Control method of vehicle suspension system, computer program product, electronic device, and storage medium

Abstract

The invention relates to the field of vehicle control, in particular to a control method of a vehicle suspension system, a computer program product, electronic equipment and a storage medium. The control method of the vehicle suspension system comprises the steps that road surface disturbance information of the suspension system is obtained and serves as an error for control; according to the error and a fuzzy control method, obtaining adjustment information of a control coefficient in a set control algorithm; optimizing the performance of the suspension system to obtain a reference value of the control coefficient; obtaining a final value of the control coefficient according to the reference value and the adjustment information; and substituting the final value into the set control algorithm to obtain a damping control instruction, and controlling the damping force of the suspension system based on the damping control instruction. According to the invention, the comfort, the controllability and the stability of the automobile in the driving process can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control, and in particular, to a control method for a vehicle suspension system, a computer program product, an electronic device, and a storage medium. Background Art

[0002] The core of a semi-active suspension system lies in dynamically adjusting the damping coefficient of the suspension according to driving conditions, thereby optimizing the handling and comfort of the vehicle. Compared with a passive suspension, a semi-active suspension does not need to change the suspension stiffness and can adapt to different road conditions and driving demands only by adjusting the damping force.

[0003] Proportional-Integral-Derivative (PID) control is a commonly used control method in suspension systems. It adjusts the error of the system through three links: proportional (P), integral (I), and derivative (D). Specifically, the proportional link adjusts the control quantity proportionally according to the magnitude of the current error and can quickly respond to the change of the error; the integral link is used to eliminate the steady-state error of the system. It performs an integral operation on the error and gradually reduces the error as time accumulates; the derivative link predicts the change trend of the error according to the change rate of the error and adjusts the control quantity in advance to improve the response speed and stability of the system. In a suspension system, a PID controller uses body acceleration, suspension displacement, etc. as feedback signals and adjusts the damping force of the suspension to keep the vehicle in good driving performance under different road conditions.

[0004] However, the parameters of the PID algorithm are fixed values preset according to the linear model of the system. When the system is affected by external disturbances or its own characteristics change, these fixed parameters cannot adapt to the new situation in time. For example, when the vehicle suddenly encounters a road bump or depression during driving, the force and motion state of the suspension system will change rapidly. Since the parameters of the PID controller cannot be adjusted in real time, it is difficult to respond to this change quickly and accurately, resulting in large vibrations and bumps of the vehicle body, affecting ride comfort and handling stability.

[0005] In view of this, the present invention is proposed. Summary of the Invention

[0006] The purpose of the present invention is to provide a control method for a vehicle suspension system, a computer program product, an electronic device, and a storage medium to improve the comfort, handling, and stability of the vehicle during driving.

[0007] To achieve the above purpose, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a control method for a vehicle suspension system, including: Obtain the road disturbance information of the suspension system as the error for control; According to the error and fuzzy control method, adjustment information of the control coefficient in the set control algorithm is obtained; By optimizing the performance of the suspension system, a reference value of the control coefficient is obtained; According to the reference value and the adjustment information, a final value of the control coefficient is obtained; The final value is substituted into the set control algorithm to obtain a damping control command, and the damping force of the suspension system is controlled based on the damping control command.

[0008] In a second aspect, the present invention provides a computer program product which, when running on a computer, causes the computer to execute the control method of the vehicle suspension system described in the first aspect or the second aspect above.

[0009] In a third aspect, the present invention provides an electronic device, including: At least one processor, and a memory communicatively connected to at least one of the processors; Wherein, the memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the control method of the vehicle suspension system described above.

[0010] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, and the computer instructions are used to cause a computer to execute the control method of the vehicle suspension system described above.

[0011] Compared with the prior art, the beneficial effects of the present invention are: In the embodiment of the present invention, by obtaining the road surface disturbance information of the suspension system as the error for control; according to the error and the fuzzy control method, adjustment information of the control coefficient in the set control algorithm is obtained; at the same time, by optimizing the performance of the suspension system, a reference value of the control coefficient is obtained; and on the basis of the reference value, adjustment is made to obtain the final value of the control coefficient, so as to control the damping force of the suspension system, taking into account both the performance of the suspension system and minimizing the road surface disturbance information, and improving the comfort, handling and stability of the vehicle during driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0013] Figure 1It is a schematic flow chart of a control method for a data vehicle suspension system provided by the present invention; Figure 2 It is a schematic flow chart of another control method for a vehicle suspension system provided by an embodiment of the present invention; Figure 3 It is a schematic structural diagram of an electronic device provided by the present invention. Specific embodiments

[0014] The following describes exemplary embodiments of the present invention with reference to the accompanying drawings. Various details of the embodiments of the present invention are included to assist understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted below.

