An adaptive fuzzy logic controller for intelligent drying

Abstract 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. Highlights Developing a systematic approach to the design of an Adaptive Fuzzy Logic Controller (AFLC) for intelligent drying Conceptual and functional design of an AFLC based on computer vision and artificial intelligence Proposing a two-stage adaptation algorithm based on multi-objective fuzzy optimization and genetic algorithm Introducing the concept of fuzzy time increment for multi-stage drying Simultaneously reducing energy consumption, drying time, and deterioration of product quality during drying