CN113110031A - Fuzzy PID active suspension control system and method based on genetic algorithm optimization - Google Patents
Fuzzy PID active suspension control system and method based on genetic algorithm optimization Download PDFInfo
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Abstract
本发明公开了一种基于遗传算法优化的模糊PID主动悬架控制系统及方法，涉及悬架控制技术领域，通过遗传算法对模糊规则进行优化，使得模糊控制器对于PID控制的参数更加合理，本发明的主要技术方案为：本发明的主动悬架控制系统包括信息采集单元、信息处理单元和控制器，基于该系统实现的主动悬架控制方法主要为，控制器接收从信息处理单元传输的预处理信号，经控制器的上层控制器通过遗传算法获得模糊规则，在根据模糊规则以及结合控制器的下层控制器实现对悬架控制。本发明主要应用于优化控制主动悬架控制系统及方法。
The invention discloses a fuzzy PID active suspension control system and method based on genetic algorithm optimization, and relates to the technical field of suspension control. The fuzzy rules are optimized by genetic algorithm, so that the parameters of the fuzzy controller for PID control are more reasonable. The main technical scheme of the invention is as follows: the active suspension control system of the present invention includes an information acquisition unit, an information processing unit and a controller, and the active suspension control method realized based on the system is mainly as follows: the controller receives the preset information transmitted from the information processing unit. After processing the signal, the upper controller of the controller obtains fuzzy rules through genetic algorithm, and the suspension control is realized according to the fuzzy rules and the lower controller combined with the controller. The invention is mainly applied to the optimal control active suspension control system and method.
Description
Technical Field
The invention relates to the technical field of suspension control, in particular to a fuzzy PID active suspension control system and method based on genetic algorithm optimization.
Background
In the development process of automobile technology, suspension design is always a key work, and suspension performance is taken as one of important standards for measuring the quality of an automobile. Traditional passive suspension can't satisfy customer's demand, and initiative suspension system can change suspension rigidity and damping, improves the holistic security of car, also improves passenger's riding comfort simultaneously, the initiative suspension control method who adopts, for example fuzzy PID control, sky shed damping control, sliding mode control etc..
At present, for fuzzy PID control, the principle of fuzzy control is to fuzzify input and output parameters, then establish a fuzzy control rule base according to expert experience, and a controller realizes fuzzy reasoning of a control system according to control rules and finally deblur the output parameters to obtain specific values.
However, although reasonable fuzzy rules and membership functions can make the system achieve more ideal control effect. However, according to seven fuzzy state variables (such as negative large, negative medium, negative small, zero, positive small, positive medium, positive large) and two-input three-output in the design, it can be known that 7 exists only in the fuzzy control rule5In addition, for a fuzzy controller which needs expert experience to determine fuzzy rules, the fuzzy controller is a very large numerical range, so that the optimization solution is time-consuming and labor-consuming, and finally the implementation control of an active suspension is not utilized, so that the driving experience of ride comfort, operation stability and riding comfort is greatly reduced.
Disclosure of Invention
The invention provides a fuzzy PID active suspension control system and method based on genetic algorithm optimization, and mainly aims to optimize a fuzzy rule through a genetic algorithm, so that a fuzzy controller is more reasonable in PID control parameters, and is more beneficial to implementation control of a suspension, and driving experience of driving smoothness, operation stability and riding comfort is guaranteed.
In order to achieve the above purpose, the present invention mainly provides the following technical solutions:
the application provides a fuzzy PID active suspension control system based on genetic algorithm optimization, which comprises:
the system comprises an information acquisition unit, a first signal generation unit and a second signal generation unit, wherein the information acquisition unit is arranged on a vehicle body and is used for acquiring road condition information and vehicle body information, the vehicle body information at least comprises vehicle body acceleration, suspension moving stroke, vehicle body pitch angle and vehicle body roll angle, and the road condition information and the vehicle body information are utilized to generate a first signal;
the information processing unit is connected with the information acquisition unit and used for performing preprocessing on the first signal by using a filter to obtain a preprocessed signal, and the preprocessed signal comprises current road condition characteristics and vehicle body information characteristics;
the controller is connected with the information processing unit and used for controlling the driving part to adjust the working state of the suspension body according to the preprocessing signal; the controller is divided into an upper layer controller and a lower layer controller, and the upper layer controller is a fuzzy PID active suspension force controller based on a genetic algorithm; the lower layer controller converts an expected controller output by the upper layer controller into control voltage required by a proportional directional solenoid valve, and the proportional directional solenoid valve realizes expected control force output by the upper layer controller by regulating flow of entering and exiting a suspension; the upper-layer controller obtains a fuzzy rule through a genetic algorithm, and the suspension is controlled according to the fuzzy control rule and by combining with the lower-layer controller.