[0015] The present invention will be further described in detail below with reference to embodiments.

[0016] Figure 1 It is a schematic flow chart of a control method for a vehicle suspension system provided by this embodiment. This method can be executed by a computer program and integrated in an electronic device, which can be an Electronic Control Unit (ECU) or an in-vehicle communication terminal (T-box). This embodiment controls the damping force of the vehicle suspension system to improve the comfort, handling, and stability of the vehicle during driving.

[0017] As Figure 1 shown, this embodiment provides a control method for a vehicle suspension system, which specifically includes the following steps: S110. Obtain the road surface disturbance information of the suspension system as the error for control.

[0018] The suspension system in this embodiment is mainly a semi-active suspension system. The semi-active suspension system mainly improves the comfort and handling of the vehicle by adjusting the characteristics of the damper. Its working principle is to use sensors to continuously monitor the road surface disturbance information received by the vehicle, and adjust the damping force of the damper according to this information, thereby changing the stiffness and damping characteristics of the suspension system. When the vehicle is driving at high speed, increasing the damping force can improve the handling stability of the vehicle; when driving at low speed or passing through a bumpy road surface, reducing the damping force can enhance the riding comfort.

[0019] The road surface disturbance information in this embodiment is the disturbance information brought to the vehicle body by road surface potholes, protrusions, etc. For example, at least one of the relative displacement between the vehicle body and the suspension (i.e., the vertical movement of the vehicle body relative to the suspension system), the vertical vibration frequency of the vehicle body, and the vertical displacement of the wheel.

[0020] When the road surface is flat and the driving is stable, the road surface disturbance information should be close to 0 or even equal to 0. Therefore, in this embodiment, the road surface disturbance information is used as an error, and the damping force of the suspension system is controlled to minimize the error.

[0021] S120. According to the error and the fuzzy control method, obtain the adjustment information of the control coefficient in the set control algorithm.

[0022] This embodiment does not limit the type of the set control algorithm, which can be a neural network control algorithm, a prediction algorithm, a proportional-integral-derivative (PID) algorithm, an adaptive control algorithm, etc. The set control algorithm outputs a damping control instruction (including the adjustment degree and adjustment direction of the damping force), and the type of the damping control instruction output mainly depends on the control coefficient in the set control algorithm.

[0023] This embodiment does not limit the type of the fuzzy control method, which can be an adaptive fuzzy control method, a Smith-fuzzy control method, a fuzzy sliding mode control method, etc. The purpose of the fuzzy control method is to obtain the adjustment direction and adjustment magnitude of the control coefficient according to the error.

[0024] S130. By optimizing the performance of the suspension system, obtain the reference value of the control coefficient.

[0025] Optimizing the performance of the suspension system specifically means, based on the current road surface disturbance information, minimizing the current road surface disturbance information (i.e., the error) by improving the damping force of the suspension system, so as to achieve the purpose of improving the comfort, handling and stability of the vehicle during driving.

[0026] This embodiment can use any one of the space optimization algorithms to obtain the reference value of the control system, such as the genetic algorithm and the particle swarm algorithm.

[0027] The genetic algorithm is an optimization algorithm based on the principles of natural selection and genetic variation. In the vehicle semi-active suspension system, the genetic algorithm can be used to optimize the PID control coefficient. The genetic algorithm continuously evolves the population through genetic operations such as selection, crossover and mutation on the individuals in the initial population to find the optimal combination of PID control coefficients. In the initialization stage, a certain number of individuals are randomly generated, and each individual represents a group of PID control coefficients. Then, the fitness value of each individual is calculated according to the fitness function, and the fitness function is constructed based on the performance indexes of the suspension system, such as vehicle body acceleration and suspension dynamic deflection. Through the selection operation, the individuals with higher fitness values are retained, and the individuals with lower fitness values are eliminated. The crossover operation exchanges part of the genes of two individuals to generate new individuals to increase the diversity of the population. The mutation operation randomly changes the genes of the individuals to prevent the algorithm from falling into a local optimum. After multiple iterations, the genetic algorithm can find the combination of PID control coefficients that makes the suspension system performance optimal.

