Abstract
By analyzing the shortcomings of the traditional fuzzy PID(Abbreviation for Proportional, Integral and Differential) control system (FPID), a multiple fuzzy PID suspension control system based on road recognition (MFRR) is proposed. Compared with the traditional fuzzy PID control system, the multiple fuzzy control system can identify the road grade and take changes in road conditions into account. Based on changes in road conditions and the variable universe and secondary adjustment of the control parameters of the PID controller were carried out, which makes up for the disadvantage of having too many single input parameters in the traditional fuzzy PID control system. A two degree of freedom 1/4 vehicle model was established. Based on the suspension dynamic parameters, a road elevation algorithm was designed. Road grade recognition was carried out based on a BP neural network algorithm. The experimental results showed that the sprung mass acceleration (SMA) of the MFRR was much smaller than that of the passive suspension system (PS) and the FPID on single-bump and sinusoidal roads. The SMA, suspension dynamic deflection (SDD) and tire dynamic load (TDL) of the MFRR were significantly less than those of the other two systems on roads of each grade. Taking grade B road as an example, compared with the PS, the reductions in the SMA, SDD and TDL of the MFRR were 40.01%, 34.28% and 32.64%, respectively. The control system showed a good control performance.
Highlights
The suspension, an important part of a vehicle, elastically connects the frame and the wheels—the performance of which is related to the various responses of the car
The third level fuzzy controller establishes a direct relationship between road conditions and PID controller control parameters, and its specific principle can be explained as follows: As shown in Figure 2, the suspension dynamic performance parameters are input into the road recognition system, and the road recognition system calculates the road elevation and road grade according to the input
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Summary
The suspension, an important part of a vehicle, elastically connects the frame and the wheels—the performance of which is related to the various responses of the car. The second level fuzzy controller establishes a direct relationship between road conditions and the universe expansion factors of the first level fuzzy controller, and its specific principle can be explained as follows: the suspension dynamic performance parameters are input into the road recognition system, and the road recognition system calculates the road elevation and road grade according to the input. The third level fuzzy controller establishes a direct relationship between road conditions and PID controller control parameters, and its specific principle can be explained as follows: the suspension dynamic performance parameters are input into the road recognition system, and the road recognition system calculates the road elevation and road grade according to the input. The third level fuzzy controller establishes a direct relationship between road conditions and the control parameters of the PID controller, and directly adjusts the PID parameters based on the road elevation and road grade information. FiFgiugruer3e. 3C.oCnotrnotlrsoylssteymstepmrinpcripinlec.iprlies.trhiesitdheealidinepaul ti.nypiustt.hyeicsotnhteroclosnigtnroall,seigisntahle, eerirsotrhseigenrarol,r signal, ecisistthhee rraattee ooff cchhaannggeeoof fththeeererrorrosrigsniganl,aαl, aαn1d αa2nadreαth2e eaxrpeatnhseioenxpfaacntosrisonofftahcetoinrspuotf vthareiainblpeut variab unuinvievresers, eβ, isβthies etxhpeanexsipoannfsaicotnorsfaocftothrse ouf ttphuet ovuartipaubtlevuanrivaebrlsee,u∆nkivp1e,rs∆ek,i1 Δakndp1 ∆, kΔd1kai1reatnhde Δkd a prtihmearpyriamdjaursytmaednjtus sotfmtheenctosnotrfolthpearcaomnettreorsl opfatrhaemPIeDtecrosnotrfoltlhere, aPnIdD∆ckop2n,t∆rokli2learn, da∆ndkd Δarkept2h,e Δki an seΔcokndd2arayraedtjhuestsmeecnotnsdoafrtyheacdojuntsrtoml peanrtasmoefttehrse of the PID controller. control parameters of the PID controller
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