The maximum torque per ampere (MTPA) is popular control strategy for interior permanent synchronous machines (PMSMs) and MTPA point is dependent on the magnetic saturation and magnet temperature. This article proposes a novel MTPA control method combining offline model and online compensation model for interior PMSM control. In the proposed approach, an offline sparse Bayesian based data driven model is derived from the machine equations to consider magnetic saturation, and an online compensation model is proposed to compensate the magnet temperature. The MTPA point can be derived by combining both the offline and online models, in which both saturation and temperature effects are considered to ensure the performance of MTPA point tracking. Compared with the offline methods, the proposed approach employs the sparse vector to represent the MTPA model with less computation and memory consumption and considers the temperature effect with better robustness. Compared with the online methods, the proposed approach only compensates the offline model with online temperature effect, which is less sensitive to noise and uncertainties and involves less computation. The proposed approach is validated with comparisons and experiments on a laboratory interior PMSM drives.

Accurate model of permanent magnet synchronous machine (PMSM) is significant for high-performance control. The machine model can be affected by various factors such as magnetic saturation and core loss effect especially in the deep saturation and high-speed regions. This paper proposes a data-driven based machine modeling and compensation approach to improve the model accuracy by considering saturation and core loss effect. In the proposed approach, magnetic saturation is initially modeled using nonlinear polynomials and core loss effect is modeled with various speed data. The model mismatch due to these effects is then derived to generate the training data for the neural network, which can accurately predict the model mismatch under various operating conditions. In comparison to conventional model, the proposed approach adds compensation terms directly to the machine models, which can achieve better accuracy with efficiency and simple implementation, which can be utilized in the motor control and parameter estimation. The proposed approach is validated on a laboratory interior PMSM and compared with existing methods under various operating conditions.

For permanent magnet synchronous machines (PMSMs), magnetic saturation is a key challenge to parameter estimation and saturation modeling using polynomials is popular with dq- axis inductances estimated independently. However, dq-axis inductances are mathematical terms derived from stator frame and are indeed correlated, which is not fully explored for improving estimation performance. Hence, this paper proposes a correlated inductance model to represent the relationship among dq- axis inductances and model their nonlinear variations with respect to stator currents due to magnetic saturation. The idea is to model the saturation in the stator frame and derive the correlated inductance model in the rotating frame. The estimation model is established based on the correlated inductance model, in which dq- axis inductances are represented using the same components and the correlation can be fully explored to improve the estimation accuracy. Moreover, the proposed estimation approach does not involve the division by the dq- axis currents, which can ensure the estimation accuracy especially under conditions with small current magnitude. While existing methods often involve the division and result in limited accuracy under such conditions. The proposed approach is evaluated on two test machines and compared with existing methods to demonstrate the performance improvement.

This paper proposes a virtual position-offset injection based permanent magnet temperature estimation approach for permanent magnet synchronous machines (PMSMs). The concept of virtual position-offset injection is mathematically transforming the machine model to a virtual frame with a position-offset. The virtual frame temperature estimation model is derived to calculate the permanent magnet temperature (PMT) directly from the measurements with computation efficiency. The estimation model involves a combined inductance term, which can simplify the establishment of saturation compensation model with less measurements. Moreover, resistance and inverter distorted terms are cancelled in the estimation model, which can improve the robustness to the winding temperature rise and inverter distortion. The proposed approach can achieve simplified computation in temperature estimation and reduced memory usage in saturation compensation. While existing model-based approaches could be affected by either the need of resistance and inverter information or complex saturation compensation. Experiments are conducted on the test machine to verify the proposed approach under various operating conditions.

We present a new statistic signal strength model, called Exponential-Rayleigh (ER) model, for device-free (DF) target localization and tracking issues in this paper. It is a single target measurement model for radio frequency (RF) based on received signal strength (RSS) measurement in outdoor regions. The model is a non-linear function between RSS measurements and target motion state. It consists of three parts: the largescale exponential attenuation part, the small-scale Rayleigh enhancement part and the noise. Different from the proposed models, while reserving the large-scale attenuation, we mainly present the small-scale Rayleigh enhancement model in ER model. The Rayleigh part depicts the multi-path caused by single target so as to reduce the multi-path error. In the context of localization and tracking experiment using particle filter, we validate the effectiveness of ER model.

For the permanent magnet synchronous machine (PMSM), parameter estimation can be greatly affected by the measurement uncertainty, but few efforts are made to reduce the uncertainty level for estimation performance improvement. Therefore, this article proposes an efficient and robust incremental Bayesian learning approach for PMSM parameter estimation. The measurement uncertainty is evaluated to guide the selection of informative measurements, and the estimation uncertainty is provided to indicate the confidence in using the estimated results. Specifically, a Bayesian learning strategy with a layered noise model is proposed for nonlinear flux linkage estimation. The measurement uncertainty level is estimated from the proposed Bayesian learning model, which is utilized to adaptively select the most informative data and delete the noninformative data for parameter estimation. This contributes to improving estimation accuracy and computation efficiency. Moreover, the estimation uncertainty is also determined by the proposed model, which can be used to indicate if the estimated results can be trusted and utilized in practical applications. The proposed approach is evaluated on a laboratory interior PMSM under various operating conditions.