A method for adjusting the posture of an adaptive suspension system

Abstract

The present invention discloses a method for adaptive suspension system posture adjustment, which belongs to the field of PID technology optimization. Specifically, the method comprises the following steps: step 1, collecting vehicle posture data of the suspension system, including vertical displacement, pitch angle, yaw angle and other data of the vehicle body, and establishing a dynamic model for adaptive suspension system posture adjustment; step 2, improving the planetary optimization algorithm, introducing a topological insulator dimensional sensitivity mechanism and an adaptive search adjustment strategy to improve the planetary optimization algorithm; step 3, optimizing the Kp, Ki, and Kd coefficients of the PID controller for suspension system posture adjustment by using an improved planetary optimization algorithm (IPOA); step 4, using the optimized PID controller to adjust the stiffness and damping parameters of the suspension system in real time based on the posture change data collected in real time. The method of the present invention effectively overcomes the problems of slow response, large overshoot and insufficient steady-state accuracy existing in traditional PID control methods in suspension system posture adjustment, thereby ensuring that the vehicle posture meets the expected requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of PID control optimization, and in particular relates to a method for adjusting the attitude of an adaptive suspension system. Background Art

[0002] The suspension system is a key component of a vehicle, designed to mitigate ground impact forces, reduce vehicle body jolting when the road surface is uneven or the load fluctuates, and thus enhance driving stability. As the automotive industry continues to demand higher performance, modern suspension systems are becoming more intelligent and adaptive. For example, intelligent suspension systems use sensors and electronic control units (ECUs) to monitor vehicle and road conditions in real time and dynamically adjust suspension components. Adaptive suspension systems, on the other hand, adjust suspension characteristics based on road conditions and driving style, automatically adjusting vehicle performance under various operating conditions.

[0003] In adaptive suspension systems, attitude control is a key control objective for maintaining vehicle stability. Its primary purpose is to ensure stable vehicle operation under various road conditions, preventing excessive pitch, roll, or bouncing, thereby improving vehicle handling and comfort. Adaptive suspension systems typically employ active or semi-active suspension technology, combining sensors and control algorithms to adjust vehicle attitude.

[0004] To fully exploit the advantages of adaptive suspension systems, optimal design of suspension parameters is crucial. With the rapid development of computer technology in recent years, intelligent optimization algorithms such as genetic algorithms, particle swarm optimization algorithms, and gray wolf optimization algorithms have been widely used for suspension parameter optimization due to their superior global search capabilities. The planetary optimization algorithm (POA) is an emerging global optimization algorithm inspired by the principles of gravitational interactions in celestial mechanics. By simulating the gravitational relationship between the sun and planets, the algorithm treats candidate solutions in the optimization problem as "planets" and searches and optimizes them through the gravitational interactions between these "planets." The POA algorithm has attracted widespread attention due to its simple structure, high efficiency, and powerful global search capabilities. However, due to the global nature of gravitational interactions, the POA algorithm may prematurely converge to a local optimum, thereby losing the ability to continue exploring the global optimum. Summary of the Invention

[0005] The purpose of the present invention is to address the deficiencies of the existing technology and propose a method for adaptive suspension system posture adjustment. The method optimizes the PID controller of the suspension system posture adjustment by improving the planetary optimization algorithm (IPOA), aiming to improve the control accuracy and response speed of the posture adjustment controller. The method combines the traditional PID control algorithm with the intelligent optimization algorithm. The proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd of the PID controller are optimized by the IPOA algorithm, so that the suspension system can be adaptively adjusted according to real-time road conditions and vehicle dynamics, thereby improving control accuracy and effectively reducing overshoot, thereby achieving precise adjustment of the vehicle posture in the adaptive suspension system and significantly improving the vehicle's driving stability and comfort under complex road conditions.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a method for adjusting the attitude of an adaptive suspension system, comprising the following specific steps:

[0007] Step 1: Collect vehicle posture data of the suspension system and establish a dynamic model for adaptive suspension system posture adjustment for subsequent optimization and adjustment of the control strategy.

[0008] Step 2: Improve the planet optimization algorithm. The specific improvement strategy is:

[0009] Im1. Introducing the topological insulator dimensionality sensitivity mechanism to improve the mass calculation equation in the POA algorithm;

[0010] Im2: To overcome the limitations of the traditional gravity model in the original algorithm, we introduce spatiotemporal nonlinear mapping and topological control mechanisms, enabling planetary individuals to perform adaptive global searches in the search space.

