Heavy air suspension truck anti-roll control method based on extended zero moment point

Abstract

The invention discloses a heavy air suspension truck anti-roll control method based on an extended zero moment point, and the method comprises the following steps: S1, building a virtual prototype model of a heavy truck, and obtaining a centroid pitch angle, a roll angle, a yaw angle and accelerations in X, Y and Z directions under a vehicle driving condition in real time; s2, based on an extended zero moment point theory, constructing a vehicle body attitude evaluation index based on extended zero moment point coordinates; s3, calculating a vehicle body posture evaluation index in real time under the vehicle driving condition, and judging whether the vehicle rolls or not; and S4, on the basis of a layered architecture control strategy, the upper layer calculates air spring force needed by anti-roll based on fuzzy PID, the lower layer inflates and deflates the air spring to output the needed air spring force, and the posture of the vehicle body is balanced. According to the method, the stability of the vehicle body can be evaluated in real time through the extended zero moment point theory, the roll risk is accurately judged, anti-roll measures are taken, and the driving stability is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle stability control, and particularly relates to a control method for anti-roll of a heavy-duty air suspension truck based on the extended zero moment point. Background Art

[0002] During the operation of heavy-duty trucks, the stability of the vehicle body attitude directly affects the safety, ride comfort and reliability of cargo transportation of the vehicle. Especially under complex terrain and high-speed driving conditions, the vehicle is prone to unstable phenomena such as roll and pitch, and in severe cases, it may even lead to overturning accidents. Although the traditional passive suspension system can relieve vehicle body vibration to a certain extent, due to its fixed stiffness and damping parameters, it is difficult to adapt to diverse road conditions and dynamic working conditions, resulting in limited anti-roll ability. In recent years, the electronically controlled air suspension system has shown significant advantages in improving vehicle stability and comfort due to its strong adjustability and fast response speed. However, how to design an efficient control strategy to fully exert its potential remains a technical problem to be solved urgently.

[0003] In the prior art, vehicle stability control mostly relies on a single sensor signal or a simple control algorithm, lacking comprehensive consideration of the overall dynamic characteristics of the vehicle and road surface features. For example, the traditional PID control method has problems with insufficient parameter adaptability when dealing with time-varying systems, and it is difficult to meet the real-time and accuracy requirements under complex working conditions. In addition, although fuzzy control can handle the uncertainties of nonlinear systems, it still has limitations in multi-objective optimization and dynamic adjustment. At the same time, as an emerging stability evaluation method, the extended zero moment point theory can quantify the stability of the vehicle body attitude through geometric relationships, but its computational complexity and the way of combining with control strategies in practical applications have not been fully studied. Summary of the Invention

[0004] The purpose of the present invention is to provide a control method for anti-roll of a heavy-duty air suspension truck based on the extended zero moment point. The present invention can evaluate the vehicle body stability in real time through the extended zero moment point theory, accurately judge the roll risk and take anti-roll measures, significantly improving the driving stability.

[0005] The technical solution of the present invention: A control method for anti-roll of a heavy-duty air suspension truck based on the extended zero moment point includes the following steps:

[0006] S1. Establish a virtual prototype model of the heavy-duty truck, and obtain the pitch angle, roll angle, yaw angle of the center of mass and the accelerations in the X, Y, and Z directions under the vehicle driving conditions in real time;

[0007] S2. Based on the extended zero moment point theory, construct a vehicle body attitude evaluation index based on the coordinates of the extended zero moment point;

[0008] S3. Calculate the body attitude evaluation index under the vehicle driving condition in real time, and determine whether the vehicle rolls over.

[0009] S4. Based on the hierarchical architecture control strategy, the upper layer calculates the air spring force required for anti-roll based on fuzzy PID, and the lower layer inflates and deflates the air spring to output the required air spring force to balance the body attitude.

