Stability redundancy method and system for controlling a vehicle equipped with a semi-active suspension

Abstract

The application discloses a kind of for controlling the stability redundancy method and system of semi-active suspension vehicle, it is related to vehicle suspension device configuration field.The application is directly optimized to multiple targets such as the acceleration of spring mass, tire dynamic load, suspension dynamic deflection by multi-objective slime fungus algorithm when there is no sensor failure, solve the optimal feedback gain matrix of LQR control, combine the damping force required by damper calculated by Kalman filtering algorithm, realize vehicle damping adjustment, solve the problem of multi-objective optimization in control process.When only displacement sensor fails, the expected damping force of damper is determined based on Kalman filtering algorithm and single sensor hybrid control algorithm, and semi-active suspension can still be used when some sensors fail;When two sensors fail, use 0A state of damper solenoid valve current as emergency state to realize vehicle control.Based on this, the application can improve control efficiency without increasing cost.

Description

Technical Field

[0001] This invention relates to the field of vehicle suspension configuration, and in particular to a method and system for controlling stability redundancy in vehicles equipped with semi-active suspension. Background Technology

[0002] In the process of configuring stability redundancy control for semi-active suspension vehicles, a current output is directly applied after a sensor failure, or the shock absorber is in a 0A protection state to achieve safety redundancy. This single-state control method is similar to a passive shock absorber and cannot well meet the vehicle's damping control requirements. Furthermore, the optimization of multi-objective optimal control problems generally transforms multiple objectives into a single objective through weighted summation, lacking a separate control process for each objective, and the values ​​of the LQR optimal control weight matrix are determined empirically. Summary of the Invention

[0003] To address the aforementioned problems in the prior art, this invention provides a method and system for controlling stability redundancy in vehicles equipped with semi-active suspension.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] A method for controlling stability redundancy in vehicles equipped with semi-active suspension, comprising:

[0006] Acquire vehicle status information; the vehicle status information includes: shock absorber displacement change information and vehicle center of gravity status information; the shock absorber displacement change information is acquired using a displacement sensor; the vehicle center of gravity status information is acquired using an IMU sensor; the vehicle center of gravity status information includes: vertical acceleration of the center of gravity, longitudinal acceleration of the center of gravity, lateral acceleration of the center of gravity, roll rate of the center of gravity, and pitch rate of the center of gravity;

[0007] Based on the relationship between the displacement change information of the vibration damper and the first set threshold, it is determined whether the displacement sensor is faulty, and a first judgment result is obtained;

[0008] When the first judgment result is negative, the multi-objective slime mold algorithm is used to optimize the control objective to obtain the optimal feedback gain matrix of LQR control; the control objective includes the acceleration of the sprung mass, the tire dynamic load, and the suspension dynamic deflection;

[0009] The optimal feedback gain matrix based on LQR control combined with the Kalman filter algorithm is used to determine the desired damping force of the shock absorber, and the vehicle damping is adjusted based on the desired damping force.

[0010] When the first judgment result is yes, the IMU sensor is judged to be faulty based on the relationship between the vehicle center of gravity state information and the second set threshold, and the second judgment result is obtained.

[0011] When the second judgment result is negative, the velocity and acceleration on the top spring of each suspension are determined based on the vehicle's center of gravity state information using the Kalman filter algorithm.

[0012] A single-sensor hybrid control algorithm is used to determine the desired damping force of the shock absorber based on the speed and acceleration on the top springs of each suspension, and the vehicle's damping is adjusted based on the desired damping force.

[0013] When the second judgment result is yes, the 0A state of the shock absorber solenoid valve current is used as the emergency state to realize vehicle control.

[0014] Optionally, a multi-objective slime mold algorithm is used to optimize the control objective to obtain the optimal feedback gain matrix for LQR control, specifically including:

[0015] Construct the objective function for LQR control, and determine the control objective based on the objective function;

[0016] Determine the weighting matrix based on the control objective;

[0017] The optimal solution of the weighting matrix is ​​determined using a multi-target slime mold algorithm;

[0018] The optimal feedback gain matrix is ​​determined based on the optimal solution of the weighting matrix.

