Design of a New Suspension System Controlled by Fuzzy-PID with Wheelbase Preview

Wong, Pak Kin; Huang, Shao Jia; Xu, Tao; Wong, Hang Cheong; Xie, Zheng Chao

doi:10.4028/www.scientific.net/amm.192.106

Cited by 6 publications

(7 citation statements)

References 6 publications

(10 reference statements)

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“…This section presents details of the application of MPC based on the mixed control approach for active suspension vehicle control. The MPC approach utilizes (6) as the internal model of the actuator dynamics to predict y(t) at a future discrete time instants [ŷ(k + H 1 /k), …, ŷ(k + H p /k)]. In this representation ŷ(k + j/k) denotes the optimal j-step ahead prediction of the system output, based on data up to time k, while H 1 and H p are respectively the lower and upper limits of the receding horizon; the predictive controller computes an optimal control sequence with respect to the following cost function [23][24][25]…”

Section: Model Predictive Controller Designmentioning

confidence: 99%

“…Therefore, active suspension systems control has attracted the attention of numerous researchers interested in ride and holding qualities. However, active vehicle suspension system models assume a small displacement about an operating point, and then it creates a linearized working model . On the other hand, the system states exhibit large deviation from the equilibrium point when the system is subjected to major impact owing to rough roads or aggressive driving.…”

Section: Introductionmentioning

confidence: 99%

“…The design of a predictive controller demands the construction of a predictor. Then the construction of the predictor in the state‐space representation can be conducted by iterating model for more detail see . In matrix representation the prediction can be seen as

\overset{Y}{true} = normalΓ + ((), normalΛ + δ normalΛ) U_{n}

…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Robust Predictive Control Design for Nonlinear Active Suspension Systems

Bououden¹,

Chadli²,

Karimi³

2015

Asian Journal of Control

100

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This paper proposes a novel method for designing robust nonlinear multivariable predictive control for nonlinear active suspension systems via the Takagi‐Sugeno fuzzy approach. The controller design is converted to a convex optimization problem with linear matrix inequality constraints. The stability of the control system is achieved by the use of terminal constraints, in particular the Constrained Receding‐Horizon Predictive Control algorithm to maintain a robust performance of vehicle systems. A quarter‐car model with active suspension system is considered in this paper and a numerical example is employed to illustrate the effectiveness of the proposed approach. The obtained results are compared with those achieved with model predictive control in terms of robustness and stability.

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Section: Model Predictive Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Robust Predictive Control Design for Nonlinear Active Suspension Systems

Bououden¹,

Chadli²,

Karimi³

2015

Asian Journal of Control

100

View full text Add to dashboard Cite

show abstract

“…As the control goes, relevant tolerance is moving toward the zero (ZE), which means the decrease of the tolerance, it is clear that the tolerance of the universe [-E, E] is bigger when compared with the shrink one. As the fuzzy control move, relevant control accuracy will be affected [9][10][11][12].…”

Section: Theory Of Variable Universe Fuzzy Controlmentioning

confidence: 99%

Application of Variable Universe Fuzzy Control PID in Temperature Command of the Animal Building

Yu¹,

Yu²,

Zhao³

2014

IJCA

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Temperature is a universe and significant data in production and scientific experiment, effective measurement and control of the temperature are widely applied in every part of national economy. Therefore, temperature control has great significance; relevant requirements regarding temperature control are also higher and higher.Temperature command system for animal house is a complex system with features of time varying, nonlinear, inaccurate model and unknown operating circumstance, temperature controlled by air-conditioner is less accurate and will affect the stability of the system performance. Regarding existing problems of long responding time, less stability and other disadvantages of the conventional PID control, we hereby raise a new approach of variable universe fuzzy PID control which is characterized with quick responding speed, better stability and stronger anti-interruption performance. Through the application of variable universe fuzzy control theory, which combined with the advantages of conventional PID and variable universe fuzzy control, forming the calculation of self-applied variable universe fuzzy control PID. Improve the accuracy of fuzzy inference by variable universe control theory, and realize the online tuning of PID data through fuzzy inference. Stimulate on Matlab, relevant stimulation results revealed that the control ways is clearly superior than conventional PID control, therefore effectively improve the dynamic property, stability precision and robustness, the system has better future of industrial application.

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“…The principle of preview control is to predict the road condition for the rear wheel based on the road information obtained from the front of the vehicle so that the control action can be performed without too much delay. This idea was firstly introduced by Bender [23] and was then proved by several studies [24][25][26][27][28][29] to be a very promising approach with the use of hydraulic active suspensions. In general, two types of preview methods are available, namely, the front preview and the wheelbase preview [26].…”

Section: Introductionmentioning

confidence: 99%

A Noise-Insensitive Semi-Active Air Suspension for Heavy-Duty Vehicles with an Integrated Fuzzy-Wheelbase Preview Control

Xie

Wong

Zhao

et al. 2013

Mathematical Problems in Engineering

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Semi-active air suspension is increasingly used on heavy-duty vehicles due to its capabilities of consuming less power and low cost and providing better ride quality. In this study, a new low cost but effective approach, fuzzy-wheelbase preview controller with wavelet denoising filter (FPW), is developed for semi-active air suspension system. A semi-active suspension system with a rolling lobe air spring is firstly modeled and a novel front axle vertical acceleration-based road prediction model is constructed. By adopting a sensor on the front axle, the road prediction model can predict more reliable road information for the rear wheel. After filtering useless signal noise, the proposed FPW can generate a noise-insensitive control damping force. Simulation results show that the ride quality, the road holding, the handling capability, the road friendliness, and the comprehensive performance of the semi-active air suspension with FPW outperform those with the traditional active suspension with PID-wheelbase preview controller (APP). It can also be seen that, with the addition of the wavelet filter, the impact of sensor noise on the suspension performance can be minimized.

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Design of a New Suspension System Controlled by Fuzzy-PID with Wheelbase Preview

Cited by 6 publications

References 6 publications

A Robust Predictive Control Design for Nonlinear Active Suspension Systems

A Robust Predictive Control Design for Nonlinear Active Suspension Systems

Application of Variable Universe Fuzzy Control PID in Temperature Command of the Animal Building

A Noise-Insensitive Semi-Active Air Suspension for Heavy-Duty Vehicles with an Integrated Fuzzy-Wheelbase Preview Control

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