This paper proposes an automatic and simple approach to design a neo-fuzzy neuron for identification purposes. The proposed approach uses the backfitting algorithm to learn multiple univariate additive models, where each additive model is a zero-order T-S fuzzy system which is a function of one input variable, and there is one additive model for each […]

This work addresses the problem of controlling unknown and time varying plants for industrial applications. The concept of “plug-and-play” was pursued using control algorithms that auto-adapt their control parameters in order to control unknown and time-varying plants. Self Tuning Controllers (STC) with PID form were studied and tested on a real process setup. The setup […]

This paper presents an approach for online learning of Takagi–Sugeno (T-S) fuzzy models. A novel learning algorithm based on a Hierarchical Particle Swarm Optimization (HPSO) is introduced to automatically extract all fuzzy logic system (FLS)’s parameters of a T–S fuzzy model. During online operation, both the consequent parameters of the T–S fuzzy model and the […]

The paper presents a new robust control strategy based on a synergetic control theory (SCT) approach for discrete-time linear time varying (LTV) systems in the presence of unknown disturbance. The proposed control scheme is featured by an integral type of SCT macro-variable manifold based on the output error. Stability analysis shows that in LTV systems, […]