Adaptive model-based tracking control for real-time hybrid simulation

Chen, Pei‐Ching; Chang, Chia‐Ming; Spencer, Billie F.; Tsai, Keh‐Chyuan

doi:10.1007/s10518-014-9681-2

Cited by 37 publications

(30 citation statements)

References 14 publications

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“…The parameters in Equation (8) relate to the physically meaningful quantities, described in Table 1. Based on the type of function h in Equation (6), coefficients c 1 to c 6 are subject to change. In the next section, control and adaptation laws will be established for this control plant: a single actuator coupled with a physical substructure.…”

Section: Nonlinear Dynamics Of the Control Plantmentioning

confidence: 99%

“…The associated errors are provided in Table 2. Note that in these experiments, the parameters associated with the MR damper is not identified and to capture the dynamics of the MR damper in the nominal control plant, a simple time-invariant linear single-degree-of-freedom system is being used for Equation (6). Clearly, the tracking performance provided in Table 2 and Figures 19 and 20 show that the SRCSys is well capable of accommodating extensive performance variations and uncertainties in the control plant.…”

Section: Normalized Error [%] =mentioning

confidence: 99%

“…Chen et al developed an adaptive control scheme to accommodate specimen nonlinearity and improve the tracking performance of a servo-hydraulic actuator. 6 Ou et al developed a linear robust integrated actuator control strategy that includes three control components: loop shaping feedback control based on H ∞ optimization, a linear-quadratic-estimation block for minimizing noise effects, and a feed-forward block that reduces small residual time delay. 7 In a numerical study, Moosavi et al established a nonlinear state-space controller for hydraulic actuators under displacement control using state feedback linearization and transformation of the state variables.…”

Section: Introductionmentioning

confidence: 99%

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A Self‐tuning Robust Control System for nonlinear real‐time hybrid simulation

Maghareh

Dyke

Silva

2020

Earthq Engng Struct Dyn

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In a real-time hybrid simulation, a transfer system is used to enforce the interface interaction between computational and physical substructures. A model-based, multilayer nonlinear control system is developed to accommodate extensive performance variations and uncertainties in a physical substructure. The aim of this work is to extend the application of real-time hybrid simulation to investigating failure, nonlinearity, and nonstationary behavior. This Self-tuning Robust Control System (SRCSys) consists of two layers: robustness and adaptation. The robustness layer synthesizes a nonlinear control law such that the closed-loop dynamics perform as intended under a broad range of parametric and nonparametric uncertainties. Sliding mode control is employed as the control scheme in this layer. Then, the adaptation layer reduces uncertainties at run time through slow and controlled learning of the control plant. The tracking performance of the SRCSys is evaluated in two experiments that have highly uncertain physical specimens.

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Section: Nonlinear Dynamics Of the Control Plantmentioning

confidence: 99%

Section: Normalized Error [%] =mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Self‐tuning Robust Control System for nonlinear real‐time hybrid simulation

Maghareh

Dyke

Silva

2020

Earthq Engng Struct Dyn

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“…Thus, Philips et al proposed a new model‐based servohydraulic tracking control method including feedforward–feedback links to achieve accurate tracking of a desired displacement in real time, which is a development of model‐based compensation . Moreover, Chen et al applied an adaptive control scheme to a model‐based feedforward–feedback controller to accommodate specimen nonlinearity. The robust integrated actuator control proposed by Ou et al can also be considered a feedforward–feedback link, in which the loop shaping feedback control based on H∞ optimization is used as the feedback controller.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al also proposed an adaptive inverse compensation method, which uses a PI controller to tune the inverse compensator using the tracking indicator. The inverse compensation algorithms can also be used to improve the performance of the EFC. The procedure and principle of the EFC combined with the inverse compensation methods are similar to EFC combined with an AFP, which is not discussed further due to limited space.…”

Section: Introductionmentioning

confidence: 99%

Equivalent force control combined with adaptive polynomial-based forward prediction for real-time hybrid simulation

Zhou

Wagg

2017

Struct Control Health Monit

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Summary The equivalent force control method uses feedback control to replace numerical iteration and solve the nonlinear equation in a real‐time hybrid simulation via the implicit integration method. During the real‐time hybrid simulation, a time delay typically reduces the accuracy of the test results and can even make the system unstable. The outer‐loop controller of the equivalent force control method can eliminate the effect of a small time delay. However, when the actuator has a large delay, the accuracy of the test results is reduced. The adaptive forward prediction method offers a solution to this problem. Thus, in this paper, the adaptive polynomial‐based forward prediction algorithm is combined with equivalent force control to improve the test accuracy and stability. The new method is shown to give good stability properties for a specimen with nonlinear stiffness by analyzing the location of the poles of the discrete transfer system. Simulations with linear and nonlinear specimens are then presented to demonstrate the effectiveness of this method. Finally, experimental results with a linear stiffness specimen and a magneto‐rheological damper are used to demonstrate that this method has better accuracy than the equivalent force control method with nonadaptive delay compensation.

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Model‐based adaptive kinematic transformation method for accurate control of multi‐DOF boundary conditions in conventional tests and hybrid simulations

Park

Kwon

2022

Earthq Engng Struct Dyn

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Several actuators need to be controlled to impose a multi-degree of freedom displacement boundary conditions on a specimen in multi-axial hybrid simulations or conventional multi-axial displacement-controlled tests. As the displacement boundary conditions are typically defined in the Cartesian coordinate system, kinematic transformation is required to transform the boundary conditions into actuator strokes. In previous studies, the kinematic transformation was carried out assuming no elastic deformation of the reaction system where the actuators and specimens are mounted. Accordingly, the kinematic transformation becomes inaccurate if the elastic deformation are not negligible, thereby impacting the accuracy of the experiments. There are methods to compensate for these errors by instrumenting specimens, but the existing methods often require many iterations or do not monotonically approach the target displacements. This study proposes a new method for kinematic transformation from the Cartesian system to the actuators' local coordinate systems. The method adopts a model identification technique by which the influence of the elastic deformation can be effectively considered in calculating the actuator strokes. Numerical verification and experimental validation with the proposed transformation method are carried out. The results show that the proposed transformation method can decrease the number of iterations to achieve the target displacement boundary conditions and thus avoiding overshooting the displacement boundary conditions and reducing the interaction between actuators. It is expected that the proposed method can reduce the overall time to run a multi-axial hybrid simulation or multi-DOF displacement-controlled experiments.

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Adaptive model-based tracking control for real-time hybrid simulation

Cited by 37 publications

References 14 publications

A Self‐tuning Robust Control System for nonlinear real‐time hybrid simulation

A Self‐tuning Robust Control System for nonlinear real‐time hybrid simulation

Equivalent force control combined with adaptive polynomial-based forward prediction for real-time hybrid simulation

Model‐based adaptive kinematic transformation method for accurate control of multi‐DOF boundary conditions in conventional tests and hybrid simulations

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