Intelligent torque observer combined with backstepping sliding-mode control for two-mass systems

Vo Thanh Ha, Pham Thi Giang


The construction of a backstepping-sliding mode control using a high-gain observer's neural network for torque estimation is presented in this research. The correctness of the load torque data is crucial to solving the two-mass system control issue. The article suggests a radial basis function neural network topology to handle load torque estimation. When a non-rigid drive shaft is present, the predicted value is merged with backstepping-sliding mode control to ensure speed tracking performance. The closed-stability loop is demonstrated analytically and quantitatively to prove it. Additionally, a high-gain observer-based structure is used to compare the effectiveness of the proposed control. The effectiveness of the proposed control structure is demonstrated by MATLAB simulation.


Backstepping method; High-gain observer; RBF-neural network; Sliding mode control; Two-mass system

Full Text:




  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.