Hongyu Tang

Dual three-phase open-winding permanent-magnet synchronous motor (PMSM) drives offer high DC-bus utilization and fault tolerance, but conventional model predictive current control (MPCC) for this topology suffers from heavy rolling optimization, large harmonic currents, and sensitivity to machine parameters. This paper proposes an incremental global optimization model predictive current control (IGOMPCC) method. First, an incremental prediction equation over adjacent control periods cancels the permanent-magnet flux term in the reference voltage solution, eliminating dependence on the flux-linkage parameter. Second, inverter 1 selects its optimal vector from the 12 outermost large voltage vectors, while inverter 2 locks the reference voltage sector by deadbeat incremental voltage prediction, restricting candidate vector groups to two, with duty cycles solved in closed form by the Lagrange multiplier method; single-cycle cost function evaluations drop from 144 to 3. Third, two adjacent active vectors and a zero vector are synthesized at the analytical duty cycle to fix the equivalent switching frequency, concentrating harmonics and suppressing z₁-z₂ sub-plane currents. Simulation results show that, versus traditional MPCC, the settling time is shortened by 34.1% for a speed step and 20% for a load step, the peak d-axis current fluctuation is reduced by 52.9%, and the q-axis current steady-state error is nearly eliminated.

As the core power equipment of the deep-sea mining system, the deep-sea mineral lifting pump continuously operates under harsh service conditions coupled with high hydrostatic pressure, complex marine environments, and solid particle media. Its operating state shows obvious strong nonlinearity, time-varying fluctuation, and local abrupt change features. Therefore, time series health state prediction of the deep-sea mineral lifting pump is of vital engineering significance for realizing predictive maintenance and ensuring the safety of offshore trials and mining operations. Taking the 500 m-level offshore sea trial conducted in the Xisha area of the South China Sea as the engineering background, four critical health characteristic parameters, including shaft power, pump efficiency, motor winding temperature, and outlet radial vibration, are selected to construct a hybrid CNN-LSTM time series prediction model. Comprehensive model evaluation metrics and ablation comparison experiments are adopted to analyze the multi-step-ahead prediction performance of the proposed model. The results show that the CNN-LSTM model achieves optimal comprehensive evaluation indices in one-step prediction and possesses excellent tracking capability for inflection points and amplitude fluctuations of time series data. Although the prediction accuracy decreases gradually with the increase in prediction steps, the model can still effectively characterize the evolutionary trend of pump operating states, and its overall prediction performance is significantly superior to that of single models. This study provides model support and technical reference for the health evaluation, early fault alarm, and maintenance optimization of deep-sea mineral lifting pumps in offshore trials.

Under the traditional control method, the dual three-phase permanent magnet synchronous motor (DTP–PMSM) has a harmonic plane with low impedance, and it can produce larger harmonic current. Model predictive control (MPC) has a simple control structure and a good dynamic performance. The MPC is usually used in a high-performance control system of multiphase motors. Aiming at the DTP–PMSM drive system, an improved MPC strategy based on the biplane virtual voltage vector is proposed in this paper. In the proposed biplane MPC scheme, the voltage vector of the α–β plane is virtual to 25 voltage vectors, while the voltage vector of the x–y plane is virtual to zero. At the same time, the voltage vector of the x–y plane is virtual to 25 voltage vectors, while the voltage vector of the α–β plane is virtual to zero. On this basis, the cost function of the biplane is evaluated. The operating time and reference voltage of each vector are calculated. The virtual voltage vector on the α–β plane is used for electromechanical energy conversion to generate the best electromagnetic torque and reduce torque ripple. The virtual voltage vector on the x–y plane is used to suppress the stator current harmonics and improve the efficiency of the DTP–PMSM. The simulation and experimental results demonstrate the superiority of the proposed biplane MPC.

Biometrics is widely used in various fields as a means of biological individual identification, and iris recognition is recognized as an efficient, stable, and secure biometric method. However, when the cooperation with users is reduced under complex scenes, the collected iris images are prone to squint or defocus, which causes serious difficulties for iris preprocessing, especially iris location, and segmentation. To solve this problem, we propose a multitask deep learning model based on deep snake. Using the closed-loop prior conditions of the inner and outer boundaries of the iris itself, the active contour method is used to achieve accurate iris location, and semantic segmentation is carried out in the effective region. First, the initial iris location and segmentation results are obtained using the target detection module based on mask regions with convolutional neural networks, then the initial contour is located by poles. The iris boundary is represented by a set of ordered point coordinates through equal spacing sampling. The initial contour is transferred as input to the active contour fitting network based on deep snake to obtain accurate iris location results. Finally, the segmentation results are obtained by the internal and external boundary constraint models. In the segmentation process, efficient channel attention mechanism is introduced to improve the segmentation accuracy and make the network focus on the effective iris region. The SoftPool layer is used to reduce the loss of image detail information caused by pooling, so that the whole network is differentiable. The performance improvements of 3.26%, 3.9%, and 3.45% are obtained on the three squint iris datasets, respectively, which proves the effectiveness of the model in solving the squint iris problem and improves the accuracy and robustness of iris recognition in complex scenes.