In some variations of the first aspect of the present application, the system further comprises: an acceleration sensor for measuring acceleration of the vehicle body; the displacement sensor is used for measuring the dynamic stroke of the suspension and also used for measuring the displacement to calculate the dynamic-static load ratio of the tire; the gyroscope is used for measuring a pitch angle and a roll angle; and the laser radar component is used for measuring road condition information and acquiring road elevation.
In some modified embodiments of the first aspect of the present application, the tire dynamic-to-static load ratio is expressed as
Wherein m is1、m2Respectively sprung mass and unsprung mass, xu、xrRespectively tire displacement during movement and input displacement to the suspension due to ground heave, ktIs the equivalent stiffness coefficient of the tire, and g is the gravity acceleration;
the dynamic-static load ratio of the tire is less than 1, and the tire is used for ensuring that positive pressure always exists between the tire and the ground in the running process of a vehicle so as to ensure reliable tire adhesion and ensure operation stability and safety.
In some modified embodiments of the first aspect of the present application, the active suspension is classified as an active air suspension or an active hydro-pneumatic suspension.
In a second aspect, the present application provides a fuzzy PID active suspension control method based on genetic algorithm optimization, which is used in the fuzzy PID active suspension control system based on genetic algorithm optimization as described in the first aspect, and the method includes:
acquiring current road condition information and vehicle body information to generate a first signal, wherein the vehicle body information at least comprises vehicle body acceleration, suspension moving distance, pitch angle and roll angle of a vehicle body;
utilizing a filter to carry out preprocessing on the first signal to obtain a preprocessed signal, wherein the preprocessed signal comprises current road condition characteristics and vehicle body information characteristics;
inputting the preprocessing signal as an input signal into a controller, wherein the controller is divided into an upper layer controller and a lower layer controller;
and obtaining fuzzy rules through a genetic algorithm in the upper layer controller and combining the fuzzy rules with the lower layer controller to realize the control of the suspension.
In some modified embodiments of the second aspect of the present application, if the active suspension is an active hydro-pneumatic suspension, the obtaining of the fuzzy rule in the upper controller by the genetic algorithm and the control of the suspension by combining with the lower controller include:
simplifying the vehicle into 1/4 vehicle model;
the oil charging and discharging process of the oil-gas spring can be switched and the oil liquid flow can be controlled by adjusting the proportional electromagnetic valve;
calculating to obtain the adjustment quantity of the height of the vehicle body according to the inclination angle, the wheelbase and the wheel base;
determining the suspension stiffness and the range of suspension damping according to the requirements of a vehicle suspension control system;
establishing a fitness function of a genetic algorithm, wherein an optimization target is to minimize the fitness value in a feasible region meeting constraint conditions, and when the fitness function is not met, the optimization is stopped, and a value meeting the requirement at the last moment is output;
establishing an initial population, wherein the initial population is composed of corresponding fuzzy state variables, namely negative large, negative medium, negative small, zero, positive small, positive medium and positive large, each variable is solved by adopting a triangular membership function, after a genetic algebra is set, the population can repeat the general optimization steps, and a new population with higher individual fitness is generated by continuous iteration;
obtaining a fuzzy rule through the processing steps of establishing an initial population, obtaining ec after derivation of a difference e signal, inputting ec into a fuzzy control model together, obtaining PID control parameter regulating quantities delta kp, delta ki and delta kd by deblurring according to a fuzzy reasoning rule obtained in advance, inputting the ec and the ec into a PID controller, obtaining a set PID controller parameter by adding the PID controller parameter regulating quantities delta kp, delta ki and delta kd after obtaining the initial PID control parameter regulating quantity, outputting a corresponding control quantity u (t) to an actuating mechanism by the PID controller after setting, adjusting the rigidity and the damping of a suspension after the actuating mechanism receives the input of a control quantity signal u (t) of the PID controller, stopping the control until a vehicle signal reaches an expected value, and realizing the active suspension control of a vehicle, wherein the vehicle signal at least comprises vehicle body acceleration, suspension dynamic stroke, pitch angle and pitch angle, The roll angle.