[0028] The particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence, which simulates the foraging behavior of bird flocks. In the semi-active suspension system of an automobile, the particle swarm optimization algorithm regards each particle as a potential solution to the PID control coefficients. The particles search for the optimal solution through mutual cooperation and information sharing in the search space. Each particle has its own position and velocity. The position represents the combination of PID control coefficients, and the velocity determines the moving direction and step size of the particle in the search space. The particle adjusts its velocity and position according to its own historical optimal position and the global optimal position of the group. In each iteration, the particle updates its velocity and position by comparing its current position with its historical optimal position and the global optimal position of the group. If the particle's current position is better than its historical optimal position, the historical optimal position is updated; if the global optimal position of the group is better, the particle moves closer to the global optimal position. The quality of the position is also obtained based on the performance indicators of the suspension system, such as vehicle body acceleration, suspension dynamic deflection, etc. Through continuous iteration, the particle swarm optimization algorithm can find the combination of PID control coefficients that optimizes the performance of the suspension system.

[0029] S140. Obtain the final value of the control coefficient according to the reference value and the adjustment information.

[0030] S150. Substitute the final value into the set control algorithm to obtain a damping control command, and control the damping force of the suspension system based on the damping control command.

[0031] The final value is obtained by superimposing the adjustment information on the reference value. For example, if the reference value is 10 and the adjustment information is to increase by 0.2 times, the final value is 1.2. Substitute 1.2 into the set control algorithm to obtain the damping control command output by the set control algorithm, such as the target value of the damping force, so as to control the damper of the suspension system to provide this target value.

[0032] The embodiment of the present invention can be applied to the control scenario of the actual vehicle suspension system or the vehicle simulation scenario. Specifically, input the damping control command into the damper of the actual or simulation environment. Through the transmission of the damping force and the interaction between the vehicle and the road surface, the suspension system will reach a new position. Then return to S110 to continue obtaining the road surface disturbance information at the next moment and control the damping force of the suspension system to achieve the purpose of real-time control.

[0033] In an embodiment of the present invention, road disturbance information of a suspension system is acquired as an error for control; according to the error and a fuzzy control method, adjustment information of a control coefficient in a set control algorithm is obtained; meanwhile, by optimizing the performance of the suspension system, a reference value of the control coefficient is obtained; and on the basis of the reference value, an adjustment is made to obtain a final value of the control coefficient, thereby controlling the damping force of the suspension system, taking into account both the performance of the suspension system and minimizing road disturbance information, and improving the comfort, handling and stability of the vehicle during driving.

[0034] Figure 2 FIG. 4 is a schematic flowchart of another control method for a vehicle suspension system provided by an embodiment of the present invention. The set control algorithm is defined as a PID algorithm. The gold panning algorithm is used to obtain the reference value of the control coefficient by optimizing the performance of the suspension system, and the reference value is input into a fuzzy PID controller. In the fuzzy PID controller, adjustment information of proportional, integral and differential is calculated, and after being fused with the reference value, a final proportional coefficient, integral coefficient and differential coefficient are formed to construct a complete PID control equation, and a damping control command is calculated.

[0035] Figure 2 The control method for the vehicle suspension system shown in FIG. 4 includes the following operations: S210. Acquire road disturbance information of the suspension system as an error for control.

[0036] S220. Perform fuzzy processing on the error and the error change rate to obtain a fuzzy set.

[0037] The error change rate is the relative displacement, the vertical vibration frequency of the vehicle body, and the change rate of the vertical displacement of the wheel relative to time. Fuzzy processing is respectively performed on the error and the error change rate.

[0038] Taking the error e as an example, assuming that the value range of e is , this range is divided into multiple fuzzy intervals, such as , which respectively correspond to the fuzzy sets "negative large (NB)", "zero (ZO)", "positive small (PS)", and "positive large (PB)". Similar processing is also performed on the error change rate Δe. Assuming its value range is , fuzzy intervals are divided and corresponding fuzzy sets are assigned. Among them, is the maximum value of the absolute value of the error, is the minimum value of the absolute value of the error, and is the maximum value of the absolute value of the error change rate.

[0039] When the actual error e and error change rate Δe are input, the membership degrees in each fuzzy set are respectively calculated through a membership function. For example, if the current value of the error e is e0 and it is located in Within the interval, the membership degree in the "Positive Small (PS)" fuzzy set is calculated through the membership function as , and the membership degree in the "Zero (ZO)" fuzzy set is , and . In this way, the precise error and error change rate are transformed into fuzzy sets, providing a basis for subsequent fuzzy inference.

[0040] S230. Perform fuzzy inference on the fuzzy sets according to the pre-established fuzzy rule base to obtain the fuzzy control output.

