Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a self-adaptive suspension system posture adjusting method, which optimizes a PID controller for suspension system posture adjustment by improving an epicyclic optimization algorithm (IPOA) and aims at improving the control precision and response speed of the posture adjusting controller, combines a traditional PID control algorithm and an intelligent optimization algorithm, the proportional coefficient Kp, the integral coefficient Ki and the differential coefficient Kd of the PID controller are optimized through the IPOA algorithm, so that the suspension system can be dynamically and adaptively adjusted according to real-time road conditions and vehicles, the control precision is improved, the overshoot is effectively reduced, the accurate adjustment of the vehicle posture is realized in the adaptive suspension system, and the running stability and the comfort of the vehicles under complex road conditions are obviously improved.
In order to achieve the aim, the invention adopts the following technical scheme that the self-adaptive suspension system posture adjusting method comprises the following specific steps:
And step 1, collecting vehicle attitude data of a suspension system, and establishing a dynamic model of attitude adjustment of the self-adaptive suspension system for optimization and adjustment of a subsequent control strategy.
Step 2, improving a planetary optimization algorithm, wherein the specific improvement strategy is as follows:
im1, introducing a topological insulator dimension sensitivity mechanism to improve a mass calculation equation in the POA algorithm;
Im2, in order to break through the limitation of the traditional gravitation model in the original algorithm, a space-time nonlinear mapping and topology control mechanism is introduced, so that the planet individuals can perform self-adaptive global search in the search space;
Im3, introducing a self-organizing criticality mechanism, so that the planetary individuals conduct self-organizing evolution under the guidance of dynamic cognitive feedback and local disturbance, and conduct more flexible local search in a complex space.
And 3, performing parameter optimization on the PID controller with the posture adjusted by improving an planetary optimization algorithm (IPOA), and optimizing a proportional coefficient Kp, an integral coefficient Ki and a differential coefficient Kd in the PID controller by simulating the law of planetary orbit motion by the IPOA algorithm.
And 4, carrying out real-time adjustment on the posture of the suspension system by adopting an optimized PID controller, and adjusting the vertical supporting force applied on the suspension element in the suspension system by the PID controller according to the posture data acquired in real time to ensure that the posture of the vehicle meets the target requirement.
Further, in the step 1, vehicle posture data of the suspension system is collected, including vertical displacement, pitch angle, roll angle, etc. of the vehicle body, and dynamic information such as road surface condition, load change, etc., and an adaptive suspension system posture control model is built for subsequent control optimization and adjustment.
Further, a vehicle dynamics model is established, wherein the vehicle dynamics model can be described by a three-degree-of-freedom system, namely vertical displacement, pitch angle and yaw angle, and the mathematical model of the vertical displacement is as follows:
(1);
In the formula (1), the components are as follows,For the quality of the car body,Is the vertical displacement of the mass center of the vehicle body,The vertical support force applied for the ith suspension;
Further, the mathematical model of pitch angle is:
(2);
in the formula (2), the amino acid sequence of the compound,For the moment of inertia of the vehicle body about the transverse axis,Is the pitching angle of the vehicle body,AndThe distances from the front axle and the rear axle to the mass center of the vehicle body are respectively,-Vertical support forces applied for the four suspensions;
Still further, the mathematical model of yaw motion is:
(3);
in the formula (3), the amino acid sequence of the compound,For the moment of inertia of the vehicle body about the longitudinal axis,Is the transverse swing angle of the vehicle body,Is the lateral distance from the center of mass to the left and right wheels.
Further, in order to implement posture adjustment, an error of each posture amount needs to be defined, the error is defined as a difference between an expected posture value and an actual posture value, and the defined error is:
(4);
in the formula (4), the amino acid sequence of the compound,、、Respectively represent a vertical displacement error, a pitch angle error and a yaw angle error,、、Respectively representing a target vertical displacement, a target pitch angle and a yaw angle,Is the actual vertical displacement of the car body,Is the actual pitch angle of the car body,Is the actual yaw angle of the vehicle body;
Further, the vertical displacement error will beThe input PID controller unit processes the error through the PID controller which is optimized on line by the IPOA algorithm and outputs a control signalSuspension element acting on an adaptive suspension system, adjusting in real time the 4 vertical support forces exerted on the suspension element-Thereby adjusting the vertical displacement, pitch angle and yaw angle of the vehicle body and ensuring the stable posture of the vehicle body suspension system;
Further, a control signal is establishedThe dynamic mapping relation with the vertical supporting force is as follows:
(5);
In the formula (5), the amino acid sequence of the compound,-For vertical supporting force in 4 suspension elements, A is a pitch angle compensation coefficient of the suspension elements, and B is a yaw angle compensation coefficient of the suspension elements;
Further, during the attitude adjustment, the Kp, ki, kd coefficients of the PID controller unit are mapped online to individual positions in the search space of the improved planetary optimization algorithm, and three dimensional values of the planetary individual positions represent [ Kp Ki Kd ], respectively.
