CN110765554A - Intelligent control method of automobile semi-active suspension system based on TS model - Google Patents
Intelligent control method of automobile semi-active suspension system based on TS model Download PDFInfo
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Abstract
An intelligent control method of an automobile semi-active suspension system based on a TS model is characterized in that a fuzzy controller is improved, an algorithm is designed based on the construction of a neural fuzzy inference system by utilizing the TS model, the algorithm is realized by calling an adaptive neural fuzzy inference system ANFIS in a fuzzy logic tool box, the trained controller is led into the semi-active suspension system, and a simulation model of the TS model fuzzy neural network control semi-active suspension system is built. Analysis of simulation test results of the model shows that the comprehensive performance of the automobile suspension system can be remarkably improved by applying the model.
Description
Technical Field
The invention relates to the technical field of intelligent control of automobiles, in particular to an intelligent control method of an automobile semi-active suspension system based on a TS model.
Background
With the improvement of material level and culture level, the requirement of people on the comprehensive control performance of automobiles is higher. The automobile is actually a complex multi-degree-of-freedom nonlinear system, and the internal subsystems have different natural frequencies. During the running process, the vibration of the whole vehicle or local part is generated due to the vibration excitation action of various internal and external factors such as the change of external environments such as the vehicle speed, the moving direction, wind, rain, uneven road surface and the like, the imbalance of a suspension, an engine, a transmission system and other components of the vehicle body and the like. Therefore, the novel technology and the novel method are adopted to reduce the vibration generated in the driving process of the vehicle as much as possible, and the improvement of the comprehensive performances such as the smoothness, the operation stability and the like has important practical significance.
For automobiles, the feasibility of improving the road surface quality and reducing the vibration source is not high, and the road surface of a newly paved expressway is slightly uneven. While proper adjustment of the natural frequency of the system itself and increasing the damping factor of the system or the use of specialized vibration damping devices such as dynamic vibration absorbers may be put into practice on automotive suspensions. The suspension system is one of the important components of the automobile, is a general term for all connecting devices between a frame and an axle or a wheel, integrates various acting forces and determines the operation stability, riding comfort and driving safety of the automobile. Therefore, in order to meet the requirements of people on the multi-aspect performance of the automobile, more advanced suspension technology must be applied to control a suspension system so as to minimize the vibration of the automobile.
As an important component of the chassis of the vehicle, the suspension system determines and influences the ride comfort and handling stability of the vehicle to a great extent. The semi-active suspension system can adjust parameters such as damping of the suspension in real time according to road impact and vehicle body motion signals so as to improve the buffer performance of the suspension, and has greater advantages than a passive suspension with fixed parameters. Accordingly, the semi-active suspension system is complex in structure, and the traditional control method cannot achieve satisfactory control effect. Therefore, research into intelligent control strategies for semi-active suspension systems is becoming increasingly important.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides an intelligent control method of a semi-active suspension system of an automobile based on a TS model, which can effectively improve the performance of the semi-active suspension system.
In order to achieve the purpose, the invention adopts the following technical scheme:
an intelligent control method of an automobile semi-active suspension system based on a TS model comprises the following steps:
firstly, determining a suspension performance evaluation index according to the characteristics of an automobile suspension system, and establishing a random filtering white noise road surface input simulation model and a simulation result which are described by power spectral density in a time domain; constructing dynamic models of a four-degree-of-freedom semi-vehicle passive suspension and a semi-active suspension;
secondly, respectively establishing block diagram simulation models of a passive suspension system and a semi-active suspension system under fuzzy control according to automobile configuration parameters and a suspension vibration mathematical model in Simulink;
step three, improving the fuzzy controller; the TS model self-adaptive neural fuzzy inference system is used as a core controller of the semi-active suspension; firstly, designing a neural fuzzy inference system algorithm based on a TS model, establishing an initial neural fuzzy inference system, and calling an adaptive neural fuzzy inference system ANFIS in a fuzzy logic tool kit to realize the algorithm; then training initial ANFIS according to relevant data obtained by simulation tests of the fuzzy control-based semi-active suspension system under the Simulink environment and the same test conditions, importing the trained controller into a semi-active suspension system model, and finally establishing a TS model-based fuzzy neural network controlled semi-active suspension system simulation model;
step four, carrying out simulation tests on the passive suspension system and the fuzzy neural network control semi-active suspension system based on fuzzy control and a TS model under the Simulink environment and the same test conditions to obtain a simulation result; and the evaluation indexes of the suspension performance, namely the root mean square values corresponding to the vertical acceleration of the vehicle body, the dynamic stroke of the suspension and the dynamic load of the tire are used as the output of the simulation module, the simulation results obtained in different control modes are compared and analyzed, and the control advantages and disadvantages of the intelligent control methods are comprehensively evaluated.
