This article presents an applicable real-time thermal model for the temperature prediction of permanent magnet synchronous motors. The load capacities of most permanent magnet synchronous motors are usually limited by the temperature, and overheating is one of the main reasons for permanent magnet synchronous motors breakdown, so an applicable temperature prediction approach is helpful to improve motor utilization and protect permanent magnet synchronous motors from thermal distortion. Compared with embedding temperature sensors into motor structures, implementing real-time thermal model in motor controllers is a cost-effective and rapid response protection method, but it still faces the challenges on the temperature estimation accuracy, the complexity of the model parameters and the computational efforts. To balance every aspect of these challenges, this article tries a simple real-time thermal model to accurately predict the thermal behavior by elaborately modeling stator core losses and considering motor itself cooling ability. The affections of the motor current and speed on the core losses are analyzed and a polynomial equation is adopted to deal with their dependencies. To simulate the motor speed impact on the cooling ability, motor speed is involved in the variable thermal conductance of the motor housing inside the surroundings by another polynomial equation. This article describes how to get the most parameters of the proposed real-time thermal model through motor basic dimensional information and introduces the test methods employed to determine the parameters of the above two polynomial equations. In the experiments, first the thermal model building process is provided by an actual permanent magnet synchronous motor with two simple tests, and then the online analytical expressions with the obtained parameters are implemented in the drive controller to verify the performance of the proposed real-time thermal model. The results of the performance tests show that the real-time thermal model has a good agreement between estimated and measured temperature values, and its performance can satisfy the most actual applications.

The existence of the resonance is usually a trouble causing instability for most elastic drive systems. Generally, the measurement of original resonance of load side in a drive system is a direct solution for resonance suppression, but exact data are difficult to come by, such as torsional torque, load speed and disturbance torque. Therefore, a developed method for resonance suppression based on adaptive filtered x-least mean square algorithm with improved convergence is presented in this research. The proposed method obtains the resonance iteratively and reduces the significant resonance oscillations through a finite impulse response filter adapted by the least mean square error principle. In order to tackle the convergence speed problem caused by the high dynamics of the forward path model, another finite impulse response filter is inserted into the control structure to smooth the current control reference signal and the speed error signal, so that the dynamics features of the forward path model are improved. Furthermore, a filtered x-least mean square control structure for elastic drive systems is also developed. From the simulation and experimental results, the resonance is more effectively suppressed with proposed modified filtered x-least mean square structure compared with notch filters, and the inserted finite impulse response improves the convergence speed.

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