Adaptive double-slip film steering wheel active return control method based on different road adhesion coefficients

Abstract

The application discloses a kind of based on different road adhesion coefficient self-adapting double slip film steering disc active return control method, first, based on the steering disc return controller and the steering angle return tracking controller of slip film control theory are established, and the active return of steering disc is realized through the collaborative control of double slip film controller;Second, the adaptive parameters of the controller are designed according to different road adhesion coefficient, and the optimal value of parameter is fitted by polynomial fitting and BP neural network, to improve the return performance of steering system under the condition of low adhesion road surface.Simulation experimental results show that the control method can effectively improve the return performance of steering system under the condition of different road adhesion coefficient, realize the fast and stable return of steering disc and steering wheel.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steering control, and particularly relates to an adaptive double-slip film steering wheel active return control method based on different road adhesion coefficients. BACKGROUND

[0002] Compared with the traditional steering system, the steer-by-wire system cancels the mechanical connection between the steering wheel and the steering gear, and realizes the steering function through the transmission of control commands by electric signals. In the steering process of the steer-by-wire system, the return torque of the tire is transmitted to the steering wheel through the road feeling motor by electric signals, helping the steering wheel to return. However, due to the structure of the steer-by-wire system and system friction and other factors, the high-speed return overshoot and the low-speed return deficiency phenomenon may occur in the return process of the vehicle, and the steering wheel cannot actively return to zero. At the same time, the return torque of the vehicle will be significantly reduced under low adhesion road conditions, which directly affects the return performance of the vehicle steering. The steering return performance of the vehicle directly affects the vehicle handling stability. Poor return performance not only makes the driver feel tired, but also affects the driving safety. Therefore, active return control of the steering wheel is needed to ensure that the steering wheel can actively return to zero after the driver releases his hand, and to improve the return performance of the vehicle.

[0003] The current commonly used steering wheel active return control methods mainly include proportional-integral-derivative control (PID), sliding mode control (SMC), fuzzy control (FC) and backstepping control. PID control is a classic linear controller, which has become the preferred control strategy for many industrial control systems due to its excellent control performance and simple implementation. A single neuron adaptive PID controller for EPS active return control is proposed in the literature. The single neuron is introduced in the traditional PID controller to realize the adaptability of the weight coefficient, and the steering wheel angular velocity feedback is added in the return control to improve the stability of the active return. The return torque compensation is based on the vehicle speed in the literature, and the switching conditions of the system assistance state and the return state are designed based on the feedback signals of the steering column torque and the steering wheel angle to avoid the sudden change of the torque. A fuzzy PID control scheme for steering wheel return based on absolute position is proposed in the literature, which improves the return ability of the wheels under various working conditions by controlling the assist motor. An adaptive PD return control strategy is designed based on the road adhesion coefficient in the literature, which improves the return performance of the vehicle on the road with adhesion. Although PID has a simple structure and high reliability, its effectiveness is often limited when dealing with complex systems and nonlinear phenomena. At the same time, the continuous accumulation of the integral term in the PID controller can easily lead to overshoot or oscillation. Based on this limitation, scholars have proposed various nonlinear control strategies to improve system performance. A sliding mode control-based active return control strategy is proposed in the literature, which can effectively reduce the return residual angle and improve the driving stability of the vehicle. A nonlinear steering wheel angle control method using self-aligning torque is proposed in the literature, which estimates the self-aligning torque by introducing a high-gain disturbance observer and designs a nonlinear controller based on backstepping control to realize the return control of the steering wheel. A fuzzy sliding mode controller based on a two-degree-of-freedom vehicle model is proposed in the literature, which combines the advantages of fuzzy control and sliding mode control. Simulation tests show that the controller improves the return characteristics of the vehicle at high and low speeds, and verifies the effectiveness of the controller. Although nonlinear control strategies can effectively improve the robustness and stability of the controller, they do not consider the influence of low adhesion road conditions on vehicle return. SUMMARY

[0004] The technical problem solved by the present application is to provide a self-adaptive double sliding film steering wheel active return control method based on different road adhesion coefficients to solve the problems of the prior art.

[0005] To achieve the above technical purposes, the technical scheme adopted by the present application is:

[0006] A self-adaptive double sliding film steering wheel active return control method based on different road adhesion coefficients, comprising:

[0007] Step 1, collect the vehicle driving state parameters when preparing to return, including steering wheel angle, vehicle speed and road adhesion coefficient;

[0008] Step 2,

[0009] 2.1, first, the vehicle determines to enter the return state:

[0010] The steering wheel return controller combines the steering wheel angle and vehicle speed collected in step 1, and obtains the optimal controller parameter k2 through the polynomial obtained by fitting;

[0011] The steering wheel return tracking controller combines the steering wheel angle, vehicle speed and road adhesion coefficient collected in step 1, and obtains the optimal controller parameter k t through the trained BP neural network;

[0012] 2.2, taking the steering wheel zero position as the control target, the difference between the current real-time steering wheel angle and the steering wheel return controller input, the steering wheel return controller combines the optimal controller parameter k2 to calculate the sliding mode control rate, and then controls the road feeling motor, continuously adjusts the torque output by the road feeling motor until the steering wheel returns to zero;

[0013] Convert the current real-time steering wheel angle to steering wheel angle through the transmission ratio, and convert the steering wheel angle and the current real-time steering wheel angle to steering execution motor angle, then subtract the two, and take the difference as the input of the steering angle return tracking controller. The steering angle return tracking controller combines the optimal controller parameter k t to calculate the sliding mode control rate, and then controls the steering execution motor to realize the tracking control of the steering wheel by the steering wheel.