In some variations of the second aspect of the present application, the method further comprises:
performing performance evaluation through a fitness function;
and outputting a target fuzzy rule table.
In some modified embodiments of the second aspect of the present application, if the active suspension is an active hydro-pneumatic suspension, the vehicle differential equation obtained by simplifying the vehicle to 1/4 vehicle model is:
wherein m is1、m2Respectively, sprung mass and unsprung mass; x is the number ofs、xu、xrRespectively vehicle body displacement, tire displacement and input displacement to a suspension due to ground fluctuation in the motion process; k is a radical oftIs the equivalent stiffness coefficient of the tire; c. CtIs the equivalent damping coefficient of the tire; p1、A1Respectively showing the pressure state and the area of the oil chamber; beta is aeThe equivalent bulk modulus of hydraulic oil; cdIs the flow coefficient; a. thekIs the effective flow area of the throttle valve bore; rho is the oil density; n is a gas adiabatic index; pg、VgThe absolute pressure and the volume state of the gas in the hydro-pneumatic spring energy accumulator are obtained; qgThe flow rate of the oil flows through a throttle valve between the oil cylinder and the air storage tank; v10The initial oil storage volume of the oil cylinder; vgo、PgoThe initial volume state and gas absolute pressure in the hydro-pneumatic spring accumulator are obtained; qaThe oil flow input and output from the external oil source to the hydro-pneumatic spring.
In some modified embodiments of the second aspect of the present application, the performing the performance evaluation by the fitness function includes:
when the comfort of the vehicle is taken as a first standard, the fitness function corresponding to the acceleration of the vehicle body and the dynamic stroke of the suspension is taken as a main function;
when the first standard is to prevent the vehicle from sliding, the fitness function corresponding to the dynamic load of the tire is taken as the main standard;
when the safety is used to prevent the vehicle from rolling over, the fitness function corresponding to the pitch angle and the roll angle is used as the main standard.
By the technical scheme, the technical scheme provided by the invention at least has the following advantages:
the invention provides a fuzzy PID active suspension control system and method based on genetic algorithm optimization, the active suspension control system comprises an information acquisition unit, an information processing unit and a controller, the active suspension control method based on the system is mainly characterized in that the controller receives a preprocessing signal transmitted from the information processing unit, a fuzzy rule is obtained through the genetic algorithm by an upper layer controller of the controller, and suspension control is realized by a lower layer controller combined with the controller according to the fuzzy rule. Compared with the prior art, the method solves the technical problem that the implementation control of the active suspension is not finally utilized due to the fact that an expert is required to intervene for establishing the fuzzy control rule base, time and labor are wasted, fuzzy rules are optimized through a genetic algorithm, so that parameters of PID control by a fuzzy controller are more reasonable, and the suspension is finally controlled by the fuzzy controller, so that driving experience of driving smoothness, operation stability and riding comfort is guaranteed.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a block diagram of a fuzzy PID active suspension control system based on genetic algorithm optimization according to an embodiment of the present invention;
FIG. 2 is a block diagram of an exemplary fuzzy PID control system in accordance with an embodiment of the invention;
FIG. 3 is a flowchart of a fuzzy PID active suspension control method based on genetic algorithm optimization according to an embodiment of the present invention;
FIG. 4 is an exemplary genetic algorithm based fuzzy rule optimization block diagram according to an embodiment of the present invention;
FIG. 5 illustrates an exemplary fuzzy PID control scheme according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The embodiment of the invention provides a fuzzy PID active suspension control system based on genetic algorithm optimization, which comprises the following components in combination with a figure 1: the information acquisition unit, the information processing unit and the controller, and the connection relationship and the working purpose between the two units and the controller are explained in detail as follows:
the information acquisition unit 100 is arranged on the vehicle body and used for acquiring road condition information and vehicle body information, wherein the vehicle body information at least comprises vehicle body acceleration, suspension moving distance, pitch angle and roll angle of the vehicle body, and a first signal is generated by utilizing the road condition information and the vehicle body information.
