Optimization method and device for structural parameters of wheel-legged all-terrain chassis and storage medium

Abstract

The invention provides an optimization method for structural parameters of a wheel-legged all-terrain chassis. The optimization method comprises the following steps: analyzing the influence of the structural parameters of the chassis on vehicle performance; a multi-objective optimization model for the structural parameters of the wheel-legged all-terrain chassis is provided, and the structural parameters of the chassis are adjusted when multi-objective optimization of a chassis system is carried out; and solving the multi-objective optimization model for the structural parameters of the wheel-legged all-terrain chassis to obtain the structural parameters of the chassis capable of enabling the overall performance of the agricultural vehicle to be optimal. The optimization method focuses on the key structure parameters of the front chassis and the rear chassis of the trolley, constructs a multi-objective optimization model, and solves by applying a heuristic algorithm, so that an optimization design scheme can be quickly obtained under a multi-constraint condition, and the agricultural machinery performance and the operation efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle chassis in hilly areas, and particularly relates to a wheel-leg type all-terrain chassis structure parameter optimization method and device and a storage medium. BACKGROUND

[0002] Hilly areas have the natural advantages of "not competing with grain for land and not competing with farmers for land", but also have the characteristics of complex topography, high and low terrain, and gully and ridge distribution. It is of great significance to improve the ride comfort of hilly agricultural machinery vehicles and to improve the optimization efficiency of the chassis. The chassis system of the vehicle is composed of multiple subsystems, and the relationship between these subsystems is complex and coupled. For a certain performance of the vehicle, it may be affected by multiple chassis subsystems, and for a certain chassis subsystem, it may also affect multiple vehicle performances. At the same time, each subsystem of the vehicle chassis is also affected by its structure parameters. Therefore, in order to effectively improve the overall performance of the vehicle, the interaction between the subsystems needs to be fully considered in the design of the optimization strategy of the chassis system, that is, multi-objective optimization needs to be considered.

[0003] In the complex topography of hilly areas, there are usually various high and low terrains, and appropriate vehicle chassis parameters need to be set in a timely manner to improve the overall performance of the vehicle for various types of hilly topography. At present, the wheel-leg composite structure has a higher degree of adaptation to hilly and rugged terrains compared to the leg-track composite structure, but it is difficult to control the front and rear ends with high precision, so a multi-objective optimization method for the structure parameters of the wheel-leg type all-terrain chassis is needed to improve the ride comfort of agricultural machinery vehicles and improve the optimization efficiency. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a wheel-leg type all-terrain chassis structure parameter optimization method, which comprises the following steps:

[0005] analyzing the influence of the chassis structure parameters on the vehicle performance;

[0006] proposing a multi-objective optimization model for the structure parameters of the wheel-leg type all-terrain chassis, and adjusting the chassis structure parameters during multi-objective optimization of the chassis system;

[0007] solving the multi-objective optimization model for the structure parameters of the wheel-leg type all-terrain chassis to obtain the chassis structure parameters that can optimize the overall performance of the agricultural machinery vehicle.

[0008] In some embodiments, analyzing the influence of the chassis structure parameters on the vehicle performance comprises:

[0009] a damping coefficient and a moment of inertia, wherein the damping coefficient determines the damping ability of the suspension system to vibration, and the moment of inertia affects the dynamic response and stability of the vehicle steering system;

[0010] A multi-objective optimization model for the structural parameters of wheel-leg all-terrain chassis is proposed. During the multi-objective optimization of the chassis system, the chassis structural parameters are adjusted, including:

[0011] The agricultural machinery chassis of the model consists of multiple subsystems, which influence each other. Heuristic algorithms are used to optimize the structural parameters of the agricultural machinery chassis for multiple objectives.

[0012] In some embodiments, the system position matrix of the chassis represents the spatial location of the snow goose population, and the chassis parameter matrix represents the individual state of the snow goose within the population, as concisely represented below:

[0013]

[0014] Here, variable n represents the total number of vehicle chassis systems, while d corresponds to the number of variables associated with the chassis optimization problem. For each subsystem in the chassis, it is assumed that there exists an array responsible for storing the performance characteristics associated with the location of each subsystem, as shown below.

[0015]

[0016] In this context, it must be emphasized that the chassis parameter performance function operates as a black box. All the user needs to do is input the candidate parameter solutions that each subsystem can obtain. The algorithm will then generate the performance value function value for each subsystem. By feeding the candidate parameter set of each subsystem and the coupling relationship between the subsystems into the performance function, the obtained performance values ​​are stored in the corresponding positions in the vector.

[0017] In some embodiments, the chassis performance of the agricultural vehicle is affected by steering system parameters and suspension system parameters. Among these, the objective functions to be optimized in the steering system include steering feel and steering sensitivity. The mathematical expression for these objective functions is...

[0018]

[0019]

[0020] in The target for optimizing steering feel is represented by the average energy of steering feel within a certain frequency range. This represents the maximum frequency value of the useful signal for steering road feel and road surface information. For steering wheel torque, The resistance torque of the steering output shaft;

[0021]

[0022]

[0023]

[0024] in, Steering wheel angle to yaw rate The transfer function, which is the steering sensitivity, is the value of the transfer function. Steering wheel angle To the front wheel corner The transfer function, and finally, the optimization index. This represents the average energy of the steering sensitivity over a certain frequency range.

