An adaptive fuzzy logic controller for intelligent drying

A systematic approach to the design of an adaptive fuzzy logic controller (AFLC) for intelligent drying with a computer vision system (CVS) in a feedback loop is proposed. Developed AFLC is based on an artificial neural network (ANN), geno-fuzzy algorithm, and multi-objective fuzzy cost function. Fuzzy sets for the moisture content and product quality are automatically generated by using principal component analysis (PCA) and fuzzy clustering. In addition, the concept of fuzzy time is introduced to optimize the duration of each control step. The fuzzy rule base for the controller was constructed through a two-stage process of (i) warming-up based on simulation and optimization (offline) and (ii) fine-tuning during real-time drying (online). The application of AFLC for shrimp drying showed advantages of the unsupervised fuzzy logic control, such as decreased drying time, less quality degradation, and smaller energy consumption.

Automated summary

This paper proposes a systematic design approach for an adaptive fuzzy logic controller (AFLC) with a computer vision system (CVS) in a feedback loop for intelligent drying. The AFLC is based on an artificial neural network (ANN), geno-fuzzy algorithm, and multi-objective fuzzy cost function. Fuzzy sets for moisture content and product quality are automatically generated using principal component analysis (PCA) and fuzzy clustering. The application of AFLC in shrimp drying shows advantages of unsupervised fuzzy logic control, including decreased drying time, less quality degradation, and smaller energy consumption.