Fuzzy Model Identification and Self Learning with Smooth Compositions

TL;DR

This paper introduces a smooth fuzzy model identification and self-learning method for dynamic systems, enabling adaptive parameter tuning on a continuous surface to improve control algorithms like MPC and robust control.

Contribution

It presents a novel smooth fuzzy modeling approach that balances model accuracy and computational efficiency for adaptive control applications.

Findings

Successfully applied to a chemical process dynamic system.

Achieved a good trade-off between model optimality and computational load.

Enhanced the adaptability of fuzzy models for control purposes.

Abstract

This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface. Running the model to adaptively gain the optimum values of the parameters on a smooth surface would facilitate further improvements in the application of other derivative based optimization control algorithms such as MPC or robust control algorithms to achieve a combined modeling-control scheme. Compared to the earlier works on the smooth fuzzy modeling structures, we could reach a desired trade-off between the model optimality and the computational load. The proposed method has been evaluated on a test problem as well as the non-linear dynamic of a chemical process.