[0010] The above control method for anti-roll of a heavy air suspension truck based on the extended zero moment point, the coordinates of the extended zero moment point are expressed as (X EZMP , Y EZMP ), and the calculation formula is as follows:

[0011]

[0012] In the formula: p x is the coordinate value of the center of mass in the X-axis direction in the fixed coordinate system OXYZ; p y is the coordinate value of the center of mass in the Y-axis direction in the fixed coordinate system OXYZ; h is the height of the center of mass; θ t is the vehicle roll angle, α x , α y and α z are the vehicle roll angular acceleration, vehicle pitch angular acceleration and vehicle yaw angular acceleration respectively; g is the acceleration due to gravity; is the terrain pitch angle; I x , I y and I z are the centroid roll moment of inertia, centroid pitch moment of inertia and centroid yaw moment of inertia respectively; M is the vehicle mass; a x and a y are the acceleration of the center of mass in the X-axis direction and the acceleration of the center of mass in the Y-axis direction respectively.

[0013] In the above control method for anti-roll of a heavy air suspension truck based on the extended zero moment point, in step S3, it is determined whether the vehicle rolls over by judging whether the coordinates of the extended zero moment point are located within the stable support polygon area. If the extended zero moment point is within this area, it is judged as stable, and when it exceeds, anti-roll control is triggered.

[0014] In the above control method for anti-roll of a heavy air suspension truck based on the extended zero moment point, the construction method of the stable support polygon is as follows:

[0015] Generate a geometric area enclosed by the connection lines of the four-wheel grounding points in the OXY coordinate system according to the vehicle geometric parameters and terrain parameters;

[0016] Among them, the vehicle geometric parameters include the front and rear wheelbases and the track width, and the terrain parameters include the terrain roll angle and the terrain pitch angle.

[0017] The above-mentioned roll prevention control method for heavy-duty air suspension trucks based on the extended zero moment point, the hierarchical architecture control strategy includes an upper decision-making layer and a lower execution layer; the upper decision-making layer calculates the vehicle body attitude evaluation index based on the signal data collected by multi-source sensors, confirms the target controller, and generates the target spring force setting value; the lower execution layer is responsible for realizing the tracking of the target air spring force.

[0018] The above-mentioned roll prevention control method for heavy-duty air suspension trucks based on the extended zero moment point, when the roll stability evaluation index in the vehicle body attitude evaluation index exceeds the stable region, the fuzzy PID algorithm is used to control the air spring inflation and deflation; if the roll stability evaluation index deviates towards the driver's side, the left air spring inflates and the right air spring deflates to generate an anti-left roll moment; if the roll stability evaluation index deviates towards the co-driver's side, the right air spring inflates and the left air spring deflates to generate an anti-right roll moment.

[0019] The above-mentioned roll prevention control method for heavy-duty air suspension trucks based on the extended zero moment point, the fuzzy PID controller is an adaptive fuzzy PID controller, which combines fuzzy control and PID control, uses the system error signal and its change rate as decision variables, and online tunes the proportional gain coefficient Kp, integral gain coefficient Ki, and differential gain coefficient Kd of the PID controller through a fuzzy logic regulator.

[0020] The above-mentioned roll prevention control method for heavy-duty air suspension trucks based on the extended zero moment point, the control of the lower execution layer is optimized and designed based on the RBF neural network, and the self-disturbance rejection control system parameter adaptive adjustment method based on RBFNN is adopted, which includes a dynamic optimization and identification two-stage adjustment mechanism. By fusing the controller input and the air spring pressure feedback signal, a neural network identification model is constructed to reconstruct the force output characteristics in real time, realize parameter iterative evolution, and improve the control performance.

[0021] Compared with the prior art, the present invention realizes a comprehensive evaluation of vehicle stability by establishing a vehicle virtual prototype model, obtaining key parameters such as centroid acceleration, roll angle, and pitch angle, and constructing a vehicle body attitude evaluation index in combination with the extended zero moment point theory. At the same time, a hierarchical architecture control strategy is adopted, combining fuzzy PID control and RBF neural network, generating the target spring force through the upper decision-making layer, and realizing the inflation and deflation adjustment of the air spring through the lower execution layer, thereby effectively suppressing the roll and pitch movements of the vehicle body. This innovative method not only overcomes the deficiencies of traditional control strategies in terms of parameter adaptability and dynamic response, but also provides a new theoretical basis and technical support for the optimal design of the electronically controlled air suspension system. Brief Description of the Drawings

[0022] Figure 1 It is a schematic diagram of a virtual prototype model of a heavy-duty truck;

[0023] Figure 2 is the extended zero moment point X EZMP and Y EZMP calculation flow chart;

[0024] Figure 3 is the graphical application of the vehicle body attitude evaluation index;