[0025] In some embodiments, the targets to be optimized in the suspension system include vertical acceleration and suspension travel. The root mean square (RMS) value of the response to each parameter is used as a performance evaluation index. Here, the RMS value of a response refers to the time history over the entire vibration analysis time T. The mathematical definition is obtained by integration:

[0026]

[0027] in, The root mean square value represents the vibration response; the smaller the value, the better the ride comfort of the agricultural machinery.

[0028] Furthermore, based on the root mean square of the system response, the objective function that needs to be optimized in the suspension system is identified as follows:

[0029]

[0030]

[0031] in, Optimize the indicators for the roll angle acceleration of agricultural machinery vehicles. It can reflect the lateral stability of agricultural machinery vehicles when cornering. Optimize the pitch angle acceleration of agricultural machinery vehicles. It can reflect the car's climbing ability.

[0032] In some embodiments, the multi-objective optimization problem of the all-terrain chassis structural parameters is:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039] The algorithm primarily optimizes the parameter matrix V to minimize the objective function, ensuring that the agricultural machinery chassis system does not experience significant bumps or vibrations under complex terrain conditions, thus guaranteeing optimal overall chassis system performance. The weighting coefficients are... The value of is generally determined by the characteristics of hilly terrain. The chassis parameters, suspension system stiffness coefficient k and suspension system damping coefficient c, in the constraints are to ensure that agricultural vehicles can drive normally and prevent excessive pitching vibration.

[0040] In some embodiments, the iterative algorithm for different parameters is calculated using the following equation:

[0041]

[0042] This equation explains the iteration parameters. The value depends on the current parameter value. Parameter error and iteration duration t, for variables t, this variable is used to represent the difference between adjacent generations, and its value is set to 1.

[0043] In some embodiments, the optimization method for the structural parameters of the wheel-leg all-terrain chassis includes the following specific steps:

[0044] Humanoid exploration phase;

[0045] In this algorithm phase, the algorithm searches for structural parameters that can improve the performance of the chassis system. These parameters are designed as performance-leading parameters, which guide the algorithm towards the global optimum. The algorithm update equation at this time is as follows:

[0046]

[0047] Where b is the generated random weight parameter;

[0048] Furthermore, while finding the optimal system parameters, the algorithm also identifies the worst-performing parameter settings for other systems. For this part, the algorithm calculates the parameters when the chassis system's performance is at the mean, and coordinates parameter conflicts through the mean of subsystem parameters. The iterative evolution equation for the parameters at this point is as follows:

[0049]

[0050] Where d is also the generated random weight parameter;

[0051] For parameters that result in moderate system performance, the algorithm will escape the parameter deadlock by perturbing the worst-case solution. In this case, the parameter update equation is as follows:

[0052]

[0053] for The algorithm generates using the following formula:

[0054]

[0055]

[0056]

[0057] During this exploratory phase, different update formulas should be used to modify the parameters of subsystems with different performance. It should be noted that the final parameters of the chassis system are obtained through iterative optimization using three update methods. Therefore, each parameter optimization update equation needs a weight coefficient to represent its contribution to the overall formula.

[0058] Linear development phase;

[0059] In this algorithm stage, the algorithm mainly performs fine optimization in the potential area of ​​chassis optimization parameters to balance the conflict of multiple objectives such as steering sensitivity and suspension vertical acceleration.

[0060] At this point, there is a significant gap between the high-performance chassis system and the low-performance subsystem. The parameter iterative update is achieved through the following equation:

[0061]

[0062] in, Represents term-by-term multiplication. Represents Brownian motion in d-dimensional space;

[0063] When random number When the system performs poorly, the algorithm will analyze the parameters of the system with good performance and then make appropriate fine adjustments to gradually optimize the performance of the system with poor performance, thereby selecting a better solution with better structural parameters; when random numbers... If a traditional heuristic algorithm gets stuck in a local solution, the parameter optimization process will produce random behavior similar to Brownian motion.

[0064] In another aspect, the present invention provides a device for optimizing the structural parameters of a wheel-legged all-terrain chassis, comprising:

[0065] The data analysis module analyzes the impact of chassis structure parameters on vehicle performance;

[0066] The data processing module proposes a multi-objective optimization model for the structural parameters of wheel-leg all-terrain chassis, and adjusts the chassis structural parameters when performing multi-objective optimization of the chassis system.

[0067] The data optimization module solves a multi-objective optimization model for the structural parameters of the wheel-leg all-terrain chassis, obtaining chassis structural parameters that enable the overall performance of agricultural machinery vehicles to reach the optimal level.

[0068] Furthermore, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for optimizing the structural parameters of the wheel-leg all-terrain chassis.

[0069] As can be seen from the above solutions, the advantages of the present invention are:

[0070] The optimization method for the structural parameters of the wheel-leg all-terrain chassis provided by this invention is a multi-objective AQ optimization method for the structural parameters of the wheel-leg all-terrain chassis. It is a new hierarchical coordinated multi-objective optimization method that optimizes the design of key structural parameters such as the steering system and suspension system of the wheel of the wheel-leg chassis. Compared with traditional optimization methods, which are not easy to handle the coupling effect between multiple performances, it can better achieve the improvement of overall performance.

[0071] The Snow Geese Algorithm (SGA) proposed in this invention is a metaheuristic optimization algorithm inspired by the flocking behavior of snow geese in nature. By simulating the dispersion and reorganization behavior of snow goose flocks, it can effectively escape local optima and is particularly suitable for multi-objective optimization problems.