[0011] Im3. Introduce a self-organized criticality mechanism to allow individual planets to self-organize and evolve under the guidance of dynamic cognitive feedback and local disturbances, and guide individual planets to conduct more flexible local searches in complex spaces.

[0012] Step 3: Optimize the parameters of the attitude control PID controller by using the improved planetary optimization algorithm (IPOA). The IPOA algorithm optimizes the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd in the PID controller by simulating the laws of planetary orbital motion.

[0013] Step 4: Use the optimized PID controller to adjust the suspension system posture in real time. The PID controller adjusts the vertical support force applied to the suspension elements in the suspension system based on the posture data collected in real time to ensure that the vehicle posture meets the target requirements.

[0014] Furthermore, in step 1, vehicle posture data of the suspension system is collected, including vertical displacement, pitch angle, roll angle, etc. of the vehicle body, as well as dynamic information such as road conditions and load changes, to establish an adaptive suspension system posture control model for subsequent control optimization and adjustment.

[0015] Furthermore, a vehicle dynamics model is established. The vehicle dynamics model can be described by a three-degree-of-freedom system, which includes vertical displacement, pitch angle, and yaw angle. The mathematical model of vertical displacement is:

[0016] (1);

[0017] In formula (1), For the body quality, is the vertical displacement of the vehicle body’s center of mass, is the vertical support force applied to the i-th suspension;

[0018] Furthermore, the mathematical model of the pitch angle is:

[0019] (2);

[0020] In formula (2), is the moment of inertia of the vehicle body around the transverse axis, is the vehicle body pitch angle, and are the distances from the front axle and rear axle to the center of mass of the vehicle body, - Vertical support forces applied to the four suspensions;

[0021] Furthermore, the mathematical model of yaw motion is:

[0022] (3);

[0023] In formula (3), is the moment of inertia of the vehicle body around the longitudinal axis, is the vehicle body yaw angle, is the lateral distance from the center of mass to the left and right wheels.

[0024] Furthermore, in order to achieve posture adjustment, it is necessary to define the error of each posture value. The error is defined as the difference between the expected posture value and the actual posture value. The error is defined as:

[0025] (4);

[0026] In formula (4), 、 、 Represent the vertical displacement error, pitch angle error and yaw angle error respectively, 、 、 represent the target vertical displacement, target pitch angle and yaw angle respectively, is the actual vertical displacement of the vehicle body, is the actual pitch angle of the vehicle body, is the actual yaw angle of the vehicle body;

[0027] Furthermore, the vertical displacement error Input PID controller unit, process the error through PID controller optimized online by IPOA algorithm, and output control signal Acting on the suspension elements of the adaptive suspension system, it adjusts the four vertical support forces applied to the suspension elements in real time - , thereby adjusting the vertical displacement, pitch angle and yaw angle of the vehicle body to ensure the stability of the vehicle body suspension system;

[0028] Furthermore, the control signal The dynamic mapping relationship with the vertical support force is:

[0029] (5);

[0030] In formula (5), - is the vertical support force of the four suspension elements, A is the pitch angle compensation coefficient of the suspension element, and B is the yaw angle compensation coefficient of the suspension element;

[0031] Furthermore, during the attitude adjustment process, the Kp, Ki, and Kd coefficients of the PID controller unit are mapped online to the individual positions in the search space of the improved planetary optimization algorithm, and the three-dimensional values ​​of the individual positions of the planets are represented by [Kp, Ki, and Kd], respectively.

[0032] Furthermore, in step 2, Im1, the topological insulator dimensional sensitivity mechanism is introduced to improve the mass calculation equation in the POA algorithm. This mechanism introduces the sensitivity activation coefficient of each dimension Distinguishing high-dimensional and low-dimensional individuals enables effective differentiation of high-dimensional and low-dimensional individuals, thereby enabling differentiated responses to the impact of distance changes in different dimensions on mass values. When a dimension is highly sensitive, the contribution of that dimension to mass evaluation can be significantly enhanced, thereby guiding the planetary individual to accelerate convergence in key dimensions and improving the algorithm's optimization capabilities in high-dimensional search space. The improved mass calculation equation is:

[0033] (6);

[0034] In formula (6), is the mass of the i-th planet, is the mass of the jth planet, i=1,…,nPop, j=1,…,nPop, nPop is the number of algorithm populations, is the fitness of the i-th or j-th planet, is the latitude value of the search space, is the sensitivity activation coefficient of the d-th dimension, is the parameter value of the ith planet in the dth dimension, is the value of the current optimal solution in the dth dimension, is the attraction parameter between the planet and the optimal solution, It is a very small constant to prevent calculation errors caused by the denominator being zero.