[0019] Optionally, a multi-target slime mold algorithm is used to determine the optimal solution of the weighting matrix, specifically including:

[0020] Using the weighted matrix as the decision variable and the objective function as the individual, the parent population is randomly generated in the feasible search space region. The non-dominated ordering and Pareto front of all individuals in the parent population are determined, and the crowding distance of each Pareto front is determined.

[0021] The first parameter, the second parameter, and the weight coefficients of slime molds are determined based on the crowding distance of the Pareto front, and the positions of slime molds in the parent population are updated to obtain a new population.

[0022] The new population and the parent population are merged to obtain the current population, and the non-dominated ordering and Pareto front of all individuals in the current population are determined.

[0023] Based on the non-dominated ranking of all individuals in the current population and the Pareto front extraction of N... pop Each individual is used as a new parent population, and the process returns to perform the steps of "randomly generating parent populations in feasible search space regions, determining the non-dominated ordering and Pareto fronts of all individuals in the parent populations, and determining the crowding distance of each Pareto front" until the maximum number of iterations is reached, at which point the optimal solution of the weighted matrix is ​​obtained.

[0024] Optionally, the velocity and acceleration on the top springs of each suspension are determined based on the vehicle's center of gravity state information using a Kalman filter algorithm, specifically including:

[0025] A seven-degree-of-freedom model of the whole vehicle is constructed, and the seven-degree-of-freedom model of the whole vehicle is discretized.

[0026] The Kalman filter algorithm is used to determine the velocity and acceleration on the top springs of each suspension based on the discretized seven-degree-of-freedom model of the vehicle and the acceleration signal at the vehicle's center of gravity.

[0027] Optionally, the implementation equation of the single-sensor hybrid control algorithm is:

[0028]

[0029] In the formula, For spring acceleration, Let be the spring velocity, α be the frequency boundary between low and high frequencies, and c be the spring velocity. min For minimum damping, c max For maximum damping, c is the desired damping force of the shock absorber determined by a single-sensor hybrid control algorithm based on the velocity and acceleration on the top springs of each suspension.

[0030] Optionally, the desired damping force of the vibration damper, determined by the optimal feedback gain matrix based on LQR control combined with the Kalman filter algorithm, is expressed as:

[0031] U L =-K·X L ;

[0032] In the formula, U L The desired damping force of the vibration damper is determined by the optimal feedback gain matrix based on LQR control and the Kalman filter algorithm, where K is the optimal feedback gain matrix and X is the damping force. L It is a state variable.

[0033] A stability redundancy system for controlling a vehicle equipped with semi-active suspension includes: an IMU sensor and multiple displacement sensors;

[0034] The IMU sensor and the plurality of displacement sensors are all mounted on the vehicle; the plurality of displacement sensors are respectively mounted on the wheels of the vehicle and are used to measure the displacement change information of the vehicle's shock absorbers; the IMU sensor is set at the center of gravity of the vehicle and is used to measure the state information of the vehicle's center of gravity; the state information of the vehicle's center of gravity includes: vertical acceleration of the center of gravity, longitudinal acceleration of the center of gravity, lateral acceleration of the center of gravity, roll rate of the center of gravity, and pitch rate of the center of gravity;

[0035] The IMU sensor and the plurality of displacement sensors are all connected to the controller; the controller has a computer program embedded therein; the controller is used to retrieve and execute the computer program based on the displacement change information of the shock absorber and the vehicle center of gravity state information, so as to implement the stability redundancy method provided above for controlling a vehicle equipped with a semi-active suspension.

[0036] Optionally, the system further includes: a linear quadratic regulator;

[0037] The linear quadratic regulator is connected to the controller and the vibration damper respectively; the linear quadratic regulator is used to optimize the control objective using a multi-objective slime mold algorithm to obtain the optimal feedback gain matrix of LQR control, and to determine the desired damping force of the vibration damper based on the optimal feedback gain matrix of LQR control combined with the Kalman filter algorithm.

