CN102501737A - Intelligent particle swarm fuzzy hybrid control method for automotive semi-active suspension systems - Google Patents
Intelligent particle swarm fuzzy hybrid control method for automotive semi-active suspension systems Download PDFInfo
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Abstract
The invention belongs to the technical field of automotive chassis control, and relates to an automotive semi-active suspension system control method, in particular to an intelligent particle swarm fuzzy hybrid control method for automotive semi-active suspension systems, which takes both riding comfort and road-friendliness into consideration. The particle swarm optimization method in the intelligent swarm theory is utilized to deeply optimize the input fuzzy variables (i.e. the vibration velocity of sprung mass and the vibration velocity of unsprung mass) and output fuzzy variables (i.e. each membership function of a damping force distribution coefficient) of a semi-active suspension system; and finally, a hybrid control core is utilized to control the semi-active suspension system in real time. The design idea of the intelligent particle swarm fuzzy hybrid control method is novel, the control principle is reliable, the safety and the stability are good, the automation degree is high, the application range is wide, and the intelligent particle swarm fuzzy hybrid control method can be widely used for controlling semi-active suspension systems of various vehicles.
Description
Technical field:
The invention belongs to car chassis control technology field, relate to a kind of vehicle semi-active suspension system control method, especially a kind of vehicle semi-active suspension system intelligence population fuzzy hybrid control method of taking into account ride comfort and road friendly.
Background technology:
Suspension system is the keystone configuration component part of car chassis; Suspension system is meant the general name that connects whole parts between vehicle body and the wheel; Its effect is application force and the moment of transmitting between wheel and the vehicle frame; And relax the impact load of being passed to vehicle frame or vehicle body by uneven road surface, the vibration of the bearing system that decay causes thus is to guarantee the rideability of automobile.The dumping force size of rigidity, shock absorber that traditional passive suspension system can not make automotive suspension is automatically regulating along with the variation of driving conditions such as automobile driving speed, condition of road surface, is difficult to reach the comprehensive raising of vehicle ride comfort, road friendly etc.; The vehicle semi-active suspension system is meant the novel suspension system that one of suspension elastic element rigidity and absorber damping force or both all can automatically adjust as required.At present; Depend on the various control methods of model; Like method for optimally controlling, various because of the state parameter of required measurement, significantly increased the hardware and software cost of vehicle semi-active suspension control system; Cause the application of above-mentioned control method to be restricted, but promoted the application of intelligence control methods such as fuzzy control in semi-active suspension system.Traditional fuzzy hybrid control method can't be optimized input and each membership function of exporting fuzz variable owing to adopt the well-distributed membership function, is difficult to realize the optimal control effect of semi-active suspension system.The intelligent population fuzzy hybrid control method that can take into account ride comfort and road friendly is a kind of novel fuzzy hybrid control method; This method utilizes intelligent group theoretical---particle group optimizing method; According to predefined fitness function; To carrying out depth optimization by the position population that constitutes of shape parameter of the membership function of each input and output fuzz variable; And through real-time adjustment dumping force partition ratio, reach reasonable distribution and mix skyhook damping power composition and the purpose of ground canopy dumping force composition in the control kernel, realization can take into account the vertical dynamics of vehicle of ride comfort and road friendly.Generally speaking; Existing vehicle semi-active suspension system control method; The overwhelming majority depends on the kinetic model of complicated controlled object; Usually the membership function that is difficult to take into account the input of ride comfort and road friendly, particularly fuzzy control simultaneously and export fuzz variable leans on the technical personnel experience to set usually, can't realize optimal control.
Summary of the invention:
The objective of the invention is to overcome the shortcoming that existing control method exists; The technical matters that exists in the traditional fuzzy mixing control method to the vehicle semi-active suspension system; Seek to propose and design a kind of particle group optimizing method capable of using input, output fuzz variable membership function are carried out system optimization respectively, realize taking into account the intelligent population fuzzy hybrid control method of vehicle ride comfort and road friendly.
