CN109927501A - A kind of intelligent control method of Vehicle Semi-active Suspension System - Google Patents
A kind of intelligent control method of Vehicle Semi-active Suspension System Download PDFInfo
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Abstract
A kind of intelligent control method of Vehicle Semi-active Suspension System, fuzzy controller is improved, the neural fuzzy inference system based on Mamdani model is designed as controller, in conjunction with BP anti-pass learning rules, fuzzy control rule is trained, while corresponding Fuzzy Neural-network Control Vehicle Semi-active Suspension System simulation model is constructed using Simulink S- function.By the Simulation results to the model analysis shows, the model utilization the comprehensive performance of automobile suspension system can be made to have obtained more significant improvement.Under same test conditions, it is unified under Simulink environment to passive suspension and l-G simulation test is carried out based on the semi-active suspension system model of fuzzy control and Mamdani Fuzzy Neural-network Control.And stroke and the corresponding root-mean-square value of tyre dynamic load lotus are moved as the output of emulation module using suspension property evaluation index, that is, vehicle body normal acceleration, suspension, intelligent control method used in the present invention can effectively improve the performance of semi-active suspension system.
Description
Technical field
The present invention relates to the field of intelligent control technology of automobile, in particular to a kind of intelligence of Vehicle Semi-active Suspension System
Control method.
Background technique
With the raising of levels of substance and levels of culture, people are higher to the comprehensively control performance requirement of automobile.Automobile is real
It is a complicated Nonlinear System with Multi degree of Freedom on border, internal subsystems have different intrinsic frequencies.It is in driving process
The middle variation because of the external environments such as speed, the direction of motion and wind, rain, Uneven road, suspension, engine, transmission system and vehicle body
The exciting of the various internal and external factors such as the imbalance of other component parts acts on and generates the vibration of vehicle or part.Therefore, it uses
New technology and method reduces vehicle generated vibration in the process of moving to the greatest extent, improves its ride comfort, control stability etc.
Comprehensive performance has important practical significance.
For automobile, improve pavement quality, the way feasibility for reducing vibration source is not high, even the height newly spread
Fast highway, road surface also can be slightly uneven.And the intrinsic frequency of appropriate adjustment system itself and increase system damping because
Son can be tried out using special dampening apparatus such as dynamic shock absorber on automotive suspension.Suspension system is the important of automobile
One of composition, is the general name of all attachment devices between vehicle frame and vehicle bridge or wheel, it combines multi-acting force, decides
Control stability, riding comfort and the driving safety of automobile.Therefore, it is wanted to meet people to automobile various aspects performance
It asks, it is necessary to apply more advanced suspension technology, suspension system is controlled, automobile vibration is cut to bone with realizing.
As the important composition of automobile chassis, suspension system largely determines and affects the smooth of running car
Property and control stability.Semi-active suspension system can adjust in real time the damping etc. of suspension according to road shocks and body movement signal
Parameter, to improve rack buffer performance, than parameter, fixed passive suspension presents bigger advantage.Correspondingly, half is actively outstanding
Frame system structure is more complex, and traditional control method tends not to obtain satisfactory control effect.Therefore, semi-active suspension
The Intelligent Control Strategy research of system seems further important.
Summary of the invention
In order to solve the problems, such as described in background technique, the present invention provides a kind of intelligent control of Vehicle Semi-active Suspension System
Method can effectively improve the performance of semi-active suspension system.
In order to achieve the above object, the present invention is implemented with the following technical solutions:
A kind of intelligent control method of Vehicle Semi-active Suspension System, includes the following steps:
Step 1: being directed to automobile suspension system own characteristic, suspension property evaluation index is specified, is established in the time domain by function
The stochastic filtering white noise road surface input simulation model and analog result of rate spectrum density description, building half vehicle of four-degree-of-freedom passively hang
The kinetic model of frame and semi-active suspension；
Step 2: establishing passive suspension respectively according to configuration of automobiles parameter and the vibrated mathematical model of suspension in Simulink
The block diagram simulation model of semi-active suspension system under system and fuzzy control；
Step 3: for fuzzy inference system, there is also shortcomings, introduce Mamdani model nerve mould on its basis
Paste inference system control algolithm improves fuzzy controller, writes S- function using the M language that software carries and realizes, then ties
The user interface that Simulink encapsulation function customizes S- function module as needed is closed, BP back propagation algorithm is embedded, uses typical number
Implement training according to the initial neural fuzzy inference system based on Mamdani model；To which creation is based on Mamdani model
Fuzzy Neural-network Control semi-active suspension system simulation model；
Training data is derived from the emulation data of Fuzzy control system, respectively vehicle body normal acceleration, suspension move stroke with
And the real time data of tyre dynamic load lotus selects pretreated typical data to initial followed by BP anti-pass learning rules
Neural fuzzy inference system based on Mamdani model implements training, finally by the trained nerve based on Mamdani model
Fuzzy inference system is imported into the core controller FNN_Mamdani of semi-active suspension system, so that creation is based on Mamdani
The Fuzzy Neural-network Control semi-active suspension system simulation model of model, and then carry out l-G simulation test.
