This paper proposes an advanced approach focused on the automatic tuning of fuzzy rules in a Self-Tunable Fuzzy Inference System (STFIS) network. By combining neural network principles with a zero-order Takagi-Sugeno-type FIS (Sugeno FIS), the approach enables continuous adaptation to external disturbances and system changes. The mini-JEAN control architecture optimizes the STFIS rule base in real-time, enabling efficient control of an underactuated, coupled system: the inverted pendulum. A backpropagation gradient descent learning algorithm is proposed to determine the STFIS network's weight values in real-time, supervised by an adaptive mechanism. A clustering algorithm is then applied to organize these values and extract decision rules from the weight data. The performance of the STFIS controller is compared with that of a fixed Rule-Based Fuzzy System (FPID controller). The results demonstrate that the STFIS controller outperforms the FPID controller in terms of efficiency and robustness, particularly when subjected to external disturbances. These findings confirm that the STFIS controller is a promising solution for real-time control applications.