This study presents an adaptation method for Sugeno fuzzy inference systems that maintain the readability and interpretability of the fuzzy model during and after the learning process. This approach can be used for the modelling of dynamical systems and for building adaptive model-based control algorithms for chemical processes. The gradient-descent based learning algorithm can be used on-line to form an adaptive fuzzy controller-this ability allows these controllers 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. The proposed approach was applied in an internal model (IMC) fuzzy control structure based on the inversion of the fuzzy model. The adaptive fuzzy controller was applied in the control of a non-linear plant and is shown to be capable of providing good overall system performance.