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 contributionpeer-review


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)
Number of pages7
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


Conference13th APCA International Conference on Automatic Control and Soft Computing (CONTROLO)
City Ponta Delgada


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


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