[0072] Compared to traditional intelligent algorithms, which have high computational costs in high-dimensional spaces, are susceptible to the "curse of dimensionality," and suffer from decreased particle collaboration efficiency in high dimensions, the Xueyan algorithm proposed in this invention explores different dimensions in parallel through hierarchical collaboration and reduces computational complexity by utilizing the correlation between different subsystems, thereby efficiently achieving multi-objective optimization of chassis parameters. Attached Figure Description

[0073] Figure 1 A schematic diagram of the overall process for optimizing the structural parameters of a wheel-legged all-terrain chassis according to an embodiment of the present invention. Figure 1 ;

[0074] Figure 2 A schematic diagram of the overall process for optimizing the structural parameters of a wheel-legged all-terrain chassis according to an embodiment of the present invention. Figure 2 ;

[0075] Figure 3 This is a schematic diagram of the SGA algorithm flow provided in an embodiment of the present invention;

[0076] Figure 4This is a schematic diagram of the overall structure of the device for optimizing the structural parameters of a wheel-leg all-terrain chassis according to an embodiment of the present invention;

[0077] in:

[0078] 310: Data Analysis Module;

[0079] 320: Data processing module;

[0080] 330: Data optimization module;

[0081] S1-S3: Steps. Detailed Implementation

[0082] To make the above features and effects of the present invention clearer and easier to understand, specific embodiments are described below, and detailed descriptions are provided in conjunction with the accompanying drawings.

[0083] See Figures 1-4 As shown in the figure, an embodiment of the present invention provides a method for optimizing the structural parameters of a wheel-legged all-terrain chassis, including:

[0084] S1. Analyze the impact of chassis structural parameters on vehicle performance;

[0085] S2. A multi-objective optimization model for the structural parameters of wheel-leg all-terrain chassis is proposed. When performing multi-objective optimization of the chassis system, the chassis structural parameters are adjusted. By adjusting these parameters, the comprehensive optimization of the performance of agricultural machinery vehicles can be achieved.

[0086] S3. Solve the multi-objective optimization model for the structural parameters of the wheel-leg all-terrain chassis to obtain the chassis structural parameters that enable the overall performance of agricultural machinery vehicles to reach the optimal level, thereby improving the ride comfort, stability and working efficiency of agricultural machinery vehicles.

[0087] This embodiment analyzes the impact of chassis structural parameters on vehicle performance, including damping coefficient and moment of inertia. The damping coefficient determines the suspension system's ability to attenuate vibrations, and appropriate damping can balance ride comfort and vehicle control. The moment of inertia affects the dynamic response and stability of the vehicle's steering system. A multi-objective optimization model for the structural parameters of wheel-leg all-terrain chassis is proposed. When performing multi-objective optimization of the chassis system, the chassis structural parameters are adjusted. The model's agricultural machinery chassis includes multiple subsystems, and these subsystems influence each other. To maximize the overall performance of the agricultural machinery, a heuristic algorithm is used to perform multi-objective optimization of the structural parameters of the agricultural machinery chassis.

[0088] In the multi-objective optimization model of vehicle chassis parameters described in this embodiment, the various subsystems of the chassis influence each other. For example, during the operation of agricultural machinery, steering motion causes body roll, affecting the stress and deformation of the suspension system. The vibration and deformation of the suspension system also feed back to the steering system, affecting steering accuracy and road feel. Therefore, optimizing the design of chassis structural parameters is crucial for improving the performance of agricultural machinery. Through multi-objective optimization and the rational design and selection of key chassis structural parameters, the vehicle's handling stability, ride comfort, and safety can be significantly improved.

[0089] In this embodiment, the system position matrix of the chassis represents the spatial location of the snow goose population, and the chassis parameter matrix represents the individual state of the snow goose within the population, as concisely represented below:

[0090]

[0091] Here, variable n represents the total number of vehicle chassis systems, while d corresponds to the number of variables associated with the chassis optimization problem. For each subsystem in the chassis, it is assumed that there exists an array responsible for storing the performance characteristics associated with the location of each subsystem, as shown below.

[0092]

[0093] In this context, it must be emphasized that the chassis parameter performance function operates as a black box. All the user needs to do is input the candidate parameter solutions that each subsystem can obtain. The algorithm will then generate the performance value function value for each subsystem. By feeding the candidate parameter set of each subsystem and the coupling relationship between the subsystems into the performance function, the obtained performance values ​​are stored in the corresponding positions in the vector.

[0094] In this embodiment, the chassis performance of the agricultural vehicle is affected by the steering system parameters and suspension system parameters. Among them, the objective functions that need to be optimized in the steering system are steering road feel and steering sensitivity. The mathematical expression of the optimization objective function is as follows:

[0095]

[0096]

[0097] in The target for optimizing steering feel is represented by the average energy of steering feel within a certain frequency range. This represents the maximum frequency value of the useful signal for steering road feel and road surface information. For steering wheel torque, The resistance torque of the steering output shaft;

[0098]

[0099]

[0100]

[0101] in, Steering wheel angle to yaw rate The transfer function, which is the steering sensitivity, is the value of the transfer function. Steering wheel angle To the front wheel corner The transfer function, and finally, the optimization index. This represents the average energy of the steering sensitivity over a certain frequency range.

[0102] In this embodiment, the targets to be optimized in the suspension system are vertical acceleration and suspension travel. The root mean square (RMS) value of the response to each parameter is used as a performance evaluation index. Here, the RMS value of a response refers to the time history over the entire vibration analysis time T. The mathematical definition is obtained by integration:

[0103]

[0104] in, The root mean square value represents the vibration response; the smaller the value, the better the ride comfort of the agricultural machinery.

