This paper proposes a hybrid methodology for one-step-ahead torsional torque estimation in an electric drive with an elastic connection. The approach integrates Nonlinear Autoregressive Neural Networks with Exogenous Inputs (NARX NNs) and model-based feedback. The NARX model uses real-time and historical motor speed and torque signals as inputs while leveraging physics-derived torsional torque as a feedback input to refine estimation accuracy and robustness. While model-based methods provide insight into system dynamics, they lack predictive capability—an essential feature for proactive control. Conversely, standalone NARX NNs often suffer from error accumulation and overfitting. The proposed hybrid architecture synergises the adaptive learning of NARX NNs with the fidelity of physics-based feedback, enabling proactive vibration damping. The method was implemented and evaluated on a two-mass drive system using an IP controller and additional torsional torque feedback. Results demonstrate high accuracy and reliability in one-step-ahead torsional torque estimation, enabling effective proactive vibration damping. MATLAB 2024a/Simulink and dSPACE 1103 were used for simulation and hardware-in-the-loop testing.

This paper presents the control problem of a drive system with an electric motor and a finitely stiff shaft. In such a system, effective damping of torsional vibrations requires the use of advanced control structures. Such structures, in turn, require precise information about all state variables, and often also about the system parameters. To ensure effective damping of torsional vibrations, the paper proposes the use of a predictive control algorithm that cooperates with a two-layer observer. Coupling these two algorithms resulted in improved state variable waveforms, particularly for unknown initial states, and the imposition of more stringent state variable constraints.

In this paper, the concept of an estimator of the parameters of the two-mass drive system is presented. Significant changes in parameters, such as the load-side time constant, can lead to large estimation errors and compromised closed-loop performance in drives with an elastic shaft. The proposed multi-layer estimators use multiple internal nonlinear extended Kalman filters (EKF) to enhance the accuracy of state estimation. After a theoretical introduction, different structures were studied with simulation tests conducted in MATLAB/Simulink (2010a) and the effect of the number of filters was presented. The results show that the use of multi-layer observers can improve state estimation and accelerate online identification compared to classical methods. The results are confirmed by experimental validation carried out using dSPACE 1103.