Further, introducing a topological insulator dimension sensitivity mechanism in the steps 2 and Im1 improves a quality calculation equation in the POA algorithm, wherein the mechanism is used for introducing sensitivity activation coefficients of each dimensionThe high dimension and the low dimension of the individual are distinguished, and the effective distinction of the high dimension and the low dimension individual is realized, so that the influence of the distance change on different dimensions on the quality value has differential response. When the sensitivity of a certain dimension is higher, the contribution of the dimension to quality evaluation can be obviously enhanced, so that a planet individual is guided to accelerate the convergence speed in the key dimension, the optimization capacity of an algorithm in a high-dimensional search space is improved, and an improved quality calculation equation is as follows:
(6);
In the formula (6), the amino acid sequence of the compound,For the mass of the ith planet,For the mass of the j-th planet, i=1,.. nPop, j=1,.. nPop, nPop is the algorithm population number,The fitness of the ith or jth planet,For the latitude value of the search space,For the sensitivity activation coefficient of the d-th dimension,For the parameter value of the ith planet in the d dimension,For the value of the current optimal solution in the d-th dimension,For the attractive force parameters between the planet and the optimal solution,Being extremely small constant, prevents denominator zero from causing calculation errors.
Furthermore, a space-time nonlinear mapping and topology control mechanism is introduced in the steps 2 and Im2, so that the planetary individuals can perform self-adaptive global search in the search space, and the improved global search mathematical model is as follows:
(7);
in the formula (7), the amino acid sequence of the compound,For the updated individual position of the ith planet, t is the current iteration number,For the individual position of the current iteration of the ith planet,As the gravitational acceleration factor,AndTo take the value of the random number between 0 and 1,For the optimal solution position in the current iteration,For the attractive force parameters between the planet and the optimal solution,For the purpose of the diversity of the search parameters,As a function of the state of the planet,In order to adjust the parameters of the influence of the neighborhood,Is the neighborhood of the ith planet in the current iteration, wherein,The mathematical model of (a) is:
(8);
in the formula (8), t is the current iteration number,For the fitness of the ith planet in the kth generation,For the fitness of the optimal solution,For the individual position of the ith planet at the kth generation,Individual positions at the kth generation are the optimal solutions.
Furthermore, a self-organizing criticality mechanism is introduced in the steps 2 and Im3, so that the planetary individuals are subjected to self-organizing evolution under the guidance of dynamic cognitive feedback and local disturbance, and are guided to perform more flexible searching in a complex space, and the improved local searching mathematical model is as follows:
(9);
In the formula (9), the amino acid sequence of the compound,For the updated individual position of the ith planet,For the individual position of the current iteration of the ith planet, t is the current iteration number,Critical locations evolved for the system's self-organization,Is a locally optimal solution in the planetary neighborhood,For the critical disturbance intensity to be a critical disturbance intensity,The intensity is guided for the neighborhood.