The information processing unit 200 is connected to the information acquisition unit 100, and is configured to perform preprocessing on the first signal, specifically, perform preprocessing on the first signal by using a filter to obtain a corresponding preprocessed signal, where the preprocessed signal includes current road condition characteristics and vehicle body information characteristics.
And the controller 300 is connected with the information processing unit 200 and is mainly used for controlling the driving part to adjust the working state of the suspension body according to the preprocessing signal transmitted by the information processing unit 200. The controller 300 is divided into an upper controller, which is an active suspension force controller based on a fuzzy PID of a genetic algorithm, and a lower controller, which converts a desired control force output from the upper controller into a control voltage required for a proportional directional solenoid valve, which can realize the desired control force output from the upper controller by adjusting a flow rate into and out of the suspension.
Illustratively, as shown in fig. 2, an example of the fuzzy PID control system block diagram according to an embodiment of the present invention is that an input signal is a preprocessed signal, which is transmitted from the information processing unit 200 to the controller, the controller 300 is divided into an upper layer controller and a lower layer controller, fig. 2 shows that the upper layer controller is a fuzzy controller, the lower layer controller is a PID controller, and the controlled object is a suspension, and the final purpose is that the upper layer controller obtains a fuzzy rule through a genetic algorithm, and the suspension 400 (controlled object) is controlled according to the fuzzy rule and by combining with the lower layer controller.
For example, in the embodiment of the present invention, a specific implementation process of the road condition information and the vehicle body information acquired by the information acquisition unit 100 may be as follows:
the active suspension control system of the embodiment of the invention also comprises: an acceleration sensor for measuring acceleration of the vehicle body; the displacement sensor is used for measuring the dynamic stroke of the suspension and also used for measuring the displacement to calculate the dynamic-static load ratio of the tire; the gyroscope is used for measuring a pitch angle and a roll angle; and the laser radar component is used for measuring road condition information and acquiring road elevation. Then, by means of the above components included in the system, the information acquisition unit 100 can efficiently and accurately acquire the current road condition information and the vehicle body information.
Further, for the ratio of dynamic load to static load of the tire, it can be expressed as
Wherein m is1、m2Respectively sprung mass and unsprung mass, xu、xrRespectively tire displacement during movement and input displacement to the suspension due to ground heave, ktIs the equivalent stiffness coefficient of the tire, and g is the gravitational acceleration.
It should be noted that, in order to ensure the operation stability and safety, a positive pressure is always required to exist between the tire and the ground during the driving process to ensure reliable tire adhesion, so that the dynamic-static load ratio of the tire is required to be less than 1.
Further, the active suspension control system provided by the embodiment of the invention has the controlled object of an active suspension, and the active suspension is divided into an active air suspension and an active hydro-pneumatic suspension.
The active suspension control system comprises an information acquisition unit 100, an information processing unit 200 and a controller 300, and the active suspension 400 control method realized based on the system mainly comprises the steps that the controller receives a preprocessing signal transmitted from the information processing unit, a fuzzy rule is obtained through a genetic algorithm by an upper-layer controller of the controller, and suspension control is realized according to the fuzzy rule and a lower-layer controller combined with the controller. Compared with the prior art, the method solves the technical problem that the implementation control of the active suspension is not finally utilized due to the fact that an expert is required to intervene for establishing the fuzzy control rule base, time and labor are wasted, fuzzy rules are optimized through a genetic algorithm, so that parameters of PID control by a fuzzy controller are more reasonable, and the suspension is finally controlled by the fuzzy controller, so that driving experience of driving smoothness, operation stability and riding comfort is guaranteed.
In order to describe the work flow of the active suspension control system provided by the embodiment of the present invention in more detail, on the other hand, as shown in fig. 3, the embodiment of the present invention further provides a fuzzy PID active suspension control method based on genetic algorithm optimization, which is used for the active suspension control system, and the method includes:
And 102, preprocessing the first signal by using a filter to obtain a preprocessed signal, wherein the preprocessed signal comprises the current road condition characteristic and the vehicle body information characteristic.
And 103, inputting the preprocessing signal as an input signal into a controller, wherein the controller is divided into an upper layer controller and a lower layer controller.
And step 104, obtaining a fuzzy rule in the upper layer controller through a genetic algorithm and realizing the control of the suspension by combining the lower layer controller.
