Nonlinear modeling and control of automotive vibration isolation systems

Wagner, John R.; Liu, X.

doi:10.1109/acc.2000.878963

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…The effect of preview control on vehicle performance characteristics in terms of ride comfort, suspension deflections and road-holding ability is investigated. Wagner and Liu (2000) have described nonlinear modelling and control of vibration isolation of suspension systems, to minimize the transmission of road surface disturbances to the vehicle occupants. Three such systems have been investigated, i.e.…”

Section: Introductionmentioning

confidence: 99%

Experimental analysis of 2 DOF quarter-car passive and hydraulic active suspension systems for ride comfort

Patil¹,

Joshi²

2014

Systems Science & Control Engineering

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This paper describes an experimental analysis of 2 degree-of-freedom (DOF) quarter-car passive suspension system and hydraulic active suspension system (QC-H-ASS) for ride comfort. The passive suspension system, which models a quartercar suspension, consists of the sprung mass, unsprung mass, a suspension spring and damper and a tyre spring. A hydraulic actuator has been considered as one of the most viable choices for an active suspension system due to its high power-to-weight ratio and low cost. Thus this model is modified to a 2 DOF QC-H-ASS by placing a hydraulic actuator, with its attendant control instrumentation, in between sprung and unsprung masses. The results show considerable improvement in ride comfort over the conventional passive system. The details of the quarter-car model development with the test set-ups for the passive and hydraulic active suspension systems, suspension elements employed, experimental analysis and results are presented.whereas in active suspension, actuator is able to both add and dissipate energy from the system. If a correct control strategy is applied to the actuator system, a better compromise between ride comfort and vehicle stability is possible compared with a passive suspension system (see Figure 2). Present theories and practicesAutomotive suspension systems have been researched exhaustively by many researchers from all over the continents since long and now have reached to a very high level of sophistication. Sharp and Crolla (1987) have described road surfaces, modelling vehicles and setting up performance criteria to passive, active and semi-active suspension systems. They have elaborated methods of deriving control laws for active systems. Crolla (1996) has reviewed the contributions of vehicle dynamics theory to practical vehicle design. He has nicely highlighted the effect of the suspension on improving the vehicle performance and safety. The practical constraints, which are restricting the commercial development of limited bandwidth active suspension systems, have been mentioned. He has observed that electro-hydraulic technology holds the most commercial promise, but a major breakthrough in electro-hydraulic valve technology will be required, leading to reduce costs if significant further developments are to occur over the next few years. Karnopp (1986) has shown using simple linear two degrees-of-freedom suspension system model that

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Section: Introductionmentioning

confidence: 99%

Experimental analysis of 2 DOF quarter-car passive and hydraulic active suspension systems for ride comfort

Patil¹,

Joshi²

2014

Systems Science & Control Engineering

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“…Nowadays, electromagnetic actuators are becoming more popular in anti-vibration applications for the automotive industry, due to their improved dynamic response, accuracy and efficiency compared to hydraulic or pneumatic solutions [1]. Anti-vibration systems require a small volume, light weight, low power consumption, and high force density.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the force density for medium-stroke reluctance actuators

Bao

Gysen

Vrijsen

et al. 2013

2013 International Electric Machines &Amp; Drives Conference

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This paper concerns the force density optimization for medium-stroke reluctance actuators applied in anti-vibration applications. The force density in a conventional E-core reluctance actuator is limited for medium strokes by the nonlinear force-displacement characteristic. In this paper, different tooth topologies are analyzed to maximize the force density along the stroke using the finite element analysis. Teeth parameters are tuned in each topology to analyze the influences on the force density. An analytic thermal model is used and verified with finite element simulations.

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“…The artificial neural networks (ANN) have been greatly developed in these years and applied in many fields [4,5,6]. Back-Propagation (BP) network is the most popular one, and a three-layer BP network has the potential to approximate any continuous nonlinear function with arbitrary accuracy.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Prediction of Vehicle Tube Hydraulic Shock Absorbers Based on BP Neural Network

Pan

Shuang-xia

Wei-rui

2006

2006 International Conference on Machine Learning and Cybernetics

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Research on modeling the tube hydraulic shock absorbers is always a challenging issue. This paper presents a modeling method through BP (Back-Propagation) neural network established by training data from experiments. Characteristic parameters of the absorbers are as the inputs of the BP network model, while damping forces as outputs. Numerical simulations are given as examples, which demonstrate that the method is effective to predict the performance of the absorber successfully.

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Nonlinear modeling and control of automotive vibration isolation systems

Cited by 7 publications

References 10 publications

Experimental analysis of 2 DOF quarter-car passive and hydraulic active suspension systems for ride comfort

Experimental analysis of 2 DOF quarter-car passive and hydraulic active suspension systems for ride comfort

Optimization of the force density for medium-stroke reluctance actuators

Modeling and Prediction of Vehicle Tube Hydraulic Shock Absorbers Based on BP Neural Network

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