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A practical implementation of self-evolving cloud-based control of a pilot plant

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This paper presents the implementation of a first order self-evolving cloud-based controller for the liquid level of a two-tank pilot plant. The controller is based on the AnYa type fuzzy rule-based system (FRB), which has a parameter-free antecedent part, and can learn autonomously on-line with each new input data collected and output generated, with no prior knowledge of the system or off-line training. Two types of controllers are considered: a PD-type controller, with simulated and real results; and a MRC-type controller, with simulated results. Regarding the practical implementation, a real continuous process didactic plant was used as a representation of a real industrial environment through the OLE for Process Control (OPC) communication protocol. It has been demonstrated the possibility of building autonomously and in an unsupervised manner a controller capable of developing and adapting itself in a real-time industrial automation application.