[0025] Figure 4 is the schematic diagram of the hierarchical architecture control principle;

[0026] Figure 5 is the fuzzy adaptive PID control logic;

[0027] Figure 6 is the Simulink model of the fuzzy adaptive PID controller;

[0028] Figure 7 is the schematic diagram of the hierarchical architecture controller model;

[0029] Figure 8 is the RBFNN training flow chart;

[0030] Figure 9 is the Rbfnn-ADRC control structure diagram;

[0031] Figure 10 is the simulation result diagram of the tracking of the air spring force;

[0032] Figure 11 is the comparison diagram of the double lane change road surface condition parameters. Among them, (a) is the pitch evaluation index; (b) is the roll evaluation index; (c) is the roll angle; (d) is the roll angular velocity; (e) is the vertical acceleration;

[0033] Figure 12 is the comparison diagram of the snake test condition parameters. Among them, (a) is the roll evaluation index; (b) is the roll angle; (c) is the roll angular velocity; (d) is the vertical acceleration. Specific implementation manner

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments, but it shall not be used as a basis for limiting the present invention.

[0035] Embodiment: A control method for anti-roll of a heavy-duty air suspension truck based on an extended zero moment point includes the following steps:

[0036] S1. Establish a virtual prototype model of a heavy-duty truck, and obtain the pitch angle, roll angle, yaw angle and accelerations in the X, Y, and Z directions of the vehicle under the driving conditions in real time;

[0037] In this step, for the virtual prototype model of a heavy truck, it is defined that when the vehicle is stationary on a horizontal road surface, the center of mass G is projected onto the ground by the vertical projection method, and this projection point is used as the origin O of the coordinate system; the positive direction of the X-axis is in the forward direction of the driver, the positive direction of the Y-axis is in the co-driver direction, and the direction of the Z-axis is determined according to the right-hand rule. During the dynamic process of the vehicle driving on a non-horizontal road surface, with the real-time changes in terrain undulation and vehicle body attitude, the coordinate system OXYZ fixedly connected to the vehicle body always maintains its relative position relationship, and the overall model is as shown in Figure 1 shown. The relevant parameters of the vehicle are defined in Table 1 as follows:

[0038]

[0039] Table 1

[0040] S2. Based on the extended zero moment point theory, construct a vehicle body attitude evaluation index based on the coordinates of the extended zero moment point;

[0041] In this step, the coordinates of the extended zero moment point are expressed as (X EZMP , Y EZMP ), and the calculation formula is as follows:

[0042]

[0043]

[0044] In the formula: p x is the coordinate value of the center of mass in the X-axis direction in the fixed coordinate system OXYZ; p y is the coordinate value of the center of mass in the Y-axis direction in the fixed coordinate system OXYZ; h is the height of the center of mass; θ t is the roll angle of the vehicle, α x , α y and α z are the roll angular acceleration, pitch angular acceleration and yaw angular acceleration of the vehicle respectively; g is the acceleration due to gravity; is the pitch angle of the terrain; I x , I y and I z are the roll moment of inertia about the center of mass, pitch moment of inertia about the center of mass and yaw moment of inertia about the center of mass respectively; M is the total vehicle mass; a x and a y are the acceleration of the center of mass in the X-axis direction and the acceleration of the center of mass in the Y-axis direction respectively.

[0045] Analysis based on the extended zero moment point theory shows that the system stability can be evaluated through the coordinates of the extended zero moment point (Y EZMP , Y EZMPQuantitatively evaluate the spatial relationship with the support polygon. According to the theoretical framework of the extended zero moment point, when the geometric position of the extended zero moment point coordinates is within the geometric inclusion range of the support polygon, the dynamic stability of the system is maintained; if the coordinates of this characteristic point break through the geometric boundary constraints of the support area, it indicates that the system enters an unstable state. Therefore, the closer the calculated extended zero moment point coordinates are to the centroid position, the better the stability of the system in this direction.

[0046] By analyzing the EZMP evaluation index X EZMP and Y EZMP From their mathematical expressions and physical meanings, it can be found that these two evaluation quantities not only incorporate the inherent properties of the vehicle and the operating conditions, but also take into account the road surface characteristics. When evaluating the dynamic characteristics of vehicle roll, this index has comprehensive characteristics, can provide a reliable basis for stability judgment, and at the same time supports real-time monitoring and measurement during driving. Figure 2 For the extended zero moment point X EZMP and Y EZMP Calculation flow chart.

