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Chemical mechanical planarization (CMP) is vital for ensuring chip fabrication uniformity at nanometer scales. The emergence of a series of phenomenological CMP process models (Stine et al., 1997; Gbondo-Tugbawa, 2002; Xie, 2007; Vasilev, 2011) suggests that the existing model upgrade approach is largely based on a change in phenomenological model assumptions, demanding deep insights into complex process mechanisms and protracted period for accuracy improvements. To tackle this issue, this paper proposes a decoupling regression strategy for model upgrades. This strategy employs a data-driven approach to enhance the coupling relationships within the model, facilitating continuous improvement of simulation accuracy based on the existing model. It is capable of achieving improvements in model accuracy even in scenarios where modelers lack insight into complex process mechanisms. We validate our method by upgrading the Density Step Height (DSH) model to the Extend-DSH model to address poor erosion predictions at the 28nm node. Comparing model predictions with silicon data reveals that the Extend-DSH model aligns better with the measured data, reducing the root mean square error from 159.31Å to 6.89Å and increasing the coefficient of determination from -0.83561 to 0.6058, showcasing the effectiveness of the proposed chip-level CMP model upgrade method grounded in the decoupling regression strategy. [ABSTRACT FROM AUTHOR]

It is known that the determination of the good-dice-in-bad-neighborhoods (GDBNs) has been regarded as an effective technique to reduce the value of the defect parts per million (DPPM) by identifying and rejecting the suspicious dice even though they are good in testing. Instead of examining eight immediate neighbors in a small-sized $3\times 3$ window or exploiting simple linear regression, a large-sized window can be used to recognize the broad-sighted neighborhoods and accurately infer the suspiciousness level for any given die. In this paper, the artificial neural networks (ANN)-based method can be proposed to solve the GDBN identification. Furthermore, two enhanced techniques can be further presented to improve the inference accuracy of the original ANN-based method by considering the variation of the time-dependent wafer patterns and the wafer-to-wafer relationship between two adjacent wafers. After applying the two enhanced techniques, the business profits can be improved in the new ANN-based method. Various experiments on two datasets clearly reveal the superiority of the proposed ANN-based method over the other existing methods. In addition to the reduction of the DPPM value, the new ANN-based method can achieve the 1.5X–2X better reduction in the cost of the return merchandise authorization (RMA). On the other hand, the experimental results show that the similar result can also be obtained in the other lower-yield products. By using the new ANN-based method, the relationships on bad dice cross wafers can be captured and the highly-accurate inference results can be simultaneously maintained. [ABSTRACT FROM AUTHOR]

Research in agriculture is a promising field, and crop prediction for particular land areas is especially critical to agriculture. Such prediction depends on the soil, minerals, and environment, the last of which has been short-changed by changing climatic conditions. Consequently, crop prediction for a particular zone presents difficulties for farmers. This is where machine learning (ML) steps in with techniques that are widely applied in agriculture. This work proposes a weighted stacked ensemble (WSE) method for the crop prediction process. It combines two base learners or classifiers to construct the WSE, which is a single predictive ensemble model, using weighted instances. The experimental outcomes show that the proposed WSE outperforms other classification and ensemble techniques in terms of improved crop prediction accuracy. [ABSTRACT FROM AUTHOR]

The adoption of adaptive neuro-fuzzy inference systems (ANFIS) and metaheuristic optimization approaches has been widely observed in recent research. Even so, integrating these methods improves the model’s capability to solve complex problems. A novel enhanced prediction method based on COOT bird optimization was developed for selecting the optimal parameters of ANFIS in the current study. This method combines COOT optimization with ANFIS to model the quality of service (QoS) characteristics of web services by using the adaptive neuro-fuzzy inference system COOT (ANFIS-COOT). In this instance, the quality of the web service (QWS) dataset was obtained from the GitHub database, which consists of 120 web services data, and then evaluated using the presented model on the dataset for estimating response time and throughput of web services. As significant evidence of ANFIS-COOT’s efficiency, the similar QWS data set is analyzed using four different prediction models: ANFIS, ANFIS-Beetle Antennae Search (ANFIS-BAS), ANFIS-Reptile Search Algorithm (ANFIS-RSA), and ANFIS-Snake Optimizer (ANFIS-SO). Moreover, the exploratory study used statistical benchmarks such as root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and determination coefficient ( $R^{2}$ ) to emphasize the accuracy of the proposed model. Based on analysis results, the presented model achieved optimal values of RMSE (59.7473), MAE (15.8531), MAPE (0.0705), and $R^{2}$ of 96.32 %, as well as RMSE (1.335), MAE (1.1255), MAPE (0.1818), and $R^{2}$ of 97.12 % for modelling response time and throughput of web services, compared to other models. Eventually, this report demonstrates the viability of the ANFIS-COOT while tackling a complex problem and improving predictive performance.