[0014] As a further improved technical scheme of the present application, the polynomial obtained in step 2.1 is:

[0015]

[0016] wherein, δ sw is the steering wheel angle; v is the vehicle speed;

[0017] a 00 , a 10 , a 01 , a 20 , a 11 , a 02 , a 30 , a 21 , a 12 , a 03 , a 40 , a 31 , a 22 , a 13 , a 04 is the k2 fitting parameter.

[0018] As a further improved technical solution of the application, in the BP neural network of step 2.1:

[0019] A plurality of groups of steering wheel angles, vehicle speeds and road adhesion coefficients under different driving conditions are collected, and the corresponding k t optimum values are obtained by simulation.

[0020] The BP neural network inputs are set as the steering wheel angle, the vehicle speed and the road adhesion coefficient, and the output is set as the k t optimum value.

[0021] After network parameter adjustment and error comparison, the parameters of the BP neural network are finally set as follows: the number of hidden layer nodes is 3, the learning rate is 0.001, the maximum number of iterations is 10000, the activation function is Softplus, the optimizer is SGD, and the training set and test set ratio is set to 9:1 respectively.

[0022] As a further improved technical solution of the application, the sliding mode control rate of the steering wheel return-to-center controller in step 2.2 is:

[0023]

[0024] wherein, k1, k3, k4, a, λ are all real numbers greater than zero, s0 is the initial state of the sliding mode surface; T a is the steering wheel active return torque, J sw is the moment of inertia of the steering wheel assembly, B sw is the damping coefficient of the steering wheel assembly; δ sw is the steering wheel angle. is the boundary value, sat is a saturation function, s is a sliding surface of the improved fast non-singular fast terminal sliding mode controller, q, p are positive odd numbers and 1 < p / q < 2; c, β are non-zero positive real numbers; e is a rotation angle error signal, e = δ t - δ sw = - δ sw , δ t is a target rotation angle, i.e. δ t = 0, δ sw is a steering wheel rotation angle.

[0025] As a further improved technical solution of the application, the sliding mode control rate of the rotation angle back-to-normal tracking controller in step 2.2 is:

[0026]

[0027] Wherein, U b is the voltage of the steering execution motor; R b is the resistance of the steering execution motor; I b is the current of the steering execution motor; L b is the inductance of the steering execution motor; k b is the back electromotive force coefficient of the steering execution motor, c t is a non-zero positive real number, ε t is a positive real number, s t is the sliding surface of the rotation angle back-to-normal tracking controller: s t = c t e m + D; D is a non-zero positive real number; e m = θ r - θ f ;

[0028] Wherein, the acquisition process of the target rotation angle θ r of the steering execution motor in the back-to-normal process is:

[0029] The current real-time steering wheel rotation angle is converted into the steering wheel rotation angle through the transmission ratio, and the rotation angle θ r of the steering execution motor is derived through the steering wheel rotation angle.

[0030] Wherein, the acquisition process of the current motor rotation angle θ f of the steering execution motor is:

[0031] The current real-time steering wheel rotation angle is converted into the steering execution motor rotation angle θ f .

[0032] The beneficial effects of the application are:

[0033] (1)Due to the influence of the structure and system friction, the vehicle with steer-by-wire system has the problem of high-speed return overshoot and low-speed return deficiency. To solve this problem, this paper proposes a double sliding film steering wheel active return control method, which mainly consists of a steering wheel return controller and a steering angle return tracking controller. The steering wheel return controller mainly controls the road feeling motor output torque to achieve active return of the steering wheel. The steering angle return tracking controller mainly controls the steering rack displacement through the steering motor to achieve the return tracking of the steering wheel angle, ensuring the synchronous change of the steering wheel and the steering wheel during the steering wheel return process. At the same time, aiming at the problem that the traditional sliding mode control effect is poor due to the significant deviation between the initial state and the target state of the steering wheel during the steering wheel return control, a nonsingular fast terminal sliding mode steering wheel return controller is designed. In the return working condition, the controller cooperates with the steering wheel return tracking controller based on the traditional sliding mode design to achieve accurate return of the steering wheel. The simulation test results show that compared with the traditional sliding mode controller, the nonsingular fast terminal sliding mode controller has faster convergence speed and smaller return residual angle, and has higher control accuracy and control efficiency.

[0034] Note: Compared with the widely used PID controller, the controller designed in this paper effectively solves the problem of PID controller overshoot, and has faster convergence speed and control accuracy.

[0035] (2)Due to the influence of road adhesion coefficient on the return torque of the steering wheel, the controller has a certain steady-state error on the road with low road adhesion coefficient. To solve this problem, this paper proposes an adaptive double sliding film steering wheel active return control method based on different road adhesion coefficients. First, the CarSim and Simulink joint simulation are used to set up different road adhesion coefficient operating conditions, and the optimal parameters under different conditions are obtained by adjusting the parameters of the steering wheel return controller and the steering angle return tracking controller. Second, polynomial and BP neural network are used for adaptive parameter fitting to design adaptive steering wheel return controller(Adaptive Nonsingular Fast Terminal Sliding Mode Control, ANFTSMC) and adaptive steering angle tracking controller(Backpropagation-Augmented Adaptive Sliding Mode Control, BPAA-SMC). The simulation results show that the adaptive double sliding film steering wheel active return control method combined with BPAA-SMC and ANFTSMC can effectively improve the return performance of the steering system under different road adhesion coefficient conditions, and realize the fast and stable return of the steering wheel and the steering wheel. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1The adaptive double slip film steering wheel active return control method framework based on different road adhesion coefficients.