[0035] Furthermore, the spatiotemporal nonlinear mapping and topological control mechanism are introduced in step 2, Im2, so that individual planets can perform adaptive global search in the search space. The improved global search mathematical model is:

[0036] (7);

[0037] In formula (7), is the updated individual position of the i-th planet, t is the current iteration number, is the individual position of the current iteration of the i-th planet, is the gravitational acceleration factor, and is a random number between [0, 1], is the optimal solution position in the current iteration, is the attraction parameter between the planet and the optimal solution, Search parameters for diversity, is the planetary state function, To adjust the parameters of neighborhood influence, is the neighborhood of the i-th planet in the current iteration, where The mathematical model is:

[0038] (8);

[0039] In formula (8), t is the current iteration number, is the fitness of the i-th planet in the k-th generation, is the fitness of the optimal solution, is the individual position of the i-th planet in the k-th generation, is the individual position of the optimal solution in the kth generation.

[0040] Furthermore, the self-organized criticality mechanism is introduced in step 2 and Im3, so that the planetary individuals can self-organize and evolve under the guidance of dynamic cognitive feedback and local disturbances, and guide the planetary individuals to conduct more flexible searches in complex spaces. The improved local search mathematical model is:

[0041] (9);

[0042] In formula (9), is the updated individual position of the i-th planet, is the individual position of the current iteration of the i-th planet, t is the number of current iterations, is the critical position of the system’s self-organization evolution. is the local optimal solution in the neighborhood of the planet, is the critical disturbance intensity, is the neighborhood guidance strength.

[0043] Furthermore, in step 3, the parameters of the attitude control PID controller are optimized by using an improved planetary optimization algorithm (IPOA). The IPOA algorithm optimizes the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd in the PID controller by simulating the laws of planetary orbital motion. The specific steps are as follows:

[0044] S1. Initialize the population size nPop of the improved planetary optimization algorithm (IPOA), the maximum number of iterations MaxIter, the search space dimension Dim, and the upper and lower bounds of the search space [Ub, Lb], where Ub and Lb are unit vectors of order Dim;

[0045] S2. Initialize the b, c, G, and Rmin parameters of the improved planetary optimization algorithm, initialize the individual positions of the improved planetary optimization algorithm, generate the initial positions of the individuals in the population, and the mathematical model for initializing the individual positions is:

[0046] (10);

[0047] In formula (10), is the initial position of the randomly generated planet, is a random number between [0, 1], and Ub and Lb have the same meaning as above;

[0048] S3. Calculate the fitness of each planet; update the individual positions and fitness of all planets through greedy selection, and select the individual position with the smallest fitness in the population as the optimal solution , and record The fitness is the best fitness in the current iteration ;

[0049] S4. Calculate and update the gravitational torque parameter M. The mathematical model of M is:

[0050] (11);

[0051] In formula (11), and is the mass of the i-th planet and the mass of the j-th planet, is the Cartesian distance between the two planets, G is the gravitational parameter;

[0052] S5. Calculate the Cartesian distance between the planet and the optimal solution , The mathematical model is:

[0053] (12);

[0054] In formula (12), the meanings of the parameters are the same as above;

[0055] S6. When > , the algorithm enters the global exploration stage, simulates the planets away from the "sun" to build motion trajectories for global search, updates individual positions, and the improved mathematical model of global exploration is the same as above, The mathematical model is:

[0056] (13);

[0057] In formula (13), is the initial Cartesian distance between the planet and the optimal solution, and the other parameters are the same as above;

[0058] S7, when < , the algorithm enters the local development stage, simulating the planets close to the "sun" to construct motion trajectories for local search and update individual positions. The improved mathematical model of local exploration is the same as above;

[0059] S8. Check whether the current number of iterations t is greater than MaxIter. If so, output the optimal solution of the improved planetary optimization algorithm and decode the optimal solution into the proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd in the PID controller. If not, execute t=t+1 and return to S3 to continue iterative optimization.

[0060] The present invention proposes an adaptive suspension system attitude adjustment method, which optimizes the PID controller of attitude adjustment in the suspension system by improving the planetary optimization algorithm (IPOA). Compared with the existing technology, the present invention has the following advantages:

[0061] P1. By introducing a dimensionality-sensitivity mechanism for topological insulators, the mass calculation equation in the Planet Optimization Algorithm (POA) is improved. This allows individual planets to adapt adaptively to changes in the search space dimension, enhancing the optimization process's ability to perceive and adapt to complex, non-uniform spatial structures. This improves the accuracy and dynamic response of PID controller parameter optimization during suspension system attitude adjustment.

