Robust tracking control of a shaking table with dry friction

Hirata, Gerardo; Álvarez, Joaquín; Cuesta, Ricardo

doi:10.1007/s11071-016-2975-y

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…where d𝑡 is the table acceleration tracking error, d𝑡𝑑 is the acceleration command. The Lyapunov function candidate V is positive-definite, as shown in Equation (8). By defining Ṡ as expressed in Equation ( 9), where K is a positive constant, then the negative definite V can be obtained, as shown in Equation (10).…”

Section: Force-based Smcmentioning

confidence: 99%

“…At present, control algorithms for shake tables can be roughly divided into displacement control, of which proportionalintegral-derivative (PID) control is most common, and hybrid control, primarily three-variable control (TVC). 7 Based on mechanical modeling and parameter adjustment, Hirata et al 8 proposed a displacement proportional-derivative (PD) controller that could compensate for friction and disturbance, and they applied this controller to a shake table driven by a brushless motor. The test results demonstrated good tracking performance of the controller.…”

Section: Introductionmentioning

confidence: 99%

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Acceleration‐based sliding mode hierarchical control algorithm for shake table tests

Yao

Tan

Yang

et al. 2021

Earthq Engng Struct Dyn

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Existing control algorithms for seismic shake table tests (STTs) generally exhibit limitations such as poor acceleration tracking for displacement control, instability that results in table drift for direct acceleration, force, or velocity control, and the lack of a theoretical justification for hybrid control. Therefore, a reliable control algorithm has become key for effective shake table control. This paper presents acceleration‐based sliding mode control (SMC) as a solution to the drawbacks of the traditional force‐based SMC; in this manner, the influence of the force of the tested structure applied on the table as well as unmodeled complex nonlinear forces, such as friction, are counteracted. An acceleration‐based sliding mode hierarchical control (ASMHC) algorithm is proposed, where the acceleration‐based SMC is used as the high‐level controller to generate the corrected acceleration command, and the low‐level controller, that includes feed‐forward and feedback control, tracks the acceleration command in real time. The high‐level controller, having zero asymptotic stability, and the low‐level controller, designed based on the system transfer function, ensure tracking stability in time and frequency domains, respectively. The proposed ASMHC algorithm was first verified by a series of bare STTs, and was then applied to a real STT of a two‐story steel structure. The experimental results show that the proposed ASMHC algorithm can achieve good tracking of displacement, velocity, and acceleration in both time and frequency domains, which ensures accurate reproduction of seismic excitation in STTs.

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Section: Force-based Smcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Acceleration‐based sliding mode hierarchical control algorithm for shake table tests

Yao

Tan

Yang

et al. 2021

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

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Earthquake Ground Motion Matching on a Small Electric Shaking Table Using a Combined NN-PDFF Controller

Larbi

Bourahla

Benchoubane

et al. 2020

Shock and Vibration

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Replicating acceleration time histories with high accuracy on shaking table platforms is still a challenging task. The complex interference between the components of the system, the inherent nonlinearities, and the coupling effect between the specimen and the shaking table are among other reasons that most affect the control performance. In this paper, a neural network- (NN-) based controller has been developed and experimentally implemented to improve the acceleration tracking performance of an electric shaking table. The latter is a biaxial shaking table driven by linear motors and controlled by a proportional-derivative-feedforward (PDFF) controller that is very efficient in reproducing displacement waveforms on the detriment of the simulation of the prescribed acceleration ground motions. In order to bypass this shortcoming, a control scheme combining the PDFF as a basic control function with a NN controller which filters the shaking table feedback signal and acts on the drive signal by compensating for acceleration distortions is proposed in this study. Several experimental tests have been carried out to build a database for offline training, validating, and testing of the proposed NN control model. Subsequently, the well-trained NN is implemented in the inner control loop of the shaking table to compensate, in parallel with the PDFF controller, the distortions during the replication of acceleration signals. Results of tests using earthquake records showed an enhancement in signal matching when integrating the NN model for both bare and loaded conditions of the shaking table. The tracking errors, estimated using the relative root-mean-square error, between the measured and the desired signal, are significantly reduced in time and frequency domains with the additional NN online controller.

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Robust tracking control of a shaking table with dry friction

Cited by 2 publications

References 18 publications

Acceleration‐based sliding mode hierarchical control algorithm for shake table tests

Acceleration‐based sliding mode hierarchical control algorithm for shake table tests

Earthquake Ground Motion Matching on a Small Electric Shaking Table Using a Combined NN-PDFF Controller

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