[0105] Furthermore, based on the root mean square of the system response, the objective function that needs to be optimized in the suspension system is identified as follows:

[0106]

[0107]

[0108] in, Optimize the indicators for the roll angle acceleration of agricultural machinery vehicles. It can reflect the lateral stability of agricultural machinery vehicles when cornering. Optimize the pitch angle acceleration of agricultural machinery vehicles. It can reflect the car's climbing ability.

[0109] The multi-objective optimization problem of the all-terrain chassis structure parameters described in this embodiment is:

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] The algorithm primarily optimizes the parameter matrix V to minimize the objective function, ensuring that the agricultural machinery chassis system does not experience significant bumps or vibrations under complex terrain conditions, thus guaranteeing optimal overall chassis system performance. The weighting coefficients are... The value of is generally determined by the characteristics of hilly terrain. The chassis parameters, suspension system stiffness coefficient k and suspension system damping coefficient c, in the constraints are to ensure that agricultural vehicles can drive normally and prevent excessive pitching vibration.

[0117] In this embodiment, the iterative algorithm for different parameters is calculated using the following equation:

[0118]

[0119] This equation explains the iteration parameters. The value depends on the current parameter value. Parameter error and iteration duration t, for variables t, this variable is used to represent the difference between adjacent generations, and for the sake of simplicity, we set its value to 1.

[0120] The optimization method for the structural parameters of the wheel-leg all-terrain chassis described in this embodiment includes the following specific steps:

[0121] Humanoid exploration phase;

[0122] In this algorithm phase, the algorithm searches for structural parameters that can improve the performance of the chassis system. These parameters are designed as performance-leading parameters, which guide the algorithm towards the global optimum. The algorithm update equation at this time is as follows:

[0123]

[0124] Where b is the generated random weight parameter;

[0125] Furthermore, while finding the optimal system parameters, the algorithm also identifies the worst-performing parameter settings for other systems. For this part, the algorithm calculates the parameters when the chassis system's performance is at the mean, and coordinates parameter conflicts through the mean of subsystem parameters. The iterative evolution equation for the parameters at this point is as follows:

[0126]

[0127] Where d is also the generated random weight parameter;

[0128] For parameters that result in moderate system performance, the algorithm will escape the parameter deadlock by perturbing the worst-case solution. In this case, the parameter update equation is as follows:

[0129]

[0130] for The algorithm generates using the following formula:

[0131]

[0132]

[0133]

[0134] During this exploratory phase, different update formulas should be used to modify the parameters of subsystems with different performance. It should be noted that the final parameters of the chassis system are obtained through iterative optimization using three update methods. Therefore, each parameter optimization update equation needs a weight coefficient to represent its contribution to the overall formula.

[0135] Linear development phase;

[0136] In this algorithm stage, the algorithm mainly performs fine optimization in the potential area of ​​chassis optimization parameters to balance the conflict of multiple objectives such as steering sensitivity and suspension vertical acceleration.

[0137] At this point, there is a significant gap between the high-performance chassis system and the low-performance subsystem. The parameter iterative update is achieved through the following equation:

[0138]

[0139] in, Represents term-by-term multiplication. Represents Brownian motion in d-dimensional space;

[0140] At this stage, the algorithm focuses more on the overall performance of the chassis system. It determines how to perform optimization operations by analyzing the magnitude of a random number generated by the chassis. When the system performs poorly, the algorithm will analyze the parameters of the system with good performance and then make appropriate fine adjustments to gradually optimize the performance of the system with poor performance, thereby selecting a better solution with better structural parameters; when random numbers... In such cases, if traditional heuristic algorithms get stuck in local optima, the parameter optimization process can exhibit random behavior similar to Brownian motion. The algorithm proposed in this embodiment employs Levy flight to help escape local optima, and this strategy has proven effective in various situations.

[0141] See Figure 4 Another embodiment of the present invention provides a device 300 for optimizing the structural parameters of a wheel-legged all-terrain chassis, comprising:

[0142] Data analysis module 310 analyzes the impact of chassis structure parameters on vehicle performance;

[0143] Data processing module 320 proposes a multi-objective optimization model for the structural parameters of wheel-leg all-terrain chassis, and adjusts the chassis structural parameters when performing multi-objective optimization of the chassis system;

[0144] The data optimization module 330 solves a multi-objective optimization model for the structural parameters of the wheel-leg all-terrain chassis, obtaining chassis structural parameters that enable the overall performance of agricultural machinery vehicles to reach the optimal level.

[0145] Furthermore, another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the above embodiment.

[0146] In this embodiment, to achieve optimal overall performance of agricultural machinery vehicles, the coupling effect between various subsystems of the agricultural machinery vehicle chassis will be fully considered, and multi-objective optimization will be performed for different subsystems. In order to ensure that agricultural machinery vehicles can better cope with various terrains in complex hilly and mountainous areas, a wheel-leg-type movement mode will be adopted, and the chassis parameters will be optimized. In order to improve the optimization efficiency of chassis parameters, a parallel multi-objective optimization method will be used to optimize the key parameters of the chassis.

[0147] More specifically and in detail, the following is a description of this solution: This invention provides a multi-objective optimization method for the structural parameters of a wheel-legged all-terrain chassis, such as... Figure 1 As shown, it includes the following steps:

[0148] (1) Analyze the key parameters of the chassis

[0149] Chassis parameters have a decisive impact on vehicle performance, especially in wheel-leg all-terrain chassis designs, where these parameters directly relate to the vehicle's handling stability, ride comfort, and safety.