[0047] S3. Real-time calculate the body attitude evaluation index under the vehicle driving conditions to determine whether the vehicle rolls;

[0048] In this step, to determine whether the vehicle rolls, it is to judge whether the extended zero moment point coordinates are within the stable support polygon area. If the extended zero moment point is within this area, it is judged as stable, and when it exceeds, the anti-roll control is triggered.

[0049] Taking a four-wheel vehicle as an example, as Figure 3 shown, when establishing the stability analysis model, vehicle geometric parameters need to be considered, including the front and rear wheelbase T, the distance from the front axle to the centroid a, and the distance from the rear axle to the centroid b; terrain feature parameters (including terrain pitch angle and terrain roll angle ); reference coordinate system, using the OXY coordinate system fixed to the vehicle.

[0050] This evaluation index realizes a comprehensive evaluation of vehicle pitch and roll characteristics by integrating multi-dimensional information such as terrain features, vehicle dynamic parameters, and structural characteristics. It can not only monitor the driving stability of the vehicle in real time, but also provide a new theoretical basis for optimizing the control strategy of the electronically controlled air suspension system. Taking this index as the control target can effectively suppress vehicle pitch and roll movements, opening up a new direction for the control research of the electronically controlled air suspension system.

[0051] S4. Based on the hierarchical architecture control strategy, the upper layer calculates the air spring force required for anti-roll based on fuzzy PID, and the lower layer inflates and deflates the air spring to output the required air spring force to balance the body attitude.

[0052] In this step, based on the unique air pressure regulation mechanism of the air spring, a vehicle equipped with an electronically controlled air suspension can achieve dynamic adjustment of the vehicle body posture through the air charging and discharging adjustment process. The control strategy is designed using a hierarchical architecture, and its control principle framework is as shown in Figure 4 Figure. This architecture includes two core control levels: the upper-level decision-making layer control module collects various signal data based on multi-source sensors, generates a target spring force setting value by calculating the vehicle body posture evaluation index and confirming the target controller in the multi-objective control switching established above. The lower-level execution layer controller is responsible for achieving the tracking of the target air spring force.

[0053] Taking the roll stability evaluation index Y EZMP as the control target, when Y EZMP exceeds the stable region and shifts towards the driver's side, the fuzzy PID algorithm is used to inflate the left air spring and deflate the right air spring, generating an anti-left roll moment to suppress the left roll of the vehicle body; when Y EZMP exceeds the stable region and shifts towards the co-driver's side, the fuzzy PID algorithm is used to inflate the right air spring and deflate the left air spring, generating an anti-right roll moment to suppress the right roll of the vehicle body.

[0054] The designed controller is an adaptive fuzzy PID controller, which combines fuzzy control and PID control. In this embodiment, the fuzzy PID control algorithm is used, which not only does not require an accurate system model but also can realize the online optimization of PID parameters. This composite control architecture effectively overcomes the problem of insufficient parameter adaptability of traditional PID control in time-varying systems and improves the dynamic response quality and anti-interference ability of the closed-loop system. The controller uses the system error signal and its change rate as decision variables, and online tunes the proportional gain coefficient, integral gain coefficient, and differential gain coefficient of the PID controller through a fuzzy logic regulator. Its control logic is as shown in Figure 5 Figure.

[0055] Among them, the adaptive tuning of PID parameters is as follows:

[0056]

[0057] In the formula: ΔK p , ΔK i , ΔK d are the coefficients adjusted by the fuzzy controller; K p0 , K i0 , K d0 are the initial coefficients of each item of PID; K p , K i , K d are the parameters after the final tuning is completed.

[0058] When determining the initial parameters K p0 , K i0, K d0 Before determining the initial parameter values of the PID control, it is also necessary to deeply analyze the influence of these parameters on the system performance. Among them, the proportional coefficient K p0 's value directly affects the system response speed. The larger the coefficient, the faster the system response, but it may lead to control overshoot. On the contrary, the smaller the coefficient, the slower the system response and the longer the system adjustment time. The integral coefficient K i0 is used to eliminate the system steady-state error. If the coefficient is too large, it is easy to cause overshoot, and if it is too small, the error cannot be completely eliminated, affecting the control accuracy. The differential coefficient K d0 can suppress the overshoot phenomenon, enhance the system damping characteristic, and form a filtering effect on high-frequency interference signals. If the coefficient is too large, it will cause the system speed to increase abnormally.