[0041] Fuzzy inference is the core part of the fuzzy PID controller, which processes the fuzzified input signal based on the pre-established fuzzy rule base. The fuzzy rule base consists of a series of "IF-THEN" rules, which are formulated according to expert experience and the understanding of the control objectives of the suspension system. For example, a typical rule is: "IF the error e is Positive Big (PB) and the error change rate Δe is Positive Small (PS), THEN the proportional coefficient K p increases, the integral coefficient K i decreases, and the differential coefficient K d increases". When the fuzzified error e and error change rate Δe are input into the fuzzy inference system, the system will match the rules in the fuzzy rule base. Suppose the current fuzzified error e belongs to the "Positive Big (PB)" and "Positive Small (PS)" fuzzy sets, with membership degrees and respectively. The error change rate Δe belongs to the "Positive Small (PS)" and "Zero (ZO)" fuzzy sets, with membership degrees and respectively. For the above rule, calculate the adjustment information of the proportional coefficient K p , the integral coefficient K i , and the differential coefficient K d . By synthesizing the calculation results of all fuzzy rules, the final fuzzy control output is obtained, that is, the adjustment direction and adjustment degree of K p , K i , and K d . For example, when the error is large and the error change rate is positive, increase the proportional coefficient K p to quickly reduce the error; when the error is small and the error change rate is negative, reduce the integral coefficient K i to avoid overshoot.

[0042] S240. Defuzzify the fuzzy control output to obtain the adjustment information of the control coefficient; the adjustment information includes the adjustment direction and adjustment degree.

[0043] The result after fuzzy inference is a fuzzy value, which needs to be defuzzified to convert it into the adjustment amount of the control coefficient of the actual fuzzy PID controller.

[0044] There are methods such as the centroid average defuzzification method and the maximum membership degree defuzzification method. Taking the centroid average defuzzification method as an example, assume that the fuzzy set of the adjustment amount of the proportional coefficient K p obtained through fuzzy inference is , and the corresponding fuzzy value is , then the actual adjustment amount after defuzzification is: ; In this way, the fuzzy control output is converted into an accurate numerical value for adjusting the proportional coefficient K p of the fuzzy PID controller. The same method can be used to calculate the adjustment amounts of the integral coefficient K i and the derivative coefficient K d .

[0045] S250. Randomly generate an initial group of gold miners, and each gold miner in the initial group of gold miners represents a set of control coefficients.

[0046] In the initialization stage of the gold mining algorithm, an initial group of gold miners needs to be randomly generated. Assume that the size of the group of gold miners is N, and the position of each gold miner is represented by a vector in the search space, where . Taking the optimization of the PID control coefficients of the automotive semi-active suspension system as an example, the search space is a three-dimensional space composed of the value ranges of K p , K i , and K d . The position of each gold miner represents a set of possible PID control coefficient combinations. These initial positions are randomly generated within the parameter value ranges. For example, the value range of the proportional coefficient K p is , the value range of the integral coefficient K i is , and the value range of the derivative coefficient K d is . Then, random numbers are generated within their respective ranges through a random number generator to determine the initial positions of each gold miner. In this way, the initial group of gold miners is distributed in the search space, providing a basis for subsequent optimization searches.

[0047] S260. Construct an objective function according to the performance index of the suspension system.

[0048] The objective function is a key tool for measuring the quality of each gold miner's position. In the optimization of the automotive semi-active suspension system, the objective function to evaluate the position of each gold miner, where X represents the position of the gold miner, i.e., the PID control coefficient combination .

[0049] Optionally, an objective function is constructed based on vehicle body acceleration, suspension dynamic deflection, and tire dynamic displacement. Taking vehicle body acceleration as an example, it directly affects ride comfort. A smaller vehicle body acceleration means a smoother driving experience. Suspension dynamic deflection reflects the working range of the suspension system. Excessive suspension dynamic deflection may cause the suspension to collide with the limit block, affecting driving safety and comfort. Tire dynamic displacement is closely related to the ground contact of the tire and driving stability. Appropriate tire dynamic displacement can ensure good contact between the tire and the ground and provide sufficient grip. The objective function can be expressed as a weighted sum of these performance indicators, for example: ; wherein, is the vehicle body acceleration, d is the suspension dynamic deflection, s is the tire dynamic displacement, is the corresponding weight coefficient, which is set according to the degree of emphasis on different performance indicators. By calculating the objective function value corresponding to each gold miner's position, the lower the objective function value, the better the performance of the suspension system represented by the PID control coefficient combination represented by the gold miner.