[0150] In hilly agricultural vehicles, a long wheelbase chassis provides high-speed stability and reduces bumps, but it also increases the turning radius and reduces maneuverability. A short wheelbase, on the other hand, enhances turning maneuverability and passability, but it is prone to drifting at high speeds.

[0151] Meanwhile, each subsystem of the chassis also has its own influencing parameters. In the steering system, the rotational inertia of the power steering motor and the steering output shaft affects the dynamic response and stability of the steering system. The damping coefficient determines the energy dissipation capacity of the steering system, and the high torsional stiffness of the torque sensor increases the handling burden while improving steering accuracy. In the suspension system, the stiffness coefficient directly affects the vertical vibration characteristics of the vehicle. Higher stiffness can improve the handling stability of agricultural machinery vehicles but reduce ride comfort, while lower stiffness has the opposite effect. Furthermore, the damping coefficient in the suspension system determines the system's ability to dampen vibrations; appropriate damping can better achieve ride comfort and vehicle control in agricultural machinery vehicles.

[0152] Furthermore, there are mutual influences among the various subsystems of the vehicle chassis. For example, during the operation of agricultural machinery, steering movements cause body roll, affecting the stress and deformation of the suspension system. The vibrations and deformations of the suspension system, in turn, are fed back to the steering system, affecting steering accuracy and road feel. Therefore, optimizing the design of chassis structural parameters is crucial for improving the performance of agricultural machinery. Through multi-objective optimization and the rational design and selection of key chassis structural parameters, the vehicle's handling stability, ride comfort, and safety can be significantly improved.

[0153] (2) Establish a multi-objective optimization model

[0154] In the proposed SNA optimization algorithm, each individual snow goose is assumed to represent a potential solution to the chassis parameter optimization problem. The variables of this problem are metaphorically related to the spatial positions of these snow geese within the search space. Therefore, snow geese can adjust their positions, thus migrating across various dimensions, including one-dimensional, two-dimensional, three-dimensional, and even extending into higher-dimensional spaces, meaning the effects of mutual coupling between multiple chassis systems can be considered. Since SGA is a population-based algorithm, this invention defines the subsystem position matrix as representing the spatial position of the snow goose population, and the chassis parameter matrix as representing the individual state of the snow goose within the population, concisely represented as follows:

[0155]

[0156] Here, variable n represents the total number of vehicle chassis systems, while d corresponds to the number of variables associated with the chassis optimization problem.

[0157] Furthermore, for each subsystem in the chassis, it must also assume the existence of an array responsible for storing the performance metrics associated with the location of each subsystem, as shown below 𝐹𝑉

[0158]

[0159] In this context, it must be emphasized that the chassis parameter performance function operates as a black box. The user only needs to input the possible candidate parameter solutions for each subsystem, and the algorithm will generate the performance function value for each subsystem. By feeding the candidate parameter set for each subsystem and the coupling relationships between the subsystems into the performance function, the resulting performance values ​​are stored in the corresponding positions in a vector.

[0160] It should be noted that SGA is a triplet designed to progressively approach the global optimum of the chassis parameter optimization problem, denoted as: Within this framework, function 𝑅 is responsible for generating chassis parameter combinations and calculating the performance values ​​of each subsystem within the chassis system, and can be represented as: .

[0161] Function U is the central function of the entire algorithm, enabling the chassis system to search and move within the parameter space. Function U retrieves a matrix from P and V and returns an updated matrix. This function can be represented as follows:

[0162]

[0163] Where t represents the current iteration number.

[0164] The function T evaluates whether the current iteration in the algorithm should terminate. If the current iteration meets the termination condition, function T returns the boolean value true; otherwise, it returns false. The function is defined as follows:

[0165]

[0166] The main function U is to continuously adjust the key structural parameters of the chassis system, bringing the entire vehicle's chassis system close to the global optimum. The core of this metaheuristic algorithm research is the definition of function U, with a focus on the design of information exploration and the straight-line formation process. In the exploration phase, the algorithm aims to ensure a thorough exploration of the entire solution space, while the development phase aims to converge when the structural parameters are close to the global optimum. Furthermore, when designing function U for optimization, the difficulty of escaping local optima must be considered. In this case, the algorithm adopts a two-stage search strategy, starting with the exploration of the herringbone pattern and transitioning to the development of straight-line formations. In the exploration phase, the chassis parameters can be widely varied to allow for comprehensive performance evaluation and adapt to constantly changing environmental conditions. The algorithm is then guided to search for potential regions using the current optimal solution, avoiding premature convergence. During straight-line formation, the algorithm performs concentrated optimization of potential solutions while incorporating Brownian motion to escape local optima. When dealing with multi-objective processing, Pareto dominance sorting is used to maintain the non-dominated solution set.

[0167] Meanwhile, to further address the optimization problem, the SGA algorithm introduces a hyperparameter. , is used to represent the transition of agricultural machinery chassis systems from the exploration stage to the development stage, and its mathematical representation is defined as follows.

[0168]

[0169] In the formula, M is the final evaluation criterion set by the function T. This is true as long as the number of iterations t performed by the function satisfies... If the iteration count exceeds M, the algorithm will continue its iterative optimization operation. When the iteration count exceeds M, the designed SGA algorithm will stop executing.