[0062] P2. By introducing spatiotemporal nonlinear mapping and topological control mechanisms, we break through the single linear evolution mode of position update in traditional gravity models and realize adaptive twisted search and topological perception migration of individual planets in the search space, effectively expanding the global exploration range and improving the search efficiency and adaptability to complex working conditions during the attitude adjustment PID parameter optimization process.

[0063] P3. By introducing a self-organized criticality mechanism, the planetary individuals form self-organized evolutionary behavior under the guidance of dynamic cognitive feedback and local disturbances. They can adaptively perform local jumps and global adjustments according to changes in the system state during suspension system posture adjustment, thereby significantly improving the adjustment accuracy of the PID controller and the posture stability and comfort of the suspension system under complex road conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 Technology roadmap for adaptive suspension system posture adjustment.

[0065] Figure 2 Optimization flow chart for improving planet optimization algorithm.

[0066] Figure 3 A fitness comparison chart of the improved planet optimization algorithm and the ordinary planet optimization algorithm.

[0067] Figure 4 A comparison chart of vertical displacement between the improved planetary optimization algorithm and the ordinary planetary optimization algorithm.

[0068] Figure 5 A comparison chart of the pitch angles of the improved planet optimization algorithm and the ordinary planet optimization algorithm.

[0069] Figure 6 A comparison chart of the yaw angles of the improved planetary optimization algorithm and the ordinary planetary optimization algorithm. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0071] The present invention provides an adaptive suspension system posture adjustment method, such as Figure 1 As shown, the specific steps are:

[0072] Step 1: Collect vehicle attitude data of the suspension system, including the vertical displacement, pitch angle, yaw angle, etc. of the vehicle body, and monitor the road conditions and the dynamic response of the vehicle. Based on this data, establish a dynamic model for the attitude adjustment of the adaptive suspension system.

[0073] Specifically, in the example of the present invention, mathematical models are established for vertical displacement, pitch angle, and yaw angle respectively by Matlab, wherein the mathematical model of vertical displacement is:

[0074] function dz = heave_model(t, z, F_z_func, m)

[0075] % Vertical displacement equation

[0076] dz1 = z(2); % Velocity is equal to the first derivative of displacement

[0077] dz2 = sum(F_z_func(t)) / m; % The acceleration is determined by the sum of the four suspension forces

[0078] dz = [dz1; dz2];

[0079] end.

[0080] Furthermore, the mathematical model of the pitch angle is:

[0081] function dtheta = pitch_model(t, theta, F1_func, F2_func, F3_func,F4_func, l_f, l_r, I_x)

[0082] % Pitch angle equation

[0083] dtheta1 = theta(2); % Angular velocity is equal to the first derivative of the pitch angle

[0084] dtheta2 = (F1_func(t) * l_f - F2_func(t) * l_f - F3_func(t) * l_r + F4_func(t) * l_r) / I_x; % Angular acceleration

[0085] dtheta = [dtheta1; dtheta2];

[0086] end.

[0087] Furthermore, the mathematical model of yaw motion is:

[0088] function dphi = roll_model(t, phi, F1_func, F2_func, F3_func, F4_func, l_w, I_y)

[0089] % Yaw angle equation

[0090] dphi1 = phi(2); % Angular velocity equals the first derivative of the yaw angle

[0091] dphi2 = (F1_func(t) * l_w + F3_func(t) * l_w - F2_func(t) * l_w -F4_func(t) * l_w) / I_y; % angular acceleration

[0092] dphi = [dphi1; dphi2];

[0093] end.

[0094] Furthermore, in order to achieve posture adjustment, it is necessary to define the error of each posture value. The error is defined as the difference between the expected posture value and the actual posture value. The error is defined as:

[0095] (4);

[0096] In formula (4), 、 、 Represent the vertical displacement error, pitch angle error and yaw angle error respectively, 、 、 are the desired vehicle body posture target values, representing the target vertical displacement, target pitch angle, and yaw angle, respectively. is the actual vertical displacement of the vehicle body, is the actual pitch angle of the vehicle body, is the vehicle body yaw angle.

[0097] Furthermore, the vertical displacement error Input PID controller unit, process the error through PID controller optimized online by IPOA algorithm, and output control signal Acting on the suspension elements of the adaptive suspension system, it adjusts the vertical support force applied to the four suspension elements in real time - , adjust the vertical displacement, pitch angle and yaw angle of the vehicle body to ensure the stability of the vehicle body suspension system.