[0037] Figure 2 The sat function image map.

[0038] Figure 3 The parameter k2 optimal value fitting result map.

[0039] Figure 4 The parameter k t optimal value result map.

[0040] Figure 4 The k t optimal value result map.

[0041] Figure 4 The k t optimal value result map.

[0042] Figure 5 The BP neural network training result map.

[0043] Figure 5 The (a) in the figure is a comparison result map of the prediction result and the true value of the test set.

[0044] Figure 5 The (b) in the figure is the k t fitting value result map.

[0045] Figure 6 The steering wheel active return test map of the vehicle under different road conditions in high speed working condition.

[0046] Figure 6 The (a) in the figure is the steering wheel angle return result map of the vehicle under the road adhesion coefficient μ of 0.9 in high speed working condition under different controller combinations.

[0047] Figure 6 The (b) in the figure is the steering wheel angle tracking result map of the vehicle under the road adhesion coefficient μ of 0.9 in high speed working condition under different controller combinations.

[0048] Figure 6 The (c) in the figure is the steering wheel angle return result map of the vehicle under the road adhesion coefficient μ of 0.5 in high speed working condition under different controller combinations.

[0049] Figure 6 The (d) in the figure is the steering wheel angle tracking result map of the vehicle under the road adhesion coefficient μ of 0.5 in high speed working condition under different controller combinations.

[0050] Figure 6 Figure (e) in the (e) is a steering wheel angle return result figure of the vehicle under different controller combinations when the road surface adhesion coefficient μ is 0.3 in the high-speed working condition.

[0051] Figure 6 Figure (f) in the (f) is a steering wheel angle tracking result figure of the vehicle under different controller combinations when the road surface adhesion coefficient μ is 0.3 in the high-speed working condition.

[0052] Figure 7 Figure (a) in the (a) is a steering wheel active return test figure of the vehicle under different road surface conditions in the low-speed working condition.

[0053] Figure 7 Figure (a) in the (a) is a steering wheel active return test figure of the vehicle under different road surface conditions in the low-speed working condition.

[0054] Figure 7 Figure (b) in the (b) is a steering wheel angle tracking result figure of the vehicle under different controller combinations when the road surface adhesion coefficient μ is 0.9 in the low-speed working condition.

[0055] Figure 7 Figure (c) in the (c) is a steering wheel angle return result figure of the vehicle under different controller combinations when the road surface adhesion coefficient μ is 0.5 in the low-speed working condition.

[0056] Figure 7 Figure (d) in the (d) is a steering wheel angle tracking result figure of the vehicle under different controller combinations when the road surface adhesion coefficient μ is 0.5 in the low-speed working condition.

[0057] Figure 7 Figure (e) in the (e) is a steering wheel angle return result figure of the vehicle under different controller combinations when the road surface adhesion coefficient μ is 0.3 in the low-speed working condition.

[0058] Figure 7 Figure (f) in the (f) is a steering wheel angle tracking result figure of the vehicle under different controller combinations when the road surface adhesion coefficient μ is 0.3 in the low-speed working condition. DETAILED DESCRIPTION

[0059] The specific embodiments of the present application are further described below with reference to the accompanying drawings:

[0060] Steer-by-wire systems, as a novel type of steering system, have greatly promoted the intelligence and ease of handling of automobiles. However, due to the influence of vehicle structure and system friction, steer-by-wire systems suffer from problems such as high-speed overshoot and low-speed undershoot during vehicle return to center. This paper proposes an adaptive dual-slip film steering wheel return-to-center control method based on different road surface adhesion coefficients. First, this method establishes a steering wheel return-to-center controller and a steering wheel return-to-center tracking controller based on slip film control theory. The vehicle return to center is achieved through the synergistic action of the dual slip film controllers. Second, adaptive parameters of the controllers are designed according to different road surface adhesion coefficients. The optimal parameter values ​​are fitted using polynomial fitting and a BP neural network to improve the return-to-center performance of the steering system under low-adhesion road surface conditions. Simulation results show that this control method effectively improves the return-to-center performance of the steering system under different road surface adhesion coefficients, achieving rapid and stable return of the steering wheel and steering wheels.

[0061] 1. Design of an adaptive dual-slippery-film active self-centering control method based on different road surface adhesion coefficients:

[0062] For steer-by-wire systems, since the steering wheel and steering wheels are controlled by a road feel motor and a steering actuator motor respectively, controlling only the road feel motor to achieve steering wheel return control while neglecting the steering wheel's tracking control of the steering wheel can easily lead to reduced vehicle stability. To address this issue, this paper designs a dual-slip diaphragm controller collaborative active return control method. This method primarily uses a steering wheel return controller and a steering angle return tracking controller to collaboratively control steering wheel return. The steering wheel active return controller controls the road feel motor to achieve precise steering wheel return during the return-to-center phase, while the steering angle return tracking controller controls the steering actuator motor to control the steering wheel angle, achieving real-time tracking control of the steering wheel's steering wheel angle. Its architecture is as follows: Figure 1 As shown.