Step 4: being unified under Simulink environment under same test conditions to passive suspension and based on fuzzy control
And the semi-active suspension system model of Mamdani Fuzzy Neural-network Control carries out l-G simulation test, obtains simulation result；And with outstanding
Frame Performance Evaluating Indexes, that is, vehicle body normal acceleration, suspension move stroke and the corresponding root-mean-square value of tyre dynamic load lotus is emulation mould
The output of block compares and analyzes the simulation result obtained under different control modes, and overall merit goes out these intelligent controls
The control superiority and inferiority effect of method.
The step two specifically includes as follows:
Step 201, using vehicle body normal acceleration, the dynamic stroke of suspension and tyre dynamic load lotus, these three are retouched from different perspectives
It states and the basic parameter that mutually restrains carries out quantitative assessment；
Step 202 establishes the stochastic filtering white noise road surface input simulation model by power spectral density description in the time domain
And analog result, vertical displacement input in front and rear wheel road surface is simulated by MATLAB/Simulink；
Step 203, the kinetic model for constructing half automobile of four-degree-of-freedom passive suspension and semi-active suspension；By passive suspension
Half vehicle model be organized into the mathematical model of following form:
Wherein, each symbol meaning is as follows:
ms- automobile spring carried mass；
Pitch angle at θ-vehicle body mass center；
IsThe rotary inertia of-spring carried mass lateral axes angular oscillation at mass center；
Z2Vertical deviation at-mass center；
A, b-antero posterior axis to mass center distance；
k2f、k2r- forward and backward suspension rate；
c2f、c2r- forward and backward suspension damping coefficient；
muf、mur- axle nonspring carried mass；
k1f、k1r- front and back tire stiffness；
Z2f、Z2rThe vertical deviation of-vehicle body front and back ends；
Z1f、Z1rThe vertical deviation of-axle nonspring carried mass；
Z0f、Z0rRoad roughness inputs at-front and rear wheel；
Semi-active suspension provides adjustable damping force in a certain range by controllable damper, establishes half vehicle of four-degree-of-freedom
Semi-active suspension kinetic model；Half vehicle model of semi-active suspension is organized into the mathematical model of following form:
Ff、Fr- forward and backward suspension damping-force adjustable；
As the damping-force adjustable F of vehicle body front and back ends semi-active suspensionf、FrInput value when being zero, semi-active suspension system
It is converted into passive suspension system；
The dynamics simulation mould of automobile suspension system is established by MATLAB/Simulink platform by the above mathematical model
Type.
The step four specifically includes as follows:
The laggard Mobile state l-G simulation test of system simulation model is constructed under step 401, Simulink environment, first has to determine each
Configuration parameter selects the algorithm of variable step mode, determines corresponding solution musical instruments used in a Buddhist or Taoist mass；
Step 402, by passive suspension and based on fuzzy control and Mamdani model Fuzzy Neural-network Control half actively
Suspension system model carries out l-G simulation test under conditions of the identical road surface B grades of and running speed are V；
Step 403 obtains and moves stroke and tyre dynamic load lotus phase with vehicle body normal acceleration, suspension under different control modes
The root-mean-square value answered；
Step 404, comparative test result data, quantitative analysis these three Performance Evaluating Indexes, that is, vehicle body normal acceleration,
Suspension moves stroke and the corresponding root-mean-square value of tyre dynamic load lotus.