This article proposes a decoupled fault-tolerant model predictive current control for a dual three-phase permanent magnet synchronous motor with harmonic compensation. The harmonics caused by nonsinusoidal back electromotive force can be well controlled under the open-circuit fault scenario by designing a harmonic closed-loop scheme. First, a decoupled predictive model and fault-tolerant voltage vectors are deduced based on a reduced-dimension matrix, which can eliminate the coupling issues between the harmonic and fundamental subspaces. Afterward, a uniform harmonic-free virtual vector is constructed by rearranging the irregular voltage vectors. More importantly, a virtual null vector is designed in the scheme, where the virtual null vector is effective in the harmonic subspace and has no components in the fundamental subspace. So, the harmonics can be further controlled without affecting torque and flux generation. Extensive experimental results verify the effectiveness of the proposed method.

Photosynthetic bacteria wastewater treatment is an efficient water pollution treatment method, but photosynthetic bacteria fermentation is a multivariable, non-linear, and time-varying process. So it is difficult to establish an accurate model. Aiming at the difficulty of online measurement of key parameters, such as bacterial concentration and matrix concentration in photosynthetic bacteria fermentation process, an improved ant colony algorithm least squares support vector machine (AC-LSSVM) soft sensing model method is proposed in this paper. Firstly, the virtual sensing subsystem of the photosynthetic bacteria fermentation process is proposed, with measurable parameters as input and unmeasurable key parameters as output, and the left inverse soft sensing model of virtual sensing is constructed. Then, the ant colony algorithm can quickly find the shortest path to optimize the parameters of the traditional PI regulation, to improve the dynamic performance and accuracy of parameter measurement in the fermentation process. After that, the ant colony algorithm is used to optimize penalty parameters C and kernel parameters σ of LSSVM, which effectively avoids the local optimization and improves the computing power and global optimization ability. Finally, the soft sensing prediction model of the photosynthetic bacteria fermentation process based on AC-LSSVM is established. Compared with SVM and LSSVM prediction models, the root mean square error of bacterial concentration and matrix concentration based on the AC-LSSVM model are 0.468 and 0.126, respectively. The simulation analysis shows that this model has less error and better prediction ability, and it can meet the needs of online prediction of key parameters of photosynthetic bacteria fermentation.

Straw fermented fuel ethanol is a complex process with multivariable, large lag, and strong nonlinearity. It is difficult to directly measure the key parameters such as ethanol concentration and cell concentration online. Aiming at the problem, a soft sensing model of straw fermented ethanol based on improved support vector regression (SVR) is proposed. Based on the analysis of the process of ethanol production from straw fermentation, the Bayesian method is used to optimize the support vector regression (BSVR). And the concepts of generation a priori and generation likelihood are introduced to optimize the data prediction model. The comparative experiment of model training and testing is carried out. The simulation results show that the proposed BSVR method is better than SVR. It can improve the generalization ability of data and the anti-interference of the model, and its prediction accuracy and stability are higher.

Marine alkaline protease (MAP) fermentation is a complex multivariable, multi-coupled, and nonlinear process. Some unmeasured parameters will affect the quality of protease. Aiming at the problem that some parameters are difficult to be detected online, a soft sensing modeling method based on improved Krill Herd algorithm RBF neural network (LKH-RBFNN) is proposed in this paper. Based on the multi-parameter RBFNN model, the adaptive RBF neural network algorithm and control law are used to approximate the unknown parameters. The adaptive Levy flight strategy is used to improve the traditional Krill Herd algorithm, improve the global search ability of the algorithm, and avoid falling into local optimization. At the same time, the location update formula of Krill Herd algorithm is improved by using the calculation methods of similarity and agglomeration degree, and the parameters of adaptive RBFNN are optimized to improve its over correction and large amount of calculation. Finally, the soft sensing prediction model of bacterial concentration and relative active enzyme in map process based on LKH-RBFNN is established. The root mean square error and maximum absolute error of this model are 0.938 and 0.569, respectively, which are less than KH-RBFNN and PSO-RBFNN prediction models. It proves that the prediction error of LKH-RBFNN model is smaller and can meet the needs of online prediction of key parameters of map fermentation.

The conventional duty ratio modulation based direct torque control (DR-DTC) for the dual three-phase permanent magnet (DTP-PM) motor reduces the torque ripple using duty ratio modulation in torque control. However, the flux was controlled by a hysteresis comparator, resulting in deficient steady-state performance. This paper proposes a novel DR-DTC strategy to improve torque and flux simultaneously. Firstly, a switching table with the multiple vector selection is newly established. Two adjacent virtual voltage vectors and a null vector are selected in each control period. Then, the duty ratios of selected adjacent virtual voltage vectors are optimized with the dual objective modulation considering torque performance and flux performance. In addition, the combination of adjacent virtual voltage vectors for the DTP-PM motor generates non-standard symmetrical switching sequences. Two switching sequences generated methods are further investigated to address the issue. So, the switching sequences of the proposed strategy can also be generated easily. Finally, the conventional strategy and the proposed strategy are compared through experiments, and the experimental results verify the effectiveness of the proposed strategy.