WIT Press

Adaptive structural control using dynamic hyperspace

Price

Free (open access)

Paper DOI

10.2495/CMEM-V3-N1-49-64

Volume

Volume 3 (2015), Issue 1

Pages

15

Page Range

49 - 64

Author(s)

S. LAFLAMME

Abstract

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.

Keywords

Adaptive control, adaptive hyperspace, adaptive input, online sequential network, self- organizing input, sequential neural network, structural control