Parameter identification is a critical factor influencing the dynamic performance of permanent magnet synchronous motor (PMSM)-driven servo systems. However, mechanical parameter identification encounters the following challenges: multiple parameters that are significantly mutually coupled and difficulties in direct measurement, especially with friction parameters. Currently, most scholars have navigated these challenges through partial parameter identification, either by ignoring Coulomb friction, neglecting the dynamic processes of Coulomb friction, or identifying only the parameters associated with the friction model. In this study, a scheme using a parallel-cascaded extended sliding-mode observer (PCESMO) is proposed to address these challenges. The PCESMO relies solely on phase currents and rotor position to identify parameters, including the moment of inertia, viscous damping, load torque, and parameters of a simplified LuGre model that considers the dynamic process of Coulomb friction during self-commission. Additionally, the PCESMO has low computational complexity and is easy to implement. The effectiveness of the PCESMO was validated through both simulations and experiments. Compared to other competitive methods, PCESMO-identified parameters result in optimal control effects.

Thermal error has a significant effect on machine tools’ precision. Traditional methods usually establish the thermal error model by temperature. As the temperature only describes the instant state of the machine tool, it is inadequate to clarify the generation process of thermal error. To improve the description completeness of thermal error generation process, the operating condition data such as spindle speed, rotational number and power are employed besides the temperature. Furthermore, Long Short-Term Memory (LSTM) based thermal error model was proposed which combined the operating condition with temperature. This model not only considers the dynamic characteristics of the thermal error, but also selects and remembers the previous thermal error data information. To improve the accuracy for the whole speed range, the K-Means clustering analysis was adopted to obtain the optimal speed categorization, then different LSTM sub-models were established separately. Experiments demonstrated that the predicting accuracy of the proposed K-Means segmental LSTM method (K-S-LSTM) is over 85.0% under different operating conditions. Moreover, comparison experiments indicated that the predicting performance of K-S-LSTM is better than Elman and IRNN models. Finally, the compensation experiment showed that the thermal error can decrease from 49 μ m to 12 μ m after compensation in actual numerical control system.

An identification method is presented for the arctan two-parameter model, which describes the deadtime effect voltage error of PMSM drives' three-phase voltage source inverter (VSI) during self-commission. In this method, the motor maintains a standstill during the whole identification process. First, a disturbance observer is used to improve the tracking performance of the total voltage error. Then, a technique is used to extract the deadtime effect voltage error from the total voltage error without a voltage sensor during the standstill condition. Finally, the core algorithm (utilizing the bisection method) is developed to solve the coupling problem in the two-parameter identification process, such that the parameters in the model converge simultaneously. Furthermore, a sufficient condition for the convergence of the algorithm is given, and a mathematical proof is presented. Experimental results show that the deadtime effect voltage error estimated by our method is closer to the measured voltage error than other state-of-the-art methods proposed in the literature.