Adaptation over Unsteady Dynamics

Jul 22, 2014 · 2 min read

A signature capability of humans and intelligent systems, adaptation has been a frequent subject of scientific inquiry and a central pillar in feedback controls. We developed a class of stability-assured adaptive controls that achieve highly robust feedback performance under unknown and/or extremely time-varying disturbances.

Performance comparison of our algorithm in an international benchmark on adaptive regulation organized by Ioan-Doré Landau, founder and the first President of the European Community Control Association.

We have open sourced the algorithms at the MACS ARB Github Repository.

Algorithmically, MACS ARB presents an adaptive control scheme for identifying and rejecting unknown and/or time-varying narrow-band vibrations. We discuss an idea of safely and adaptively inverting a (possibly non-minimum phase) plant dynamics at selected frequency regions, so that structured disturbances (especially vibrations) can be estimated and canceled from the control perspective. By taking advantage of the disturbance model in the design of special infinite-impulse-response (IIR) filters, we can reduce the adaptation to identify the minimum amount of parameters, achieve accurate parameter estimation under noisy environments, and flexibly reject the narrow-band disturbances with clear tuning intuitions.

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Adaptation Learning Research
Xu Chen
Authors
Xu Chen