[0063] 1.1 Steering wheel return controller:

[0064] When the steering wheel enters the return-to-center state, the torque input by the driver to the steering wheel is zero, and only the return-to-center torque provided by the road feel motor exists on the steering shaft. Ignoring uncertainties such as friction, the dynamic equation for the return-to-center process is:

[0065]

[0066] In the formula, T a J is the active return torque of the steering wheel. sw B sw These represent the moment of inertia and damping coefficient of the steering wheel assembly, respectively; δ sw is the steering wheel angle; u is the sliding mode control signal.

[0067] In the steering wheel return control, the control target is to achieve the steering wheel from any position quickly and accurately return to zero position. Because there may be significant deviation between the initial state and the target state of the steering wheel, the traditional sliding film control is easy to be affected by the nonlinear disturbance torque or the sharp change of the control amount when facing such mutation control scene, and even the system may be chattering due to parameter mismatch or disturbance. In view of the above problems, a non-singular fast terminal sliding mode controller NFTSMC is proposed, which introduces nonlinear term and dynamic sliding mode surface to improve the convergence speed and suppress overshoot, thereby significantly improving the control efficiency and robustness of the steering wheel active return. The approach rate expression of the designed controller is:

[0068]

[0069] In the formula, k1, k2, k3, k4, a, λ are all real numbers greater than zero; s0 is the initial state of the sliding mode surface. At the same time, the tangent function term is introduced in the approach rate, which improves the convergence speed when the control system approaches the initial state by adjusting the parameter λ.

[0070] In order to reduce the chattering of the controller, the saturation function sat is used instead of the sign function sgn, where is the boundary value, and β is a real number greater than zero. The corresponding image of the sat function is as follows Figure 2 The expression is:

[0071]

[0072] Substituting equation (3) into equation (2), the designed approach rate expression is:

[0073]

[0074] The target steering angle is zero in the return state, assuming that δ t is the target steering angle, i.e. δ t = 0, and δ sw is the steering wheel angle. Thus, the steering angle error signal e can be expressed as:

[0075] e = δ t - δ sw = - δ sw (5);

[0076] The improved fast non-singular fast terminal sliding mode controller sliding mode surface is defined as:

[0077]

[0078] Where c and β are non-zero positive real numbers.

[0079] Taking the derivative of it can be obtained:

[0080]

[0081] Will Substituting, we get:

[0082]

[0083] Substituting equations (1) and (8) into equation (4), we can obtain the sliding mode control rate of the control system as follows:

[0084]

[0085] The stability of the established control system is analyzed using Lyapunov theory, verifying that the time for the characteristic point moving outside s=0 to reach the sliding surface is convergent. The derivative of the Lyapunov function is:

[0086]

[0087] When s≠0, |s / φ|<1:

[0088]

[0089] According to Lyapunov's theory, the designed controller can converge within a finite time, ensuring system stability.

[0090] 1.2, Angle return tracking controller:

[0091] The steering wheel return tracking controller designed in this section primarily functions to convert the steering wheel angle change into a rack displacement change in the rack steering mechanism during the vehicle's active steering wheel return process. By controlling the rack displacement, it achieves steering wheel return tracking control, ensuring synchronous changes between the steering wheel and the wheels during the return process. In a drive-by-wire steering system, rack displacement is mainly controlled by the steering actuator motor. Therefore, a controller is designed specifically for the steering actuator motor's angle to achieve steering wheel angle tracking control during the return process.

[0092] Based on Kirchhoff's laws, the voltage equation for the steering actuator motor can be derived. Therefore, the control equation for the steering return tracking controller is as follows:

[0093]

[0094] In the formula, U b The voltage for the steering actuator motor; R b The resistance of the motor for steering; I b The current for the motor that drives the steering; L b The inductance of the steering actuator motor; k b The back electromotive force coefficient of the steering actuator motor; θf For steering execution motor angle, u t For the sliding film control signal.

[0095] The sliding surface of the steering execution motor angle return-to-zero tracking controller adopts a linear sliding surface, and its expression is:

[0096] s t = c t e m + D (13) ;

[0097] Assuming that the target angle of the steering execution motor during the return-to-zero process is θ r , and the current angle of the steering execution motor is θ f , the steering error signal e m can be expressed as:

[0098] e m = θ r - θ f (14) ;

[0099] Wherein, the process of obtaining the target angle θ r of the steering execution motor during the return-to-zero process is:

[0100] The current real-time steering wheel angle is converted into the steering wheel angle through the transmission ratio, and the steering execution motor angle θ r is obtained through the steering wheel angle.

[0101] Wherein, the process of obtaining the current motor angle θ f of the steering execution motor is:

[0102] The steering execution motor angle θ f is obtained through the current real-time steering wheel angle.

[0103] Substitute equation (12) and equation (14) into the sliding surface function equation (13), and derive it to obtain:

[0104]

[0105] The sliding film controller adopts an exponential approach rate, which is expressed as:

[0106]

[0107] Assuming that u t is the control amount of the controller, and combining equation (15) and equation (16), the sliding film control rate of the control system is:

[0108]

[0109] The stability of the control system is analyzed by using Lyapunov theory. Substituting equation (15) into equation (17) gives

[0110]

[0111] From equation (18), it can be concluded that The stability condition of the above Lyapunov function is satisfied, that is, the established steering wheel return control system is stable and effective.