[0098] Furthermore, the control signal The dynamic mapping relationship with the vertical support force is:

[0099] (5);

[0100] In formula (5), - is the vertical support force of the four suspension elements, A is the pitch angle compensation coefficient of the suspension element, which is 0.1, and B is the yaw angle compensation coefficient of the suspension element, which is 0.08.

[0101] Furthermore, the attitude difference is input into the PID controller unit, which processes the difference through the online optimized PID control algorithm and outputs a control signal. The control signal acts on the suspension elements of the adaptive suspension system to adjust the stiffness and damping characteristics of the suspension system in real time.

[0102] Step 2: Improve the planet optimization algorithm. The specific improvement strategy is:

[0103] Im1. Introducing the topological insulator dimensional sensitivity mechanism to improve the mass calculation equation in the algorithm, the improved mass calculation equation is:

[0104] (6);

[0105] In formula (6), is the mass of the i-th planet, is the mass of the jth planet, i=1,…,nPop, j=1,…,nPop, nPop is the number of algorithm populations, is the fitness of the i-th or j-th planet, is the latitude value of the search space, is the sensitivity activation coefficient of the d-th dimension, which is 0.8. is the parameter value of the ith planet in the dth dimension, is the value of the current optimal solution in the dth dimension, is the attraction parameter between the planet and the optimal solution, and its value is 2. is a very small constant, with a value of 0.000001;

[0106] Im2. The global search mathematical model of the improved algorithm is improved by introducing the time-space nonlinear mapping and topology control mechanism. The improved global search mathematical model is:

[0107] (7);

[0108] In formula (7), is the updated individual position of the i-th planet, t is the current iteration number, is the individual position of the current iteration of the i-th planet, is the gravitational acceleration factor, and is a random number between [0, 1], is the optimal solution position in the current iteration, is the attraction parameter between the planet and the optimal solution, Search parameters for diversity, is the planetary state function, To adjust the parameters of neighborhood influence, the value is 0.05. is the neighborhood of the i-th planet in the current iteration, where The mathematical model is:

[0109] (8);

[0110] In formula (8), t is the current iteration number, is the fitness of the i-th planet in the k-th generation, is the fitness of the optimal solution, is the individual position of the i-th planet in the k-th generation, is the individual position of the optimal solution in the kth generation;

[0111] Im3. The local search mathematical model of the algorithm is improved by introducing the self-organized criticality mechanism. The improved local search mathematical model is:

[0112] (9);

[0113] In formula (9), is the updated individual position of the i-th planet, is the individual position of the current iteration of the i-th planet, t is the number of current iterations, is the critical position of the system’s self-organization evolution. is the local optimal solution in the neighborhood of the planet, is the critical perturbation intensity that decreases linearly from 0.5 to 0, is the neighborhood guidance strength, with a value of 0.2;

[0114] Step 3: Optimize the parameters of the attitude control PID controller by using the improved planetary optimization algorithm (IPOA). The IPOA algorithm optimizes the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd in the PID controller by simulating the laws of planetary orbital motion. The specific steps are as follows:

[0115] S1. Initialize the population size nPop of the improved planetary optimization algorithm (IPOA) to 30, the maximum number of iterations MaxIter to 20, the search space dimension Dim to 3, the upper bound Ub of the search space to [20, 20, 20], and the lower bound to [0, 0.000001, 0];

[0116] S2. Initialize the parameters b of the improved planetary optimization algorithm to 5, G to 1, R0 to 1000, and c to a value that decreases linearly from 2 to 1. Initialize the individual positions of the improved planetary optimization algorithm to generate the initial positions of the planetary individuals. The mathematical model for initializing the individual positions is:

[0117] (10);

[0118] In formula (10), is the initial position of the randomly generated planet, is a random number between [0, 1], and Ub and Lb have the same meaning as above;

[0119] S3. Calculate the fitness of each planet; update the individual positions and fitness of all planets through greedy selection, and select the individual position with the smallest fitness in the population as the optimal solution , and record The fitness is the best fitness in the current iteration ;

[0120] S4. Calculate and update the gravitational torque parameter M. The mathematical model of M is:

[0121] (11);

[0122] In formula (11), and is the mass of the i-th planet and the mass of the j-th planet, is the Cartesian distance between the two planets, G is the gravitational parameter;

[0123] S5. Calculate the Cartesian distance between the planet and the optimal solution , The mathematical model is:

[0124] (12);