Compared with prior art, the beneficial effects of the present invention are:
1, fuzzy controller is improved, designs the neural fuzzy inference system based on Mamdani model as control
Device processed trains fuzzy control rule in conjunction with BP anti-pass learning rules, while corresponding using the building of Simulink S- function
Fuzzy Neural-network Control Vehicle Semi-active Suspension System simulation model.Pass through the analytical table of the Simulation results to the model
Bright, the utilization of the model can make the comprehensive performance of automobile suspension system obtain more significant improvement.
2, under same test conditions, be unified under Simulink environment to passive suspension and based on fuzzy control and
The semi-active suspension system model of Mamdani Fuzzy Neural-network Control carries out l-G simulation test.And with suspension property evaluation index
I.e. vehicle body normal acceleration, suspension move stroke and the corresponding root-mean-square value of tyre dynamic load lotus is the output of emulation module, to imitative
True result compares and analyzes the property for showing that intelligent control method used in the present invention can effectively improve semi-active suspension system
Energy.
3, semi-active suspension is the compromise comprehensively considered in vehicle comprehensively control performance, energy loss and cost.This hair
Bright automotive suspension damping property of the technique study based on intelligent control combined by theory with l-G simulation test, in suspension design
Stage combines different control modes to be emulated, and primary success of the test rate can be improved, and saves cost, while improving vehicle
The comfortable quality of the entirety of ride comfort, control stability and vehicle.
Detailed description of the invention
Fig. 1 is that front-wheel pavement displacement inputs simulation model；
Fig. 2 be front and rear wheel at home highway B grade road surfaces when vertical displacement input analog result；
Fig. 3 is the passive dynamic suspension system of vehicles model of half vehicle of four-degree-of-freedom；
Fig. 4 is half vehicle semi-active suspension kinetic model of four-degree-of-freedom；
Fig. 5 is passive suspension system block diagram simulation model；
Fig. 6 is the semi-active suspension system block diagram simulation model under fuzzy control；
Fig. 7 is S- function control flow chart；
Fig. 8 is the Fuzzy Neural-network Control semi-active suspension system block diagram simulation model based on Mamdani model.
Specific embodiment
Specific embodiment provided by the invention is described in detail below in conjunction with attached drawing.
A kind of intelligent control method of Vehicle Semi-active Suspension System, includes the following steps:
Step 1: firstly, specifying suspension property evaluation index, foundation is in time domain for automobile suspension system own characteristic
In by power spectral density description stochastic filtering white noise road surface input simulation model and analog result.Construct half vehicle of four-degree-of-freedom
The kinetic model of passive suspension and semi-active suspension.
The step one specifically includes as follows:
1, stroke and tyre dynamic load lotus are moved using vehicle body normal acceleration, suspension these three is described and phase from different perspectives
The basic parameter mutually restrained carries out quantitative assessment.
2, it establishes in the time domain by the stochastic filtering white noise road surface input simulation model of power spectral density description and simulation
As a result
Front and rear wheel road surface vertical displacement input is simulated by MATLAB/Simulink.Its simulation model is as shown in Figure 1.
Gain in Fig. 1Gain G ain1=2 π f0=0.0628.For rear-wheel, road surface
The only more Postponement modules of input model, this is embodied in passive suspension simulation model below, corresponding delay time T meter
Calculating is 0.1327s.Front and rear wheel at home highway B grades of road surfaces when vertical displacement input analog result it is as shown in Figure 2.
3, the kinetic model of half automobile of four-degree-of-freedom passive suspension and semi-active suspension is constructed
For a certain general car vehicle, the passive dynamic suspension system of vehicles model of half vehicle of four-degree-of-freedom as shown in Figure 3 is established.
Wherein, each symbol meaning is as follows:
ms- automobile spring carried mass；
Pitch angle at θ-vehicle body mass center；
IsThe rotary inertia of-spring carried mass lateral axes angular oscillation at mass center；
Z2Vertical deviation at-mass center；
A, b-antero posterior axis to mass center distance；
k2f、k2r- forward and backward suspension rate；
c2f、c2r- forward and backward suspension damping coefficient；
muf、mur- axle nonspring carried mass；
k1f、k1r- front and back tire stiffness；
Z2f、Z2rThe vertical deviation of-vehicle body front and back ends；
Z1f、Z1rThe vertical deviation of-axle nonspring carried mass；
Z0f、Z0rRoad roughness inputs at-front and rear wheel.