[0038] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0039] In the absence of sensor failure, this invention directly optimizes multiple objectives such as sprung mass acceleration, tire dynamic load, and suspension dynamic deflection using a multi-objective slime mold algorithm to solve for the optimal feedback gain matrix of LQR control. Combined with a Kalman filter algorithm, the required damping force of the shock absorber is calculated, achieving vehicle damping adjustment. This solves the problem of multi-objective optimization in the control process, avoids empirically determined optimal control values, and allows for selection of appropriate parameters based on project requirements. When only the displacement sensor fails, the desired damping force of the shock absorber is determined based on a Kalman filter algorithm and a single-sensor hybrid control algorithm, achieving vehicle damping adjustment. When both the displacement sensor and IMU sensor fail, the 0A state of the shock absorber solenoid valve current is used as an emergency state to achieve vehicle control, enabling the semi-active suspension to function even with partial sensor failure. Therefore, this invention can achieve redundant control in sensor failure scenarios without increasing costs, solving the problem of multi-objective optimization in the control process, avoiding subjective selection of relevant parameters, and improving control efficiency. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram illustrating the implementation process of the stability redundancy method for controlling vehicles equipped with semi-active suspension provided by the present invention.

[0042] Figure 2This is a representation of the optimal solution form of the Pareto front provided in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of a seven-degree-of-freedom vehicle model provided in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the structure of a stability redundancy system for controlling a vehicle equipped with semi-active suspension, provided by the present invention.

[0045] Explanation of reference numerals in the attached figures:

[0046] 1-Displacement sensor, 2-IMU sensor. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] The purpose of this invention is to provide a stability redundancy method and system for controlling vehicles equipped with semi-active suspension. This method enables redundant control of sensor failure scenarios without increasing costs, solves the problem of multi-objective optimization during control, avoids subjective selection of relevant parameters, and improves control efficiency.

[0049] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] The following terms, "semi-active suspension," refer to adjustable damping in vehicle shock absorbers. This invention uses an adjustable shock absorber equipped with a solenoid valve (CDC, Continuous Damping Control) as an example for method discussion. Based on this, the present invention provides a method for controlling the stability redundancy of vehicles equipped with semi-active suspension, comprising:

[0051] Step 100: Acquire vehicle status information. Vehicle status information includes: shock absorber displacement change information and vehicle center of gravity status information. The shock absorber displacement change information is acquired using a displacement sensor. The vehicle center of gravity status information is acquired using an IMU sensor. The vehicle center of gravity status information includes: vertical acceleration of the center of gravity, longitudinal acceleration of the center of gravity, lateral acceleration of the center of gravity, roll rate of the center of gravity, and pitch rate of the center of gravity.

[0052] Step 101: Determine whether the displacement sensor is faulty based on the relationship between the displacement change information of the shock absorber and the first set threshold, and obtain the first judgment result.

[0053] In practical applications, the fault diagnosis process for displacement sensors is as follows:

[0054] 1) Outlier Detection: Controllers typically expect displacement sensors to provide measurements within a reasonable range. If the displacement sensor output value exceeds the predetermined range, the controller may determine that an anomaly has occurred. This is achieved by setting a threshold or calibrating the sensor.

[0055] 2) Fault Detection Codes: Some displacement sensors and controllers have built-in fault detection codes or diagnostic functions. Displacement sensors may report their status via error codes or status bits. Controllers can periodically query these statuses to identify whether the displacement sensor is in a faulty state.

[0056] 3) Periodic self-test: The controller can periodically send self-test commands to the displacement sensor, requiring the sensor to perform an internal self-test. If the displacement sensor detects a problem during the self-test, it can notify the controller through feedback information.

[0057] 4) Continuous monitoring: The controller can detect anomalies by continuously monitoring the changing trend of the displacement sensor output. For example, if the rate of change of the displacement sensor output exceeds a set threshold, the controller may determine that the sensor has a problem.

[0058] 5) Communication Fault Detection: If a communication channel exists between the displacement sensor and the controller, the controller can monitor the reliability of the communication. If the displacement sensor fails to send data normally or communication with the controller is interrupted, this may indicate a fault in the displacement sensor.

[0059] Based on the hardware's inherent characteristics and functionalities, fault detection is added to the strategy, and strategy switching is performed accordingly. For example, if the value received by the controller exceeds the set threshold range, a default fault is detected, and the system switches to a single-sensor hybrid control algorithm.

