Servo-hydraulic actuator in controllable canonical form: Identification and experimental validation

Maghareh, Amin; Silva, Christian E.; Dyke, Shirley J.

doi:10.1016/j.ymssp.2017.07.022

Cited by 33 publications

(25 citation statements)

References 21 publications

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“…13 To model the dynamics of a typical hydraulic transfer system, the fluid flow rate is often linearized about the origin. In this model by Dyke et al 2 and Maghareh et al, 11,12 the interaction between the hydraulic actuator and the physical specimen is captured as shown in Figure 3, where s ∈ C denotes the Laplace variable. The equations describing the fluid flow rate q in an actuator can be linearized about the origin to obtain the following equations: where F a , , V, A a , K q , i, K c , x, and x 1 indicate actuator force, bulk modulus of the fluid, half the volume of the actuator, piston area, valve flow gain, valve input, leakage coefficient, and actuator displacement and velocity, respectively.…”

Section: Nonlinear Dynamics Of the Control Plantmentioning

confidence: 99%

“…The equations describing the fluid flow rate q in an actuator can be linearized about the origin to obtain the following equations: where F a , , V, A a , K q , i, K c , x, and x 1 indicate actuator force, bulk modulus of the fluid, half the volume of the actuator, piston area, valve flow gain, valve input, leakage coefficient, and actuator displacement and velocity, respectively. 2,4,[11][12][13][14][15] By combining Equations (1) and (2), and solving for . F a , the following expression is obtained:…”

Section: Nonlinear Dynamics Of the Control Plantmentioning

confidence: 99%

“…In a previous study and based on Equations (1) to (5), a physics-based nonlinear model for a servo-hydraulic actuator coupled with a nonlinear physical specimen was developed, validated, and transformed into a controllable canonical form. 11,12 This controllable canonical model captures the nonlinear dynamics in Figure 3. In this dynamical model, the physical specimen-here, physical substructure-is described with a general mathematical form of…”

Section: Nonlinear Dynamics Of the Control Plantmentioning

confidence: 99%

“…where x, x 1 , x 2 , F a , and m denote actuator displacement, velocity, acceleration, force, and attached mass, respectively. 11 Hereafter, x n indicates the nth derivative of actuator displacement with respect to time, and nonlinear function h is a continuous, twice-differentiable function. Maghareh et al 11,12 showed that the block diagram provided in Figure 3 can be mathematically described as…”

Section: Nonlinear Dynamics Of the Control Plantmentioning

confidence: 99%

See 3 more Smart Citations

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

Maghareh

Dyke

Silva

2020

Earthq Engng Struct Dyn

Self Cite

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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: Nonlinear Dynamics Of the Control Plantmentioning

confidence: 99%

Section: Nonlinear Dynamics Of the Control Plantmentioning

confidence: 99%

Section: Nonlinear Dynamics Of the Control Plantmentioning

confidence: 99%

See 2 more Smart Citations

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

Maghareh

Dyke

Silva

2020

Earthq Engng Struct Dyn

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the fields of precision machines, intelligent manufacturing, robotics [1][2][3], and other active devices [4] for moving and driving an object precisely, actuators play an irreplaceable role. Conventionally, hydraulic pressure [5][6][7], pneumatic pressure [8,9], etc., can be transferred into mechanical motion. However, electric energy is more conveniently converted into motion and output force via electrostatic [6,10], piezoelectric, electroactive [11,12], electromagnetic induction, or magnetomotive force [13][14][15] coupling effects.…”

Section: Introductionmentioning

confidence: 99%

A Piezoelectric and Electromagnetic Dual Mechanism Multimodal Linear Actuator for Generating Macro- and Nanomotion

Gao

et al. 2019

Research

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Fast actuation with nanoprecision over a large range has been a challenge in advanced intelligent manufacturing like lithography mask aligner. Traditional stacked stage method works effectively only in a local, limited range, and vibration coupling is also challenging. Here, we design a dual mechanism multimodal linear actuator (DMMLA) consisted of piezoelectric and electromagnetic costator and coslider for producing macro-, micro-, and nanomotion, respectively. A DMMLA prototype is fabricated, and each working mode is validated separately, confirming its fast motion (0~50 mm/s) in macromotion mode, micromotion (0~135 μm/s) and nanomotion (minimum step: 0~2 nm) in piezoelectric step and servomotion modes. The proposed dual mechanism design and multimodal motion method pave the way for next generation high-precision actuator development.

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A simple strategy for active structural control against control–structure–excitation effects: Concept, framework, and experiments

Yao

Tan

Liu

et al. 2022

Earthq Engng Struct Dyn

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Active control is among the most effective control techniques for mitigating structural vibrations. Control–structure interaction (CSI) plays a significant role in modeling a control system. Popular methods incorporate the actuator dynamics and structure into the control system to account for CSI. The transfer function of the actuator can be identified through frequency response tests without physical modeling. However, this method ignores the effects of the excitation. Another approach is to create a physics‐based model of the actuator and include it in the control design, but its implementation may be challenging and requires a thorough understanding of the complex control system. In this study, the concept of control–structure–excitation effects (CSEE) is presented to account for the effects of the control, structure, and excitation on the transfer function of the actuator. In contrast to the existing control methods that consider CSI in system modeling, an improved control framework is proposed to eliminate the influence of CSI/CSEE on the control performance and thus decouple the tracking control of the actuator from the control design of the structure. This is achieved by introducing the control floor acceleration into the actuator controller and transforming the tracking of the absolute control force into that of the relative acceleration. The control strategy is simple and easy to implement based on conventional actuator control algorithms, such as three‐variable control. Frequency response tests and shake table tests were performed on a two‐story small‐scale structure to evaluate the effectiveness of the control strategy. It was confirmed that the improved control framework shows good tracking performance with respect to the control force and thus allows for ideal structural response control.

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Servo-hydraulic actuator in controllable canonical form: Identification and experimental validation

Cited by 33 publications

References 21 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

A Piezoelectric and Electromagnetic Dual Mechanism Multimodal Linear Actuator for Generating Macro- and Nanomotion

A simple strategy for active structural control against control–structure–excitation effects: Concept, framework, and experiments

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