[0170] Finally, it should be noted that the chassis performance of the agricultural machinery vehicle in this invention is affected by the parameters of the steering system and the suspension system. Specifically, the objective functions to be optimized in the steering system include steering feel and steering sensitivity, while those in the suspension system include vertical acceleration and suspension travel. The mathematical expressions for each optimization objective function are given below.

[0171]

[0172]

[0173] in The target for optimizing steering feel is represented by the average energy of steering feel within a certain frequency range. This represents the maximum frequency value of the useful signal for steering road feel and road surface information. For steering wheel torque, This is the resistance torque of the steering output shaft.

[0174]

[0175]

[0176]

[0177] in, Steering wheel angle to yaw rate The transfer function is the steering sensitivity. Steering wheel angle To the front wheel corner The transfer function, and finally, the optimization index. This represents the average energy of the steering sensitivity over a certain frequency range.

[0178] In the chassis suspension system, this invention uses the root mean square (RMS) value of the response to each parameter as a performance evaluation index. Specifically, the RMS value of a response refers to the response over the entire vibration analysis time T. The mathematical definition is obtained by integration:

[0179]

[0180] in, The root mean square value represents the vibration response; the smaller the value, the better the ride comfort of the agricultural machinery.

[0181] Furthermore, based on the root mean square of the system response, the objective function that needs to be optimized in the suspension system is identified as follows:

[0182]

[0183]

[0184] in, Optimize the indicators for the roll angle acceleration of agricultural machinery vehicles. It can reflect the lateral stability of agricultural machinery vehicles when cornering. Optimize the pitch angle acceleration of agricultural machinery vehicles. It can reflect the car's climbing ability.

[0185] Therefore, this paper proposes an objective function for the multi-objective optimization of agricultural machinery chassis. The definition is as follows:

[0186]

[0187] The algorithm primarily optimizes the parameter matrix V to minimize the objective function, ensuring that the agricultural machinery chassis system does not experience significant bumps or vibrations under complex terrain conditions, thus guaranteeing optimal overall chassis system performance. (Regarding the weighting coefficients...) The value of is generally determined by the characteristics of hilly terrain.

[0188] At the same time, constraints are set for the steering system and suspension system:

[0189]

[0190]

[0191]

[0192]

[0193]

[0194] Among them, the chassis parameters, suspension system stiffness coefficient k and suspension system damping coefficient c, are both used to ensure that agricultural machinery vehicles can drive normally and prevent excessive pitching vibrations.

[0195] (3) Solve the model using the SGA algorithm to obtain the optimal design scheme.

[0196] 3.1) Humanoid Exploration Phase

[0197] In the initial stage of the algorithm, the chassis performance changes according to the parameter variations. Then, as the structural parameters approach the optimal solution, the chassis system exhibits better performance. The iterative algorithm for different parameters uses the following equation for calculation:

[0198]

[0199] This equation explains the iteration parameters. The value depends on the current parameter value. Parameter error and iteration duration t. For variables t, this variable represents the difference between adjacent generations; for simplicity, we set its value to 1. w, this variable is a weighting factor for the iteration parameters. In the SGA algorithm environment, the magnitude of the current chassis structure parameters significantly affects the changes in the next generation. Therefore, we introduce w as a weighting factor to represent this process, with the following mathematical expression:

[0200]

[0201] Among them, the weighting factor w balances the relationship between the current parameters and the previous generation parameters. During the algorithm iteration process, the overall performance of the agricultural machinery chassis system first increased the performance of some subsystems, then decreased, and finally the overall performance converged.

[0202] In this algorithm phase, the algorithm searches for structural parameters that can improve the performance of the chassis system. These parameters are designed as performance-leading parameters, which guide the algorithm towards the global optimum. The algorithm update equation at this time is as follows:

[0203]

[0204] Where b is the generated random weight parameter.

[0205] Furthermore, while finding the optimal system parameters, the algorithm also identifies the worst-performing parameter settings for other systems. For this part, the algorithm calculates the parameters when the chassis system's performance is at the mean, and coordinates parameter conflicts through the mean of subsystem parameters. The iterative evolution equation for the parameters at this point is as follows:

[0206]

[0207] Where d is also the generated random weight parameter.

[0208] For parameters that result in moderate system performance, the algorithm will escape the parameter deadlock by perturbing the worst-case solution. In this case, the parameter update equation is as follows:

[0209]

[0210] for The algorithm generates using the following formula:

[0211]

[0212]

[0213]

[0214] During this exploratory phase, different update formulas should be used to modify the parameters of subsystems with different performance characteristics. It is important to note that the final parameters of the chassis system are obtained through iterative optimization using three update methods; therefore, each parameter optimization update equation requires a weighting coefficient to represent its contribution to the overall formula.

[0215] 3.2) Linear Development Phase

[0216] In this algorithm stage, the algorithm mainly performs fine optimization in the potential area of ​​chassis optimization parameters to balance the conflict between multiple objectives such as steering sensitivity and suspension vertical acceleration.

[0217] At this point, there is a significant gap between the high-performance chassis system and the low-performance subsystem. The parameter iterative update is achieved through the following equation:

[0218]

[0219] in, Represents term-by-term multiplication. This represents Brownian motion in d-dimensional space.

[0220] At this stage, the algorithm focuses more on the overall performance of the chassis system. It determines how to perform optimization operations by analyzing the magnitude of a random number generated by the chassis. In this case, the algorithm will analyze the parameters of the system with good performance and then make appropriate fine adjustments, gradually optimizing the performance of the system with poor performance, and finally selecting the optimal solution with better structural parameters. When random numbers... In such cases, if traditional heuristic algorithms get stuck in local optima, the parameter optimization process can exhibit random behavior similar to Brownian motion. The algorithm proposed in this invention employs Levy flight to help escape local optima, and this strategy has proven effective in various situations.