[0060] Step 102: When the first judgment result is negative, the Multi-objective SlimeMould Algorithm (MOSMA) is used to optimize the control objectives to obtain the optimal feedback gain matrix for LQR control. The control objectives include the acceleration of the sprung mass, tire dynamic load, and suspension dynamic deflection.

[0061] In practical applications, to facilitate understanding of the combination of LQR and MOSMA algorithms, we will first introduce how LQR is applied in suspension control. This will provide a foundation for the subsequent implementation of the MOSMA algorithm.

[0062] The state space of the 1 / 4 suspension model is described as follows:

[0063]

[0064] In the formula, A L Let B be the LQR state variable matrix. L U is the control force input matrix. L For control force, E L For the excitation input matrix, W L For road surface input, C L For the output matrix, D L To directly pass the matrix, X L Y is a state variable. L For output variables, These are the predicted values ​​for the state variables.

[0065] LQR (Linear Quadratic Regulator) is a mechanism that uses an objective function as its object, achieving effective control of that function with relatively small inputs. Taking a semi-active vehicle suspension as an example, it allows for the optimal design of vertical acceleration, suspension deflection, and tire dynamic load based on design intent. Furthermore, it is necessary to limit the active control force to reduce energy consumption. Therefore, the objective function is established as follows:

[0066]

[0067] In the formula, q1, q2, and q3 are the weighting coefficients for the vertical acceleration of the vehicle body (sponged), the dynamic deflection of the suspension, and the dynamic travel of the tires, respectively. r is the weighting coefficient for the control force, which is a constant. u is the damping force. z b z represents the vertical displacement of the car body (sprout). w For the vertical displacement of the spring, z r The vertical excitation is for the road surface. Ts is the specified time period, and dt is the time increment.

[0068] Writing the above weighting coefficients in matrix form q = diag(q1, q2, q3), we get the optimization problem as follows: Substitute Y L =C L X L +D L U L The optimization problem then becomes state controller regulation, i.e.:

[0069]

[0070] In the formula, Q L =C L T qC L Let N be the weighted matrix of the state variables. L =C LT qD L R is a weighted matrix of correlations. L =r+D L T qD L This is a weighted matrix for the control variables.

[0071] Once the vehicle parameters and weighting coefficients are determined, the optimal control feedback gain matrix K can be obtained from the Riccati equation. The Riccati equation is:

[0072] P L A L +A L T P L -(P L B L +N L )R L -1 (B L T P L +N L T )+Q L =0.

[0073] P L The solution to the Riccati equation is P, which can be obtained from the Riccati equation. Then, the optimal feedback gain matrix K can be obtained as follows:

[0074] K = R L -1 B L T P L .

[0075] Therefore, the optimal control force is: U L =-K·X L .

[0076] Based on this, the process in step 102 of optimizing the control objective using a multi-objective slime mold algorithm to obtain the optimal feedback gain matrix for LQR control can be described as follows:

[0077] Step 1: Determine the control objectives. The optimization quantities for the multi-objective slime mold algorithm are set as q1, q2, and q3 in the weighted matrix q. Considering the relativity of the improvement and the differences in magnitude, the root mean square values ​​of the state quantities related to the passive suspension are used as the optimization objectives (F1, F2, and F3) of the multi-objective slime mold algorithm. Furthermore, considering balance, the sum of the ratios of the three (F4) is also used as the objective function for optimization, and its expression is as follows:

[0078]

[0079] In the formula, RMS represents the root mean square value. (Z b -Z w ) p and (Z) w -Z r ) p These are the acceleration, dynamic stroke, and tire deformation of the passive shock absorber, respectively.

[0080] Step 2: MOSMA optimization.

[0081] The MOSMA algorithm, based on SMA, employs an elitist non-dominated sorting mechanism and a crowding distance mechanism that preserves diversity, making it an effective strategy for multi-objective optimization. This algorithm utilizes an elitist non-dominated sorting mechanism and a crowding distance mechanism that preserves diversity.