The third step specifically comprises the following steps:
step 301, introducing a self-adaptive neuro-fuzzy inference system, and utilizing the self-adaptive neuro-fuzzy inference system under a TS model of MATLAB software and a GUI construction module thereof; improving a fuzzy controller; the TS model self-adaptive neural fuzzy inference system is used as a core controller of the semi-active suspension; firstly, designing a neural fuzzy inference system algorithm based on a TS model, establishing an initial neural fuzzy inference system, and calling an adaptive neural fuzzy inference system ANFIS in a fuzzy logic tool kit to realize the algorithm;
step 302, loading data in an ANFIS establishment process;
loading input/output data for training and learning, wherein the input/output data are derived from simulation data of a semi-active suspension system based on fuzzy control, and are directly imported into a working interval of MATLAB after being preprocessed;
step 303, generating an initial neural fuzzy inference system in the ANFIS establishment process;
selecting a grid segmentation method to construct an initial system, setting the language value subset division number corresponding to each language variable, and selecting a membership function type and an output accurate function description type corresponding to the language value subset;
step 304, training an initial system in the ANFIS establishment process; the training method is BP back-propagation learning rule;
305, generating an IF-THEN rule set in the ANFIS establishment process;
after training is finished, the IF-THEN rule set of ANFIS is generated, and the designed ANFIS model can be introduced into a core controller FNN _ TS of the semi-active suspension system for subsequent simulation tests.
Compared with the prior art, the invention has the beneficial effects that:
1. and improving the fuzzy controller, designing a neural fuzzy inference system construction algorithm based on a TS model, calling an adaptive neural fuzzy inference system ANFIS in a fuzzy logic tool kit to realize the algorithm, guiding the trained controller into a semi-active suspension system, and establishing a simulation model of the TS model fuzzy neural network control semi-active suspension system. Analysis of simulation test results of the model shows that the comprehensive performance of the automobile suspension system can be remarkably improved by applying the model.
2. A passive suspension system and a fuzzy neural network control semi-active suspension system based on fuzzy control and a TS model are subjected to simulation tests under the Simulink environment and the same test conditions, and suspension performance evaluation indexes, namely root mean square values corresponding to vertical acceleration of a vehicle body, dynamic stroke of the suspension and dynamic load of tires, are used as output of a simulation module, and comparison analysis on simulation results shows that the intelligent control method can effectively improve the performance of the semi-active suspension system.
3. Semi-active suspension is a compromise of comprehensive considerations on vehicle overall control performance, energy consumption and cost. The invention researches the vibration damping performance of the automobile suspension based on intelligent control by a method combining theory and simulation test, and simulates by combining different control modes in the suspension design stage, thereby improving the success rate of one-time test, saving the cost, and simultaneously improving the smoothness, the operating stability and the overall comfort quality of the vehicle.
Drawings
FIG. 1 is a front wheel road surface displacement input simulation model;
FIG. 2 is a simulation result of vertical displacement input of front and rear wheels on a level B road of a domestic highway;
FIG. 3 is a four degree of freedom semi-vehicle passive suspension dynamics model;
FIG. 4 is a four degree of freedom semi-vehicle semi-active suspension dynamics model;
FIG. 5 is a block diagram simulation model of a passive suspension system;
FIG. 6 is a block diagram simulation model of a semi-active suspension system under fuzzy control;
FIG. 7 is a block diagram of an adaptive neural fuzzy controller for a TS model based semi-active suspension system;
FIG. 8 is a load training data interface;
FIG. 9 is a fuzzy inference system initial setup block;
FIG. 10 is a network hierarchy representation of an adaptive neuro-fuzzy inference system;
FIG. 11 is a training process for the system;
FIG. 12 is a surface depiction of system rules;
FIG. 13 is a bar graph of the RMS distribution of vertical acceleration of the vehicle body obtained for different control modes;
FIG. 14 is a bar graph of RMS distribution of suspension dynamic travel for different control modes;
FIG. 15 is a bar graph of RMS distribution of dynamic tire loads obtained for different control regimes.
Detailed Description
The following detailed description of the present invention will be made with reference to the accompanying drawings.
An intelligent control method of an automobile semi-active suspension system based on a TS model comprises the following steps:
firstly, determining a suspension performance evaluation index according to the characteristics of an automobile suspension system, and establishing a random filtering white noise road surface input simulation model and a simulation result which are described by power spectral density in a time domain. And (3) constructing dynamic models of the four-degree-of-freedom semi-vehicle passive suspension and the semi-active suspension.