To achieve these goals, the intelligent population fuzzy hybrid control method of taking into account vehicle ride comfort and road friendly that the present invention relates to may further comprise the steps:
(1) utilizes four pairs of acceleration pick-ups and filtering integration module thereof earlier; Gather the vertical vibration speed of left front, left back, right front, the right back place spring carried mass of semi-active suspension system vehicle, the vertical vibration speed of nonspring carried mass respectively, generate the incoming signal of intelligent population fuzzy hybrid control device;
(2) utilize fuzzy control that each spring carried mass vertical vibration speed described in the step (1) and nonspring carried mass vertical vibration speed incoming signal are carried out Fuzzy processing; Form the input fuzz variable, then each input blurred signal is sent to fuzzy controller;
(3) set up the fuzzy rule base of vehicle semi-active suspension system intelligence population fuzzy hybrid control device; Utilize the vertical vibration speed of each spring carried mass described in (1), the vertical vibration speed of nonspring carried mass to import the position shape parameter of the membership function of fuzz variable; And particle group optimizing method is implemented to optimize to above-mentioned population and is prepared to combine output fuzz variable---the position shape parameter of each membership function of dumping force partition ratio is to form the primary crowd, for utilizing the intelligent group theory---;
(4) setting can take into account the fitness function of the intelligent population fuzzy hybrid control of ride comfort and road friendly simultaneously, calculates the fitness value of each particle, analyzes simultaneously to draw the historical optimal value p of individual particles
Best[] and global history optimal value g
Best[];
F＝std(a
u)×(std(a
s))^ψ
In the following formula, F is a fitness value; Std () is a standard deviation function; a
uBe the nonspring carried mass vibration acceleration; a
sBe the spring carried mass vibration acceleration; ψ is a dimension adjustment coefficient;
(5) utilize following speed, position renewal equation that speed, the position of primary described in the step (3) are upgraded; With the depth optimization of realization, and give the vertical dynamics that the vehicle semi-active suspension system can be taken into account vehicle ride comfort and road friendly to the position shape parameter of the membership function of input and output fuzz variable;
v[]＝w×v[]+c1×rand1()(p
best[]-present[])+c2×rand2()(g
best[]-present[])
present[]＝present[]+v[]
In above-mentioned two formulas, v [] is a particle rapidity, and w is the inertia weight coefficient, and present [] is current particle, p
Best[] is individual optimal value, g
Best[] is all optimal values, and rand1 (), rand2 () are two random numbers of scope in [0～1], and c1, c2 are accelerator coefficient;
(6) repeating step (3)～(5) are until the optimization that realizes each membership function position shape parameter;
(7) utilize center of gravity defuzzification method that the output fuzz variable of intelligent population fuzzy hybrid control device is carried out defuzzification and handle, and obtain reflecting the dumping force partition ratio of ceiling control composition and ground canopy control component ratio;
(8) the dumping force partition ratio that utilizes defuzzification to draw; And be controlled in mixing control kernel one, that can adjust vehicle ride comfort and road friendly simultaneously through collection ceiling control and ground canopy and form output control current signal, and 4 magnetorheological damping devices of vehicle semi-active suspension system are implemented independent control.
The present invention is theoretical through intelligent group---and particle group optimizing method carries out depth optimization to the input of vehicle semi-active suspension system, each membership function of output fuzz variable, and through said mixing control kernel semi-active suspension is controlled in real time.
The present invention compared with prior art; Can evade a complicated vehicle semi-active suspension system modeling difficult problem; Need not to set up on the accurate model based of controlled suspension system; Can be according to the fitness function degree of depth, the position shape parameter of the membership function of each input of system optimization, output fuzz variable, technological merit with comprehensive raising ride comfort and road friendly; Its design philosophy is novel, and control principle is reliable, and security and stability is good, and degree of automation is high, and is applied widely.
Description of drawings:
Fig. 1 realizes the theory of constitution schematic block diagram of fuzzy hybrid control for the present invention.
Fig. 2 realizes particle swarm optimization algorithm diagram of circuit in the fuzzy hybrid control for the present invention.
Fig. 3 is the nonspring carried mass degree of membership curve behind the particle group optimizing according to the invention.
Fig. 4 is the spring carried mass degree of membership curve behind the particle group optimizing according to the invention.
Fig. 5 is the dumping force partition ratio degree of membership curve behind the particle group optimizing according to the invention.
The specific embodiment:
Below through embodiment and combine accompanying drawing to be described further.