[0125] In formula (12), the meanings of the parameters are the same as above;

[0126] S6. When > , the algorithm enters the global exploration stage, simulates the planets away from the "sun" to build motion trajectories for global search, updates individual positions, and the improved mathematical model of global exploration is the same as above, The mathematical model is:

[0127] (13);

[0128] In formula (13), is the initial Cartesian distance between the planet and the optimal solution, and the other parameters are the same as above;

[0129] S7, when < , the algorithm enters the local development stage, simulating the planets close to the "sun" to construct motion trajectories for local search and update individual positions. The improved mathematical model of local exploration is the same as above;

[0130] S8. Check whether the current number of iterations t is greater than MaxIter. If so, output the optimal solution of the improved planetary optimization algorithm and decode the optimal solution into the proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd in the PID controller. If not, execute t=t+1 and return to S3 to continue iterative optimization.

[0131] Furthermore, considering the control accuracy, response time and system energy consumption of the adaptive suspension system attitude adjustment, the objective function of the improved planetary optimization algorithm is selected as:

[0132] (14);

[0133] In formula (13), J is the fitness value calculated by the objective function, 、 、 Represent the vertical displacement error, pitch angle error and yaw angle error respectively, is the magnitude of the overshoot, is the integral of the square of the PID controller output control signal, T is the total system operation time, n is the number of accumulated errors, 、 is the weight coefficient, The value is 0.03, The value is 0.01, which is used to balance the control accuracy and energy consumption of the system.

[0134] Step 4: Use the optimized PID controller to adjust the suspension system posture in real time. The PID controller adjusts the stiffness and damping parameters of the suspension system based on the posture change data collected in real time to ensure that the vehicle posture meets the target requirements. The specific steps are as follows:

[0135] Step 1. Set the running time of the adaptive suspension system attitude adjustment simulation model to 20 seconds, the sampling time to 0.5 seconds, the vehicle's initial vertical displacement to 0, the initial pitch angle to 0°, the initial yaw angle to 0°, the target vertical displacement to 3, the target pitch angle to 0°, and the target yaw angle to 1°.

[0136] Step 2: Establish a mathematical model of the improved planetary optimization algorithm and write a connection function through Matlab to transfer data between the simulation model and the mathematical model of the improved planetary optimization algorithm;

[0137] Step 3: Connect the Kp, Ki, and Kd coefficients of the PID controller to the individual planet positions of the improved planetary optimization algorithm through the connection function. Related, =[Kp, Ki, Kd];

[0138] Step 4. Run the improved planetary optimization algorithm mathematical model, iterate the algorithm to find the best solution, output the individual solution in each iteration, and decode the values ​​of each individual solution in different dimensions into the Kp, Ki, and Kd coefficients of the PID controller;

[0139] Step 5. Output the Kp, Ki, and Kd parameters to the control system simulation model and run the control system simulation model;

[0140] Step 6. Determine whether the iteration is terminated. If terminated, output the optimal solution of the improved planetary optimization algorithm and decode the values ​​of each latitude of the optimal solution into Kp, Ki, and Kd parameters. The optimal Kp, Ki, and Kd parameters are: 14.7367, 0.0642, and 16.2716.

[0141] In this implementation step, the curves of fitness value changing with the number of iterations during the optimization process of the common planetary optimization algorithm and the improved planetary optimization algorithm are compared and analyzed. Figure 3 As shown, the fitness value of the conventional planetary optimization algorithm drops rapidly during the first six iterations, then enters a convergence plateau, stabilizing at approximately 0.035. The overall convergence speed is fast, but the final convergence quality is limited, preventing further reduction in the fitness value. The improved planetary optimization algorithm also experiences a significant drop in fitness around the sixth iteration, but subsequently maintains a moderate downward trend. Further optimization is achieved after the tenth iteration, ultimately converging to approximately 0.018 within 20 iterations, significantly outperforming the conventional algorithm. Overall, the improved planetary optimization algorithm not only converges quickly in the early stages but also maintains good optimization capabilities in the middle and late stages, ultimately achieving a lower fitness value. This demonstrates the effectiveness and superiority of the proposed method in improving optimization accuracy and accelerating convergence.