In the embodiment of the present invention, mainly the step 104 is described in detail, and for example, the description in conjunction with the fuzzy rule optimizing block diagram based on the genetic algorithm illustrated in fig. 4 may include the following steps:
firstly, a vehicle is simplified into 1/4 vehicle models, it should be noted that a controlled object (active suspension) in the embodiment of the present invention may be an active air suspension or an active hydro-pneumatic suspension, and for example, if the active suspension is an active hydro-pneumatic suspension, a vehicle differential equation obtained may be as follows formula (1):
wherein m is1、m2Respectively, sprung mass and unsprung mass; x is the number ofs、xu、xrRespectively vehicle body displacement, tire displacement and input displacement to a suspension due to ground fluctuation in the motion process; k is a radical oftIs the equivalent stiffness coefficient of the tire; c. CtIs the equivalent damping coefficient of the tire; p1、A1Respectively showing the pressure state and the area of the oil chamber; beta is aeThe equivalent bulk modulus of hydraulic oil; cdIs the flow coefficient; a. thekIs the effective flow area of the throttle valve bore; rho is the oil density; n is a gas adiabatic index; pg、VgThe absolute pressure and the volume state of the gas in the hydro-pneumatic spring energy accumulator are obtained; qgThe flow rate of the oil flows through a throttle valve between the oil cylinder and the air storage tank; v10The initial oil storage volume of the oil cylinder; vgo、PgoThe initial volume state and gas absolute pressure in the hydro-pneumatic spring accumulator are obtained; qaThe oil flow input and output from the external oil source to the hydro-pneumatic spring.
It should be noted that n is a gas adiabatic index, when vibration is severe, the gas in the spring does not have time to exchange heat with the outside, and can be considered as an adiabatic change process for analysis, and at this time, n generally takes a value of about 1.4.
Secondly, the oil charging and discharging process of the oil-gas spring can be switched and the oil liquid flow Q can be controlled by adjusting a proportional electromagnetic valveaSo as to achieve the purpose of controlling the pressure of the hydro-pneumatic spring and determine the flow QaIs as follows, equation (2):
wherein, PsFor supply pressure, PrFor outlet pressure during oil drainage, AcIs the valve port area of a proportional solenoid valve, which has a certain relationship with the voltage input to the solenoid valve. When A isc>When 0, the oil filling process of the oil gas spring is represented; when A isc<And when 0, the oil-gas spring oil unloading process is represented.
And then, calculating to obtain the adjustment quantity Dt of the vehicle body height according to the inclination angle, the wheelbase and the wheel track, and obtaining the adjustment quantity Dt of the vehicle body height according to the following formula (3):
wherein x isbijIs the displacement of four wheels, theta,
The roll angle and the pitch angle of the vehicle body measured by the gyroscope are L, BF, and the wheel base are respectively.
And step two, determining the suspension stiffness and the suspension damping range according to the requirements of the vehicle suspension system.
And step three, establishing a fitness function of the genetic algorithm, wherein the optimization target is to minimize the fitness value in a feasible domain meeting the constraint condition, and when the fitness function is not met, the optimization is stopped, and the value met at the last moment is output.
The fitness represents the adaptive capacity of an individual to the environment in the population evolution process, the survival probability of the individual with low fitness is small, the survival probability of the individual with high fitness is large in the biological evolution process, and after the individual with high fitness is reserved, a better individual can be generated through multiple iterations, so that the fitness function is a key function for optimizing the genetic algorithm.
The genetic algorithm selects a time-by-absolute-error integration criterion (ITAE) as an optimized performance index, and a fitness function of the genetic algorithm is expressed as a formula (4):
and e is expressed as a difference value obtained by comparing the signal values actually obtained by the vehicle body acceleration, the suspension dynamic travel, the tire dynamic-static load ratio, the pitch angle and the roll angle with the system signal expected value.
Illustratively, fitness represents the adaptive capacity of an individual to the environment in the population evolution process, and in the biological evolution process, the survival probability of an individual with low fitness is small, the survival probability of an individual with high fitness is large, and after the individual with high fitness is reserved, a better individual can be generated through multiple iterations.