It, can be by half vehicle mould of passive suspension according to Newton's second law or dAlembert principle or Analytical Mechanics method
Type is organized into the mathematical model of following form:
Semi-active suspension provides adjustable damping force in a certain range, half Che Banzhu of four-degree-of-freedom by controllable damper
Dynamic dynamic suspension system of vehicles model is as shown in Figure 4.
Wherein, each symbol meaning is as follows:
Ff、Fr- forward and backward suspension damping-force adjustable.
According to the methods of dAlembert principle, half vehicle model of semi-active suspension is organized into the mathematical modulo of following form
Type:
As the damping-force adjustable F of vehicle body front and back ends semi-active suspensionf、FrInput value when being zero, semi-active suspension system
Passive suspension system can be converted into.
By the above mathematical model, the dynamics that can establish automobile suspension system by MATLAB/Simulink platform is imitative
True mode.
Step 2: then, according to configuration of automobiles parameter and the vibrated mathematical model of suspension in Simulink, establishing quilt respectively
The block diagram simulation model of dynamic suspension system and the semi-active suspension system under fuzzy control.
The step two specifically includes as follows:
1, suspension basic configuration parameter
The present invention is shown in Table 1 with reference to the expansion research of certain general car model, suspension basic configuration parameter.
1 suspension basic configuration parameter of table
2, passive suspension system block diagram simulation model
Fig. 5 is shown in the block diagram simulation model of passive suspension system creation.
3, the semi-active suspension system block diagram simulation model under fuzzy control
Fig. 6 is shown in the block diagram simulation model of the semi-active suspension system creation under fuzzy control.
Step 3: since fuzzy inference system is there is also shortcoming, Mamdani model is introduced on its basis
Neural fuzzy inference system control algolithm.The algorithm is write S- function using the M language that software carries and is realized, in conjunction with
Simulink encapsulation function customizes the user interface of S-Function module as needed, embeds BP back propagation algorithm, uses typical case
Data implement training to the initial neural fuzzy inference system based on Mamdani model；To which creation is based on Mamdani model
Fuzzy Neural-network Control semi-active suspension system simulation model.
The control flow chart of S- function is shown in Fig. 7.
Fuzzy Neural-network Control semi-active suspension system block diagram simulation model based on Mamdani model, such as Fig. 8 institute
Show.Training data is derived from the emulation data of Fuzzy control system, and respectively vehicle body normal acceleration, suspension moves stroke and tire
The real time data of dynamic loading.Followed by BP anti-pass learning rules, select pretreated typical data to it is initial based on
The neural fuzzy inference system of Mamdani model implements training, and drafting frequency of training is 2000 times, and error is limited to 0.1.Finally will
The trained neural fuzzy inference system based on Mamdani model imported into the core controller FNN_ of semi-active suspension system
In Mamdani, thus Fuzzy Neural-network Control semi-active suspension system simulation model of the creation based on Mamdani model, into
And carry out l-G simulation test.
Step 4: being unified under Simulink environment the semi-active suspension system to fuzzy control and Fuzzy Neural-network Control
Model of uniting carries out l-G simulation test, obtains simulation result.And row is moved with suspension property evaluation index, that is, vehicle body normal acceleration, suspension
Journey and the corresponding root-mean-square value of tyre dynamic load lotus are the output of emulation module, the emulation knot that will be obtained under different control modes
Fruit compares and analyzes, and overall merit goes out the control superiority and inferiority effect of these intelligent control methods.
The step four specifically includes as follows:
1, it is constructed under Simulink environment after system simulation model it is necessary to being ready for Dynamic Simulating Test.It first has to really
Fixed each configuration parameter: simulation time is set as 0~30s.The characteristics of suspension system according to the present invention, selects the calculation of variable step mode
Method determines that corresponding solution musical instruments used in a Buddhist or Taoist mass is ode45, it corresponds to the Dormand-Prince algorithm of four/five rank Runge-Kuttas of display.
2, by passive suspension and semi-active suspension system based on fuzzy control and Mamdani model Fuzzy Neural-network Control
System model carries out l-G simulation test under conditions of the identical road surface B grades of and running speed are 72km/h.
3, it obtains corresponding equal with vehicle body normal acceleration, the dynamic stroke of suspension and tyre dynamic load lotus under different control modes
Root value, respectively as shown in table 2, table 3 and table 4.