Further, in the step 3, parameter optimization is performed on the PID controller with posture adjustment by improving an planetary optimization algorithm (IPOA), the IPOA algorithm performs optimization by simulating a law of planetary orbit motion, and a proportional coefficient Kp, an integral coefficient Ki and a differential coefficient Kd in the PID controller are optimized, and the specific steps are as follows:
S1, initializing a population scale nPop of an Improved Planetary Optimization Algorithm (IPOA), a maximum iteration number MaxIter, a search space dimension Dim, and search space upper and lower bounds [ Ub, lb ], wherein Ub and Lb are unit vectors of Dim order;
s2, initializing b, c, G and Rmin parameters of the improved planetary optimization algorithm, initializing individual positions of the improved planetary optimization algorithm, generating initial positions of population individuals, and initializing a mathematical model of the individual positions to be:
(10);
In the formula (10), the amino acid sequence of the compound,For a randomly generated initial position of the planet,For random numbers with values between [0,1], ub and Lb are the same as the meaning;
S3, calculating fitness of each planet, updating individual positions and fitness of all the planets in a greedy selection mode, and selecting the individual position with the minimum fitness in the population as an optimal solutionAnd recordIs the best fitness in the current iteration;
S4, calculating and updating a moment parameter M, wherein a mathematical model of the moment parameter M is as follows:
(11);
In the formula (11), the amino acid sequence of the compound,AndFor the mass of the ith planet and the mass of the jth planet,The Cartesian distance between two planets, G is the gravitational parameter;
S5, calculating Cartesian distance between the planet and the optimal solution,The mathematical model of (a) is:
(12);
in the formula (12), each parameter has the same meaning as above;
s6, when>The algorithm enters a global exploration stage, simulates a planet construction motion track far away from the sun to carry out global search, updates individual positions, and adopts the improved mathematical model of global exploration as above,The mathematical model of (a) is:
(13);
In the formula (13), the amino acid sequence of the compound,The initial Cartesian distance between the planet and the optimal solution is the same as the other parameters;
s7, when<The algorithm enters a local development stage, a planet construction motion track close to the sun is simulated to perform local search, the individual position is updated, and an improved mathematical model for local exploration is the same as the above;
S8, checking whether the current iteration times t is larger than MaxIter, if so, outputting an optimal solution of the improved planetary optimization algorithm, decoding the optimal solution into a proportional coefficient Kp, an integral coefficient Ki and a differential coefficient Kd in the PID controller, if not, executing t=t+1, and returning to S3 to continue iterative optimization.
The invention provides a self-adaptive suspension system posture adjustment method, which optimizes a PID controller for posture adjustment in a suspension system by improving an planetary optimization algorithm (IPOA), and has the following beneficial effects compared with the prior art:
P1, by introducing a topological insulator dimension sensitivity mechanism, a mass calculation equation in a Planetary Optimization Algorithm (POA) is improved, so that a planetary individual can be adaptively adjusted according to sensitivity characteristics of search space dimension change, and the sensing and adaptation capacity of an optimization process to a complex non-uniform space structure is enhanced, so that the accuracy and dynamic response capacity of PID controller parameter optimization in the suspension system attitude adjustment process are improved;
p2, by introducing a space-time nonlinear mapping and topology control mechanism, a single linear evolution mode of position updating in a traditional gravity model is broken through, self-adaptive distortion searching and topology perception migration of a planet individual in a search space are realized, a global exploration range is effectively expanded, and searching efficiency and adaptability to complex working condition changes in a gesture adjustment PID parameter optimization process are improved;
And P3, by introducing a self-organizing criticality mechanism, the planetary individuals form self-organizing evolution behaviors under the dynamic cognitive feedback and the local disturbance guidance, and the local jump and the global adjustment can be adaptively carried out according to the system state change in the posture adjustment of the suspension system, so that the adjustment precision of the PID controller is obviously improved, and the posture stability and the comfort of the suspension system under the complex road condition are improved.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.
The invention provides a self-adaptive suspension system posture adjusting method, which is shown in figure 1 and comprises the following specific steps:
and 1, acquiring vehicle attitude data of a suspension system, including vertical displacement, pitch angle, yaw angle and the like of a vehicle body, monitoring road surface conditions and dynamic response of the vehicle, and establishing a dynamic model of the attitude adjustment of the self-adaptive suspension system based on the data.
Specifically, in the embodiment of the invention, mathematical models are respectively built for vertical displacement, pitch angle and yaw angle through Matlab, wherein the vertical displacement mathematical model is as follows:
function dz = heave_model(t, z, F_z_func, m)
% vertical displacement equation
Dz1=z (2);% velocity is equal to the first derivative of displacement
Dz2=sum (f_z_func (t))/m%acceleration is determined by the sum of the four suspension forces
dz = [dz1; dz2];
end。
Further, the mathematical model of pitch angle is:
function dtheta = pitch_model(t, theta, F1_func, F2_func, F3_func, F4_func, l_f, l_r, I_x)
% pitch equation
Dtheta =theta (2);% angular velocity is equal to the first derivative of pitch angle
Dtheta = (f1_func (t) ×l_f-f2_func (t) ×l_f-f3_func (t) x l_r+f4_func (t) x l_r)/i_x;% angular acceleration
dtheta = [dtheta1; dtheta2];
end。
Still further, the mathematical model of yaw motion is:
function dphi = roll_model(t, phi, F1_func, F2_func, F3_func, F4_func, l_w, I_y)
% yaw equation
Dphi =phi (2);% angular velocity is equal to the first derivative of the yaw angle
Dphi = (f1_func (t) ×l/u w+F3_func (t) ×l_w-f2_func (t) x l_w-f4_func (t) x l_w)/i_y;% angular acceleration
dphi = [dphi1; dphi2];
end。
Further, in order to implement posture adjustment, an error of each posture amount needs to be defined, the error is defined as a difference between an expected posture value and an actual posture value, and the defined error is:
(4);
in the formula (4), the amino acid sequence of the compound,、、Respectively represent a vertical displacement error, a pitch angle error and a yaw angle error,、、The target vehicle body posture value represents the target vertical displacement, the target pitch angle and the yaw angle respectively,Is the actual vertical displacement of the car body,Is the actual pitch angle of the car body,Is the yaw angle of the vehicle body.