[0082] The application process of the semi-active suspension control algorithm is as follows:

[0083] Step 1: Define control parameters: Set the decision variable q = [q1 q2 q3] and its upper and lower limits, and set the initial population size to N. pop (For example, N) pop =30), set the optimization objective function (F1, F2, F3 and F4 mentioned above), set the maximum number of iterations (e.g. 10), and set different parameters according to different vehicles.

[0084] Step 2: Randomly generate a parent population P0 in the feasible search space region S, and evaluate each objective function in the objective space of P0.

[0085] Step 3: Apply the non-dominated ranking (NDR) and crowding distance (CD) to P0 to find the non-dominated ranking and Pareto front for all individuals, and calculate the crowding distance of the Pareto front for each individual.

[0086] Step 4: Calculate the parameters based on the results obtained in Step 3. (i.e., the first parameter), parameter (i.e., the second parameter) and the weighting coefficient of slime mold Update the slime mold population At the location, create a new population P j .in, For a parameter whose value ranges from [-a, a], The parameters are set to [-b, b], and a and b are set according to the actual situation.

[0087] Step 5: Place P j Merge with P0 to obtain population Pi (P i =P0∪P j The algorithm evaluates each objective function F1, F2, F3, and F4 of the target space vector and applies elite-based NDR and CD selection methods. pop Each individual replaces P0.

[0088] Step 6: If the loop termination criterion is met (e.g., the maximum number of iterations is reached), then output the result; otherwise, go to step 2.

[0089] Based on the above description, assuming a Class C road surface is used as an example for simulation analysis, with a population size of 30 and 10 iterations, the optimized Pareto front optimal solutions for q1, q2, and q3 are obtained as follows: Figure 2 As shown.

[0090] Step 3: Optimize the weight matrix q.

[0091] Depending on the focus of the vehicle development, an appropriate solution is selected from the Pareto front optimal solutions, and the value of the optimal feedback gain matrix K is obtained. For example, if the overall vehicle balance is taken as the control objective, then a smaller F4 value is chosen. If the focus is on comfort, then a smaller F1 value is sufficient. Based on this, the present invention provides more alternative solutions and avoids frequent attempts at subjective values.

[0092] Step 103: Determine the desired damping force of the shock absorber based on the optimal feedback gain matrix of LQR control combined with the Kalman filter algorithm, and adjust the vehicle's damping based on the desired damping force. Calculate and output the damping force U based on the solved optimized weight matrix. L =-K·X L (The principle is explained in the LQR section above).

[0093] Step 104: When the first judgment result is yes, determine whether the IMU sensor is faulty based on the relationship between the vehicle center of gravity state information and the second set threshold, and obtain the second judgment result.

[0094] In practical applications, the fault detection process for IMU sensors can be as follows:

[0095] 1) Outlier Detection: The controller expects the IMU sensor to provide acceleration and angular velocity within a reasonable range. If the value output by the IMU sensor exceeds the predetermined range, an anomaly is identified, which is achieved by setting a threshold or calibrating the IMU sensor.

[0096] 2) Self-test function: IMU sensors typically have a built-in self-test function, which can be triggered by the controller. During the self-test, the IMU sensor performs a series of internal tests to check whether its components and functions are normal. If the self-test fails, the IMU sensor issues a fault signal.

[0097] 3) Fault detection codes: IMU sensors typically report fault codes or status bits indicating their status. The controller can periodically query these statuses to detect whether the IMU sensor is in a faulty state.

[0098] 4) Continuous monitoring: The controller detects anomalies by continuously monitoring the changing trends of the IMU sensor output. For example, if the rate of change of acceleration or angular velocity output by the IMU sensor exceeds a set threshold, the controller may determine that the sensor has a problem.

[0099] Step 105: When the second judgment result is negative, the speed and acceleration on the top spring of each suspension are determined based on the vehicle's center of gravity state information using the Kalman filter algorithm.

[0100] In practical applications, in order to obtain the suspension speed signals required for subsequent strategies, this invention needs to first construct a seven-degree-of-freedom model of the entire vehicle (e.g., Figure 3 As shown), the seven-degree-of-freedom model of the entire vehicle is discretized. Then, based on the Kalman filter algorithm, the velocity and acceleration on the top springs of each suspension are estimated according to the acceleration signal at the center of gravity of the entire vehicle. Therefore, only one acceleration sensor (i.e., IMU sensor) is needed to acquire the relevant signals. Among them, we have:

[0101]

[0102] The data collected by the accelerometer are shown in Table 1.

