim 6, characterized in that: In step 4, the minimum-maximum-centroid method is used from the fuzzy reasoning process of the fuzzy controller, as follows: First, the current input information E and E c Substitute the membership degrees of the two inputs of the same rule that have been triggered into Rule Table 2 respectively, and the membership function of the output fuzzy set corresponding to each rule can be obtained; the rule whose two inputs are not zero is triggered, and the smaller membership degree of the two inputs of the same rule that has been triggered is calculated, and the result of each rule is called the premise credibility of the rule; the result of taking the intersection of the premise credibility of each rule and the membership function of the fuzzy set output by each rule is the fuzzy value finally output by each rule; finally, the fuzzy values ​​output by all rules are calculated by taking the union, and the fuzzy value of the angle of attack correction A0 output from the fuzzy controller at this time can be obtained.

8. The method for controlling the angle of attack of an active rear wing of a vehicle based on fuzzy logic according to claim 7, characterized in that: In step 5, the centroid method is used to defuzzify the fuzzy quantities of the master fuzzy controller and the slave fuzzy controller.

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