This paper proposes a linear regression model for scalar-valued responses and two-way functional (bivariate) predictors. Our motivation stems from the quality evaluation of products based on optical emission spectroscopy data from virtual metrology of semiconductor manufacturing. We focus on multivariate cases where the smoothness and shapes of the data vary significantly across variables. We propose a two-step solution to this problem, consisting of decomposition and prediction. First, we decompose the two-way functional data into pairs of component functions using functional singular value decomposition. Next, we build functional linear models for the decomposed functional variables and obtain the final predictor by averaging the models. Results from numerical studies, including simulation studies and real data analysis, demonstrate the promising empirical properties of the proposed approach, especially when the number of predictors is large. [ABSTRACT FROM AUTHOR]

Doing high throughput high accuracy metrology in small geometries is challenging. One approach is to build easily measurable proxy targets onto dies and make a predictive model based on those signals. We use optical Pattern Shift Response (PSR) proxy targets to build predictive models of the electrical characteristics of devices in the Back End Of Line (BEOL). Given the wide choice of PSR targets, we explore how to select combinations of them to maximise the utility of the features for building an accurate Machine Learning (ML) model; we call this approach Multiplexed Optical Metrology. We also explore the trade-off between chip area dedicated to targets and achievable accuracy. We run ML experiments using different selections of targets measured at different stages of BEOL processing: post-lithography and post-Chemical-Mechanical-Planarisation (CMP). Our results show that a) reasonable predictive performance can be achieved for a reasonable area budget; b) ML model performance across target families varies significantly, thus justifying the need for careful selection of targets; c) longitudinal measurements of targets increases accuracy for no extra area penalty; d) increasing the number of targets gives some improvement in accuracy for a dataset of this size, but relatively small compared to the increase in area budget needed. Ultimately we aim to do die-level yield prediction using these techniques. We discuss how collecting a larger dataset with appropriate yield information is the logical next step to achieving this. [ABSTRACT FROM AUTHOR]

In motion-control applications, noise and dynamic nonlinearities influence the performance of control systems and lead to unpredictable disturbances. The dc servo motors used in motion control applications should have precise control methods to achieve the desired responses. Therefore, predicting and compensating for the disturbance are essential for increasing system robustness and achieving high precision and fast reaction. This article introduces the polynomial predictive filtering (PPF) method to estimate the states of a system using polynomial extrapolation of consecutive and evenly spaced sensor data. Acceleration-/torque-based experiments are conducted to validate the effectiveness and viability of the proposed method. The difference between the real-time sensor data and the PPF-based predicted value shows a standard deviation of less than 0.15 and $1 \times 10^{-5}$ for the velocity and disturbance torque, respectively. [ABSTRACT FROM AUTHOR]

A direct speed control strategy based on finite control set model predictive control (FCS–MPC) with a voltage smoother is presented herein. In the proposed concept, a finite set of smoothed voltage vectors characterized by an adjustable amplitude and a movable origin is introduced as voltage candidates in the FCS–MPC scheme. The controller predicts the future current and speed and outputs the optimal smoothed voltage using pulse-width modulation. Owing to this control scheme, an abrupt change in the output voltage, which causes a large current ripple, is avoided without additional computational costs. Simulation and experimental results obtained using a permanent magnet synchronous motor fed by a two-level three-phase inverter show that the proposed method effectively reduces the current ripple while achieving a high dynamic drive compared with the conventional FCS–MPC scheme. [ABSTRACT FROM AUTHOR]

