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Robust evolving cloud-based controller for a hydraulic plant

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Abstract

In this paper a novel online self-evolving cloud-based controller (RECCo) is introduced. This type of controller has a parameter-free antecedent (IF) part. Two types of consequents are proposed - a locally valid PID-type controller and a locally valid MRC-type one. Corresponding adaptive laws are proposed to tune the parameters in consequent part autonomously. This RECCo controller learns autonomously from its own actions while performing the control of the plant. It does not use any off-line pre-training nor the explicit model (e.g. in a form of differential equations) of the plant. It has been demonstrated that a fully autonomously and in an unsupervised manner (based only on the data density and selecting representative prototypes/focal points from the control hyper-surface acting as a data space) it is possible to generate and self-tune/learn a non-linear controller structure and evolve it in on-line mode. The proposed algorithm is tested on a simulated hydraulic plant. The example is provided aiming mainly to prove the proposed concept.