Further, the vertical displacement error will beThe input PID controller unit processes the error through the PID controller which is optimized on line by the IPOA algorithm and outputs a control signalSuspension element acting on an adaptive suspension system, adjusting in real time the vertical support forces exerted on the 4 suspension elements-And the vertical displacement, pitch angle and yaw angle of the vehicle body are regulated, so that the stable posture of the vehicle body suspension system is ensured.
Further, a control signal is establishedThe dynamic mapping relation with the vertical supporting force is as follows:
(5);
In the formula (5), the amino acid sequence of the compound,-For vertical supporting force in 4 suspension elements, A is the pitch angle compensation coefficient of the suspension elements, the value is 0.1, B is the yaw angle compensation coefficient of the suspension elements, and the value is 0.08.
Furthermore, the attitude difference value is input into a PID controller unit, the difference value is processed through an online optimized PID control algorithm, and a control signal is output, and acts on a suspension element of the self-adaptive suspension system to adjust the rigidity and damping characteristics of the suspension system in real time.
Step 2, improving a planetary optimization algorithm, wherein the specific improvement strategy is as follows:
Im1, introducing a mass calculation equation in a topological insulator dimension sensitivity mechanism improvement algorithm, wherein the improved mass calculation equation is as follows:
(6);
In the formula (6), the amino acid sequence of the compound,For the mass of the ith planet,For the mass of the j-th planet, i=1,.. nPop, j=1,.. nPop, nPop is the algorithm population number,The fitness of the ith or jth planet,For the latitude value of the search space,For the sensitivity activation coefficient of dimension d, the value is 0.8,For the parameter value of the ith planet in the d dimension,For the value of the current optimal solution in the d-th dimension,The attractive force parameter between the planet and the optimal solution is 2,Is extremely small constant and takes the value of 0.000001;
Im2, introducing a global search mathematical model of a space-time nonlinear mapping and topology control mechanism improvement algorithm, wherein the improved global search mathematical model is as follows:
(7);
in the formula (7), the amino acid sequence of the compound,For the updated individual position of the ith planet, t is the current iteration number,For the individual position of the current iteration of the ith planet,As the gravitational acceleration factor,AndTo take the value of the random number between 0 and 1,For the optimal solution position in the current iteration,For the attractive force parameters between the planet and the optimal solution,For the purpose of the diversity of the search parameters,As a function of the state of the planet,In order to adjust the parameters of the neighborhood influence, the value is 0.05,Is the neighborhood of the ith planet in the current iteration, wherein,The mathematical model of (a) is:
(8);
in the formula (8), t is the current iteration number,For the fitness of the ith planet in the kth generation,For the fitness of the optimal solution,For the individual position of the ith planet at the kth generation,Individual position at the kth generation for the optimal solution;
im3, introducing a local search mathematical model of a self-organizing criticality mechanism improvement algorithm, wherein the improved local search mathematical model is as follows:
(9);
In the formula (9), the amino acid sequence of the compound,For the updated individual position of the ith planet,For the individual position of the current iteration of the ith planet, t is the current iteration number,Critical locations evolved for the system's self-organization,Is a locally optimal solution in the planetary neighborhood,To linearly decrease the critical disturbance intensity from 0.5 to 0,The neighborhood guiding strength is 0.2;
step 3, parameter optimization is carried out on the PID controller with the posture adjusted by improving an IPOA algorithm, the IPOA algorithm carries out optimization by simulating the law of planetary orbit motion, and the proportional coefficient Kp, the integral coefficient Ki and the differential coefficient Kd in the PID controller are optimized, wherein the specific steps are as follows:
s1, initializing a population scale nPop of an Improved Planetary Optimization Algorithm (IPOA) to be 30, wherein the maximum iteration number MaxIter is 20, the dimension Dim of a search space is 3, the upper bound Ub of the search space is [20,20,20], and the lower bound is [0,0.000001,0];
S2, initializing individual positions of the improved planetary optimization algorithm, generating initial positions of planetary individuals, and initializing mathematical models of the individual positions to be:
(10);
