Sliding mode control design for the benchmark problem in real-time hybrid simulation

Li, Hongwei; Maghareh, Amin; Montoya, Herta; Uribe, Johnny Wilfredo Condori; Dyke, Shirley J.; Xu, Zhao‐Dong

doi:10.1016/j.ymssp.2020.107364

Cited by 21 publications

(12 citation statements)

References 38 publications

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“…However, in the previous study on the same RTHS benchmark problem that adopted the sliding mode controller without coupling it with a parameter estimator, the coefficient being 1.6 leads to the best RTHS performance 26 (the smallest mean evaluation values of perturbed cases are achieved). The corresponding control system 26 is denoted as NSMC and will be compared with ASMC in Section 3.3. The nominal values of α 1 and α 2 listed in Table 2 are treated as the initial estimated values α1 ð0Þ and α1 ð0Þ for the ASMC.…”

Section: Control Plantmentioning

confidence: 87%

“…Simulation results where the coefficient is varied from 1.0 to 2.0 show that the performance of ASMC is not sensitive to the values of the coefficient. However, in the previous study on the same RTHS benchmark problem that adopted the sliding mode controller without coupling it with a parameter estimator, the coefficient being 1.6 leads to the best RTHS performance 26 (the smallest mean evaluation values of perturbed cases are achieved). The corresponding control system 26 is denoted as NSMC and will be compared with ASMC in Section 3.3.…”

Section: Control Plantmentioning

confidence: 87%

“…Simulation results demonstrate that high performance and robustness are achievable with ASMC, even when extreme damage is induced in the structure. Compared with the previous studies that adopted sliding mode controllers in RTHS, 10,23,25,26 ASMC further enhances the capability of the controller to deal with model uncertainties in the plant by coupling with the BLS estimator that is able to track time-varying parameters. This paper shows that with the adoption of ASMC, the plant used for controller design and parameter estimation can be greatly simplified without compromising the RTHS performance and robustness.…”

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confidence: 99%

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An adaptive sliding mode control system and its application to real‐time hybrid simulation

Maghareh

Uribe

et al. 2021

Structural Contr & Hlth

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Real-time hybrid simulation (RTHS) is intended to serve as a technique able to conduct experiments when the behavior of the plant is not well understood, i.e., when deep uncertainties are present in the physical specimen. By combining strategies from robust control and adaptive control, this paper develops an adaptive sliding mode control (ASMC) system for uncertain control plants. The ASMC consists of a bounded-gain forgetting least-squares estimator and a sliding mode controller, aimed at estimating parameters of the control plant and eliminating the negative effects of estimation errors, respectively. The ASMC is evaluated by applying it to the benchmark control problem in RTHS, where the fifth-order control plant is reduced to a second-order control plant to facilitate the control system's design and execution. High performance and robustness are achieved with the adoption of ASMC. The results demonstrate that an effective ASMC can be designed based on a significantly simplified control plant, making it a potent control system for RTHS. K E Y W O R D Sadaptive control, model uncertainties, real-time hybrid simulation, robust control, sliding mode control | INTRODUCTIONReal-time hybrid simulation (RTHS) is a cyber-physical technique to conduct experimental studies of large-scale and complex structures subject to dynamic loading. 1 In RTHS, the structure under investigation is divided into the numerical substructure-computational model simulated in a computer and the physical substructure-experimental specimen(s). The numerical substructure should contain the majority or larger part of the structure. It is typically assumed to be well understood and can be numerically modeled precisely, while the rest of structure, normally individual elements(s) or device(s) inside of the structure, e.g., a damper, is treated as a physical substructure and is

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Section: Control Plantmentioning

confidence: 87%

Section: Control Plantmentioning

confidence: 87%

mentioning

confidence: 99%

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An adaptive sliding mode control system and its application to real‐time hybrid simulation

Maghareh

Uribe

et al. 2021

Structural Contr & Hlth

Self Cite

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“…Additionally, a feedforwardfeedback control scheme based on linear-quadratic regulator (LQR) and Kalman filter was developed for controlling both single and multi-actuation setups [18,19]. A modelbased sliding mode control approach has also been developed for RTHS making use of a reduced plant [20]. Recently, a control scheme based on model predictive control (MPC) [21] was also presented.…”

Section: Introductionmentioning

confidence: 99%

Adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation

Tsokanas¹,

Pastorino²,

Stojadinović³

2021

Preprint

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Real-time hybrid simulation is an experimental method used to obtain the dynamic response of a system whose components consist of loading-rate-sensitive physical and numerical substructures. The coupling of these substructures is achieved by actuation systems, i.e., an arrangement of motors or actuators, which are responsible for continuously synchronizing the interfaces of the substructures and are commanded in closed-loop control setting. To ensure high fidelity of such hybrid simulations, performing them in real-time is necessary. However, real-time hybrid simulation poses challenges as the inherent dynamics of the actuation system introduce time delays, thus modifying the dynamic response of the investigated system and hence compromising the simulation's fidelity and trust in the obtained response quantities. Therefore, a reference tracking controller is required to adequately compensate for such time delays.In this study, a novel tracking controller is proposed for dynamics compensation in real-time hybrid simulations. It is based on an adaptive model predictive control approach, a linear time-varying Kalman filter, and a real-time model identification algorithm. Within the latter, auto-regressive exogenous polynomial models are identified in real-time to estimate the changing plant dynamics and used to update the prediction model of the tracking controller. A parametric virtual real-time hybrid simulation case study is used to validate the performance and robustness of the proposed control scheme. Results demonstrate the effectiveness of the proposed controller for real-time hybrid simulations.

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“…From the coupling of substructures, several challenges arise, e.g., time delays due to inherent transfer system dynamics or due to computational power needed to compute the NS response. Advanced control techniques [1][2][3][4] and model order reduction methods [5,6] have been used to tackle such issues. Albeit the challenges, the HS approach is beneficial since it can be used to experimentally study the inner workings of specific substructures over their linear regime and, hence, acquire realistic results but without constructing the entire considered system nor risking damaging it.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Surrogate Modeling Techniques for Global Sensitivity Analysis in Hybrid Simulation

Tsokanas

Pastorino

Stojadinović

2021

MAKE

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Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The system under consideration is divided into multiple individual substructures, out of which one or more are tested physically, whereas the remaining are simulated numerically. The coupling of all substructures forms the so-called hybrid model. Although hybrid simulation is extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation are conceived as being deterministic. However, associated uncertainties are present, whilst simulation deviation, due to their presence, could be significant. In this regard, global sensitivity analysis based on Sobol’ indices can be used to determine the sensitivity of the hybrid model response due to the presence of the associated uncertainties. Nonetheless, estimation of the Sobol’ sensitivity indices requires an unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques using machine learning data-driven regression are utilized to alleviate this burden. This study extends the current global sensitivity analysis practices in hybrid simulation by employing various different surrogate modeling methodologies as well as providing comparative results. In particular, polynomial chaos expansion, Kriging and polynomial chaos Kriging are used. A case study encompassing a virtual hybrid model is employed, and hybrid model response quantities of interest are selected. Their respective surrogates are developed, using all three aforementioned techniques. The Sobol’ indices obtained utilizing each examined surrogate are compared with each other, and the results highlight potential deviations when different surrogates are used.

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Sliding mode control design for the benchmark problem in real-time hybrid simulation

Cited by 21 publications

References 38 publications

An adaptive sliding mode control system and its application to real‐time hybrid simulation

An adaptive sliding mode control system and its application to real‐time hybrid simulation

Adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation

A Comparison of Surrogate Modeling Techniques for Global Sensitivity Analysis in Hybrid Simulation

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