[0221] Throughout the convergence phase of the metaheuristic algorithm proposed in this invention, there is a clear transition from an initial emphasis on exploration to a subsequent focus on utilization. As the algorithm enters the later stages of iteration, it is prone to getting trapped in local optima, leading to premature convergence. It is worth noting that Brownian motion is characterized by the high randomness of particle movement, which typically occurs due to a lack of external driving force or control. The SGA algorithm proposed in this invention utilizes the inherent randomness of Brownian motion to reduce the risk of the algorithm getting trapped in local optima.

[0222] The Snow Goose Algorithm (SGA), one of the core innovations of this invention, is inspired by the intelligent behavior of snow goose flocks in nature, cleverly simulating their dispersal and regrouping characteristics. During implementation, SGA achieves efficient global search for chassis parameter optimization through the synergistic effect of two key stages: humanoid exploration and linear development. The humanoid exploration stage, by introducing random weight parameters, effectively balances the relationship between current and previous generation parameters, ensuring the algorithm can broadly explore the solution space in the early stages and avoid getting trapped in local optima. The linear development stage, through the organic combination of the Lévy flight strategy and Brownian motion, not only achieves fine optimization of the potential region but also significantly reduces the risk of getting trapped in local optima, ensuring the algorithm's stable convergence. This innovative algorithm demonstrates superior performance in multi-objective optimization problems, especially in handling high-dimensional complex optimization problems. With its unique hierarchical collaboration mechanism, it effectively reduces computational complexity and significantly improves computational efficiency, successfully overcoming the bottleneck problem of traditional intelligent algorithms being susceptible to the "curse of dimensionality" and experiencing decreased particle collaboration efficiency in high-dimensional spaces.

[0223] This invention fully considers the stringent performance requirements of agricultural machinery vehicles in hilly and mountainous areas due to their complex topography and undulating terrain. Focusing on the key area of ​​wheel-leg all-terrain chassis, it proposes a multi-objective optimization method for the structural parameters of wheel-leg all-terrain chassis. This process comprehensively analyzes the profound impact of chassis parameters such as damping coefficient and moment of inertia on various aspects of vehicle handling stability, ride comfort, and safety. It also fully considers the complex coupling relationships between chassis subsystems, ensuring that the optimization results comprehensively improve the overall performance of agricultural machinery vehicles, enabling them to have better adaptability and efficiency when dealing with complex hilly terrain. This invention first conducts an in-depth analysis of the significant impact of key chassis parameters on vehicle performance, successfully constructing a comprehensive and accurate multi-objective optimization model. Then, it uses the innovative Snow Goose Algorithm (SGA) for efficient solution, thereby quickly obtaining a high-performance optimized design scheme under complex constraints. This provides strong technical support and a novel solution for optimizing the chassis structural parameters of hilly agricultural machinery vehicles, demonstrating significant technical advantages and practical value.

[0224] The multi-objective optimization method proposed in this invention achieves significant results in improving the ride comfort, stability, and working efficiency of agricultural machinery vehicles. This multi-objective optimization method for the structural parameters of wheel-leg all-terrain chassis is theoretically highly innovative and forward-looking, and demonstrates strong adaptability and effectiveness in practical applications, which is of great significance for promoting the level of agricultural and forestry mechanization in hilly areas.

[0225] In this embodiment of the invention, the computer-readable storage medium can be an internal storage unit of any data processing device described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be any data processing device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., mounted on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.

[0226] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only.

[0227] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the structural parameters of a wheel-legged all-terrain chassis, characterized in that: include: Analyze the impact of chassis structural parameters on vehicle performance; A multi-objective optimization model for the structural parameters of wheel-leg all-terrain chassis is proposed, and the chassis structural parameters are adjusted when performing multi-objective optimization of the chassis system. Solve the multi-objective optimization model for the structural parameters of the wheel-leg all-terrain chassis to obtain the chassis structural parameters that enable the overall performance of agricultural machinery vehicles to reach the optimal level.

2. The method for optimizing the structural parameters of the wheel-leg all-terrain chassis according to claim 1, characterized in that: The analysis of the impact of chassis structural parameters on vehicle performance includes: Damping coefficient and moment of inertia, wherein the damping coefficient determines the suspension system's ability to dampen vibrations, and the moment of inertia affects the dynamic response and stability of the vehicle's steering system; A multi-objective optimization model for the structural parameters of wheel-leg all-terrain chassis is proposed. During the multi-objective optimization of the chassis system, the chassis structural parameters are adjusted, including: The agricultural machinery chassis of the model consists of multiple subsystems, which influence each other. Heuristic algorithms are used to optimize the structural parameters of the agricultural machinery chassis for multiple objectives.

3. The method for optimizing the structural parameters of the wheel-leg all-terrain chassis according to claim 1, characterized in that: The system position matrix of the chassis represents the spatial location of the snow goose population, and the chassis parameter matrix represents the individual state of the snow goose within the population, as concisely represented below: Here, variable n represents the total number of vehicle chassis systems, while d corresponds to the number of variables associated with the chassis optimization problem. For each subsystem in the chassis, it is assumed that there exists an array responsible for storing the performance characteristics associated with the location of each subsystem, as shown below. In this context, it must be emphasized that the chassis parameter performance function operates as a black box. All the user needs to do is input the candidate parameter solutions that each subsystem can obtain. The algorithm will then generate the performance value function value for each subsystem. By feeding the candidate parameter set of each subsystem and the coupling relationship between the subsystems into the performance function, the obtained performance values ​​are stored in the corresponding positions in the vector.

