Adaptive Control Learning Based on a Similarity Measure

T. Rocha, S. Paredes, J. Henriques, P. Carvalho, A. Cardoso, P. Gil

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work proposes a learning methodology to be employed in an adaptive controller strategy. It is based on a similarity approach along with a pole placement technique, by combining ideas from adaptive control and machine learning areas. The learning scheme is propped on the hypothesis that the current characterization of a given system can be achieved from the analysis of past similar behaviors. The main assumption is that data gathered from past experiments, during the operation, can be used on-to line reduce the uncertainty of a model that describes the system. As result, the strategy contributes to improve the performance of a controller based on that model. Two main steps are involved. In the first step a similarity analysis is performed, enabling to find in the historical a set of patterns (input/output time series) similar to the current condition. Then, in a second step, these time series are used to estimate the parameters of a linear model, that are employed afterwards in a pole placement control tuning. The applicability of the proposed approach is assessed on a benchmark nonlinear process, namely a continuous stirred tank reactor (CSTR), showing a better performance than with fixed PI and pole placement controllers
Original languageEnglish
Title of host publication2018 13th APCA International Conference on Control and Soft Computing (CONTROLO)
Pages91-97
Number of pages7
DOIs
Publication statusPublished - 1 Jun 2018
Event13th APCA International Conference on Automatic Control and Soft Computing (CONTROLO) - Ponta Delgada, Portugal
Duration: 4 Jun 20186 Jun 2018

Conference

Conference13th APCA International Conference on Automatic Control and Soft Computing (CONTROLO)
CountryPortugal
City Ponta Delgada
Period4/06/186/06/18

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Keywords

  • adpative control
  • case based reasoning
  • Machine Learning
  • Chemical reactors
  • continuous stirred tank reactor

Cite this

Rocha, T., Paredes, S., Henriques, J., Carvalho, P., Cardoso, A., & Gil, P. (2018). Adaptive Control Learning Based on a Similarity Measure. In 2018 13th APCA International Conference on Control and Soft Computing (CONTROLO) (pp. 91-97) https://doi.org/10.1109/CONTROLO.2018.8514277