2019
DOI: 10.1016/j.ymssp.2019.106260
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A robust linear-quadratic-gaussian controller for the real-time hybrid simulation on a benchmark problem

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Cited by 22 publications
(16 citation statements)
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References 31 publications
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“…Based on the principles of ATS, Palacio-Betancur and Gutierrez Soto (2019) proposed a conditional adaptive time series (CATS) compensator for a benchmark problem in which a recursive least square (RLS) algorithm is adopted for the parameter estimation of the controller to reduce computational efforts. Zhou (Zhou et al, 2019) combined a linear-quadratic gaussian controller and a polynomial-based feedforward prediction algorithm to compensate the adverse effects of time delay and uncertainties on the RTHS testing system.…”
Section: Delay Compensation Schemesmentioning
confidence: 99%
“…Based on the principles of ATS, Palacio-Betancur and Gutierrez Soto (2019) proposed a conditional adaptive time series (CATS) compensator for a benchmark problem in which a recursive least square (RLS) algorithm is adopted for the parameter estimation of the controller to reduce computational efforts. Zhou (Zhou et al, 2019) combined a linear-quadratic gaussian controller and a polynomial-based feedforward prediction algorithm to compensate the adverse effects of time delay and uncertainties on the RTHS testing system.…”
Section: Delay Compensation Schemesmentioning
confidence: 99%
“…In response to this benchmark problem, various compensation methods were proposed. [29][30][31][32][33][34][35][36][37][38][39] Fermandois 29 applied a feedback regulator to model-based compensation (MBC) approach to improve its robustness for undesired disturbance and sensor noise. Najafi and Spencer 31 introduced an adaptive model reference control method for actuator tracking.…”
Section: Introductionmentioning
confidence: 99%
“…More recently, Silva et al 28 proposed a benchmark problem for RTHS of a three‐story steel frame. In response to this benchmark problem, various compensation methods were proposed 29–39 . Fermandois 29 applied a feedback regulator to model‐based compensation (MBC) approach to improve its robustness for undesired disturbance and sensor noise.…”
Section: Introductionmentioning
confidence: 99%
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“…Based on the constant-delay assumption, polynomial extrapolation [11] and kinematic predictors [12] were proposed and then improved [13], among which the third-order polynomial extrapolation is being widely applied [11,14]. Time delay resulting from the dynamics of control plant can be theoretically viewed as the phase lag divided by the corresponding frequency, and methods from control theory have thus been employed, such as the phase-lead network [15], pole assignment [16], feedforward control [17], inverse control [18], model-based control [19], state-based control [20,21], H ∞ control [14,22], and sliding mode control [23].…”
Section: Introductionmentioning
confidence: 99%