Inverse fuzzy-process-model based direct adaptive control

This paper proposes a direct adaptive fuzzy-model-based control algorithm. The controller is based on an inverse semi-linguistic fuzzy process model, identified and adapted via input-matching technique. For the adaptation of the fuzzy model a general learning rule has been developed employing gradient-descent algorithm. The on-line learning ability of the fuzzy model allows the controller to be used in applications, where the knowledge to control the process does not exist or the process is subject to changes in its dynamic characteristics. To demonstrate the applicability of the method, a realistic simulation experiments were performed for a non-linear liquid level process. The proposed direct adaptive fuzzy logic controller is shown to be capable of handling non-linear and time-varying systems dynamics, providing good overall system performance.