[0059] According to the control requirements and the characteristics of the two-dimensional fuzzy controller, taking the vehicle body attitude stability evaluation index Y EZMP and the difference e between the ideal reference input Y EZMP and its change rate ec as the input quantities of the controller, the fuzzy control output ΔK p , ΔK i , ΔK d are the PID controller parameter adjustment amounts, and the PID controller output is the target spring force of the air suspension.

[0060] The initial parameters of the fuzzy PID vertical controller in this embodiment are obtained as follows through continuous simulation tests:

[0061] Initial parameters of the fuzzy PID roll controller:

[0062]

[0063] Among them, the establishment of the fuzzy controller has three steps as follows:

[0064] (1) Selection of fuzzy language variables

[0065] Usually, fuzzy language expressions such as "Positive Big (PB)", "Positive Medium (PM)", "Positive Small (PS)", "Zero (ZO)", "Negative Small (NS)", "Negative Medium (NM)", and "Negative Big (NB)" are used.

[0066] (2) Determination of the basic domain and the fuzzy domain

[0067] For the fuzzy domains of the input and output quantities, both are taken as [-3, 3]. For the basic domains of the input quantity deviation and the output quantity deviation change rate of the roll, pitch, and vertical controllers, they are [-0.8, 0.8] and [-30, 30], [-1.5, 1.5] and [-1.5, 1.5], [-0.3, 0.3] and [-3, 3] respectively.

[0068] (3) Formulation of Fuzzy PID Control Rules

[0069] In view of the significant transient characteristics of the dynamic excitation received during vehicle driving, in order to ensure that the control system has real-time response capabilities, triangular membership functions with higher computational efficiency are preferentially adopted in the configuration of membership functions.

[0070] Based on the experience of electronic control air suspension control engineering, control rules are formulated using "If...then..." statements, so fuzzy rule tables as shown in Tables 2, 3 and 4 are constructed.

[0071]

[0072] Table 2 ΔK p Fuzzy Control Rule Table

[0073]

[0074] Table 3 ΔK i Fuzzy Control Rule Table

[0075]

[0076] Table 4 ΔK d Fuzzy Control Rule Table

[0077] At the same time, combined with the previous text, the designed fuzzy PID controller model is built and implemented in the Simulink platform, as Figure 6 shown.

[0078] Furthermore, based on the evaluation index of vehicle body attitude stability, the vehicle body is controlled to switch for different vehicle body attitudes under different road conditions. The upper-layer control switches the control objectives in the Stateflow model, through the switching of vehicle body vertical control, anti-roll control and anti-pitch control. First, different controllers are determined and output through the Stateflow model, and the target spring force is output by the fuzzy PID controller corresponding to the control objective. The lower layer is based on different controllers to track the target spring force. Thus, a hierarchical architecture controller model is established respectively, which includes vehicle body attitude evaluation index, upper-layer multi-objective switching fuzzy PID controller, and lower-layer target force tracking controller, as Figure 7 shown.

[0079] In this embodiment, the lower-layer control is optimized based on the RBF neural network, as Figure 8As shown, it includes a two-stage regulation mechanism of dynamic optimization and identification. First, by fusing the controller input and the air spring pressure feedback signal, a neural network identification model is constructed to reconstruct the force output characteristic in real time; when the identification error converges to a preset threshold, the parameter optimization process is triggered, and the sensitivity analysis of the Jacobian matrix of the control input is carried out using the identification output, and the parameter increment is generated to implement dynamic regulation. This strategy embeds the neural network in the control closed-loop and realizes the iterative evolution of parameters through the online learning mechanism, improving the control performance while ensuring the system stability.