[0103] Table 1 Data Table

[0104]

[0105]

[0106] Based on this, the implementation process of the Kalman filter algorithm is as follows:

[0107] 1) State prediction:

[0108]

[0109] In the formula, A is the state transition matrix, which represents how the current state is inferred from the previous time step. B is the control matrix, which represents how the control variable U acts on the current state. This represents the estimate of X at time t-1, rather than the actual value of X. This represents the X value inferred from time t. This inferred X value will be further corrected based on observed state values, and only after correction is it considered the final optimal estimate. With the state prediction formula, the state at the current time can be clearly predicted; however, all predictions involve an update process that includes noise.

[0110] 2) Noise covariance matrix prediction:

[0111] In a car suspension, the uncertainty of the state at each moment is represented by white noise v. k Let the covariance matrix P be used to represent v, then v k It satisfies a normal distribution p(v)~N(0,P).

[0112] According to the properties of the covariance matrix, the covariance matrix at the next time step is obtained by multiplying both sides of the covariance matrix P at this time step by the state transition matrix A (i.e., ...). The suspension system model established is not completely accurate and contains some external noise interference. Therefore, the noise covariance prediction matrix still needs to be adjusted in the equation. To the right of the matrix, we add a matrix Q to represent the noise caused by the uncertainty of the prediction model itself. The process noise is defined as w. k Then Q is the covariance matrix of the process noise, w k The condition p(w) ~ N(0,Q) is satisfied.

[0113] Based on the above description, we have: P t - =AP t-1 A T +Q represents the relationship between uncertainty and time.

[0114] 3) In vehicle observation, observation noise also exists, let it be V. The measurement matrix of a seven-DOF vehicle is: Y = CX + V, and the covariance matrix of the observation noise is R. Each item in the measurement vector of the vehicle system is only an incomplete representation of the true state. The fusion function of the Kalman filter can precisely deduce a state quantity that is relatively close to the true value from the inaccurate description. Integrating the measurement data into the previous estimate of state X, we have already obtained the state inferred from time t. Simply add one more term to the right side of the previous prediction to correct it. The best estimate can be obtained by taking the value of , and the expression is:

[0115]

[0116] In the formula, K t K represents the Kalman coefficient. t =P t - C T(CP t - C T +R) -1 Update the noise covariance matrix P t =(IK t C)P t - .

[0117] Step 106: Use a single-sensor hybrid control algorithm [i.e., a single-sensor hybrid algorithm (Mix-1-Sensor) control strategy] to determine the desired damping force of the shock absorber based on the speed and acceleration on the top springs of each suspension, and adjust the vehicle's damping based on the desired damping force.

[0118] Based on the description of step 105 above, the principle of realizing the desired damping force of the vibration damper obtained by the single-sensor hybrid control algorithm is expressed by the following formula.

[0119]

[0120] In the formula, For spring acceleration, The speed is the speed of the spring. The value can be considered a simple "band selector". α is the frequency boundary between low and high frequencies. The specific division between high and low frequencies can be set according to actual needs, that is, the part greater than the set value is regarded as high frequency, and the part not greater than the set value is regarded as low frequency. min For minimum damping, c max For maximum damping, c is the desired damping force of the shock absorber determined by a single-sensor hybrid control algorithm based on the velocity and acceleration on the top springs of each suspension.

[0121] Step 107: When the second judgment result is yes, the 0A state of the shock absorber solenoid valve current is used as an emergency state to realize vehicle control. At this time, the semi-active suspension damping adjustment function is no longer available.

[0122] Based on the above description, the implementation process of the stability redundancy method for controlling vehicles equipped with semi-active suspension provided by the present invention is as follows: Figure 1 As shown, its implementation process is mainly divided into four parts: vehicle status acquisition, strategy execution when there is no sensor failure, strategy execution when the displacement sensor fails, and strategy execution when both the displacement sensor and the IMU sensor fail.