Embodiment:
The intelligent population fuzzy hybrid control method of taking into account vehicle ride comfort and road friendly that present embodiment relates to may further comprise the steps:
(1) utilizes four pairs of acceleration pick-ups and filtering integration module thereof; Gather the vertical vibration speed of left front, left back, right front, the right back place spring carried mass of semi-active suspension system vehicle, the vertical vibration speed of nonspring carried mass respectively, generate the incoming signal of intelligent population fuzzy hybrid control device;
(2) utilize fuzzy control that incoming signals such as each spring carried mass vertical vibration speed described in the step (1), nonspring carried mass vertical vibration speed are carried out Fuzzy processing; Form the input fuzz variable, then each input blurred signal is sent to fuzzy controller;
(3) set up the fuzzy rule base of vehicle semi-active suspension system intelligence population fuzzy hybrid control device; The position shape parameter of the membership function of the vertical vibration speed of each spring carried mass described in the utilization (1), the fuzzy input variables such as vertical vibration speed of nonspring carried mass; And particle group optimizing method is implemented to optimize to above-mentioned population and is prepared to combine fuzzy output variable---the position shape parameter of each membership function of dumping force partition ratio is to form the primary crowd, for utilizing the intelligent group theory---;
(4) setting can take into account the fitness function of the intelligent population fuzzy hybrid control of ride comfort and road friendly simultaneously, calculates the fitness value of each particle, analyzes simultaneously to draw the historical optimal value p of individual particles
Best[] and global history optimal value g
Best[];
F＝std(a
u)×(std(a
s))^ψ
In the following formula, F is a fitness value; Std () is a standard deviation function; a
uBe the nonspring carried mass vibration acceleration; a
sBe the spring carried mass vibration acceleration; ψ is a dimension adjustment coefficient;
(5) utilize following speed, position renewal equation that speed, the position of primary described in the step (3) are upgraded; With the depth optimization of realization, and give the vertical dynamics that the vehicle semi-active suspension system can be taken into account vehicle ride comfort and road friendly to the position shape parameter of the membership function of input and output fuzz variable;
v[]＝w×v[]+c1×rand1()(p
best[]-present[])+c2×rand2()(g
best[]-present[])
present[]＝present[]+v[]
In above-mentioned two formulas, v [] is a particle rapidity, and w is the inertia weight coefficient, and present [] is current particle, p
Best[] is individual optimal value, g
Best[] is all optimal values, and rand1 (), rand2 () are two random numbers of scope in [0～1], and c1, c2 are accelerator coefficient;
(6) repeating step (3)～(5) are until the optimization that realizes each membership function position shape parameter;
(7) utilize center of gravity defuzzification method that the fuzzy output variable of intelligent population fuzzy hybrid control device is carried out defuzzification and handle, and obtain reflecting the dumping force partition ratio of ceiling control composition and ground canopy control component ratio;
(8) the dumping force partition ratio that utilizes defuzzification to draw; And be controlled in mixing control kernel one, that can adjust vehicle ride comfort and road friendly simultaneously through collection ceiling control and ground canopy and form output control signal (electric current), and 4 magnetorheological damping devices of vehicle semi-active suspension system are implemented independent control.
Present embodiment is through the input fuzz variable of the particle group optimizing method in the intelligent group theory to semi-active suspension system---vibration velocity of spring carried mass and the vibration velocity of nonspring carried mass, output fuzz variable---, and each membership function of dumping force partition ratio carries out depth optimization; And finally semi-active suspension is controlled in real time through said mixing control kernel.
Present embodiment adopts following method to set up the fuzzy rule base of intelligent population fuzzy hybrid control device, makes fuzzy input variable---the vibration velocity V of spring carried mass that locates such as left front, left back, right front, right back
1, nonspring carried mass vibration velocity V
2Get minimum (ES), less (VS), little (SM), intermediate value (ME), big (LA), big (VL), the very big linguistic variable of (EL) respectively, α gets Z with output in season fuzz variable dumping force partition ratio
1, Z
2, Z
3, Z
4, Z
5, Z
6, Z
7, Z
8, Z
9Linguistic variable, each input is all chosen suitable membership function with the output fuzz variable, and sets up the fuzzy rule base with intelligent population fuzzy hybrid control as shown in table 1.