The first step specifically comprises the following steps:
1. the three basic parameters of the vertical acceleration of the vehicle body, the dynamic stroke of the suspension and the dynamic load of the tire, which are described from different angles and are mutually contained, are adopted for quantitative evaluation.
2. And establishing a random filtering white noise road surface input simulation model described by power spectral density in a time domain, and simulating the vertical displacement input of the front and rear wheel road surfaces by MATLAB/Simulink simulation. The simulation model is shown in FIG. 1.
Gain in FIG. 1
Gain1 ═ 2 pi f00.0628. For the rear wheel, the road surface input model is added by only one delay module, which is reflected in that in the following passive suspension simulation model, the corresponding delay time tau is calculated as 0.1327 s. The vertical displacement input simulation results of the front and rear wheels on the level-B road surface of the domestic highway are shown in figure 2.
3. Dynamic model for constructing four-degree-of-freedom semi-vehicle passive suspension and semi-active suspension
Aiming at a certain common car model, a four-degree-of-freedom semi-car passive suspension dynamic model shown in figure 3 is established.
Wherein, each symbol means as follows:
ms-a sprung mass of the vehicle;
theta is the pitch angle at the center of mass of the vehicle body;
Is-spring carrierMeasuring the moment of inertia of the angular vibration about the lateral axis at the centroid;
Z2-vertical displacement at the centre of mass;
a. b-distance from the front-back axis to the centroid;
k2f、k2r-front and rear suspension stiffness;
c2f、c2r-front and rear suspension damping coefficients;
muf、mur-front and rear axle unsprung masses;
k1f、k1r-front and rear tire stiffness;
Z2f、Z2r-vertical displacement of the front and rear ends of the body;
Z1f、Z1r-vertical displacement of unsprung masses of the front and rear axles;
Z0f、Z0rinputting the unevenness of the road surface at the front wheel and the rear wheel.
According to Newton's second law or Darwebel's principle or analytical mechanics methods, the semi-vehicle model of the passive suspension can be organized into a mathematical model of the form:
the semi-active suspension provides adjustable damping force in a certain range through the controllable shock absorber, and a four-degree-of-freedom semi-active suspension dynamic model of the semi-vehicle is shown in figure 4.
Wherein, each symbol means as follows:
Ff、Frfront and rear suspension adjustable damping forces.
According to the Daronbel principle and other methods, a half car model of the semi-active suspension is arranged into a mathematical model in the following form:
adjustable damping force F of semi-active suspension at front end and rear end of vehicle bodyf、FrWhen the input value is zero, the semi-active suspension system can be converted into a passive suspension system.
By the aid of the mathematical model, a dynamic simulation model of the automobile suspension system can be established by means of the MATLAB/Simulink platform.
And secondly, respectively establishing block diagram simulation models of the passive suspension system and the semi-active suspension system under fuzzy control in Simulink according to the automobile configuration parameters and the suspension vibration mathematical model.
The second step specifically comprises the following steps:
1. basic configuration parameters of suspension
The invention develops research by referring to a certain common car model, and the basic configuration parameters of the suspension of the invention are shown in a table 1.
TABLE 1 suspension basic configuration parameters
2. Passive suspension system block diagram simulation model
A block diagram simulation model created for a passive suspension system is shown in fig. 5.
3. Semi-active suspension system block diagram simulation model under fuzzy control
A block diagram simulation model created for a semi-active suspension system under fuzzy control is shown in fig. 6.
And step three, improving the fuzzy controller. And the TS model adaptive neural fuzzy inference system is used as a core controller of the semi-active suspension. The self-adaptive neural fuzzy controller block diagram of the semi-active suspension system based on the TS model is characterized in that firstly, a neural fuzzy inference system algorithm based on the TS model is designed, an initial neural fuzzy inference system is established, and the algorithm is realized by calling a self-adaptive neural fuzzy inference system ANFIS in a fuzzy logic toolbox. A block diagram of an adaptive neural fuzzy controller of a semi-active suspension system based on a TS model is shown in fig. 7. Then, initial ANFIS is trained after relevant data obtained by simulation tests of the fuzzy control-based semi-active suspension system under the Simulink environment and the same test conditions are preprocessed, the trained controller is led into a semi-active suspension system model, and finally the TS model-based fuzzy neural network controlled semi-active suspension system simulation model is established.