[0080] Regarding the optimization problem of the core parameter β of the extended state observer in the second-order active disturbance rejection control system 01 , β 02 , β 03 , a parameter self-learning framework based on neural network is constructed. By defining the deviation index between the observer state estimation error and the system output, an error backpropagation channel is established:

[0081] error=r(k)-y(k);

[0082] In the formula: r(k) is the ideal reference input value, and y(k) is the actual output value;

[0083] At the same time, variables are constructed:

[0084]

[0085] The corresponding control algorithm is:

[0086] Δu(k)=β 01 (error(k)-error(k - 1))+β 02 (error(k))+β 03 (error(k)-2error(k - 1)+error(k - 2));

[0087] The performance index function is selected as:

[0088]

[0089] The tuning of the core parameter β 01 , β 02 , β 03 is as follows:

[0090]

[0091] Among them, represents the Jacobian information of the object, and η1, η2, η3 are the learning rates respectively.

[0092] To meet the real-time constraints of the control system and ensure that a single neural network parameter update process is completed within the sampling period, this embodiment uses a gradient descent algorithm with an inertial momentum term to implement iterative updates of network parameters. This optimization algorithm synchronously adjusts the connection weight matrix from the hidden layer to the output layer, and jointly optimizes the center vector of the basis function and the node basis width parameter. Through a multi-dimensional parameter collaborative optimization mechanism, it reduces the scale of hidden layer nodes and improves the calculation efficiency while ensuring the approximation accuracy. This improved parameter update strategy accelerates the gradient descent process by introducing a momentum term and achieves rapid convergence in the parameter space while maintaining numerical stability.

[0093] Let the performance index function of the neural network identifier be:

[0094]

[0095] where: J is the performance index function of the neural network identifier, which is used to measure the closeness between the network output and the desired output. The network parameters are optimized by minimizing this function, and y m (k) is the output value of the neural network model.

[0096] The output layer weight correction formula is as follows:

[0097]

[0098] where: Δw j is the change in the j-th output layer weight at the current moment; h j is the output of the j-th node in the hidden layer, w j () is the j-th output layer weight, w j (k - 1) is the value at discrete time point k - 1, η is the learning rate, Φ j is the gradient value related to the j-th node in the hidden layer; α is the momentum factor;

[0099] The correction formula for the node basis width parameter is as follows:

[0100]

[0101] where: Δb j is the change in the j-th node basis width parameter at the current moment; b j (k) is the value of the j-th node basis width parameter at discrete time point k; X is the input vector; C j represents the center of the j-th node; is the parameter related to the j-th node;

[0102] The node center correction formula is as follows:

[0103]

[0104] where Δc ji is the change of the i-th component of the center of the j-th node at the current moment, x j is the input variable related to the j-th node.

[0105] The self-disturbance rejection control structure of the RBF neural network is established as Figure 9 shown. In order to verify the feasibility and effectiveness of the control scheme for online tuning the parameters of the neural network self-disturbance rejection controller proposed in the present invention in the electronic control air suspension system, a comparative analysis is carried out on four control schemes: traditional PID control, fuzzy PID control, self-disturbance rejection control, and the neural network self-disturbance rejection control proposed in this embodiment. The simulation is carried out in a joint simulation environment constructed by the single air spring charging and discharging model and the MATLAB control algorithm. The input step signal simulates the target air spring force of 40 KN, and the actual air spring force is output through AMESim to form a closed-loop control. The PID control, fuzzy PID control, self-disturbance rejection control, and neural network self-disturbance rejection control are respectively connected to track the air spring force. The simulation results are as Figure 10 shown. The results show that under the traditional PID control strategy, there are obvious delay and overshoot phenomena in the air spring force tracking response. After adopting the fuzzy adaptive PID controller, the overshoot of the system is reduced, but the response speed is still not significantly improved. After introducing the self-disturbance rejection control, the response time of the system is effectively optimized, but the overshoot problem still exists. Finally, through the adaptive optimization of the ADRC controller by the RBF neural network, the system not only achieves the optimal response speed but also significantly reduces the overshoot. Compared with the other three control strategies, this optimization scheme can more accurately achieve the tracking of the target control force.