……………
R
i＝IF?V
1?IS?EL?AND?V
2?IS?ES?THEN α＝Z9
R
i+1＝IF?V
1?IS?ES?AND?V
2?IS?EL?THEN α＝Z1
R
i+2＝IF?V
1?IS?EL?AND?V
2?IS?EL?THEN α＝Z5
Table 1 is the fuzzy rule base of intelligent population fuzzy hybrid control strategy
Claims (1)
1. vehicle semi-active suspension system intelligence population fuzzy hybrid control method is characterized in that may further comprise the steps:
(1) utilizes four pairs of acceleration pick-ups and filtering integration module thereof earlier; Gather the vertical vibration speed of left front, left back, right front, the right back place spring carried mass of semi-active suspension system vehicle, the vertical vibration speed of nonspring carried mass respectively, generate the incoming signal of intelligent population fuzzy hybrid control device;
(2) utilize fuzzy control that each spring carried mass vertical vibration speed described in the step (1) and nonspring carried mass vertical vibration speed incoming signal are carried out Fuzzy processing; Form the input fuzz variable, then each input blurred signal is sent to fuzzy controller;
(3) set up the fuzzy rule base of vehicle semi-active suspension system intelligence population fuzzy hybrid control device; Utilize the vertical vibration speed of each spring carried mass described in (1), the vertical vibration speed of nonspring carried mass to import the position shape parameter of the membership function of fuzz variable; And combination output fuzz variable; The position shape parameter of each membership function that is the dumping force partition ratio is to form the primary crowd; Utilize intelligent group theoretical, promptly particle group optimizing method is implemented to optimize to above-mentioned population and is prepared;
(4) setting can take into account the fitness function of the intelligent population fuzzy hybrid control of ride comfort and road friendly simultaneously, calculates the fitness value of each particle, analyzes simultaneously to draw the historical optimal value p of individual particles
Best[] and global history optimal value g
Best[];
F＝std(a
u)×(std(a
s))^ψ
In the following formula, F is a fitness value; Std () is a standard deviation function; a
uBe the nonspring carried mass vibration acceleration; a
sBe the spring carried mass vibration acceleration; ψ is a dimension adjustment coefficient;
(5) utilize following speed, position renewal equation that speed, the position of primary described in the step (3) are upgraded; With the depth optimization of realization, and give the vertical dynamics that the vehicle semi-active suspension system can be taken into account vehicle ride comfort and road friendly to the position shape parameter of the membership function of input and output fuzz variable;
v[]＝w×v[]+c1×rand1()(p
best[]-present[])+c2×rand2()(g
best[]-present[])
present[]＝present?[]+v[]
In above-mentioned two formulas, v [] is a particle rapidity, and w is the inertia weight coefficient, and present [] is current particle, p
Best[] is individual optimal value, g
Best[] is all optimal values, and rand1 (), rand2 () are two random numbers of scope in [0～1], and c1, c2 are accelerator coefficient;
(6) repeating step (3)～(5) are until the optimization that realizes each membership function position shape parameter;
(7) utilize center of gravity defuzzification method that the output fuzz variable of intelligent population fuzzy hybrid control device is carried out defuzzification and handle, and obtain reflecting the dumping force partition ratio of ceiling control composition and ground canopy control component ratio;
(8) the dumping force partition ratio that utilizes defuzzification to draw; And be controlled in mixing control kernel one, that can adjust vehicle ride comfort and road friendly simultaneously through collection ceiling control and ground canopy and form output control current signal, and four magnetorheological damping devices of vehicle semi-active suspension system are implemented independent control.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103646280A (en) * | 2013-11-28 | 2014-03-19 | 江苏大学 | Particle swarm algorithm-based vehicle suspension system parameter optimization method |
| CN106347059A (en) * | 2016-09-09 | 2017-01-25 | 山东理工大学 | Hub-driven electric car active suspension two-loop PID control method based on particle swarm optimization |
| CN106647256A (en) * | 2016-10-08 | 2017-05-10 | 西南交通大学 | H-infinite PID-based active suspension rack control system and control method |
| CN108891220A (en) * | 2018-07-19 | 2018-11-27 | 燕山大学 | A kind of capricorn bettle innovatory algorithm of Vehicle Semi-active Suspension System |
