Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Journal of Intelligent and Robotic Systems
On-line learning of a fuzzy controller for a precise vehicle cruise control system
Expert Systems with Applications: An International Journal
Indirect adaptive control with fuzzy neural networks via kernel smoothing
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
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This paper presents an adaptive control architecture, where evolutionary learning is applied for initial learning and real-time tuning of a fuzzy logic controller. The initial learning phase involves identification of an artificial neural network model of the process and subsequent development of a fuzzy controller with parameters obtained via a genetic search. The neural network model is utilized for evaluating trial fuzzy controllers during the genetic search. The proposed adaptive mechanism is based on the concept of perpetual evolution, where parameters of the fuzzy controller are updated at each time step with solutions extracted from a continuously evolving population of trials. There are two mechanisms that accommodate the real-time changes in the control task and/or the process into the continuous genetic search: a scheme that dynamically modifies the fitness evaluation criteria of the genetic algorithm, and an online learning of the neural network model used for evaluating the trial controllers. The potential of using evolutionary learning for real-time adaptive control is illustrated through computer simulations, where the proposed technique is applied to a chemical process control problem