[0112] 1.3. Design of adaptive steering wheel return controller

[0113] The steering wheel return controller is mainly responsible for controlling the road feel motor to realize the control of the steering wheel angle. The control effect of the controller is mainly affected by the steering wheel angle deviation and the vehicle speed. Combined with the sliding mode approach rate and the control equation of the controller as shown in equations (4) and (9), and considering that the approach rate affects the robustness and convergence speed of the sliding mode controller, the exponential approach rate coefficient k2 in the approach rate is selected as an adaptive parameter, and an adaptive steering wheel return controller (Adaptive Nonsingular Fast Terminal Sliding Mode Control, ANFTSMC) is established. The k2 is designed as a function of the vehicle speed and the steering wheel angle. The controller can adjust the k2 parameter value in real time according to different driving conditions, ensuring good steering wheel return performance. In order to determine the optimal value of the control parameter that can balance the return accuracy and return time under different conditions, CarSim and Simulink are used for joint simulation test of steering wheel active return. The vehicle speed in the simulation condition is set to 30 km / h, 40 km / h, 50 km / h, 60 km / h, 70 km / h, and 80 km / h, and the steering wheel angle range is set to 30°-100° with a step of 10°. The return overshoot, return residual and return time are used as the judgment standard, and the k2 parameter value with good control effect under different conditions is recorded. The simulation results are shown in Table 1.

[0114] Table 1. Optimal value of parameter k2

[0115]

[0116]

[0117] From the table, it can be seen that at the same vehicle speed, the optimal value of k2 is negatively correlated with the steering wheel angle, and at the same steering wheel angle, the optimal value of k2 is negatively correlated with the vehicle speed, so k2 has a certain linear relationship with the steering wheel angle and the vehicle speed, and the expression of the optimal parameter k2 can be obtained by fitting. Therefore, the optimal value of k2 collected by simulation is fitted by a quartic polynomial using the curve fitting toolbox in MATLAB software, and the expression of the optimal parameter k2 is as follows:

[0118]

[0119] where δ sw is the steering wheel angle; v is the vehicle speed; a 00 ~ a 04 is the k2 fitting parameter, and its specific value is shown in Table 2.

[0120] Table 2, k2 fitting expression parameter value:

[0121] Parameter Value Parameter Value a 00 ]]> 371.3 a 12 ]]> -1.769 x 10 -3 ]] a 10 ]]> -5.99 a 03 ]]> -2.776 x 10 -3 ]]> a 01 ]]> -17.16 a 40 ]]> 6.045 x 10 -7 <!-- 9 -->]]> a 20 ]]> 0.0583 a 31 ]]> 2.258 x 10 -6 ]] a 11 ]]> 0.1682 a 22 ]]> 3.418 x 10 -6 ]]> a 02 ]]> 0.3219 a 13 ]]> 6.83 x 10 -6 ]]> a 30 ]]> -3.104 x 10 -4 ]] a 04 ]]> 9.115 x 10 -6 ]] a 21 ]]> -8.782 x 10 -4 ]]>

[0122] Figure 3 The figure shows the fitting result of the optimal value of the parameter k2. The small black dots in the figure represent the optimal values obtained by simulation, and it can be found that the fitting surface is relatively smooth, and the test optimal values are all on the fitting plane. At the same time, the determination coefficient R-Square of the fitting result of the optimal value of k2 is 0.995, so the error between the optimal value fitting result and the true data is small, the fitting accuracy is high, and the fitting result can be used for subsequent adaptive parameter research.

[0123] 1.4, adaptive steering angle return tracking controller design:

[0124] The steering angle return tracking controller is mainly responsible for controlling the steering execution motor to realize the tracking control of the steering wheel angle of the steering wheel. The steering execution motor controls the displacement of the gear rack to control the steering angle of the steering wheel, and the displacement of the gear rack is affected by the left and right return torques of the steering wheel. The return torque of the wheel is closely related to the road adhesion coefficient, so when designing the adaptive parameters of the steering angle return tracking controller, in addition to considering the steering wheel angle and the vehicle speed, the influence of the road adhesion coefficient on the steering wheel return tracking effect also needs to be considered.

[0125] According to the control equation (17) of the steering angle return tracking controller, and considering that the approach rate affects the robustness and convergence speed of the sliding mode controller, the exponential approach rate coefficient k t is selected as an adaptive parameter, which can adjust k tThe parameter value ensures good steering wheel steering angle tracking performance. To determine the parameter optimal value that can balance control accuracy and control time under different working conditions, CarSim and Simulink are used for steering wheel active return-to-center joint simulation test. In the simulation working condition, the vehicle speed is set to 30-80 km / h with a step of 10 km / h, the steering wheel steering angle range is set to 30°-100° with a step of 10°, and the road adhesion coefficient range is set to 0.25-0.9. The return-to-center overshoot, return-to-center residual amount and return-to-center time are used as the judgment standard, and the parameter k t optimal value with good control effect under different working conditions is recorded Figure 4 .