And step four, establishing an initial population, wherein the initial population consists of positive large (PB), Positive Medium (PM), Positive Small (PS), Zero (ZO), Negative Small (NS), Negative Medium (NM) and negative large (NB) corresponding fuzzy state variables. And all variables are solved by adopting a triangular membership function. (1-7), after the genetic algebra is set, the population can repeat the general optimization steps, and a new population with higher individual fitness is generated by continuous iteration. The purpose of 'selection' is to select the individual with better fitness after the individual in the population is acted by an operator and directly transmit the individual to the next generation. The 'crossing' process is to randomly select two chromosomes, randomly select the positions of the chromosomes for exchange, and set the crossing probability, wherein the lengths of the selected positions of the two chromosomes are the same. The mutation process is to replace the value of a certain position in the chromosome with a random number between 1 and 7, set the mutation probability, prevent the premature convergence phenomenon, and make the genetic algorithm have local random search capability to accelerate convergence to the optimal solution.
Illustratively, when the fuzzy control rule is optimized by using a genetic algorithm, the number of solutions and the running time of the control process are considered, so that the optimal solution of the membership function and the domain-of-discourse range is not carried out. According to the design of fuzzy control input and output quantity, five variables are provided, and seven fuzzy language values are set in the discourse domain of each variable, so that 7 fuzzy rules are provided5And (3) strips.
The fuzzy language values are digitally coded according to the design of the fuzzy controller, and seven fuzzy language values of PB, PM, PS, ZO, NS, NM and NB are represented by 1, 2, 3, 4, 5, 6 and 7 respectively.
Thus obtaining the input variables e (k) and ec(k) Is a 49 x 2 matrix of the formula (5)
According to design, the output value Δ KP、ΔKI、ΔKDThe fuzzy language value matrix is a result required by the genetic algorithm to solve. The fuzzy linguistic value matrix of the output variables is denoted by X, which is a 49X 3 matrix. The matrix thus obtained for the fuzzy rule digitization is the following matrix (6):
rule＝[in,X]
the matrix row vector represents the fuzzy control rule. And rearranging fuzzy rules of the three output variables into a row of vectors in sequence according to the coding requirements of the genetic algorithm, wherein the row of vectors is the chromosome length of the individuals in the population, and the length of the vectors is 7 multiplied by 3.
And step five, obtaining the fuzzy rule through the step four. Exemplary, and further detailed explanation of the fuzzy PID control system block diagram illustrated in conjunction with fig. 2 is as follows:
deriving the difference e signal to obtain ec, inputting the ec and the ec into a fuzzy control model, resolving the fuzzy to obtain PID control parameter regulating variables delta kp, delta ki and delta kd, and inputting the PID control parameter regulating variables delta kp, delta ki and delta kd into a PID controller according to the obtained fuzzy inference rule; the PID controller obtains an initial PID control parameter regulating quantity and then adds the initial PID controller parameters kp, ki and kd to obtain a set PID controller parameter, and the set PID controller outputs a corresponding control quantity u (t) to an actuating mechanism (electromagnetic valve); after receiving the input of a control quantity signal u (t) of the PID controller, the executing mechanism adjusts the rigidity and the damping of the suspension until vehicle signals (vehicle body acceleration, suspension moving stroke, pitch angle and roll angle) reach expected values, and then stops, so that the active suspension control of the vehicle is realized, and the optimal running smoothness, operation stability and riding comfort of the vehicle are achieved on the premise of ensuring the overall safety of the vehicle.
And step six, performing performance evaluation through a fitness function.
When the comfort of the vehicle is taken as a first standard, the fitness function corresponding to the acceleration of the vehicle body and the dynamic stroke of the suspension is taken as a main function; when the first standard is to prevent the vehicle from sliding, the fitness function corresponding to the dynamic load of the tire is taken as the main standard; when the safety is used to prevent the vehicle from rolling over, the fitness function corresponding to the pitch angle and the roll angle is used as the main standard.
And seventhly, outputting a target fuzzy rule table, wherein the target rule table is an optimal fuzzy rule table. Exemplarily, as shown in fig. 5, the fuzzy PID control rule exemplified by the embodiment of the present invention shows in fig. 5 positive large (PB), Positive Middle (PM), Positive Small (PS), Zero (ZO), Negative Small (NS), Negative Middle (NM), and negative large (NB), E is the difference E signal shown in fig. 2, and EC is EC obtained by deriving the difference E signal shown in fig. 2. On the controller side, processing is carried out according to an optimized fuzzy control rule and by combining with a lower layer controller, a controlled object (an active suspension) is controlled according to an output signal, and illustratively, a proportional directional electromagnetic valve is adjusted through the signal output by the controller to change the flow of the inlet and the outlet of the suspension, so that the rigidity damping of the suspension is adjusted.
