Adaptive variable domain fuzzy PID control strategy based on road excitation for semi-active suspension using CDC shock absorber

Abstract

Electronic suspensions can take into account the ride comfort and safety of the vehicle, the continuous damping control (CDC) shock absorber is the core component of the electronic suspension. CDC shock absorber and electronic suspension have a promising future for application in automotive. This paper proposed an adaptive variable domain fuzzy PID control strategy for semi-active suspensive to effectively improve the vibration reduction effect of the automobile suspension system. By analyzing the dynamic performance of the semi-active suspension system coupled with the CDC shock absorber to get the control variables, we deduce the math function of semi-active suspension. In addition, the simulation model of the semi-active suspension based on the bench test of shock absorbers was established. Under the observation of performance indicators, though comparing the new control and other different control strategies, which can prove the effectiveness of the new method. From the simulation results, the shock absorber simulation model is correct and the performance of the proposed control strategies are effective than the traditional PID control and fuzzy control under the random road excitation. In particular, in the 120 km/h case by using VAC, the peak values of the suspension dynamic deflection, vehicle body acceleration, and car body dynamic load are reduced by 49.6%, 50%, and 50%. For a semi-active suspension using the CDC shock absorber, the proposed control method provides better ride comfort to passengers due to lower peak compared with the fuzzy control strategy and the PID control strategy, it can be used as an optimized design method for suspension.

Research on variable universe fuzzy PID control for semi-active suspension with CDC dampers based on dynamic adjustment functions

“…And the RMS values of FSD, RSD, FTDL and RTDL controlled by In summary, the references 15,16,28,31,33,35,37 all used the specific functions to design the scaling factor controller. The references 11,17,18,32 all used the fuzzy reasoning to design the scaling factor controller. We take references 32,33 as examples to compare and analyze their advantages and disadvantages with the proposed novel scaling factor controller in this paper.…”

“…However, it is difficult to determine the parameters of the functional contracting-expanding factor and accurately describe the real-time change of the contracting-expanding factor during the control process with fixed parameters. To solve this problem, Zhang et al 17 designed a variable universe fuzzy controller based on fuzzy reasoning, used the fuzzy controller to replace the traditional function formula, which achieved good control results through simulation research. However, the determination of fuzzy rules of the controller overly relies on expert experience, which has the same problems as traditional fuzzy controllers, making it difficult to ensure control accuracy, and the tedious integration operation make this method unsuitable for practical engineering structures.…”

Gust Factor Approach for Estimating Maximum Response and Control Force in High‐Rise Base‐Isolated Buildings with Active Structural Control

“…Semiactive control requires using a small external energy to generate signifcant control force by dynamically altering the structural damping coefcient or stifness and is inherently stable. Its efectiveness has been validated via analytical and experimental studies [26][27][28]. However, semiactive control devices usually have a relatively small control force and cannot add or remove energy to the structure.…”

“…Structural Control and Health Monitoring Te variables are defned as follows: g D : peak factor of along-wind vibration, C g : overturning moment coefcient in along-wind direction, C g ′ : RMS overturning moment coefcient in along-wind direction, ϕ D : mode shape correction factor, R D : resonance factor for along-wind vibration, v D (Hz): level crossing rate, α: exponent of the power law for wind speed profle, I H : turbulence intensity at reference height given by equation (25), B (m): building width, L H (m): turbulence scale at reference height given by equation (28), F D : along-wind force spectral factor, ζ m1,eq : damping ratio for the 1st mode of the equivalent model, f m1,eq (Hz): natural frequency for the 1st mode of the equivalent model, F: wind force spectrum factor, S D : size efect factor, R: correlation coefcient between wind pressures on the windward and leeward faces, U H (m/s): design wind velocity (see Table 3), β: exponent of the power law for the frst translational vibration mode in the along-wind direction defned in equation ( 29), M D (kg): generalized mass of building for along-wind vibration, M D1 (kg): generalized mass of building for along-wind vibration calculated based on β � 1, λ: mode correction factor of general wind force, m(h) (kg/m): mass per unit height at h m, and μ(h): 1st mode shape of building in each direction.…”

Fuzzy logic control of active suspension system equipped with a hydraulic actuator

“…There have been a lot of research papers published that study the development of such controllers. Although those studies greatly enhance the accuracy and adaptability of the suspension system controller, this topic still needs more work, especially in the logic rules [12][13][14].…”