[0050] S270. Optimize the objective function by optimizing the position of each gold miner.

[0051] Optionally, the method for optimizing the position includes at least one of migration, panning, and cooperation. Each optimization method is introduced below.

[0052] Migration is an important operation in the panning algorithm where the gold miner adjusts its own position according to the optimal solution position. During migration, each gold miner moves by referring to the position of the currently searched optimal solution. Assume that the currently searched best gold mine position (i.e., the position with the minimum objective function value among the current multiple gold miners) is , and the current position of the i-th gold miner is . The migration of the gold miner is achieved through the following formula: ; ; wherein, D 1 is the position of the i-th gold miner X i the distance between X * and the best gold mine position C multiplied by the random number is the new location where the gold miners migrate to, and are vector coefficients determined by a random process and can be expressed as follows: ; ; Here, is a balance factor used to adjust the step size and direction of migration, and are random numbers within the interval [0, 1].

[0053] Through the aforementioned formula, the gold miners can move in the search space according to the position of the optimal solution and random factors to explore better positions. If the proportionality coefficient K p of the current optimal solution has a large value and the objective function value of the new location is smaller than that of the current location, then the position of the gold miners will move in the direction of increasing K p value to find a better combination of PID control coefficients. The migration operation enables the gold miners to jump out of the current local area and search for the optimal solution in a broader search space.

[0054] The gold panning operation is a process in which the gold miners conduct local searches near the current position to find a better solution. During this process, each gold miner explores within the neighborhood of its current position. Assume the current position of the i-th gold miner is , and local search is carried out through the following formula: ; ; where, is the position of another randomly selected gold miner, is the gold miner and the gold miner the distance between them, is the new position where the gold miner migrates to. is the weight and can be expressed as follows: ; is a random number within the interval [0, 1]. The gold miner generates a new position near the current position by comparing and operating with the position of another randomly selected gold miner . If the integral coefficient K value of the randomly selected gold miner position i is the same as the integral coefficient K of the current gold miner position iIf the values are different and the objective function value at the new position is less than that at the current position, then the gold miner will generate a new position near the current position, and the integral coefficient K of the new position i will change. By continuously performing gold panning operations, the gold miner can explore potential better solutions near the current position and improve the search accuracy.

[0055] Collaboration is an important mechanism for gold miners to share information and jointly explore new areas in the gold panning algorithm. During the collaboration process, each gold miner randomly selects the positions of two other gold miners and explores new areas through information sharing. Suppose the gold miner randomly selects the positions of two gold miners as and , and conducts collaborative exploration through the following formula: ; ; where, is a coefficient that controls the degree of collaboration and can be adjusted according to the actual situation. is the distance between gold miner and gold miner , is the new position that the gold miner migrates to. Through collaboration, the gold miner can draw on the position information of other gold miners and discover new search directions. If the differential coefficient K values of the two gold miners and selected by the gold miner d differ greatly, then the differential coefficient K of the new position d calculated through the collaboration formula may be in a new range, thus exploring a new area. The collaboration operation increases the information exchange and interaction among gold miners, helps improve the global search ability of the algorithm, and avoids falling into local optimal solutions.

[0056] The relocation operation is a process of determining whether the gold miner updates its position based on the evaluation result of the gold mine position value, that is, whether to update the current position to a new position. According to the foregoing description, there are multiple new positions. In each iteration, the current position and the new position are evaluated by calculating the objective function. Suppose the current position of the i-th gold miner is , and the newly generated position is . The values of the two positions are evaluated by calculating the objective function, that is, calculating and . If , it means that the combination of PID control coefficients corresponding to the new position makes the performance of the suspension system better, then the gold miner will update the position and set As the new current position; otherwise, the gold digger will stay at the previous position . This process can be expressed as: ; where is the position of the i-th gold digger at time t, which is also the position at the current moment, is the position of the i-th gold digger at time t + 1.

[0057] Through the relocation operation, the gold digger can gradually move to a better position, continuously optimize the PID control parameters of the suspension system, and improve the performance of the suspension system.

[0058] S280. Take the control coefficient corresponding to the gold digger with the optimal position as the reference value.

[0059] As the iteration progresses, the gold digger will get closer and closer to the global optimal solution, that is, obtain the position of the gold digger that makes the objective function value the smallest, which is also the optimal set of PID control coefficients, as the reference value.

[0060] S290. Obtain the final value of the control coefficient according to the reference value and the adjustment information; substitute the final value into the set control algorithm to obtain the damping control command, and control the damping force of the suspension system based on the damping control command.