[0126] Figure 4 The parameter k t optimal value result diagram is shown, where Figure 4 (a) in the figure shows the k t optimal value obtained by a total of 432 tests, X, Y and Z axes represent the road adhesion coefficient, steering wheel steering angle and vehicle speed respectively, and the dots in the figure represent the corresponding k t optimal value under each working condition; Figure 4 (b) in the figure is the k t optimal value result diagram under the working condition of a steering wheel steering angle of 70°; t optimal value under different vehicle speeds and steering wheel steering angle driving working conditions; t optimal value distribution diagram. It can be seen from the diagram that under the same vehicle speed, with the increase of the road adhesion coefficient, the k t optimal value first increases and then decreases, when the road adhesion coefficient is about 0.5, the k t optimal value reaches the highest value.

[0127] From the k t optimal value result diagram, it can be seen that the k t optimal value has a nonlinear relationship with the vehicle speed, steering wheel steering angle and road adhesion coefficient. Polynomial fitting has defects such as large error and too many required parameters when processing the multi-element nonlinear relationship, while the backpropagation neural network (BPNN) has high nonlinear fitting capability and can describe the corresponding relationship between variables in the interval in the form of a function expression through training. Therefore, the k t optimal value data obtained through simulation test is used for BP neural network training to establish a backpropagation-augmented adaptive sliding mode control (BPAA-SMC) controller, so as to realize the self-adaptation of the steering angle return-to-center tracking controller parameter k t under different working conditions and ensure high control accuracy of the controller.

[0128] The training dataset for the BP neural network consists of 432 sets of k data obtained through co-simulation using Simulink and CarSim in this section. t The optimal value is achieved by setting the network input layer to three parameters: vehicle speed, road surface adhesion coefficient, and steering wheel angle, and setting the output layer to a single element, k. t Optimal values. After adjusting network parameters and comparing errors, the final parameters of the BP neural network are set as follows: 3 hidden layer nodes, learning rate of 0.001, maximum number of iterations of 10000, activation function of Softplus, optimizer of SGD, and training set to test set ratio of 9:1. BP neural network k t The optimal value fitting training results are as follows: Figure 5 As shown.

[0129] The training results of the BP neural network are shown in the figure above. Figure 5 (a) in the figure represents the comparison between the predicted results and the true values ​​on the test set. The blue dots in the figure represent the target values, and the red dots represent the fitted values ​​after training the BP neural network. It can be clearly seen that the k obtained during training... t The fitted values ​​have high accuracy; Figure 5 (b) represents the k obtained by inputting the simulation data of each operating condition into the trained fitting model. t Fitted values, where the x-axis represents k t The target optimal value is shown on the y-axis, and the fitted value is represented on the ordinate. The closer the blue dot is to y=x, the closer the fitted value is to the target value. The graph clearly shows that the trained model can fit a highly accurate k under different operating conditions. t The value is calculated. Furthermore, the R-squared coefficient of determination is used to characterize the accuracy of the neural network fitting model. The calculated R-squared coefficient of determination for the BP neural network fitting the training set reaches 0.9992, meaning the trained BP neural network fitting model can effectively describe k. t The relationship between vehicle speed, road surface adhesion coefficient, and steering wheel angle.

[0130] 2. Simulation Analysis:

[0131] To verify the steering wheel active return controller designed in this invention based on different road surface conditions, the vehicle steering wheel return condition was simulated by CarSim and Simulink. Simulation return tests were conducted under different road surface adhesion coefficients for high-speed return and low-speed return conditions.

[0132] 2.1 High-speed steering wheel active return-to-center test:

[0133] In high-speed working conditions, the vehicle was tested under the conditions of road adhesion coefficient of 0.9, 0.5 and 0.3, respectively, the vehicle speed was 70 km / h, and the steering wheel angle was constant input of 60°. After the vehicle ran smoothly for five seconds, the steering wheel angle input was canceled and changed to steering wheel torque input, and the input torque was set to 0 Nm. The steering wheel angle response change during the return-to-straight process was recorded, and the simulation results are shown in Figure 6 Figure 6 SMC+SMC indicates that both the steering wheel return-to-straight controller and the steering angle return-to-straight tracking controller use the traditional sliding mode controller SMC, SMC+NFTSMC indicates that the steering wheel return-to-straight controller uses the non-singular fast terminal sliding mode controller NFTSMC, and the steering angle return-to-straight tracking controller uses the traditional sliding mode controller SMC, and BPAA-SMC+ANFTSMC indicates the adaptive double sliding mode steering wheel return-to-straight controller based on different road adhesion coefficients proposed in this paper.