Body of a motor car front and back ends normal acceleration root-mean-square value table under 2 different control modes of table
The forward and backward suspension of automobile moves stroke root-mean-square value table under 3 different control modes of table
The tyre dynamic load lotus root-mean-square value table of automobile front and rear wheel under 4 different control modes of table
4, comparative test result data, quantitative analysis these three Performance Evaluating Indexes, that is, vehicle body normal acceleration, suspension are dynamic
Stroke and the corresponding root-mean-square value of tyre dynamic load lotus.As can be seen that based on investigation of fuzzy control of semi-active suspension system and passively outstanding
Frame system is compared: body structure normal acceleration root-mean-square value decline 24.5%, under body rear end normal acceleration root-mean-square value
Drop 36.3%；Front suspension moves stroke root-mean-square value and increases by 4.9%, and rear suspension moves stroke root-mean-square value and reduces 1.8%；Front-wheel movement of the foetus
Load root-mean-square value reduces 18.1%, and rear tyre dynamic loading root-mean-square value in turn reduces 21.8%.Therefore, it is based on Fuzzy Control
The control effect of semi-active suspension system processed is more preferable.Based on Mamdani model Fuzzy Neural-network Control semi-active suspension system
Compared with passive suspension system: body structure normal acceleration root-mean-square value decline 29.0%, body rear end normal acceleration is equal
Root value decline 37.4%；Front suspension moves stroke root-mean-square value and increases 3.3%, and rear suspension moves stroke root-mean-square value and reduces 1.8%；
Front tyre dynamic loading root-mean-square value reduces 21.8%, and rear tyre dynamic loading root-mean-square value reduces 22.5%.Therefore, above three
Kind intelligent control method is sequentially increased the improvement of suspension system comprehensive performance.Wherein, based on the fuzzy mind of Mamdani model
It is best through effectiveness in vibration suppression of the network control mode to suspension system.
Above embodiments are implemented under the premise of the technical scheme of the present invention, give detailed embodiment and tool
The operating process of body, but protection scope of the present invention is not limited to the above embodiments.Method therefor is such as without spy in above-described embodiment
Not mentionleting alone bright is conventional method.
Claims (4)
1. a kind of intelligent control method of Vehicle Semi-active Suspension System, which comprises the steps of:
Step 1: being directed to automobile suspension system own characteristic, suspension property evaluation index is specified, is established in the time domain by power spectrum
The stochastic filtering white noise road surface input simulation model and analog result of density description, the building passive suspension of half vehicle of four-degree-of-freedom and
The kinetic model of semi-active suspension；
Step 2: establishing passive suspension system respectively according to configuration of automobiles parameter and the vibrated mathematical model of suspension in Simulink
With the block diagram simulation model of the semi-active suspension system under fuzzy control；
Step 3: introducing Mamdani model neural fuzzy inference system control algolithm on the basis of fuzzy inference system, use is soft
The included M language of part writes the realization of S- function, customizes the use of S- function module as needed in conjunction with Simulink encapsulation function
Family interface embeds BP back propagation algorithm, real to the initial neural fuzzy inference system based on Mamdani model using typical data
Train white silk；To Fuzzy Neural-network Control semi-active suspension system simulation model of the creation based on Mamdani model；
Step 4: under same test conditions, be unified under Simulink environment to passive suspension and based on fuzzy control and
The semi-active suspension system model of Mamdani Fuzzy Neural-network Control carries out l-G simulation test, obtains simulation result；And with suspension
Performance Evaluating Indexes, that is, vehicle body normal acceleration, suspension move stroke and the corresponding root-mean-square value of tyre dynamic load lotus is emulation module
Output, the simulation result obtained under different control modes is compared and analyzed, overall merit goes out these intelligent control sides
The control superiority and inferiority effect of method.