[0142] In this implementation step, the vertical displacement curves of the common planetary optimization algorithm and the improved planetary optimization algorithm are compared and analyzed, such as Figure 4 As shown, the conventional planetary optimization algorithm rises rapidly in the initial stage, but suffers from significant overshoot, with a maximum displacement approaching 4 cm and accompanied by large oscillations. The convergence process is relatively slow, and it takes about 10 seconds to stabilize near the target displacement value. The transition process is also not smooth. The improved planetary optimization algorithm also rises rapidly in the initial stage, but the maximum overshoot is significantly reduced, and the amplitude and number of oscillations are significantly reduced. The overall displacement response curve is more stable, converging to the target displacement value within approximately 6 seconds, and maintaining good stability. Comprehensive analysis shows that the improved planetary optimization algorithm outperforms the conventional algorithm in terms of overshoot suppression, convergence speed, and system stability, verifying the effectiveness and superiority of the proposed improvement strategy.

[0143] In this implementation step, the pitch angle response curves of the common planetary optimization algorithm and the improved planetary optimization algorithm are compared and analyzed, such as Figure 5 As shown in the figure, the conventional planetary optimization algorithm changes rapidly in the initial stage, but exhibits significant negative overshoot, with the maximum pitch angle approaching -0.6°. Positive overshoot then occurs, with large oscillation amplitude and slow convergence, ultimately stabilizing after approximately 10 seconds. The improved planetary optimization algorithm also exhibits a certain degree of negative excursion in the initial stage, but the overshoot amplitude is significantly reduced, and positive oscillation is also greatly reduced, resulting in a smoother overall curve. It converges to the target pitch angle of near 0° within 6 seconds, with minimal subsequent fluctuations. Overall, the improved planetary optimization algorithm outperforms the conventional algorithm in overshoot suppression, oscillation control, and convergence speed, effectively improving the system's dynamic response performance and stability.

[0144] In this implementation step, the yaw angle response curves of the common planetary optimization algorithm and the improved planetary optimization algorithm are compared and analyzed, such as Figure 6 As shown, the conventional planetary optimization algorithm rises rapidly in the initial stage, but exhibits significant overshoot, with the maximum yaw angle exceeding the target value by approximately 0.4°. This is followed by a certain amplitude of oscillation, and the overall convergence speed is slow, stabilizing near the target value after approximately 8 seconds. The improved planetary optimization algorithm, on the other hand, also rises rapidly in the initial stage, but exhibits virtually no significant overshoot. The response is smooth, and it quickly converges to the target yaw angle of approximately 1° in approximately 2 seconds. Subsequent fluctuations are minimal, demonstrating excellent dynamic performance and steady-state characteristics. Overall, the improved planetary optimization algorithm effectively reduces the system's overshoot, significantly improves convergence speed, and enhances system stability, offering significant advantages over the conventional planetary optimization algorithm.

[0145] In summary, the present invention provides an adaptive suspension system attitude adjustment method. This method optimizes the Kp, Ki, and Kd coefficients of the PID controller for suspension system attitude adjustment by using an improved planetary optimization algorithm (IPOA), thereby effectively overcoming the problems of slow response, large overshoot, and insufficient steady-state accuracy in the traditional PID control method during the suspension system attitude adjustment process. By introducing the topological insulator dimensional sensitivity mechanism and an adaptive search and adjustment strategy to improve the planetary optimization algorithm, the algorithm can realize efficient optimization of the PID control coefficients, significantly improving the attitude control performance of the suspension system under different working conditions, especially when facing external disturbances such as complex road conditions and vehicle load changes. The method can maintain rapid response and high stability of the system, thereby effectively improving the comfort and safety of vehicle driving.