In summary, the active suspension control system according to the embodiment of the present invention includes an information acquisition unit 100, an information processing unit 200, and a controller 300, and the active suspension 400 control method implemented based on the system mainly includes that the controller receives a preprocessing signal transmitted from the information processing unit, obtains a fuzzy rule through a genetic algorithm by an upper controller of the controller, and implements suspension control according to the fuzzy rule and a lower controller combined with the controller. Compared with the prior art, the technical problem that expert human intervention is needed for establishing the fuzzy control rule base, time and labor are wasted, and the implementation control of the active suspension is not finally utilized is solved.
The embodiment of the invention provides an active suspension control system and method, aiming at the characteristics of nonlinearity, complexity and time-varying property of a suspension system when a vehicle runs, a genetic algorithm is used for optimizing a fuzzy rule, the vehicle active suspension control system is established based on a fuzzy PID control algorithm, a current state value of the vehicle is measured through a sensor, and a corresponding control instruction is generated after the current state value is sent to an electronic control unit to adjust the vehicle suspension.
It is to be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (9)
1. A fuzzy PID active suspension control system based on genetic algorithm optimization, characterized in that the system comprises:
the system comprises an information acquisition unit, a first signal generation unit and a second signal generation unit, wherein the information acquisition unit is arranged on a vehicle body and is used for acquiring road condition information and vehicle body information, the vehicle body information at least comprises vehicle body acceleration, suspension moving stroke, vehicle body pitch angle and vehicle body roll angle, and the road condition information and the vehicle body information are utilized to generate a first signal;
the information processing unit is connected with the information acquisition unit and used for performing preprocessing on the first signal by using a filter to obtain a preprocessed signal, and the preprocessed signal comprises current road condition characteristics and vehicle body information characteristics;
the controller is connected with the information processing unit and used for controlling the driving part to adjust the working state of the suspension body according to the preprocessing signal; the controller is divided into an upper layer controller and a lower layer controller, and the upper layer controller is a fuzzy PID active suspension force controller based on a genetic algorithm; the lower layer controller converts an expected controller output by the upper layer controller into control voltage required by a proportional directional solenoid valve, and the proportional directional solenoid valve realizes expected control force output by the upper layer controller by regulating flow of entering and exiting a suspension; the upper-layer controller obtains a fuzzy rule through a genetic algorithm, and the suspension is controlled according to the fuzzy control rule and by combining with the lower-layer controller.
2. The active suspension control system of claim 1, further comprising: an acceleration sensor for measuring acceleration of the vehicle body; the displacement sensor is used for measuring the dynamic stroke of the suspension and also used for measuring the displacement to calculate the dynamic-static load ratio of the tire; the gyroscope is used for measuring a pitch angle and a roll angle; and the laser radar component is used for measuring road condition information and acquiring road elevation.
3. The active suspension control system of claim 2 wherein the tire dynamic to static load ratio is expressed as
Wherein m is1、m2Respectively sprung mass and unsprung mass, xu、xrRespectively tire displacement during movement and input displacement to the suspension due to ground heave, ktIs the equivalent stiffness coefficient of the tire, and g is the gravity acceleration;
the dynamic-static load ratio of the tire is less than 1, and the tire is used for ensuring the reliable tire adhesion by always having positive pressure between the tire and the ground in the running process of a vehicle.
4. The active suspension control system of any one of claims 1 to 3 wherein the active suspension is classified as an active air suspension or an active hydro-pneumatic suspension.
5. A fuzzy PID active suspension control method based on genetic algorithm optimization, which is used for the fuzzy PID active suspension control system based on genetic algorithm optimization according to any one of claims 1-4, and is characterized by comprising the following steps:
acquiring current road condition information and vehicle body information to generate a first signal, wherein the vehicle body information at least comprises vehicle body acceleration, suspension moving distance, pitch angle and roll angle of a vehicle body;
utilizing a filter to carry out preprocessing on the first signal to obtain a preprocessed signal, wherein the preprocessed signal comprises current road condition characteristics and vehicle body information characteristics;
inputting the preprocessing signal as an input signal into a controller, wherein the controller is divided into an upper layer controller and a lower layer controller;
and obtaining fuzzy rules through a genetic algorithm in the upper layer controller and combining the fuzzy rules with the lower layer controller to realize the control of the suspension.