[0106] Furthermore, the double lane change condition mainly simulates the condition of a vehicle overtaking or avoiding obstacles during driving, and can reflect the anti-roll ability of the vehicle. During the test, the vehicle driving speed is set to 60 km / h. The vehicle will first maintain a uniform straight-line driving, then the vehicle will turn, and after completing the turn, it will keep going straight, and then turn back to the original position. The results of the double lane change condition are as Figure 11 shown, Figure 11 where (a) is the pitch evaluation index; (b) is the roll evaluation index; (c) is the roll angle; (d) is the roll angular velocity; (e) is the vertical acceleration. Since in the double lane change condition, the road surface is a two-dimensional ideal plane terrain, the pitch angle and roll angle excited by this terrain both remain zero. When the vehicle performs a turning operation and the roll stability index breaks through the threshold, the upper layer control will set the control target to right roll control according to the judgment logic, and then complete overtaking and the steering wheel returns to the straight position, and then switch to left roll motion control. To further analyze the ECAS control effect, the root mean square values of each index under the double lane change condition are statistically shown in Table 5:

[0107] Parameter Passive ECAS <![CDATA[Vertical acceleration / (m / s 2 )]]> 0.2938 0.0616 Roll angle / (deg) 0.8728 0.5611 Roll angular velocity / (deg / s) 0.0477 0.0314 Pitch evaluation index / (m) 0.0672 0.0689 Roll evaluation index / (m) 0.1042 0.0973

[0108] Table 5

[0109] According to the data in the above table, the electronically controlled air suspension shows significant advantages compared with the passive suspension: the root mean square value of the vertical acceleration is reduced by 79.02%, the root mean square value of the roll angle is reduced by 35.72%, the roll angle speed is decreased by 34.17%, and the roll evaluation index is also correspondingly reduced by 6.62%. These data indicate that the electronically controlled air suspension performs excellently in roll control. Although during the roll control process, the root mean square value of the pitch evaluation index will slightly increase by 2.537%, since this index is still within a stable range, its impact can be ignored. In summary, this control strategy performs well in the roll condition.

[0110] Furthermore, a serpentine condition event is selected for serpentine simulation. In this embodiment, the handling and stability performance of a certain commercial vehicle is simulated for a serpentine test, and Figure 12 the change curves of (a) the roll evaluation index, (b) the roll angle, (c) the roll angle speed, and (d) the vertical acceleration evaluation index at different vehicle speeds as shown are obtained. It can be seen from the figure that under the serpentine test condition, the electronically controlled air suspension plays a certain role in suppressing the roll of the vehicle body. To further quantitatively analyze its actual control effect, the root mean square values of the vertical acceleration, roll angle, roll angle speed, pitch, and roll evaluation index under this condition are statistically shown in Table 6 as follows:

[0111] Parameter Passive ECAS <![CDATA[Vertical acceleration / (m / s 2 )]]> 0.3099 0.0844 Roll angle / (deg) 1.1954 0.9792 Roll angular velocity / (deg / s) 0.0851 0.0346 Pitch evaluation index / (m) 0.0671 0.0687 Roll evaluation index / (m) 0.1439 0.1293

[0112] Table 6

[0113] The table shows that compared with the passive suspension, the root mean square values of the vertical acceleration, roll angle, roll angle speed, and roll evaluation index of the electronically controlled air suspension under this condition are respectively decreased by 72.77%, 18.08%, 59.32%, and 10.11%; similarly, the slight increase in the pitch evaluation index does not affect the overall control. Therefore, this serpentine test further verifies the roll resistance ability of the control strategy proposed by the invention.

[0114] In summary, the present invention realizes a comprehensive evaluation of vehicle stability by establishing a vehicle virtual prototype model, obtaining key parameters such as the centroid acceleration, roll angle, and pitch angle, and constructing a vehicle body attitude evaluation index in combination with the extended zero moment point theory. At the same time, a hierarchical architecture control strategy is adopted, combining fuzzy PID control with RBF neural network, generating the target spring force through upper-layer decision-making, and realizing the charging and discharging adjustment of the air spring through lower-layer execution, thereby effectively suppressing the roll and pitch motions of the vehicle body. This innovative method not only overcomes the deficiencies of traditional control strategies in parameter adaptability and dynamic response, but also provides a new theoretical basis and technical support for the optimal design of electronically controlled air suspension systems.