Claims

1. A method for adjusting the attitude of an adaptive suspension system, characterized in that: Specifically include: Step 1: Collect vehicle attitude data of the suspension system, including vertical displacement, pitch angle, and yaw angle data of the vehicle body, and establish a dynamic model for attitude adjustment of the adaptive suspension system; Step 2: Improve the planet optimization algorithm. The specific improvement strategy is: Im1. Introducing the topological insulator dimensional sensitivity mechanism to improve the mass calculation equation in the planet optimization algorithm; the improved mass calculation equation is: （6）； In formula (6), is the mass of the i-th planet, is the mass of the jth planet, i=1,…,nPop, j=1,…,nPop, nPop is the number of algorithm populations, is the fitness of the i-th or j-th planet, is the latitude value of the search space, is the sensitivity activation coefficient of the d-th dimension, is the parameter value of the ith planet in the dth dimension, is the value of the current optimal solution in the dth dimension, is the attraction parameter between the planet and the optimal solution, It is a very small constant to prevent the denominator from being zero and causing calculation errors; Im2. Introducing spatiotemporal nonlinear mapping and topological control mechanisms enables planetary individuals to perform adaptive global searches in the search space. Specifically, introducing spatiotemporal nonlinear mapping and topological control mechanisms improves the global search mathematical model of the planetary optimization algorithm. The improved global search mathematical model is: （7）； In formula (7), is the updated individual position of the i-th planet, t is the current iteration number, is the individual position of the current iteration of the i-th planet, is the gravitational acceleration factor, and is a random number between [0, 1], is the optimal solution position in the current iteration, is the attraction parameter between the planet and the optimal solution, For the diversity search parameter, is the planetary state function, To adjust the parameters of neighborhood influence, is the neighborhood of the i-th planet in the current iteration, where The mathematical model is: （8）； In formula (8), t is the current iteration number, is the fitness of the i-th planet in the k-th generation, is the fitness of the optimal solution, is the individual position of the i-th planet in the k-th generation, is the individual position of the optimal solution in the kth generation; Im3. Introducing a self-organized criticality mechanism to enable individual planets to self-organize and evolve under the guidance of dynamic cognitive feedback and local perturbations, and to guide individual planets to conduct local searches in complex spaces; Step 3: Optimize the parameters of the PID controller for attitude control by improving the planetary optimization algorithm. The improved planetary optimization algorithm optimizes the parameters by simulating the laws of planetary orbital motion, thereby optimizing the proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd in the PID controller. Step 4: Use the optimized PID controller to adjust the suspension system posture in real time. The PID controller adjusts the stiffness and damping parameters of the suspension system according to the posture change data collected in real time.

2. The method for adjusting the attitude of an adaptive suspension system according to claim 1, characterized in that: In step 2, the introduction of the self-organized criticality mechanism is specifically: introducing the self-organized criticality mechanism to improve the local search mathematical model of the planetary optimization algorithm, and the improved local search mathematical model is: （9）； In formula (9), is the updated individual position of the i-th planet, is the individual position of the current iteration of the i-th planet, t is the number of current iterations, is the critical position evolved by the self-organization of the system. is the local optimal solution in the neighborhood of the planet, is the critical disturbance intensity, is the neighborhood guidance strength.

3. The method for adjusting the attitude of an adaptive suspension system according to claim 1, characterized in that: In step 3, the parameters of the PID controller for attitude adjustment are optimized by using an improved planetary optimization algorithm. The specific steps are as follows: S1. Initialize the population size nPop, the maximum number of iterations MaxIter, the search space dimension Dim, and the upper and lower bounds of the search space [Ub, Lb] of the improved planetary optimization algorithm, where Ub and Lb are unit vectors of order Dim; S2. Initialize the b, c, G, and Rmin parameters of the improved planetary optimization algorithm, initialize the individual positions of the improved planetary optimization algorithm, generate the initial positions of the individuals in the population, and the mathematical model for initializing the individual positions is: （10）； In formula (10), is the initial position of the randomly generated planet, is a random number between [0, 1], and Ub and Lb have the same meaning as above; S3. Calculate the fitness of each planet; update the individual positions and fitness of all planets through greedy selection, and select the individual position with the smallest fitness in the population as the optimal solution , and record The fitness is the best fitness in the current iteration , calculate and update the gravitational moment parameter M; S4. Calculate the Cartesian distance between the planet and the optimal solution ,when > , the algorithm enters the global exploration stage, and the improved mathematical model of global exploration is the same as above; S5. When < , the algorithm enters the local development stage, and the improved mathematical model of local exploration is the same as above; S6. Check whether the current number of iterations t is greater than MaxIter. If so, output the optimal solution of the improved planetary optimization algorithm and decode the optimal solution into the proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd in the PID controller. If not, execute t=t+1 and return to S3 to continue iterative optimization.

4. The method for adjusting the attitude of an adaptive suspension system according to claim 3, characterized in that: In the steps 3 and S2 The mathematical model is: （11）； In formula (11), is the initial Cartesian distance between the planet and the optimal solution, is the latitude value of the search space, and Ub and Lb are the upper and lower bounds of the search space.

5. The method for adjusting the attitude of an adaptive suspension system according to claim 3, characterized in that: The mathematical model of M in step 3 and S3 is: （12）； In formula (12), and is the mass of the i-th planet and the mass of the j-th planet, is the Cartesian distance between the two planets, and G is the gravitational parameter.

6. The method for adjusting the attitude of an adaptive suspension system according to claim 3, characterized in that: In the steps 3 and S4 The mathematical model is: （13）； In formula (13), is the individual position of the current iteration of the i-th planet, d takes the value of 1,…,Dim, is the latitude value of the search space, is the optimal solution of the algorithm.

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