Experimental Validation of Robust Backstepping Control for TRMS Using an Interval Type-2 Fuzzy Observer

This research focuses on the trajectory tracking control of a Twin Rotor MIMO System (TRMS) with time-varying sinusoidal inputs. Initial design considerations include a backstepping controller integrated with a high-gain observer (HGO) to estimate unmeasured states. While the outcomes of the simulation show good accuracy of tracking, real-time implementation shows instability and performance degradation. This divergence is attributed to the static high gains of the observer that amplify measurement noise and inject inaccurate state estimates into the controller during actual deployment. To overcome this drawback without altering the core control structure, we propose a strategy of online gain tuning based on Interval Type-2 Takagi–Sugeno (TS) fuzzy logic. The proposed mechanism dynamically adjusts the observer gain based on estimation errors to balance the trade-off between convergence speed and noise sensitivity. Experimental evaluations on the physical TRMS confirm that the fuzzy-tuned observer eliminates instability in real-time. Quantitative analysis demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) by 65.6% in the Pitch axis and 92.3% in the Yaw axis compared to the fixed-gain counterpart.