The third step specifically comprises the following steps:
step 301, introducing a self-adaptive neural fuzzy inference system. MATLAB software comprises an adaptive neural fuzzy inference system (ANFIS for short) under a TS model and a GUI construction module thereof. The neural network and TS type fuzzy inference are fused together, and a fuzzy inference system (ANFIS) is established by combining a plurality of acquired data through a self-adaptive modeling method, wherein rules and membership function parameters are calculated by using BP back-propagation learning rules or a hybrid method (hybrid), so that the manual summary and induction of expert intuitional operation experience or intuitional perception are avoided. Improvements are made to the fuzzy controller. And the TS model adaptive neural fuzzy inference system is used as a core controller of the semi-active suspension. Firstly, designing a neural fuzzy inference system algorithm based on a TS model, establishing an initial neural fuzzy inference system, and calling an adaptive neural fuzzy inference system ANFIS in a fuzzy logic tool box to realize the algorithm.
Step 302, load data during ANFIS setup.
Loading input/output data for training learning is derived from simulation data of a semi-active suspension system based on fuzzy control, and is directly imported in a working interval of MATLAB after being preprocessed, and a loading training data interface is shown in FIG. 8.
Step 303, generating an initial neuro-fuzzy inference system in the ANFIS establishment process.
And selecting a grid segmentation method to construct an initial system, setting the language value subset division number corresponding to each language variable, and selecting the membership function type and the output accurate function description type corresponding to the language value subset. The input of the text is 2 linguistic variables, each of which has 7 membership functions, the type of the membership function is gausssf, the output membership function is linear, and the parameters are shown in the initial setting box of the fuzzy inference system in fig. 9.
The network hierarchical representation of the established adaptive neuro-fuzzy inference system is shown in fig. 10.
Step 304, train initial system in ANFIS setup process.
The training method is still a BP back propagation learning rule, the error precision is 0, and the training times are 1000. The training process of the system is shown in fig. 11.
Step 305, generating IF-THEN rule set in ANFIS setup.
After training, the IF-THEN rule set of ANFIS has been generated, and a surface description of the system rules in fuzzy space is shown in FIG. 12. Therefore, the designed ANFIS model can be led into a core controller FNN _ TS of the semi-active suspension system for subsequent simulation tests.
And step four, carrying out simulation tests on the passive suspension system and the fuzzy control-based and TS model-based fuzzy neural network control semi-active suspension system under the Simulink environment and under the same test conditions to obtain a simulation result. And the evaluation indexes of the suspension performance, namely the root mean square values corresponding to the vertical acceleration of the vehicle body, the dynamic stroke of the suspension and the dynamic load of the tire are used as the output of the simulation module, the simulation results obtained in different control modes are compared and analyzed, and the control advantages and disadvantages of the intelligent control methods are comprehensively evaluated.
The fourth step specifically comprises the following steps:
1. after a system simulation model is built in a Simulink environment, a dynamic simulation test is prepared. First, the configuration parameters are determined: the simulation time is set to 0-30 s. According to the characteristics of the suspension system of the present invention, the algorithm of the variable step mode is selected, and the corresponding solution operator is determined to be ode45, which corresponds to the dorend-Prince algorithm showing the fourth/fifth order longge-kutta.
2. And carrying out simulation tests on a passive suspension system and a semi-active suspension system model based on fuzzy control and TS model fuzzy neural network control under the same B-level road surface and the condition that the driving speed is 72 km/h.
3. Root mean square values corresponding to the vehicle body vertical acceleration, the suspension dynamic stroke, and the tire dynamic load in the different control modes were obtained as shown in table 2, fig. 13, table 3, fig. 14, table 4, and fig. 15, respectively.
TABLE 2 RMS value table of vertical acceleration of front and rear ends of automobile body under different control modes
TABLE 3 RMS value table for the front and rear suspension moving distance of automobile under different control modes
TABLE 4 RMS table of dynamic loads of front and rear wheels of vehicle under different control modes
4. A passive suspension system, fuzzy control and a TS model-based fuzzy neural network control semi-active suspension system are subjected to simulation tests under the Simulink environment and the same test conditions, test result data are compared, and the three performance evaluation indexes, namely the root mean square values corresponding to the vertical acceleration of a vehicle body, the dynamic stroke of a suspension and the dynamic load of a tire, are quantitatively analyzed. It can be seen that the semi-active suspension system based on fuzzy control compares to the passive suspension system: the root mean square value of the vertical acceleration at the front end of the vehicle body is reduced by 24.5 percent, and the root mean square value of the vertical acceleration at the rear end of the vehicle body is reduced by 36.3 percent; the root mean square value of the dynamic stroke of the front suspension is increased by 4.9 percent, and the root mean square value of the dynamic stroke of the rear suspension is reduced by 1.8 percent; the root mean square value of the dynamic load of the front tire is reduced by 18.1 percent, and the root mean square value of the dynamic load of the rear tire is reduced by 21.8 percent. Therefore, the control effect of the semi-active suspension system based on fuzzy control is better.