[0061] Specifically, the following formula is used for calculation to obtain the final value of the control system.

[0062] ; where is the proportional coefficient at time t + 1, is the proportional coefficient at time t (i.e., the reference value), is the adjustment information of the proportional coefficient. is the integral coefficient at time t + 1, is the integral coefficient at time t (i.e., the reference value), is the adjustment information of the integral coefficient. is the differential coefficient at time t + 1, is the differential coefficient at time t (i.e., the reference value), is the adjustment information of the differential coefficient.

[0063] The finally obtained adjusted fuzzy PID controller parameters are used to control the suspension system, enabling the system to perform adaptive adjustment according to the actual road conditions and vehicle driving states.

[0064] In this embodiment, the gold rush algorithm is used to take the PID parameter space as the search space, and the position of each gold miner represents a combination of PID control coefficients. Through continuous iteration, the gold miners perform operations such as migration, gold panning, and collaboration in the search space to find the combination of PID control coefficients that optimizes the objective function. In each iteration, the performance indexes of the suspension system, such as vehicle body acceleration, suspension dynamic deflection, and tire dynamic displacement, are calculated according to the current position of the gold miners, and the objective function value is calculated based on these performance indexes. The position of the gold miners is evaluated and adjusted according to the objective function value, so that the gold miners gradually move towards a better combination of control coefficients. After multiple iterations, the gold rush algorithm can find a set of better PID control coefficient combinations.

[0065] The better combination of PID control coefficients obtained by the gold rush algorithm is input into the fuzzy PID controller to achieve the real-time control of the suspension system. Specifically, according to the reference value obtained by the gold rush algorithm and the adjustment information obtained by the fuzzy rules, the final value of the control coefficient is obtained. The final value is brought into the PID algorithm to obtain the damping control instruction, which can dynamically adjust the working state of the suspension according to the dynamic changes of the vehicle body and the road surface conditions.

[0066] In the actual application scenario, during the vehicle driving process, the sensor real-time monitors information such as the acceleration, speed, and suspension displacement of the vehicle body, and feeds this information back to the fuzzy PID controller. The fuzzy PID controller adjusts the damping characteristics of the semi-active suspension according to the input road surface disturbance signal and the optimized PID control coefficients. When the vehicle is driving at a high speed and the road surface is relatively flat, the damping force of the suspension is adjusted to keep it at a suitable level to improve the handling stability of the vehicle. When the vehicle is driving at a low speed through a bumpy road surface, the controller will adjust the damping force to reduce the vibration of the vehicle body and improve the riding comfort. Through this real-time control, the suspension system can quickly adapt to various working condition changes, improve the overall performance, and provide better driving stability and comfort for the vehicle.

[0067] Combining the above embodiments, the present invention achieves the following technical effects: I. Optimizing parameters and improving performance The application of the gold panning algorithm automates and intelligentizes the optimization process of PID control coefficients, effectively avoiding the cumbersome and uncertain manual adjustment of PID control coefficients. Traditional PID control coefficient adjustment often relies on engineers' experience and repeated trials, which requires a large amount of time and effort and it is difficult to find the global optimal solution. The gold panning algorithm simulates the process of gold miners looking for gold and conducts efficient search and optimization in the PID control coefficient space. In the initialization stage, a randomly generated group of gold miners is distributed at various positions in the parameter space, providing a wide starting point for the search. During operations such as migration, gold panning, cooperation, and relocation, the gold miners continuously adjust their positions according to the current search results, approaching a better combination of PID control coefficients. Through continuous iterative evolution, the gold panning algorithm can automatically find the optimal combination of PID control coefficients, enabling the suspension system to achieve the best performance under various working conditions. Compared with traditional optimization algorithms, the gold panning algorithm has higher search efficiency and stronger global optimization ability, can find better PID control coefficients in a shorter time, thus significantly improving the performance of the suspension system and making the vehicle have better comfort, handling, and stability during driving.

[0068] II. Adaptive Ability and Driving Experience The fuzzy PID controller combined with the gold panning algorithm endows the suspension system with strong adaptive ability. The fuzzy PID controller can dynamically adjust the PID control coefficients through fuzzy inference according to the vehicle's real-time driving state and road surface conditions, enabling the suspension system to quickly adapt to different working conditions. When the vehicle is driving on a bumpy road surface, the fuzzy PID controller can quickly adjust the PID control coefficients to increase the damping force of the suspension, reduce the vibration and bumps of the vehicle body, and improve ride comfort. The optimization effect of the gold panning algorithm further enhances this adaptive ability, can optimize the PID control coefficients in real time according to different road conditions and driving states, and keep the suspension system in the optimal working state all the time. Through precise control and optimization, the fuzzy PID controller combined with the gold panning algorithm can balance handling stability and comfort during vehicle driving.