4. The method for optimizing the structural parameters of the wheel-leg all-terrain chassis according to claim 1, characterized in that: The chassis performance of the agricultural machinery is affected by the parameters of the steering system and the suspension system. Among these, the objective functions that need to be optimized in the steering system are steering feel and steering sensitivity. The mathematical expression for these objective functions is as follows: in The target for optimizing steering feel is represented by the average energy of steering feel within a certain frequency range. This represents the maximum frequency value of the useful signal for steering road feel and road surface information. For steering wheel torque, The resistance torque of the steering output shaft; in, Steering wheel angle to yaw rate The transfer function, which is the steering sensitivity, is the value of the transfer function. Steering wheel angle To the front wheel corner The transfer function, and finally, the optimization index. This represents the average energy of the steering sensitivity over a certain frequency range.

5. The method for optimizing the structural parameters of the wheel-leg all-terrain chassis according to claim 1, characterized in that: In suspension systems, the objectives to be optimized include vertical acceleration and suspension travel. The root mean square (RMS) value of the response to each parameter is used as a performance evaluation metric. Specifically, the RMS value of a response refers to the response over the entire vibration analysis time T. The mathematical definition is obtained by integration: in, The root mean square value represents the vibration response; the smaller the value, the better the ride comfort of the agricultural machinery. Furthermore, based on the root mean square of the system response, the objective function that needs to be optimized in the suspension system is identified as follows: in, Optimize the indicators for the roll angle acceleration of agricultural machinery vehicles. It can reflect the lateral stability of agricultural machinery vehicles when cornering. Optimize the pitch angle acceleration of agricultural machinery vehicles. It can reflect the car's climbing ability.

6. The method for optimizing the structural parameters of the wheel-leg all-terrain chassis according to claim 1, characterized in that: The multi-objective optimization problem for the all-terrain chassis structural parameters is as follows: The algorithm primarily optimizes the parameter matrix V to minimize the objective function, ensuring that the agricultural machinery chassis system does not experience significant bumps or vibrations under complex terrain conditions, thus guaranteeing optimal overall chassis system performance. The weighting coefficients are... The value of is generally determined by the characteristics of hilly terrain. The chassis parameters, suspension system stiffness coefficient k and suspension system damping coefficient c, in the constraints are to ensure that agricultural vehicles can drive normally and prevent excessive pitching vibration.

7. The method for optimizing the structural parameters of the wheel-leg all-terrain chassis according to claim 6, characterized in that: The iterative algorithm for different parameters uses the following equation for calculation: This equation explains the iteration parameters. The value depends on the current parameter value. Parameter error and iteration duration t, for variables t, this variable is used to represent the difference between adjacent generations, and its value is set to 1.

8. The method for optimizing the structural parameters of the wheel-leg all-terrain chassis according to claim 1, characterized in that: The specific steps include: Humanoid exploration phase; In this algorithm phase, the algorithm searches for structural parameters that can improve the performance of the chassis system. These parameters are designed as performance-leading parameters, which guide the algorithm towards the global optimum. The algorithm update equation at this time is as follows: Where b is the generated random weight parameter; Furthermore, while finding the optimal system parameters, the algorithm also identifies the worst-performing parameter settings for other systems. For this part, the algorithm calculates the parameters when the chassis system's performance is at the mean, and coordinates parameter conflicts through the mean of subsystem parameters. The iterative evolution equation for the parameters at this point is as follows: Where d is also the generated random weight parameter; For parameters that result in moderate system performance, the algorithm will escape the parameter deadlock by perturbing the worst-case solution. In this case, the parameter update equation is as follows: for The algorithm generates using the following formula: During this exploratory phase, different update formulas should be used to modify the parameters of subsystems with different performance. It should be noted that the final parameters of the chassis system are obtained through iterative optimization using three update methods. Therefore, each parameter optimization update equation needs a weight coefficient to represent its contribution to the overall formula. Linear development phase; In this algorithm stage, the algorithm mainly performs fine optimization in the potential area of ​​chassis optimization parameters to balance the conflict of multiple objectives such as steering sensitivity and suspension vertical acceleration. At this point, there is a significant gap between the high-performance chassis system and the low-performance subsystem. The parameter iterative update is achieved through the following equation: in, Represents term-by-term multiplication. Represents Brownian motion in d-dimensional space; When random number When the system performs poorly, the algorithm will analyze the parameters of the system with good performance and then make appropriate fine adjustments to gradually optimize the performance of the system with poor performance, thereby selecting a better solution with better structural parameters; when random numbers... If a traditional heuristic algorithm gets stuck in a local solution, the parameter optimization process will produce random behavior similar to Brownian motion.

9. A device for optimizing the structural parameters of a wheel-legged all-terrain chassis, characterized in that: include: The data analysis module analyzes the impact of chassis structure parameters on vehicle performance; The data processing module proposes a multi-objective optimization model for the structural parameters of wheel-leg all-terrain chassis, and adjusts the chassis structural parameters when performing multi-objective optimization of the chassis system. The data optimization module solves a multi-objective optimization model for the structural parameters of the wheel-leg all-terrain chassis, obtaining chassis structural parameters that enable the overall performance of agricultural machinery vehicles to reach the optimal level.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-8.

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