[0134] Figure 6 (b), (d), (f) in the steering wheel active return-to-straight test of the vehicle under different road conditions in high-speed working conditions, Figure 6 (b), (d), (f) in the steering wheel active return-to-straight test of the vehicle under different road conditions in high-speed working conditions, Figure 6 ​(a), (c), (e) in Fig. 2 are the steering wheel angle return-to-zero result graphs. It can be found from the graphs that the steering wheel of the vehicle without the steering wheel return-to-zero controller cannot return to zero position on the road surface with the adhesion coefficient of 0.9, 0.5 and 0.3, and the return-to-zero overshoot phenomenon occurs, and the steering wheel angle change gradient is large, which is easy to cause the steering wheel to shake and lead to low vehicle driving stability. In the driving condition of the vehicle on the road surface with the adhesion coefficient of 0.9, after the combination of the double traditional sliding film controller, the return-to-zero overshoot of the steering wheel is obviously reduced, and the steering wheel angle changes gently, which prevents the steering wheel from shaking due to the sharp change of the steering wheel angle in the return-to-zero process, but the return-to-zero time of the steering wheel is long, and the average return-to-zero time is 2.6 s. After the steering wheel return-to-zero controller is replaced by the NFTSMC controller, compared with the traditional sliding film controller, the NFTSMC controller has a faster convergence speed, the steering wheel angle changes stably in the return-to-zero process, the return-to-zero time is short, the average return-to-zero time is 1.06 s, and the residual angle is less than 0.1°. However, as the road surface adhesion coefficient decreases, the controller accuracy decreases obviously, and when the road surface adhesion coefficient is 0.3, the steering wheel has a steady-state error of about 4°, and the return-to-zero performance is poor. The adaptive adjustment parameters of the steering wheel active return-to-zero controller and the steering wheel return-to-zero tracking controller are introduced respectively, that is, the adaptive double sliding film steering wheel active return-to-zero controller of BPAA-SMC and ANFTSMC combination can ensure the return-to-zero speed while obviously improving the problem of large steady-state error under different road surface adhesion coefficients, and the steady-state error is about 0.03°. The steering wheel return-to-zero overshoot phenomenon in the high-speed working condition is improved, and the stability and accuracy are high.

[0135] 2.1.2, steering wheel active return-to-zero test in low-speed working condition:

[0136] In the low-speed working condition, the active return-to-zero test is carried out under the road conditions of the road surface adhesion coefficient of 0.9, 0.5 and 0.3, the vehicle speed is 40 km / h, the steering wheel angle is constant input of 90°, the vehicle returns to zero after five seconds of stable driving, and the rest of the settings are consistent with the high-speed working condition return-to-zero simulation test. The simulation results are shown in Fig. 2. Figure 7

[0137] Figure 7 Fig. 2 shows the steering wheel active return-to-zero test of the vehicle under different road conditions in the low-speed working condition, Figure 7 Fig. 2(b), (d), (f) are steering wheel angle tracking result graphs. It can be seen from the graphs that the designed controller can well track the steering wheel angle and has strong stability. Figure 7 ​(a), (c), (e) in the figure are the steering wheel angle return results, it can be found from the figure that the steering wheel of the vehicle without the steering wheel return controller cannot return to zero position on the road surface with adhesion coefficient of 0.9, 0.5 and 0.3, and the return is insufficient, which is the same as the high-speed working condition, the steering wheel return gradient is large, and it is easy to cause the steering wheel to shake and other problems. In the driving condition of the vehicle on the road surface with adhesion coefficient of 0.9, after using the combination of double traditional sliding film controllers, the amount of insufficient return of the steering wheel is obviously reduced, the change of the steering wheel angle is relatively smooth, and the residual angle of the return is about 0.12°, but the return time of the steering wheel is still long, and the average return time is 2.7s. After replacing the steering wheel return controller with the NFTSMC controller, compared with the traditional sliding film controller, it has a faster convergence speed, the change of the steering wheel angle is stable during the return process, and it has a faster convergence speed, the average return time is 1.4s, and the residual angle of the return is less than 0.1°. But with the decrease of the road adhesion coefficient, the accuracy of the controller decreases obviously, when the road adhesion coefficient is 0.3, the steering wheel has a steady-state error of about 5.4°, and the return is poor. The introduction of adaptive adjustment parameters into the steering wheel active return controller and the steering wheel return tracking controller respectively, that is, the adaptive double sliding film steering wheel active return controller combined with BPAA-SMC and ANFTSMC can ensure the return speed while obviously improving the problem of large steady-state error under different road adhesion coefficients, the steady-state error is about 0.01°, improves the steering wheel return overshoot phenomenon of the vehicle in low-speed working condition, and has high stability and accuracy.

[0138] From the above simulation test, it can be seen that the designed adaptive double sliding film steering wheel return controller based on road adhesion coefficient has the characteristics of fast convergence speed, high return accuracy and stable change of steering angle during return, and can effectively improve the return performance of the steering system under different road conditions, and ensure that the steer-by-wire system can normally return under different adhesion coefficient roads and different working conditions.

[0139] 3. Conclusion:

[0140] In this paper, a kind of adaptive double sliding film steering wheel return control method based on different road adhesion coefficients is proposed by combining the sliding mode control principle, polynomial fitting and BP neural network fitting model. According to the theoretical analysis and simulation test results, the following conclusions are drawn:

[0141] (1) A double sliding film steering wheel active return control method is proposed, which mainly controls the steering wheel return through the steering wheel return controller and the steering angle return tracking controller. At the same time, aiming at the problem that the initial state and the target state of the steering wheel return control exist significant deviation, which leads to poor effect of traditional sliding film control, a non-singular fast terminal sliding mode steering wheel return controller NFTSMC is designed, which cooperates with the steering wheel return tracking controller based on traditional sliding film design to realize accurate return of the steering wheel. The simulation results show that the designed double sliding film steering wheel active return control method can effectively realize the return control of the steering wheel under different speed conditions.