Claims

1. A control method for anti-roll of a heavy-duty air suspension truck based on the extended zero moment point, characterized in that: It includes the following steps: S1. Establish a virtual prototype model of a heavy truck, and obtain the pitch angle, roll angle, yaw angle of the center of mass and the accelerations in the X, Y, and Z directions in real time under vehicle driving conditions; S2. Based on the extended zero moment point theory, construct a vehicle body attitude evaluation index based on the coordinates of the extended zero moment point; S3. Calculate the vehicle body attitude evaluation index in real time under vehicle driving conditions, and determine whether the vehicle rolls; S4. Based on a hierarchical architecture control strategy, the upper layer calculates the air spring force required for roll prevention based on fuzzy PID, and the lower layer inflates and deflates the air spring to output the required air spring force to balance the vehicle body attitude.

2. The control method for roll prevention of a heavy-duty air suspension truck based on the extended zero moment point according to claim 1, wherein: The coordinates of the extended zero moment point are expressed as (X EZMP , Y EZMP ), and the calculation formula is as follows: where: p x is the coordinate value of the centroid in the X-axis direction of the fixed coordinate system OXYZ; p y is the coordinate value of the centroid in the Y-axis direction of the fixed coordinate system OXYZ; h is the height of the center of mass; θ t is the vehicle roll angle, α x , α y and α z are the vehicle roll angular acceleration, the vehicle pitch angular acceleration, and the vehicle yaw angular acceleration, respectively; g is the acceleration due to gravity; is the terrain pitch angle; I x 、I y and I z are the roll moment of inertia about the center of mass, the pitch moment of inertia about the center of mass, and the yaw moment of inertia about the center of mass, respectively; M is the vehicle mass; a x and a y are the acceleration of the center of mass in the X-axis direction and the acceleration of the center of mass in the Y-axis direction, respectively.

3. The control method for anti-roll of a heavy-duty air suspension truck based on the extended zero moment point according to claim 1, characterized in that: In step S3, it is determined whether the vehicle rolls by judging whether the coordinates of the extended zero moment point are within the stable support polygon area. If the extended zero moment point is within this area, it is judged as stable, and when it exceeds, anti-roll control is triggered.

4. The control method for anti-roll of a heavy-duty air suspension truck based on the extended zero moment point according to claim 3, characterized in that: The construction method of the stable support polygon is as follows: According to the vehicle geometric parameters and terrain parameters, generate a geometric area enclosed by the connection lines of the four-wheel contact points in the OXY coordinate system; Among them, the vehicle geometric parameters include the front and rear wheelbases and the track width, and the terrain parameters include the terrain roll angle and the terrain pitch angle.

5. The control method for anti-roll of a heavy-duty air suspension truck based on the extended zero moment point according to claim 1, characterized in that: The hierarchical architecture control strategy includes an upper decision-making layer and a lower execution layer; the upper decision-making layer calculates the vehicle body attitude evaluation index based on the signal data collected by multi-source sensors, confirms the target controller, and generates the target spring force setting value; the lower execution layer is responsible for realizing the tracking of the target air spring force.

6. The control method for anti-roll of a heavy-duty air suspension truck based on the extended zero moment point according to claim 5, characterized in that: When the roll stability evaluation index in the vehicle body attitude evaluation index exceeds the stable domain, the fuzzy PID algorithm is used to control the inflation and deflation of the air spring; if the roll stability evaluation index deviates towards the driver's side, the left air spring is inflated and the right air spring is deflated to generate an anti-left roll moment; if the roll stability evaluation index deviates towards the co-driver's side, the right air spring is inflated and the left air spring is deflated to generate an anti-right roll moment.

7. The control method for anti-roll of a heavy-duty air suspension truck based on the extended zero moment point according to claim 6, characterized in that: The fuzzy PID controller is an adaptive fuzzy PID controller, which combines fuzzy control and PID control, uses the system error signal and its change rate as decision variables, and online tunes the proportional gain coefficient Kp, integral gain coefficient Ki, and differential gain coefficient Kd of the PID controller through a fuzzy logic regulator.

8. The control method for preventing roll of a heavy-duty air suspension truck based on the extended zero moment point according to claim 6, characterized in that: The control of the lower execution layer is optimized and designed based on the RBF neural network. The self-disturbance rejection control system parameter adaptive adjustment method based on RBFNN is adopted, which includes a dynamic optimization and identification two-stage adjustment mechanism. By fusing the controller input and the air spring pressure feedback signal, a neural network identification model is constructed to reconstruct the force output characteristics in real time, realize parameter iterative evolution, and improve the control performance.

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