5. Compared with a passive suspension system, the fuzzy neural network control semi-active suspension system based on the TS model has the advantages that: the root mean square value of the vertical acceleration at the front end of the vehicle body is reduced by 29.2 percent, and the root mean square value of the vertical acceleration at the rear end of the vehicle body is reduced by 40.3 percent; the moving stroke root mean square value of the front suspension is increased by 3.3 percent, and the moving stroke root mean square value of the rear suspension is reduced by 3.6 percent; the root mean square value of the dynamic load of the front tire is reduced by 21.9 percent, and the root mean square value of the dynamic load of the rear tire is reduced by 23.3 percent. Therefore, the improvement effect of the three intelligent control modes on the comprehensive performance of the suspension system is increased in sequence. The fuzzy neural network control mode based on the TS model has the best vibration reduction effect on the suspension system.
The above embodiments are implemented on the premise of the technical solution of the present invention, and detailed embodiments and specific operation procedures are given, but the scope of the present invention is not limited to the above embodiments. The methods used in the above examples are conventional methods unless otherwise specified.
Claims (2)
1. An intelligent control method of an automobile semi-active suspension system based on a TS model is characterized by comprising the following steps:
firstly, determining a suspension performance evaluation index according to the characteristics of an automobile suspension system, and establishing a random filtering white noise road surface input simulation model and a simulation result which are described by power spectral density in a time domain; constructing dynamic models of a four-degree-of-freedom semi-vehicle passive suspension and a semi-active suspension;
secondly, respectively establishing block diagram simulation models of a passive suspension system and a semi-active suspension system under fuzzy control according to automobile configuration parameters and a suspension vibration mathematical model in Simulink;
step three, improving the fuzzy controller; the TS model self-adaptive neural fuzzy inference system is used as a core controller of the semi-active suspension; firstly, designing a neural fuzzy inference system algorithm based on a TS model, establishing an initial neural fuzzy inference system, and calling an adaptive neural fuzzy inference system ANFIS in a fuzzy logic tool kit to realize the algorithm; then training initial ANFIS according to relevant data obtained by simulation tests of the fuzzy control-based semi-active suspension system under the Simulink environment and the same test conditions, importing the trained controller into a semi-active suspension system model, and finally establishing a TS model-based fuzzy neural network controlled semi-active suspension system simulation model;
step four, carrying out simulation tests on the passive suspension system and the fuzzy neural network control semi-active suspension system based on fuzzy control and a TS model under the Simulink environment and the same test conditions to obtain a simulation result; and the evaluation indexes of the suspension performance, namely the root mean square values corresponding to the vertical acceleration of the vehicle body, the dynamic stroke of the suspension and the dynamic load of the tire are used as the output of the simulation module, the simulation results obtained in different control modes are compared and analyzed, and the control advantages and disadvantages of the intelligent control methods are comprehensively evaluated.
2. The intelligent control method for the semi-active suspension system of the automobile based on the TS model as claimed in claim 1, wherein the third step specifically comprises the following steps:
step 301, introducing a self-adaptive neuro-fuzzy inference system, and utilizing the self-adaptive neuro-fuzzy inference system under a TS model of MATLAB software and a GUI construction module thereof; improving a fuzzy controller; the TS model self-adaptive neural fuzzy inference system is used as a core controller of the semi-active suspension; firstly, designing a neural fuzzy inference system algorithm based on a TS model, establishing an initial neural fuzzy inference system, and calling an adaptive neural fuzzy inference system ANFIS in a fuzzy logic tool kit to realize the algorithm;
step 302, loading data in an ANFIS establishment process;
loading input/output data for training and learning, wherein the input/output data are derived from simulation data of a semi-active suspension system based on fuzzy control, and are directly imported into a working interval of MATLAB after being preprocessed;
step 303, generating an initial neural fuzzy inference system in the ANFIS establishment process;
selecting a grid segmentation method to construct an initial system, setting the language value subset division number corresponding to each language variable, and selecting a membership function type and an output accurate function description type corresponding to the language value subset;
step 304, training an initial system in the ANFIS establishment process; the training method is BP back-propagation learning rule;
305, generating an IF-THEN rule set in the ANFIS establishment process;
after training is finished, the IF-THEN rule set of ANFIS is generated, and the designed ANFIS model can be introduced into a core controller FNN _ TS of the semi-active suspension system for subsequent simulation tests.
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