[0069] III. Analysis of Simulation Results The simulation model built in the simulation software has comprehensively verified the performance of this technical solution. By simulating various actual road conditions and vehicle driving states, key performance indicators such as vehicle body acceleration and suspension dynamic deflection have been tested and analyzed. In terms of the vehicle body acceleration performance indicator, the semi-active suspension system of the vehicle based on the gold panning algorithm-based fuzzy PID control has shown significant improvement. This means that during the vehicle's driving process, the vibration of the vehicle body has been more effectively suppressed, and the riding comfort has been greatly improved. The smaller vehicle body acceleration makes the bumps and shakes felt by the passengers and drivers significantly reduced, and they can enjoy the journey more comfortably. In terms of the suspension dynamic deflection performance indicator, the optimization amplitude is also very obvious. This shows that the working range of the suspension system under different working conditions has been better controlled, which can avoid excessive compression or stretching of the suspension, reduce the collision between the suspension and the limit block, and improve the driving safety and comfort. The reasonable suspension dynamic deflection can ensure that the suspension system can effectively buffer the road surface impact while maintaining good driving stability. These simulation results fully prove the effectiveness and superiority of the embodiments of the present invention in improving the performance of the vehicle semi-active suspension system, providing strong support for practical applications.

[0070] IV. The embodiments of the present invention have broad application potential in different vehicle models.

[0071] In the field of sedans, whether it is a small sedan, a medium-sized sedan or a large luxury sedan, the semi-active suspension system of the vehicle based on the gold panning algorithm-based fuzzy PID control can significantly improve the comfort and handling performance of the vehicle. Small sedans usually focus on fuel economy and the flexibility of urban driving. This system can effectively reduce the impact of road bumps on passengers and drivers while ensuring the stable handling of the vehicle, improving the comfort of urban driving. For medium-sized sedans, when meeting the needs of family daily use and business trips, good suspension performance is the key to improving the user experience. This embodiment can enable medium-sized sedans to maintain a stable driving posture under different road conditions and provide a more comfortable riding environment. Large luxury sedans have higher requirements for comfort and handling performance. The high-precision control and adaptive ability of this embodiment can meet its high-end positioning and provide passengers with an ultimate riding experience.

[0072] In sports utility vehicle (SUV) models, due to their higher body center of gravity and the characteristics of often facing complex road conditions, the requirements for the suspension system are more stringent. This embodiment can effectively improve the passability and stability of SUVs under off-road conditions by adjusting the suspension parameters in real time, reduce the roll and bumps of the vehicle body, and make the vehicle drive more smoothly on complex terrains. It can also optimize the suspension performance during high-speed driving to improve the handling stability of the vehicle and ensure driving safety.

[0073] In the field of commercial vehicles, such as buses and trucks, this embodiment also has important application value. Buses need to provide passengers with a comfortable long-distance travel experience. This embodiment can reduce the impact of road vibrations on passengers, improve ride comfort, and enhance the service quality of buses. When transporting goods, trucks need to ensure the safety of the goods and the driving stability of the vehicle. The fuzzy PID control method based on the gold panning algorithm can adjust the suspension parameters in real time according to the weight of the goods and road conditions, ensuring that the truck can maintain good driving performance when fully loaded and unloaded, and improving transportation efficiency and safety.

[0074] As Figure 3 shown, this embodiment provides an electronic device, including: At least one processor 301; and A memory 302 communicatively connected to the at least one processor; wherein, The memory 302 stores instructions executable by the at least one processor 301. The instructions are executed by the at least one processor 302 so that the at least one processor 302 can execute the above method. At least one processor in this electronic device can execute the above method, and thus has at least the same advantages as the above method.

[0075] Optionally, the electronic device further includes an interface for connecting various components, including a high-speed interface and a low-speed interface. Each component is interconnected using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processor can process instructions executed within the electronic device, including instructions for storing graphical information in the memory or on the memory to display a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to the interface). In other embodiments, if needed, multiple processors and multiple memories can be used together, and / or multiple buses and multiple memories can be used together. Similarly, multiple electronic devices can be connected (for example, as a server array, a set of blade servers, or a multi-processor system), and each device provides some necessary operations. Figure 3 Take one processor 301 as an example in

[0076] The memory 302, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the control method of the vehicle suspension system in the embodiments of the present invention. The processor 301 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 302, that is, implements the above control method of the vehicle suspension system.