The current error is hard to avoid for finite control set model predictive current control (FCS-MPCC) in nine-phase open-end winding permanent magnet synchronous motors. To overcome this problem, an online virtual voltage vector (V $^{3}$) synthesis strategy is proposed in this article. First, a group of V $^{3}$ s without harmonic voltage components are designed as the basic vectors for online synthesis. Then, two adjacent basic V $^{3}$ s and a zero vector are used to synthesize a new V $^{3}$ , which can output arbitrary amplitude and phase angle in the fundamental space. The two basic V $^{3}$ s are directly selected from the located sector of the predicted reference voltage vector (RVV), and their duration ratio can be simply calculated according to the angle of the RVV in the sector. In this way, the zero error is realized between the new V $^{3}$ and RVV. Moreover, an online pulse generation algorithm corresponding to the new V $^{3}$ is proposed, which can calculate the symmetrical pulse sequences in real-time for the multiphase OW drive system without the space vector modulator. Finally, experimental results have verified the effectiveness and superiority of the proposed strategy, in comparison to existing FCS-MPCC in multiphase OW motor drive systems. [ABSTRACT FROM AUTHOR]

In order to relieve the computational burden and reduce the ripples in thrust force and current of linear vernier permanent-magnet motors, this article proposes three-vector-based model predictive current control (MPCC) with efficient voltage vectors selection. The proposed method avoids the traversal of all the possible voltage vectors. Only three nonadjacent active voltage vectors are predicted and evaluated by the cost function. Two active voltage vectors can be precisely determined according to the relationship of three cost function values. The prediction workload can be reduced with the efficient voltage vectors selection. Also, the two active voltage vectors along with the null voltage vector are used to synthesize the final applied voltage vector based on deadbeat principle. The proposed three-vector-based MPCC method extends the modulation area from discrete points to the whole regular hexagon. Thus, the ripples in thrust force and current can be minimized. Theoretical analyses and experimental results are given to verify the effectiveness of the proposed MPCC. [ABSTRACT FROM AUTHOR]

Wire and arc additive manufacturing (WAAM) is a promising method for directly manufacturing parts with complex shapes. However, the accuracy of the existing welding parameter planning methods would dramatically decrease when the bead geometry changes dynamically due to the long-term dependence, strong coupling, and hysteresis properties of the WAAM process. To this end, a nonautoregressive dynamic model is proposed to predict the bead geometry, and an adaptive model predictive control (aMPC) method is proposed to plan welding parameters to achieve high manufacturing accuracy. First, in the proposed dynamic model, the long-term dynamic characteristics of the WAAM process are modeled by a resample long short-term memory network by considering the fluidity of the welding pool, which is the crucial factor of dynamic characteristics of the welding process. Second, in the proposed aMPC method, the strong coupling is addressed by high multiobjective performance, and the hysteresis is considered by a long control horizon. Thus, the aMPC method can reduce the control latency and improve the manufacturing accuracy for varying geometry beads. The proposed methods are validated by experiments, which indicate that the proposed methods are effective. [ABSTRACT FROM AUTHOR]

The parameter estimators and the disturbance observers are two widely used methods for the robustness improvement of the model predictive control schemes. This article presents a hybrid solution to improve the robustness of predictive torque control (PTC) for induction motor (IM) drive. A novel integral sliding mode observer (ISMO)-based ultralocal model and an adaptive observer are combined in the proposed method to establish a robust prediction model for the PTC. The stator current prediction model of the conventional PTC contains different parameters and variables of the IM that increase the sensitivity of the method. The proposed method solves this problem by replacing the conventional stator current prediction model with the ISMO-based ultralocal model, which does not require the IM’s parameters. On the other hand, the stator flux prediction model of the PTC just depends on the stator resistance. So, an adaptive Luenberger observer is utilized to cancel the effect of resistance variation from the stator flux prediction model. The proposed ISMO and the Luenberger observer are constructed based on the Lyapunov theory to guarantee the stability of the proposed control method. The experimental validation of the proposed method is performed. Also, the robustness of this method has been validated experimentally. [ABSTRACT FROM AUTHOR]