2. a kind of intelligent control method of Vehicle Semi-active Suspension System according to claim 1, which is characterized in that described
The step of two specifically include it is as follows:
Step 201, moved using vehicle body normal acceleration, suspension stroke and tyre dynamic load lotus these three describe from different perspectives and
The basic parameter mutually restrained carries out quantitative assessment；
Step 202 establishes the stochastic filtering white noise road surface input simulation model and mould by power spectral density description in the time domain
It is quasi- to be inputted as a result, simulating the vertical displacement of front and rear wheel road surface by MATLAB/Simulink；
Step 203, the kinetic model for constructing half automobile of four-degree-of-freedom passive suspension and semi-active suspension；By the half of passive suspension
Vehicle model is organized into the mathematical model of following form:
Wherein, each symbol meaning is as follows:
ms- automobile spring carried mass；
Pitch angle at θ-vehicle body mass center；
IsThe rotary inertia of-spring carried mass lateral axes angular oscillation at mass center；
Z2Vertical deviation at-mass center；
A, b-antero posterior axis to mass center distance；
k2f、k2r- forward and backward suspension rate；
c2f、c2r- forward and backward suspension damping coefficient；
muf、mur- axle nonspring carried mass；
k1f、k1r- front and back tire stiffness；
Z2f、Z2rThe vertical deviation of-vehicle body front and back ends；
Z1f、Z1rThe vertical deviation of-axle nonspring carried mass；
Z0f、Z0rRoad roughness inputs at-front and rear wheel；
Semi-active suspension provides adjustable damping force in a certain range by controllable damper, establishes half Che Banzhu of four-degree-of-freedom
Dynamic dynamic suspension system of vehicles model；Half vehicle model of semi-active suspension is organized into the mathematical model of following form:
Ff、Fr- forward and backward suspension damping-force adjustable；
As the damping-force adjustable F of vehicle body front and back ends semi-active suspensionf、FrInput value when being zero, semi-active suspension system turns
Turn to passive suspension system；
The Dynamics Simulation Model of automobile suspension system is established by MATLAB/Simulink platform by the above mathematical model.
3. a kind of intelligent control method of Vehicle Semi-active Suspension System according to claim 1, which is characterized in that described
The step of three in: training data is derived from the emulation data of Fuzzy control system, and respectively vehicle body normal acceleration, suspension move stroke
And the real time data of tyre dynamic load lotus selects pretreated typical data to initial followed by BP anti-pass learning rules
Neural fuzzy inference system based on Mamdani model implement training, finally by the trained mind based on Mamdani model
It is imported into the core controller FNN_Mamdani of semi-active suspension system through fuzzy inference system, so that creation is based on
The Fuzzy Neural-network Control semi-active suspension system simulation model of Mamdani model, and then carry out l-G simulation test.
4. a kind of intelligent control method of Vehicle Semi-active Suspension System according to claim 1, which is characterized in that described
The step of four specifically include it is as follows:
The laggard Mobile state l-G simulation test of system simulation model is constructed under step 401, Simulink environment, first has to determine each configuration
Parameter selects the algorithm of variable step mode, determines corresponding solution musical instruments used in a Buddhist or Taoist mass；
Step 402, by passive suspension and semi-active suspension based on fuzzy control and Mamdani model Fuzzy Neural-network Control
System model carries out l-G simulation test under conditions of the identical road surface B grades of and running speed are V；
Step 403, obtain under different control modes with vehicle body normal acceleration, suspension move stroke and tyre dynamic load lotus it is corresponding
Root-mean-square value；
Step 404, comparative test result data, quantitative analysis these three Performance Evaluating Indexes, that is, vehicle body normal acceleration, suspension
Dynamic stroke and the corresponding root-mean-square value of tyre dynamic load lotus.
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| CN111231595B (en) * | 2020-03-09 | 2022-08-02 | 哈尔滨工业大学 | Semi-active suspension control method considering dynamic coupling of front axle and rear axle of automobile |
| CN111231595A (en) * | 2020-03-09 | 2020-06-05 | 哈尔滨工业大学 | Semi-active suspension control method considering dynamic coupling of front axle and rear axle of automobile |
| CN112440643A (en) * | 2020-11-30 | 2021-03-05 | 辽宁工业大学 | Fuzzy self-adaptive sampling controller of active suspension system, structure and design method |
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| CN113703312B (en) * | 2021-09-02 | 2022-05-17 | 北京理工大学 | Active suspension control method based on power balance |
| CN113570057A (en) * | 2021-09-27 | 2021-10-29 | 岚图汽车科技有限公司 | Vehicle wheel center vertical displacement measuring method and device based on model training |
| CN114611211A (en) * | 2022-03-11 | 2022-06-10 | 重庆大学 | Vehicle suspension performance degradation parameter identification method |
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