6. The method of claim 5, wherein if the active suspension is an active hydro-pneumatic suspension, the obtaining of fuzzy rules in the upper controller by genetic algorithm and the control of the suspension by the lower controller comprise:
simplifying the vehicle into 1/4 vehicle model;
the oil charging and discharging process of the oil-gas spring can be switched and the oil liquid flow can be controlled by adjusting the proportional electromagnetic valve;
calculating to obtain the adjustment quantity of the height of the vehicle body according to the inclination angle, the wheelbase and the wheel base;
determining the suspension stiffness and the range of suspension damping according to the requirements of a vehicle suspension control system;
establishing a fitness function of a genetic algorithm, wherein an optimization target is to minimize the fitness value in a feasible region meeting constraint conditions, and when the fitness function is not met, the optimization is stopped, and a value meeting the requirement at the last moment is output;
establishing an initial population, wherein the initial population is composed of corresponding fuzzy state variables, namely negative large, negative medium, negative small, zero, positive small, positive medium and positive large, each variable is solved by adopting a triangular membership function, after a genetic algebra is set, the population can repeat the general optimization steps, and a new population with higher individual fitness is generated by continuous iteration;
obtaining a fuzzy rule through the processing steps of establishing an initial population, obtaining ec after derivation of a difference e signal, inputting ec into a fuzzy control model together, obtaining PID control parameter regulating quantities delta kp, delta ki and delta kd by deblurring according to a fuzzy reasoning rule obtained in advance, inputting the ec and the ec into a PID controller, obtaining a set PID controller parameter by adding the PID controller parameter regulating quantities delta kp, delta ki and delta kd after obtaining the initial PID control parameter regulating quantity, outputting a corresponding control quantity u (t) to an actuating mechanism by the PID controller after setting, adjusting the rigidity and the damping of a suspension after the actuating mechanism receives the input of a control quantity signal u (t) of the PID controller, stopping the control until a vehicle signal reaches an expected value, and realizing the active suspension control of a vehicle, wherein the vehicle signal at least comprises vehicle body acceleration, suspension dynamic stroke, pitch angle and pitch angle, The roll angle.
7. The method of claim 6, further comprising:
performing performance evaluation through a fitness function;
and outputting a target fuzzy rule table.
8. The method of claim 6, wherein if the active suspension is an active hydro-pneumatic suspension, the vehicle differential equation corresponding to the vehicle model reduced to 1/4 is:
wherein m is1、m2Respectively, sprung mass and unsprung mass; x is the number ofs、xu、xrRespectively vehicle body displacement, tire displacement and input displacement to a suspension due to ground fluctuation in the motion process; k is a radical oftIs the equivalent stiffness coefficient of the tire; c. CtIs the equivalent damping coefficient of the tire; p1、A1Respectively showing the pressure state and the area of the oil chamber; beta is aeThe equivalent bulk modulus of hydraulic oil; cdIs the flow coefficient; a. thekIs the effective flow area of the throttle valve bore; rho is the oil density; n is a gas adiabatic index; pg、VgThe absolute pressure and the volume state of the gas in the hydro-pneumatic spring energy accumulator are obtained; qgThe flow rate of the oil flows through a throttle valve between the oil cylinder and the air storage tank; v10The initial oil storage volume of the oil cylinder; vgo、PgoThe initial volume state and gas absolute pressure in the hydro-pneumatic spring accumulator are obtained; qaThe oil flow input and output from the external oil source to the hydro-pneumatic spring.
9. The method of claim 7, wherein performing the performance evaluation by a fitness function comprises:
when the comfort of the vehicle is taken as a first standard, the fitness function corresponding to the acceleration of the vehicle body and the dynamic stroke of the suspension is taken as a main function;
when the first standard is to prevent the vehicle from sliding, the fitness function corresponding to the dynamic load of the tire is taken as the main standard;
when the safety is used to prevent the vehicle from rolling over, the fitness function corresponding to the pitch angle and the roll angle is used as the main standard.
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