Improving Drive Files for Vehicle Road Simulations

Cherng, John G.; Goktan, Ali G.; French, Mark; Yu, Gu; Jacob, Anil G.

doi:10.1006/mssp.2000.1333

Cited by 6 publications

(5 citation statements)

References 5 publications

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“…In Figure 1, an illustration of a 4-poster system is shown. Mainly being obtained through the wheels of the vehicle mostly as a magnitude of acceleration, the road data are examined in terms of fatigue and realizability and converted into a reference signal defined in a limited duration (Cherng et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy iterative learning control for 4-poster test rig

Dursun

Cansever

Üstoğlu

2020

Transactions of the Institute of Measurement and Control

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In this paper, a new control method is presented for the 4-poster test systems. The primary aim of the paper is to improve the convergence speed and decrease the error rate for model-based iterative learning control (ILC), a widely used method as a tracking control. First, the dynamic equations of the system are generated, and the control problem is formulated. Then, an inverse model of the system is established directly through the adaptive neuro-fuzzy inference system (ANFIS) with auxiliary parameter (piston position) as a serial combination of two sub-models. In order to construct a neuro-fuzzy ILC (NFILC) structure, these sub-models are integrated into the neuro-fuzzy inverse controller (NFIC). Because of this new structure, the modified ILC rule has two layers. In the first layer, the controlled parameter, namely, the acceleration is iterated, whereas, in the second layer, the auxiliary parameter is iterated. The outcomes of the proposed control method are scrutinized by testing through a numerical simulation. Finally, it is demonstrated that the modified ILC rule dramatically increase the convergence speed and reduce the final error rate.

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Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy iterative learning control for 4-poster test rig

Dursun

Cansever

Üstoğlu

2020

Transactions of the Institute of Measurement and Control

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“…To overcome the shortcomings of the conventional ILC in terms of convergence rate and tracking accuracy, De Cuyper et al (2002; extend the conventional ILC method with an augmented real-time feedback controller, and optimize the controller by a mixed sensitivity approach. Cherng et al (2001) employ a weighting function created by normalizing the frequency response function, and the iteration process is shortened and the error is reduced for the road vehicle simulator.…”

Section: Introductionmentioning

confidence: 99%

Improved feedforward inverse control with adaptive refinement for acceleration tracking of electro-hydraulic shake table

Zhu

Shen

et al. 2016

Journal of Vibration and Control

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Shake tables are essential facilities in the laboratory for evaluating structural performance subject to vibration excitation. In this paper, to improve the time waveform replication accuracy of electro-hydraulic shake tables (EHSTs), an improved feedforward inverse control algorithm with adaptive refinement is proposed. The EHST system transfer function and stable inverse model is firstly estimated and designed by multi-step recursive extended least squares algorithm and zero magnitude error tracking controller technology, respectively. To reduce the side effect of model identification errors between the estimated and actual system, a system model corrector is further identified based on the previous estimated EHST model and an inverse corrector is obtained so as to constitute the improved feedforward inverse controller (iFIC) by cascading the inverse corrector to the previous designed inverse model. Then, an adaptive refinement controller using a least mean square algorithm is applied to the iFIC controlled system to cope with system uncertainties and nonlinear effects. Therefore, the proposed algorithm combines the merits of feedforward inverse control and adaptive control. Finally, with the help of xPC rapid prototyping technology, experiments are conducted on a real uniaxial EHST system and the experimental results demonstrate that the proposed algorithm exhibits a better tracking accuracy than the conventional controllers for shake tables.

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“…Redundant electro-hydraulic shaking tables (RESTs) are extensively employed to evaluate vibration performances for civil engineering structures (Hassan et al, 2014; Luco et al, 2010; Severn, 2011), the automotive industry (Cherng et al, 2001; Kim et al, 2008), earthquake-proof testing (Chen et al, 2010; Ji et al, 2009), and so on, and analyse and modify durability properties and seismic performances for evaluating the behaviour of structures subjected to actual vibration conditions (Tagawa and Kajiwara, 2007). A number of RESTs have been developed, for example, E-defence (Tagawa and Kajiwara, 2007) and the CGS-ALGERIA shaking table (Hassan et al, 2014), and many RESTs developed by MTS Systems Corporation (2011).…”

Section: Introductionmentioning

confidence: 99%

Acceleration waveform replication on six-degree-of-freedom redundant electro-hydraulic shaking tables using an inverse model controller with a modelling error

Shen

Zhu

et al. 2016

Transactions of the Institute of Measurement and Control

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A redundant electro-hydraulic shaking table (REST) of six degrees-of-freedom (6 DOFs) with eight hydraulic actuators is an essential experimental tool in many industrial applications for real-time simulation of actual vibrations, such as structural vibration, earthquake simulation and fatigue testing. In order to obtain a high-fidelity acceleration waveform on the REST, a feed-forward inverse model (FFIM) controller with a modelling error compensator is proposed in this study. A recursive extended least-squares algorithm is employed to identify an acceleration closed-loop transfer function of the REST. A zero phase error compensation technology is employed to guarantee stability of the designed FFIM because the identified acceleration closed-loop transfer function is a typical non-minimum phase system and its direct inverse transfer function is unstable. The modelling error compensator is designed to compensate for the modelling error between the identified transfer function and the actual experimental REST, which deteriorates the acceleration waveform replication accuracy of the REST. A 6 DOF REST experimental system was used to verify the proposed controller. Experimental results demonstrated that the proposed controller gave satisfactory acceleration tracking performances on the REST.

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Improving Drive Files for Vehicle Road Simulations

Cited by 6 publications

References 5 publications

Neuro-fuzzy iterative learning control for 4-poster test rig

Neuro-fuzzy iterative learning control for 4-poster test rig

Improved feedforward inverse control with adaptive refinement for acceleration tracking of electro-hydraulic shake table

Acceleration waveform replication on six-degree-of-freedom redundant electro-hydraulic shaking tables using an inverse model controller with a modelling error

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