In this paper, evolutionary and dynamic programming based reinforcement learning techniques are combined to form an unsupervised learning scheme for designing autonomous optimal fuzzy logic control systems. A messy genetic algorithm, and an advantage learning scheme are first compared as reinforcement learning paradigms. The messy genetic algorithm enables flexible coding of a fuzzy structure f...

In this paper a hierarchical control scheme is proposed to improve vibration suppression using an electro-mechanical system based in the lever principle. The hierarchical control consists of two controllers, a LQG/LTR and a fuzzy system, that present high efficiency under different operation conditions and a supervisor based on fuzzy logic that combines the control signals from each one of the ...

Decentralized supervisory control has been introduced to deal with a class of fuzzy discrete event systems (DES) in which state transitions are uncertain. In this paper, we develop the work on fuzzy DES to consider the reliable decentralized supervisor problem with possible failures of some local supervisors. By presenting a new definition of co-observable language, the reliable decentralized s...

Fuzzy-logic-based modelling and control is very efficient in dealing with imprecision and nonlinearity [1]. However, the conventional approaches for designing Fuzzy Inference Systems (FISs) are subjective, which require significant human’s efforts. Other than time consuming, the subjective approaches may not be successful if the system is too complex or uncertain. Therefore, many researchers ha...

This paper presents a novel navigation method for Autonomous Mobile Vehicle in unknown environments. The proposed navigator consists of an Obstacle Avoider (OA), a Goal Seeker (GS), a Navigation Supervisor (NS) and an Environment Evaluator (EE). The fuzzy actions inferred by the OA and the GS are weighted by the NS using the local and global environmental information and fused through fuzzy set...

In this work we propose a Dynamical Fuzzy Cognitive Map (DFCM) where its casual relationships are based on fuzzy rules, in a way that the structure of the map changes during the phase of execution (runtime). We propose the modification of the values of the relationships between the concepts through of fuzzy rules derived from the concept states that represent the system modeled by the map. Our ...