Intelligent probabilistic recurrent fuzzy control of human-machine systems

A novel self-learning control algorithm for human-machine systems is presented. The designed controller is based on a probabilistic extension of recurrent fuzzy systems, which allows the consideration of non-deterministic information in addition to deterministic control signals. The behavior of the controller is adapted by varying the conditional probabilities of state switching, wherefore the automation-like structure of a recurrent fuzzy system is exploited. The adaptation is done by statistically evaluating the results from an objective and a subjective point of view. The developed transient probabilistic recurrent fuzzy controller (TPRFC) considers two control objectives of different time scales. First, the actual control of the mechatronical subsystem and second, the consideration (self-leaning) of disturbances and the user’s idiosyncrasy in a long term. An application of the proposed TP-RFC to a washing machine is shown by simulation.     «

A novel self-learning control algorithm for human-machine systems is presented. The designed controller is based on a probabilistic extension of recurrent fuzzy systems, which allows the consideration of non-deterministic information in addition to deterministic control signals. The behavior of the controller is adapted by varying the conditional probabilities of state switching, wherefore the automation-like structure of a recurrent fuzzy system is exploited. The adaptation is done by statistic...     »