Wavelet Network for Semi-Active Control

Laflamme, Simon; Slotine, Jean‐Jacques E.; Connor, Jerome J.

doi:10.1061/(asce)em.1943-7889.0000248

Cited by 35 publications

(38 citation statements)

References 47 publications

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“…We have presented in Laflamme et al [23] an adaptive WNN augmented by a sliding controller for semi-active control mechanisms. This WNN will be utilized with the novel SOI algorithm.…”

Section: Wavelet Neural Networkmentioning

confidence: 99%

“…Note that some bounds exist on the new parameters given that we are using wavelet activation functions. They are not listed here for conciseness; details are given in Laflamme et al [23]. Additionally, nodes can be pruned from the network.…”

Section: Self-organizing Rulesmentioning

confidence: 99%

“…5 LARGE-SCALE SIMULATION The WNN controller presented in this paper has been simulated with fixed inputs in Laflamme et al [23]. The simulations were conducted on a 39-storey office tower located in downtown Boston, MA, USA.…”

Section: Verificationmentioning

confidence: 99%

“…The minimum nodal distance and error are set equal and at 5% of max t y*. The network resolution and pruning parameters are arbitrary and based on previous simulations in Laflamme et al [23]. The adaptation weights are set to unity based on the sampling rate of 100 Hz, a sampling rate typical in structural control, except for the wavelet weights, which are augmented to 10 for enhanced adaptation speed.…”

Section: Verificationmentioning

confidence: 99%

“…A delay is induced in the actuator using the following dynamics: act = -h(u act -u n ), where h is a voltage delay taken as h = 20 s −1 to be consistent with the actuator dynamics taken in Laflamme et al [23]. A sliding window size of n = 100 time steps is selected.…”

Section: Verificationmentioning

confidence: 99%

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Adaptive structural control using dynamic hyperspace

Laflamme¹

2015

Int. J. CMEM

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The design of closed-loop structural control systems necessitates a certain level of robustness to cope with system uncertainties. Neurocontrollers, a type of adaptive control system, have been proposed to cope with those uncertainties. However, the performance of neural networks can be substantially influenced by the choice of the input space, or the hyperspace in which the representation lies. For instance, input selection may influence computation time, adaptation speed, effects of the curse of dimensionality, understanding of the representation, and model complexity. Input space selection is often overlooked in literature, and inputs are traditionally determined offline for an optimized performance of the neurocontroller. Such offline input selection is often unrealistic to conduct in the case of civil structures. In this paper, a novel method for automating the input selection process for neural networks is presented. The method is purposefully designed for online input selection during adaptive identification and control of nonlinear systems. Input selection is conducted online and sequentially, while the excitation is occurring. The algorithm designed for the adaptive input space assumes local quasi-stationarity of the time series, and embeds local maps sequentially in a delay vector using the embedding theorem. The input space of the representation is subsequently updated. The performance of the proposed dynamic input selection method is demonstrated through simulating semi-active control of an existing structure located in Boston, MA, U.S.A. Simulation results show the substantial performance of the proposed algorithm over traditional fixed-inputs strategies.

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“…We have presented in Laflamme et al [23] an adaptive WNN augmented by a sliding controller for semi-active control mechanisms. This WNN will be utilized with the novel SOI algorithm.…”

Section: Wavelet Neural Networkmentioning

confidence: 99%

Section: Self-organizing Rulesmentioning

confidence: 99%

Section: Verificationmentioning

confidence: 99%

Section: Verificationmentioning

confidence: 99%

Section: Verificationmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive structural control using dynamic hyperspace

Laflamme¹

2015

Int. J. CMEM

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Active tendons control of structures using block pulse functions

Ghaffarzadeh

Younespour²

2014

Struct. Control Health Monit.

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SUMMARY Structural active control is one of the most efficient systems available to protect building structures from destructive effects of earthquakes. Active control efficiency is highly dependent on control algorithm. One of the most important and challenging components of structural control is the development of an accurate analytical approach having less computational expenses. Block pulse functions have been studied and applied frequently in recent years as a basic set of functions for signal characterizations in systems science and control. The proposed method evolves minimizing computational expenses of analytical approaches by a novel method to suppress the responses of the structure based on block pulse functions. To prove the validity and feasibility of the proposed control method, numerical simulations of earthquake‐excited 10‐story shear buildings with active tendons are performed, and comparative results are presented. The uncontrolled and controlled responses of structural system are obtained by using the proposed method and compared with linear quadratic regulator method. The results reveal that the proposed method can be beneficial in reducing seismic responses of structures with high accuracy and less computational expenses. Copyright © 2014 John Wiley & Sons, Ltd.

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Study of input space for state estimation of high‐rate dynamics

Hong

Laflamme

Dodson

2018

Struct Control Health Monit

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Summary High‐rate dynamic systems are defined as systems being exposed to highly dynamic environments that comprise high‐rate and high‐amplitude events. Examples of such systems include civil structures exposed to blast, space shuttles prone to debris strikes, and aerial vehicles experiencing in‐flight changes. The high‐rate dynamic characteristics of these systems provides several possibilities for state estimators to improve performance, including a high potential to reduce injuries and save lives. In this paper, opportunities and challenges that are specific to state estimation of high‐rate dynamic systems are presented and discussed. It is argued that a possible path to design of state estimators for high‐rate dynamics is the utilization of adaptive data‐based observers but that further research needs to be conducted to increase their convergence rate. An adaptive neuro‐observer is designed to examine the particular challenges in selecting an appropriate input space in high‐rate state estimation. It is found that the choice of inputs has a significant influence on the observer performance for high‐rate dynamics when compared against a low‐rate environment. Additionally, misrepresentation of a system dynamics through incorrect input spaces produces large errors in the estimation, which could potentially trick the decision‐making process in a closed‐loop system in making bad judgments.

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Wavelet Network for Semi-Active Control

Cited by 35 publications

References 47 publications

Adaptive structural control using dynamic hyperspace

Adaptive structural control using dynamic hyperspace

Active tendons control of structures using block pulse functions

Study of input space for state estimation of high‐rate dynamics

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