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

Abstract

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.