[0142] (2) Because the low road adhesion coefficient affects the return torque of the vehicle steering wheel, the controller has a certain steady-state error on the road with low road adhesion coefficient. To solve this problem, the invention proposes an adaptive double sliding film steering wheel return control method based on different road adhesion coefficients. First, the CarSim and Simulink joint simulation are used to set different road adhesion coefficient operating conditions, and the optimal parameters under different conditions are obtained by adjusting the double sliding film steering wheel active return controller parameters. Second, polynomial and BP neural network are used for adaptive parameter fitting, and adaptive steering wheel return controller ANFTSMC and adaptive steering angle tracking controller BPAA-SMC are designed. The simulation results show that the adaptive double sliding film steering wheel active return controller composed of BPAA-SMC and ANFTSMC can effectively improve the return performance of the steering system under different road adhesion coefficient road conditions, and realize the rapid and stable return of the steering wheel and the steering wheel.

[0143] The protection scope of the present application includes but is not limited to the above embodiments, and the protection scope of the present application is subject to the claims, any replacement, deformation, improvement of the present technology easily thought by the person skilled in the art falls within the protection scope of the present application.

Claims

1. An adaptive dual-film steering wheel active return-to-center control method based on different road surface adhesion coefficients, characterized in that, include: Step 1: Collect vehicle driving status parameters when preparing to return to center, including steering wheel angle, vehicle speed, and road surface adhesion coefficient; Step 2 2.1 First, after the vehicle is determined to have entered the straightening state: The steering wheel return controller combines the steering wheel angle and vehicle speed collected in step 1, and obtains the optimal controller parameter k2 by fitting the polynomial. The steering wheel return tracking controller combines the steering wheel angle, vehicle speed, and road adhesion coefficient collected in step 1, and obtains the optimal controller parameters k through a pre-trained BP neural network. t ; 2.

2. Taking the steering wheel zero position as the control target, the difference between the steering wheel zero position and the current real-time steering wheel angle is used as the input of the steering wheel return controller. The steering wheel return controller then calculates the sliding mode control rate in combination with the optimal controller parameter k2, and then controls the road feel motor to continuously adjust the torque output by the road feel motor until the steering wheel returns to the zero position. The current real-time steering wheel angle is converted into the steering wheel angle through the transmission ratio. This steering wheel angle is then converted into the steering actuator motor angle, and the difference between the two is used as the input to the angle return tracking controller. The angle return tracking controller then combines this difference with the optimal controller parameter k. t The sliding mode control rate is calculated, and then the steering actuator motor is controlled to achieve the tracking control of the steering wheel on the steering wheel.

2. The adaptive dual-slip film steering wheel active return-to-center control method based on different road surface adhesion coefficients as described in claim 1, characterized in that, The polynomial obtained by fitting in step 2.1 is: Where, δ sw v is the steering wheel angle; v is the vehicle speed; a 00 、a 10 、a 01 、a 20 、a 11 、a 02 、a 30 、a 21 、a 12 、a 03 、a 40 、a 31 、a 22 、a 13 、a 04 is the k2 fitting parameter.

3. The adaptive dual-film steering wheel active return-to-center control method based on different road surface adhesion coefficients according to claim 1, characterized in that, In the BP neural network of step 2.1: Multiple sets of steering wheel angles, vehicle speeds, and road adhesion coefficients were collected under different driving conditions, and the corresponding k values ​​were obtained through simulation. t Optimal value; The inputs to the BP neural network are set to the steering wheel angle, vehicle speed, and road surface adhesion coefficient, respectively, and the output is set to k. t Optimal value; After adjusting the network parameters and comparing the errors, the parameters of the BP neural network were finally set as follows: 3 hidden layer nodes, 0.001 learning rate, 10000 maximum iterations, Softplus activation function, SGD optimizer, and a training set to test set ratio of 9:

1.

4. The adaptive dual-film steering wheel active return-to-center control method based on different road surface adhesion coefficients according to claim 2, characterized in that, The sliding mode control rate of the steering wheel return controller in step 2.2 is: where k1, k3, k4, a, and λ are all real numbers greater than zero, and s0 is the sliding mode surface in the initial state; T a is the active return torque of the steering wheel, and J sw is the moment of inertia of the steering wheel assembly, and B sw is the damping coefficient of the steering wheel assembly; δ sw is the steering wheel angle; is the boundary value, sat is the saturation function, s is the sliding mode surface of the improved fast non-singular fast terminal sliding mode controller, q and p are positive odd numbers and 1 < p / q < 2; c and β are non-zero positive real numbers; e is the angle error signal, e = δ t -δ sw = -δ sw , δ t is the target angle, that is, δ t = 0, δ sw is the steering wheel angle.

5. The adaptive dual-slip diaphragm steering wheel active return-to-center control method according to claim 3, characterized in that, The sliding mode control rate of the corner return tracking controller in step 2.2 is: Among them, U b The voltage for the steering actuator motor; R b The resistance of the steering actuator motor; I b The current for the motor that drives the steering; L b The inductance of the steering actuator motor; k b c is the back electromotive force coefficient of the steering actuator motor. t ε is a non-zero positive real number. t s is a positive real number. t The sliding surface for the corner return tracking controller: s t =c t e m +D; D is a non-zero positive real number; e m =θ r -θ f ; Among them, during the return-to-center process, the target rotation angle θ of the steering motor is executed. r The acquisition process is as follows: The current real-time steering wheel angle is converted into the steering wheel angle through the transmission ratio, and the steering actuator motor is then driven by this steering wheel angle. r ; Among them, the current motor rotation angle θ of the steering actuator motor f The acquisition process is as follows: The steering angle θ of the steering actuator motor is derived from the current real-time steering wheel angle. f .

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