In the formula (10), the amino acid sequence of the compound,For a randomly generated initial position of the planet,For random numbers with values between [0,1], ub and Lb are the same as the meaning;
S3, calculating fitness of each planet, updating individual positions and fitness of all the planets in a greedy selection mode, and selecting the individual position with the minimum fitness in the population as an optimal solutionAnd recordIs the best fitness in the current iteration;
S4, calculating and updating a moment parameter M, wherein a mathematical model of the moment parameter M is as follows:
(11);
In the formula (11), the amino acid sequence of the compound,AndFor the mass of the ith planet and the mass of the jth planet,The Cartesian distance between two planets, G is the gravitational parameter;
S5, calculating Cartesian distance between the planet and the optimal solution,The mathematical model of (a) is:
(12);
in the formula (12), each parameter has the same meaning as above;
s6, when>The algorithm enters a global exploration stage, simulates a planet construction motion track far away from the sun to carry out global search, updates individual positions, and adopts the improved mathematical model of global exploration as above,The mathematical model of (a) is:
(13);
In the formula (13), the amino acid sequence of the compound,The initial Cartesian distance between the planet and the optimal solution is the same as the other parameters;
s7, when<The algorithm enters a local development stage, a planet construction motion track close to the sun is simulated to perform local search, the individual position is updated, and an improved mathematical model for local exploration is the same as the above;
S8, checking whether the current iteration times t is larger than MaxIter, if so, outputting an optimal solution of the improved planetary optimization algorithm, decoding the optimal solution into a proportional coefficient Kp, an integral coefficient Ki and a differential coefficient Kd in the PID controller, if not, executing t=t+1, and returning to S3 to continue iterative optimization.
Further, comprehensively considering the control precision, response time and system energy consumption of the attitude adjustment of the self-adaptive suspension system, and selecting and improving an objective function of the planetary optimization algorithm as follows:
(14);
In the formula (13), J is an fitness value calculated by the objective function,、、Respectively represent a vertical displacement error, a pitch angle error and a yaw angle error,In order to adjust the magnitude of the overshoot,The integral of the square of the control signal is output by the PID controller, T is the total running time of the system, n is the accumulated times of errors,、As the weight coefficient of the light-emitting diode,The value of the water-soluble fluorescent powder is 0.03,The value is 0.01, and the method is used for balancing the control precision and the energy consumption of the system.
And 4, carrying out real-time adjustment on the posture of the suspension system by adopting an optimized PID controller, wherein the PID controller adjusts the rigidity and damping parameters of the suspension system according to the posture change data acquired in real time to ensure that the posture of the vehicle meets the target requirement, and specifically comprises the following steps:
Step1, setting the running time of a self-adaptive suspension system attitude adjustment simulation model to be 20s, setting the sampling time to be 0.5s, setting the initial vertical displacement of the vehicle to be 0, setting the initial pitching angle to be 0 degrees, setting the initial yaw angle to be 0 degrees, setting the target vertical displacement to be 3, setting the target pitching angle to be 0 degrees and setting the target yaw angle to be 1 degree;
Step2, establishing an improved planetary optimization algorithm mathematical model, and writing a connection function through Matlab for transmitting data between the simulation model and the improved planetary optimization algorithm mathematical model;
Step3, connecting Kp, ki and Kd coefficients of the PID controller with planetary individual positions of the improved planetary optimization algorithm through a connecting functionIn association with the correlation of the two,=[Kp、Ki、Kd];
Step4, running an improved planetary optimization algorithm mathematical model, carrying out algorithm iterative optimization, outputting individual solutions in each iteration, and decoding values of each individual solution in different dimensions into Kp, ki and Kd coefficients of the PID controller;
Step5, outputting Kp, ki and Kd parameters to a control system simulation model, and running the control system simulation model;
Step6, judging whether iteration is terminated, if so, outputting an optimal solution of the improved planetary optimization algorithm, and decoding values of each latitude of the optimal solution into Kp, ki and Kd parameters, wherein the optimal Kp, ki and Kd parameters are 14.7367, 0.0642 and 16.2716.