[0123] Furthermore, the present invention also provides a stability redundancy system for controlling vehicles equipped with semi-active suspension, such as... Figure 4 As shown, the system includes: IMU sensor 2 and multiple displacement sensors 1.

[0124] IMU sensor 2 and multiple displacement sensors 1 are both mounted on the vehicle. The multiple displacement sensors 1 are mounted on the vehicle's wheels to measure the displacement changes of the vehicle's shock absorbers. IMU sensor 2 is positioned at the vehicle's center of gravity to measure the vehicle's center of gravity state information. This information includes: vertical acceleration, longitudinal acceleration, lateral acceleration, roll rate, and pitch rate.

[0125] IMU sensor 2 and multiple displacement sensors 1 are all connected to a controller (not shown in the figure, which can be directly adopted as an on-board computer). The controller contains a computer program. The controller is used to retrieve and execute the computer program based on the displacement change information of the shock absorbers and the vehicle's center of gravity state information to implement the stability redundancy method described above for controlling vehicles equipped with semi-active suspension.

[0126] In practical applications, in order to achieve LQR control, a linear quadratic regulator can be set in the system to optimize the control objective using a multi-objective slime mold algorithm to obtain the optimal feedback gain matrix for LQR control. Based on the optimal feedback gain matrix of LQR control, the desired damping force of the vibration damper can be determined by combining the Kalman filter algorithm.

[0127] Based on the above description, this invention achieves redundant control in the event of sensor failure without increasing costs. Furthermore, it solves the problem of multi-objective optimization during control, avoids subjective selection of relevant parameters, and provides multiple alternative solutions, allowing parameter selection based on requirements. Compared to existing technologies, this invention improves control efficiency, avoids empirically determined optimal control values, provides multiple parameter schemes, and allows selection of appropriate parameters according to project needs. Even with partial sensor failure, it can still function as a semi-active suspension, providing redundancy at the software level.

[0128] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0129] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of these embodiments are merely for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for controlling stability redundancy in vehicles equipped with semi-active suspension, characterized in that, include: Obtain vehicle status information; The vehicle status information includes: shock absorber displacement change information and vehicle center of gravity status information; the shock absorber displacement change information is acquired using a displacement sensor; the vehicle center of gravity status information is acquired using an IMU sensor; the vehicle center of gravity status information includes: vertical acceleration of the center of gravity, longitudinal acceleration of the center of gravity, lateral acceleration of the center of gravity, roll rate of the center of gravity, and pitch rate of the center of gravity. Based on the relationship between the displacement change information of the vibration damper and the first set threshold, it is determined whether the displacement sensor is faulty, and a first judgment result is obtained; When the first judgment result is negative, the multi-objective slime mold algorithm is used to optimize the control objective to obtain the optimal feedback gain matrix of LQR control; the control objective includes the acceleration of the sprung mass, the tire dynamic load, and the suspension dynamic deflection; The optimal feedback gain matrix based on LQR control combined with the Kalman filter algorithm is used to determine the desired damping force of the shock absorber, and the vehicle damping is adjusted based on the desired damping force. When the first judgment result is yes, the IMU sensor is judged to be faulty based on the relationship between the vehicle center of gravity state information and the second set threshold, and the second judgment result is obtained. When the second judgment result is negative, the velocity and acceleration on the top spring of each suspension are determined based on the vehicle's center of gravity state information using the Kalman filter algorithm. A single-sensor hybrid control algorithm is used to determine the desired damping force of the shock absorber based on the velocity and acceleration on the top springs of each suspension element, and the vehicle's damping is adjusted based on this desired damping force; wherein, the implementation equation of the single-sensor hybrid control algorithm is: ； In the formula, For spring acceleration, The speed is the speed of the spring. This is the frequency boundary between low and high frequencies. c min For minimum damping, c max For maximum damping, c The desired damping force of the shock absorber is determined based on the velocity and acceleration on the top springs of each suspension using a single-sensor hybrid control algorithm. When the second judgment result is yes, the 0A state of the shock absorber solenoid valve current is used as the emergency state to realize vehicle control.