| CN110712490A (en) * | 2018-07-13 | 2020-01-21 | 山东大学 | Active suspension system based on stack type self-coding and working method thereof |
| CN111439086A (en) * | 2020-03-27 | 2020-07-24 | 江苏大学 | Particle swarm optimization-based PDD (plant stability data) control ideal model for ISD (in-service brake) suspension of vehicle |
| CN111891111A (en) * | 2020-04-30 | 2020-11-06 | 南京航空航天大学 | Hybrid electric vehicle interval II type fuzzy logic self-adaptive control method based on MCPSO |
| CN113665311A (en) * | 2021-07-16 | 2021-11-19 | 中国北方车辆研究所 | Vibration absorber control method and system based on frequency domain analysis |
| CN115782496A (en) * | 2022-12-01 | 2023-03-14 | 西南交通大学 | Intelligent evolution method of semi-active suspension system based on MAP control |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1083520A2 (en) * | 1999-07-27 | 2001-03-14 | Yamaha Hatsudoki Kabushiki Kaisha | System and method for control using quantum soft computing |
| CN1672171A (en) * | 2002-07-31 | 2005-09-21 | 雅马哈发动机株式会社 | Intelligent mechatronic control suspension system based on quantum soft computing |
| CN101916113A (en) * | 2010-07-23 | 2010-12-15 | 江苏大学 | Automotive body gesture decoupling control method based on active suspension evaluation indicator |
| CN201914049U (en) * | 2010-12-14 | 2011-08-03 | 江苏技术师范学院 | Intelligent control system for vehicle semi-active suspension |
-
2011
- 2011-11-02 CN CN 201110342142 patent/CN102501737B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1083520A2 (en) * | 1999-07-27 | 2001-03-14 | Yamaha Hatsudoki Kabushiki Kaisha | System and method for control using quantum soft computing |
| CN1672171A (en) * | 2002-07-31 | 2005-09-21 | 雅马哈发动机株式会社 | Intelligent mechatronic control suspension system based on quantum soft computing |
| CN101916113A (en) * | 2010-07-23 | 2010-12-15 | 江苏大学 | Automotive body gesture decoupling control method based on active suspension evaluation indicator |
| CN201914049U (en) * | 2010-12-14 | 2011-08-03 | 江苏技术师范学院 | Intelligent control system for vehicle semi-active suspension |
Non-Patent Citations (1)
| Title |
|---|
| 邱英杰: "《基于模糊控制的半主动空气悬架对平顺性影响的仿真分析》", 《机械设计与制造》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103646280A (en) * | 2013-11-28 | 2014-03-19 | 江苏大学 | Particle swarm algorithm-based vehicle suspension system parameter optimization method |
| CN103646280B (en) * | 2013-11-28 | 2016-03-02 | 江苏大学 | A kind of vehicle suspension system parameter optimization method based on particle cluster algorithm |
| CN106347059A (en) * | 2016-09-09 | 2017-01-25 | 山东理工大学 | Hub-driven electric car active suspension two-loop PID control method based on particle swarm optimization |
| CN106347059B (en) * | 2016-09-09 | 2018-08-21 | 山东理工大学 | A kind of wheel hub driving electric vehicle active suspension double loop PID control method based on particle cluster algorithm |
| CN106647256A (en) * | 2016-10-08 | 2017-05-10 | 西南交通大学 | H-infinite PID-based active suspension rack control system and control method |
| CN110712490A (en) * | 2018-07-13 | 2020-01-21 | 山东大学 | Active suspension system based on stack type self-coding and working method thereof |
| CN108891220A (en) * | 2018-07-19 | 2018-11-27 | 燕山大学 | A kind of capricorn bettle innovatory algorithm of Vehicle Semi-active Suspension System |
| CN111439086B (en) * | 2020-03-27 | 2022-10-25 | 荣成市莫林汽车科技有限公司 | Particle swarm optimization-based PDD (plant stability data) control ideal model for ISD (in-service brake) suspension of vehicle |
| CN111439086A (en) * | 2020-03-27 | 2020-07-24 | 江苏大学 | Particle swarm optimization-based PDD (plant stability data) control ideal model for ISD (in-service brake) suspension of vehicle |
| CN111891111A (en) * | 2020-04-30 | 2020-11-06 | 南京航空航天大学 | Hybrid electric vehicle interval II type fuzzy logic self-adaptive control method based on MCPSO |
| CN111891111B (en) * | 2020-04-30 | 2021-11-23 | 南京航空航天大学 | Hybrid electric vehicle interval II type fuzzy logic self-adaptive control method based on MCPSO |
| CN111891111B8 (en) * | 2020-04-30 | 2021-12-24 | 南京航空航天大学 | Hybrid electric vehicle interval II type fuzzy logic self-adaptive control method based on MCPSO |
| CN113665311A (en) * | 2021-07-16 | 2021-11-19 | 中国北方车辆研究所 | Vibration absorber control method and system based on frequency domain analysis |
| CN113665311B (en) * | 2021-07-16 | 2024-02-20 | 中国北方车辆研究所 | Vibration damper control method and system based on frequency domain analysis |
| CN115782496A (en) * | 2022-12-01 | 2023-03-14 | 西南交通大学 | Intelligent evolution method of semi-active suspension system based on MAP control |
| CN115782496B (en) * | 2022-12-01 | 2023-10-03 | 西南交通大学 | Intelligent evolution method of semi-active suspension system based on MAP control |
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