[0077] The memory 302 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory 302 may further include a memory remotely provided with respect to the processor 301, and these remote memories may be connected to the device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0078] The electronic device may further include: an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303, and the output device 304 may be connected through a bus or other means. Figure 3 Taking the connection through the bus as an example.

[0079] The input device 303 may receive input digital or character information, and the output device 304 may include a display device, an auxiliary lighting device (e.g., an LED), a tactile feedback device (e.g., a vibration motor), etc. The display device may include but is not limited to a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0080] This embodiment provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the above method. The computer instructions on the computer-readable storage medium are used to cause a computer to execute the above method, and thus have at least the same advantages as the above method.

[0081] The medium in the present invention may adopt any combination of one or more computer-readable media. The medium may be a computer-readable signal medium or a computer-readable storage medium. The medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the medium (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0082] A computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0083] The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical fiber cable, RF (Radio Frequency), etc., or any suitable combination of the foregoing.

[0084] The computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0085] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means, such as coaxial cables, optical fibers, digital subscriber line (DSL), or wireless means, such as infrared, wireless, microwave, etc. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium, or a semiconductor medium, etc. It should be noted that the computer-readable storage medium mentioned in the embodiments of the present invention can be a non-volatile storage medium, in other words, a non-transitory storage medium.

[0086] It should be understood that various forms of the processes shown above can be used, reordering, adding, or deleting steps. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. No limitations are imposed herein.

[0087] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A control method for a vehicle suspension system, characterized in that Including: Obtain the road surface disturbance information of the suspension system as the error for control; According to the error and the fuzzy control method, obtain the adjustment information of the control coefficients in the set control algorithm; Obtain the reference value of the control coefficient by optimizing the performance of the suspension system; According to the reference value and the adjustment information, obtain the final value of the control coefficient; Substitute the final value into the set control algorithm to obtain a damping control command, and control the damping force of the suspension system based on the damping control command.

2. The control method of the vehicle suspension system according to claim 1, characterized in that, The set control algorithm is a proportional-integral-derivative control algorithm, and the control coefficients include a proportional coefficient, an integral coefficient, and a derivative coefficient.

3. The control method of the vehicle suspension system according to claim 2, wherein, According to the error and the fuzzy control method, obtaining the adjustment information of the control coefficients in the set control algorithm includes: Perform fuzzy processing on the error and the error change rate to obtain a fuzzy set; Perform fuzzy inference on the fuzzy set according to a pre-established fuzzy rule base to obtain a fuzzy control output; Defuzzify the fuzzy control output to obtain the adjustment information of the control coefficient; the adjustment information includes an adjustment direction and an adjustment degree.

4. The control method of the vehicle suspension system according to claim 1, wherein Obtain the reference value of the control coefficient by optimizing the performance of the suspension system, including: Use the gold mining algorithm to optimize the performance of the suspension system to obtain the reference value of the control coefficient.

5. The control method of the vehicle suspension system according to claim 4, characterized in that, Use the gold mining algorithm to optimize the performance of the suspension system to obtain the reference value of the control coefficient, including: Randomly generate an initial gold miner population, and each gold miner in the initial gold miner population represents a set of control coefficients; Construct an objective function according to the performance index of the suspension system; Optimize the position of each gold miner to optimize the objective function; Use the control coefficient corresponding to the gold miner with the optimal position as the reference value.

6. The control method of the vehicle suspension system according to claim 5, wherein, Construct an objective function according to the performance index of the suspension system, including: Construct an objective function according to the vehicle body acceleration, the suspension dynamic deflection, and the tire dynamic displacement.

7. The control method of the vehicle suspension system according to any one of claims 1-6, characterized in that, The error for control includes at least one of the relative displacement between the vehicle body and the suspension, the vertical vibration frequency of the vehicle body, and the vertical displacement of the wheel.

8. A computer program product, characterized in that, Including: The computer program product stores computer instructions, and when the computer instructions are executed by a processor, the steps of the control method of the vehicle suspension system according to any one of claims 1-7 are implemented.

9. An electronic device, characterized in that, Including: At least one processor, and a memory communicatively connected to at least one of the processors; Wherein, the memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the control method of the vehicle suspension system according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the medium, and the computer instructions are used to cause a computer to execute the control method of the vehicle suspension system according to any one of claims 1-7.

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