In the implementation step, the change curve of the fitness value along with the iteration times in the optimization process of the common planetary optimization algorithm and the improved planetary optimization algorithm is compared and analyzed, as shown in fig. 3, the fitness value of the common planetary optimization algorithm is rapidly reduced in the previous 6-generation iteration process and then enters a convergence platform period, the fitness value is basically stabilized at about 0.035, the overall convergence speed is relatively high but the final convergence quality is limited, the fitness value cannot be further reduced, the improved planetary optimization algorithm is obviously reduced at about 6-generation, but the follow-up still can keep a moderate reduction trend, and is further optimized after 10-generation, and finally converges to about 0.018 in 20-generation, which is obviously superior to the common algorithm. In a comprehensive view, the improved planetary optimization algorithm not only has higher convergence speed in the early stage, but also keeps better optimization capacity in the middle and later stages, finally obtains a lower fitness value, and verifies the effectiveness and superiority of the improved method in the processes of improving optimizing precision and accelerating convergence.
In the implementation step, the vertical displacement curve of the common planetary optimization algorithm and the vertical displacement curve of the improved planetary optimization algorithm are compared and analyzed, as shown in fig. 4, the common planetary optimization algorithm rises rapidly in the initial stage, but has obvious overshoot phenomenon, the maximum displacement is close to 4cm, the convergence process is slower and can be basically stabilized near the target displacement value only after about 10 seconds, the transition process is not smooth enough, the improved planetary optimization algorithm also rises rapidly in the initial stage, but the maximum overshoot is obviously reduced, the oscillation amplitude and the frequency are obviously reduced, the whole displacement response curve is more stable, the displacement response curve converges near the target displacement value within about 6 seconds, and the stability is kept better. Comprehensive analysis shows that the improved planetary optimization algorithm is superior to the common algorithm in aspects of overshoot suppression, convergence speed, system stability and the like, and the effectiveness and superiority of the improved strategy are verified.
In the implementation step, the pitch angle response curves of the common planetary optimization algorithm and the improved planetary optimization algorithm are compared and analyzed, as shown in fig. 5, the common planetary optimization algorithm is changed rapidly in the initial stage, but obvious negative overshoot appears, the maximum pitch angle is close to-0.6 degrees, then positive overshoot appears, the oscillation amplitude is larger, the convergence process is slower, and finally the stability can be gradually achieved within about 10 seconds, the improved planetary optimization algorithm also has negative offset with a certain amplitude in the initial stage, but the overshoot amplitude is obviously reduced, the positive oscillation is also greatly reduced, the overall curve change is more gentle, the convergence is close to the target pitch angle of 0 degrees within 6 seconds, and the follow-up fluctuation is extremely small. In a comprehensive view, the improved planetary optimization algorithm is superior to the common algorithm in the aspects of overshoot suppression, oscillation control and convergence speed, and the dynamic response performance and stability of the system are effectively improved.
In this implementation step, the yaw angle response curves of the ordinary planetary optimization algorithm and the modified planetary optimization algorithm are compared and analyzed, as shown in fig. 6, the ordinary planetary optimization algorithm rises rapidly in the initial stage, but obviously overshoots occur, the maximum yaw angle exceeds the target value by about 0.4 degrees, then a certain amplitude of oscillation is generated, the overall convergence speed is slower, and the stability is basically near the target value after about 8 seconds, while the modified planetary optimization algorithm rises rapidly in the initial stage, but basically has no obvious overshooting, the response process is smooth, and converges rapidly to near the target yaw angle by about 1 degrees in about 2 seconds, the subsequent fluctuation is extremely small, and excellent dynamic performance and steady state characteristics are shown. In a comprehensive view, the improvement of the planetary optimization algorithm can effectively reduce the overshoot of the system, remarkably improve the convergence speed and enhance the stability of the system, and has obvious advantages compared with the common planetary optimization algorithm.
In summary, the invention provides a self-adaptive suspension system attitude adjustment method, which effectively solves the problems of slow response, large overshoot and insufficient steady-state precision in the suspension system attitude adjustment process of the traditional PID control method by improving the Kp, ki and Kd coefficients of a PID controller for optimizing the suspension system attitude adjustment by an epicyclic optimization algorithm (IPOA), and can realize efficient optimization of the PID control coefficient by introducing a topological insulator dimension sensitivity mechanism and a self-adaptive search adjustment strategy, remarkably improve the attitude control performance of a suspension system under different working conditions, and particularly maintain the quick response and high stability of the system in the face of external disturbance such as complex road conditions, vehicle load change and the like, thereby effectively improving the travelling comfort and safety of a vehicle.