Finite-control-set model predictive control (FCS-MPC) has many advantages in electric drive control systems but needs the accurate knowledge of the system parameters. The performance of the FCS-MPC will be deteriorated under parameter mismatches. This study proposes an adaptive FCS-MPC current control method for interior permanent magnet synchronous machine (IPMSM) drives subject to the inductance variations. The inductances are identified online by an adaptive observer with a recursive algorithm, which is inherently incorporated into the FCS-MPC control process to reduce the additional computational cost. Compensation methods are also proposed to improve the identification accuracy. The simulation and experimental results validate that, the IPMSM current control performance, speed-extension capability and drive efficiency are all improved by the proposed method.

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This study used visible/near-infrared hyperspectral imaging (HSI) technology combined with chemometric methods to assess the freshness of pearl gentian grouper. The partial least square discrimination analysis (PLS-DA) and competitive adaptive reweighted sampling-PLS-DA (CARS-PLS-DA) models were used to classify fresh, refrigerated, and frozen–thawed fish. The PLS-DA model achieved better classification of fresh, refrigerated, and frozen–thawed fish with the accuracy of 100%, 96.43%, and 96.43%, respectively. Further, the PLS regression (PLSR) and CARS-PLS regression (CARS-PLSR) models were used to predict the storage time of fish under different storage conditions, and the prediction accuracy was assessed using the prediction correlation coefficients (Rp2), root mean squared error of prediction (RMSEP), and residual predictive deviation (RPD). For the prediction of storage time, the CARS-PLS model presented the better result of room temperature (Rp2 = 0.948, RMSEP = 0.255, RPD = 4.380) and refrigeration (Rp2 = 0.9319, RMSEP = 1.188, RPD = 3.857), while the better prediction of freeze was by obtained by the PLSR model (Rp2 = 0.9250, RMSEP = 2.910, RPD = 3.469). Finally, the visualization of storage time based on the PLSR model under different storage conditions were realized. This study confirmed the potential of HSI as a rapid and non-invasive technique to identify fish freshness.

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