Data‐driven nonlinear autoregressive with external input model‐based compensation for real‐time testing

Xu, Weijie; Chen, Cheng; Gao, Xiaoshu; Chen, Menghui; Guo, Tong; Peng, Changle

doi:10.1002/stc.3119

Cited by 5 publications

(8 citation statements)

References 62 publications

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“…4a is also referred to as the jth order NARX model based on the terms required. Table 1 outlines the coefficients for first, second, and third order NARX model-based compensations, denoted as F-, S-, and T-NARX, respectively (Xu et al, 2022).…”

Section: Simplified Form Of Narx Model Based Compensationmentioning

confidence: 99%

“…As a result, adaptive compensation methods have become the predominant approach in RTHS, with many validated through benchmark problems (Ning et al, 2019;Wang et al, 2019;Xu et al, 2019;Zhou et al, 2019;Silva et al, 2020). Among these methods, the Nonlinear Autoregressive with External Input Model (NARX) compensation (Xu et al, 2022) constructs the actuator dynamics as a function of previously predicted displacements and current or previous measured displacements. The relationship between calculated displacement and predicted displacement is determined based on this actuator dynamics model.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, data-driven NARX model-based compensation is evaluated for the maRTHS benchmark model (Uribe et al, 2023), which features a steel frame, two hydraulic actuators, and a highstiffness steel coupler. Expanding upon the groundwork laid by the NARX controller proposed by Xu et al (2022), this study delves into the integration of nonlinear terms into the NARX model-based compensation framework. Whereas previous applications primarily focused on linear terms, this research introduces the inclusion of nonlinear elements for enhanced practical implementation.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of data-driven NARX model based compensation for multi-axial real-time hybrid simulation benchmark study

Xu,

Meng,

Chen

et al. 2024

Front. Built Environ.

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Actuator control takes a pivotal role in achieving stability and accuracy, particularly in the context of multi-axial real-time hybrid simulation (maRTHS). In maRTHS, multiple hydraulic actuators are necessitated to apply precise motions to experimental substructures thus necessitating the application of multiple-input multiple-output (MIMO)control strategies. This study evaluates the data-driven nonlinear autoregressive with external input (NARX) based compensation for the servo-hydraulic dynamics within the maRTHS benchmark model. Different from previous study, nonlinear terms are incorporated into the NARX model. Online least square and ridge regression techniques are utilized to estimate the model coefficients to achieve optimal compensation. The influence of various model order and window length is assessed for the NARX model-based compensation. The findings of this research demonstrate that NARX-based compensation has significant potential not only in facilitating precise actuator control for maRTHS but also in enabling robust control in the presence of unknown uncertainties inherent to the servo-hydraulic system.

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Section: Simplified Form Of Narx Model Based Compensationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of data-driven NARX model based compensation for multi-axial real-time hybrid simulation benchmark study

Xu,

Meng,

Chen

et al. 2024

Front. Built Environ.

Self Cite

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“…The unconditionally stable explicit CR algorithm 56 is used to solve the structural dynamics. A recently developed NARX model‐based compensation 57 is used to minimize the effect of servo‐hydraulic dynamics. More specifically, the parameters of the first order NARX model‐based compensation is determined online through ordinary least square regression in a real‐time manner.…”

Section: Experimental Validation Of Rths‐based Mfmc For Seismic Fragi...mentioning

confidence: 99%

Integrating real‐time hybrid simulation with multi‐fidelity Monte Carlo predictor for seismic fragility assessment

Gao,

Chen,

Peng

et al. 2023

Earthq Engng Struct Dyn

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Seismic fragility evaluates the performance of a structure regarding to its capability to withstand earthquake loads by describing the exceedance probability for selected engineering demand parameters. High‐fidelity (HF) models are essential for simulation‐based techniques to obtain accurate fragility assessment in the presence of uncertainties. Dynamic structural behavior however is often difficult to replicate realistically through numerical simulation, especially when parts of the structure are difficult for accurate modeling due to their inherent complex nonlinearity. This study presents the integration of real‐time hybrid simulation (RTHS) with multi‐fidelity Monte Carlo (MFMC) predictor for seismic fragility analysis. RTHS combines physical testing of experimental substructures and numerical modeling of analytical substructures thus presenting a cyber‐physical alternative for HF seismic performance evaluation. The MFMC predictor provides an optimized allocation algorithm that makes full use of data from both HF and low‐fidelity (LF) models to achieve unbiased estimation for response quantities of interests. Experimentally expensive HF data from RTHS is thus integrated with computationally cheap simulation of LF numerical models through MFMC to facilitate seismic fragility analysis. A two‐story steel moment resisting frame (MRF) with self‐centering viscous dampers (SC‐VDs) is utilized to demonstrate the effectiveness and efficiency of the proposed method. Computational simulation is first performed to numerically verify the feasibility of the proposed method for the MRF subjected to non‐stationary stochastic ground motions. RTHS is then conducted experimentally as HF model and integrated with linear elastic LF models through MFMC for fragility analysis of the MRF equipped with SC‐VDs. Both computational and experimental analysis demonstrate that the proposed method provides a promising technique for seismic fragility assessment especially when accurate modeling is not available due to complex structural behavior.

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“…In RTHS, the emulated structure is typically divided into two parts: the structural portion with a clear constitutive model is modeled numerically by a computer software, which is generally regarded as the numerical substructure (NS), and the remaining member exhibiting stronger nonlinearities is loaded physically and termed as physical substructure (PS). Nowadays, some new progresses have been made in RTHS considering structural or external excitation uncertainties [4][5][6][7]. In RTHS, the key issue is that displacement compatibility and force equilibrium on the boundary between NS and PS are enforced in real time [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Model-Based Adaptive Feedforward-Feedback Control Method for Real-Time Hybrid Simulation considering Additive Error Model

Ning,

Huang,

et al. 2023

Structural Control and Health Monitoring

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Adaptive control methods have been widely adopted to handle the variable time delay in real-time hybrid simulation (RTHS). Nevertheless, the initial parameter settings in adaptive control law, the parameter estimation method, and the testing system nonlinearity will affect RTHS’s accuracy and stability at different levels. To this end, this study proposes a novel model-based adaptive feedforward-feedback control method that considers an additive error model. In the proposed method, the time delay and amplitude discrepancy are roughly compensated by a feedforward controller and then finely reduced by an adaptive controller, and an outer-loop control formed by the feedback controller is introduced to improve the ability and robustness furthermore. What’s more, the testing system, composed of the transfer system and physical specimen, is divided into the nominal and additive error models. The feedforward controller is devised using the inverse nominal model, whose parameters are constant. The adaptive controller is designed to adopt a discrete-time additive error model, in which the parameters are identified online by the Kalman filter. Numerical simulations, parametric studies, and actual experiments were carried out to inspect the feasibility and effectiveness of this method thoroughly. Results indicate that the proposed method can effectively improve the accuracy and stability of RTHS and significantly reduce the dependence on the adaptive control law. Moreover, the proposed method exhibits strong robustness and is, therefore, useful in RTHS.

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Data‐driven nonlinear autoregressive with external input model‐based compensation for real‐time testing

Cited by 5 publications

References 62 publications

Evaluation of data-driven NARX model based compensation for multi-axial real-time hybrid simulation benchmark study

Evaluation of data-driven NARX model based compensation for multi-axial real-time hybrid simulation benchmark study

Integrating real‐time hybrid simulation with multi‐fidelity Monte Carlo predictor for seismic fragility assessment

A Novel Model-Based Adaptive Feedforward-Feedback Control Method for Real-Time Hybrid Simulation considering Additive Error Model

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