2. The method for controlling stability redundancy in a vehicle equipped with semi-active suspension according to claim 1, characterized in that, The optimal feedback gain matrix for LQR control is obtained by optimizing the control objective using a multi-objective slime mold algorithm, specifically including: Construct the objective function for LQR control, and determine the control objective based on the objective function; Determine the weighting matrix based on the control objective; The optimal solution of the weighting matrix is ​​determined using a multi-target slime mold algorithm; The optimal feedback gain matrix is ​​determined based on the optimal solution of the weighting matrix.

3. The method for controlling stability redundancy in a vehicle equipped with semi-active suspension according to claim 2, characterized in that, The optimal solution of the weighting matrix is ​​determined using a multi-target slime mold algorithm, specifically including: Using the weighted matrix as the decision variable and the objective function as the individual, the parent population is randomly generated in the feasible search space region. The non-dominated ordering and Pareto front of all individuals in the parent population are determined, and the crowding distance of each Pareto front is determined. The first parameter, the second parameter, and the weight coefficients of slime molds are determined based on the crowding distance of the Pareto front, and the positions of slime molds in the parent population are updated to obtain a new population. The new population and the parent population are merged to obtain the current population, and the non-dominated ordering and Pareto front of all individuals in the current population are determined. Based on the non-dominated ranking of all individuals in the current population and the Pareto front extraction of N... pop Each individual is used as a new parent population, and the process of "randomly generating parent populations in feasible search space regions, determining the non-dominated ordering and Pareto fronts of all individuals in the parent populations, and determining the crowding distance of each Pareto front" is repeated until the maximum number of iterations is reached, at which point the optimal solution of the weighted matrix is ​​obtained.

4. The method for controlling stability redundancy in a vehicle equipped with semi-active suspension according to claim 1, characterized in that, Based on the Kalman filter algorithm, the velocity and acceleration on the top springs of each suspension are determined according to the vehicle's center of gravity state information, specifically including: A seven-degree-of-freedom model of the whole vehicle is constructed, and the seven-degree-of-freedom model of the whole vehicle is discretized. The Kalman filter algorithm is used to determine the velocity and acceleration on the top springs of each suspension based on the discretized seven-degree-of-freedom model of the vehicle and the acceleration signal at the vehicle's center of gravity.

5. The method for controlling stability redundancy in a vehicle equipped with semi-active suspension according to claim 1, characterized in that, The desired damping force of the vibration damper, determined by the optimal feedback gain matrix of LQR control combined with the Kalman filter algorithm, is expressed as: ； In the formula, U L The desired damping force of the vibration damper is determined by the optimal feedback gain matrix based on LQR control combined with the Kalman filter algorithm. K For the optimal feedback gain matrix, X L It is a state variable.

6. A stability redundancy system for controlling a vehicle equipped with semi-active suspension, characterized in that, include: IMU sensor and multiple displacement sensors; The IMU sensor and the plurality of displacement sensors are all mounted on the vehicle; the plurality of displacement sensors are respectively mounted on the wheels of the vehicle and are used to measure the displacement change information of the vehicle's shock absorbers. The IMU sensor is located at the center of gravity of the vehicle and is used to measure the state information of the vehicle's center of gravity. The vehicle's center of gravity state information includes: vertical acceleration of the center of gravity, longitudinal acceleration of the center of gravity, lateral acceleration of the center of gravity, roll rate of the center of gravity, and pitch rate of the center of gravity; The IMU sensor and the plurality of displacement sensors are all connected to the controller; the controller has a computer program embedded therein; the controller is used to retrieve and execute the computer program based on the displacement change information of the shock absorber and the vehicle center of gravity state information, so as to implement the stability redundancy method for controlling a vehicle equipped with a semi-active suspension as described in any one of claims 1-5.

7. The stability redundancy system for controlling a vehicle equipped with semi-active suspension according to claim 6, characterized in that, The system also includes: a linear quadratic regulator; The linear quadratic regulator is connected to the controller and the vibration damper respectively; the linear quadratic regulator is used to optimize the control objective using a multi-objective slime mold algorithm to obtain the optimal feedback gain matrix of LQR control, and to determine the desired damping force of the vibration damper based on the optimal feedback gain matrix of LQR control combined with the Kalman filter algorithm.

Images