In this article, a low computational burden model predictive control (MPC) strategy without weighting factor is proposed for the single-phase cascaded H-bridge CHB converters. To reduce the switching state candidates, a hierarchy control algorithm is proposed. The grid current is controlled by selecting a subregion from the designed 2-D control plane, instead of the entire area. Two vectors are chosen in one sampling period for more accurate tracking. Then, the voltage balancing is achieved by selecting the optimal switching state from the subregion candidates to form the above two vectors. The cost function can be constructed of one variable: load voltage. Therefore, the weighting factor can be eliminated. No tuning or retuning processes are required in the proposed method. To reduce the computational time further, the principle of eliminating the switching state candidates operating the same voltage balancing performance is proposed. Conventional and proposed MPC methods are verified by experimental tests via a laboratory setup of a three-cell connected CHB converter. Steady-state and transient operations demonstrate that the proposed method guarantees less distortion grid current and shorter execution time (reduced from 15 to 3 $\mu s$). Fast response speed to variations in voltage reference and load resistance can be achieved t. [ABSTRACT FROM AUTHOR]

In this article, two dual-loop control methods are proposed for attitude control and reaction wheels momentum management of the spacecraft simulator by assimilating the advantages of sliding mode and model predictive control (MPC). In the first method, by utilizing the high accuracy and constrained features of the MPC, an optimal controller is designed in the inner loop. MPC drives the angular velocity to the desired value while keeping the reaction wheel velocity to a small region near the origin, considering the constraints of the actuators. Furthermore, based on the fixed-time terminal sliding mode (TSM) the proper angular velocity is produced in the outer layer of this composite controller for the MPC part. In the second method, contrary to the first one, in the exterior loop MPC and in the interior loop finite-time TSM have been designed. The incorporated twofold nonlinear controllers’ policy insures a good dynamic and robustness against external disturbances. The high yielding of the control algorithms has been analyzed by simulation and experimental validation utilizing MATLAB software with real-time connection based on LabVIEW. [ABSTRACT FROM AUTHOR]

Redundant manipulators have been investigated and employed in various fields, and its trajectory tracking is of much importance in the field of robotic control. In this article, a model predictive control (MPC) scheme for the trajectory tracking of redundant manipulators is constructed, which minimizes the tracking error, velocity norm, and acceleration norm simultaneously. The commonly used trajectory tracking schemes for redundant manipulators, such as the minimum-velocity-norm scheme and minimum-acceleration-norm scheme, handle joint limits at different levels by introducing additional parameters, which reduces the feasible region of decision variables. In contrast, the proposed scheme directly considers these limits at three different levels, without reducing the feasible region of decision variables. In addition, to compensate for the deficiencies of most existing algorithms in noise environments, an error-summation enhanced Newton (ESEN) algorithm is proposed for solving the MPC scheme. Through theoretical analysis, it is determined that the proposed ESEN algorithm has a small steady-state error under noise conditions. Finally, in contrast with the comparative trajectory tracking schemes, as determined through computer simulations and experiments, the proposed MPC scheme solved by the ESEN algorithm not only enables the redundant manipulator to perform the trajectory tracking task in excellent fashion, but also offers advantages of high efficiency, fast responsiveness, and strong noise tolerance. [ABSTRACT FROM AUTHOR]

This article proposes a lateral vehicle trajectory planning and control algorithm using a model predictive control (MPC) scheme for an automated perpendicular parking system. Previous work showed that approximated clothoid-based local path planning using a virtual towing distance provides low computational time and the stability of lateral vehicle motion in a parking system. However, this approach cannot function in certain parking situations and may cause undesirable steering maneuvers due to state-dependent planning. We present a new approximated clothoid path based on lateral vehicle kinematics to cope with these problems. Using the proposed kinematic model, we formulate an MPC problem for path planning. Then, the proposed lateral vehicle trajectory planning becomes an MPC problem under constraints. Besides, we show that lateral vehicle motion for vertical parking can be implemented using simple kinematic lateral motion control. Experimental results show that the vehicle with the proposed method moves smoothly and completes the given parking mission under constraints, even under tight parking space conditions. [ABSTRACT FROM AUTHOR]

The dual-motor drive is common in many heavy-duty swinging arm servo systems. A well-tuned dual-motor drive can provide better stability and durability than the regular single motor servo. However, the backlash is usually unavoidable in the heavy transmission chains. In this article, the issues provoked by the backlash are resolved by a virtual average motor model and a simple anti-backlash torsion torque. The transient performance is guaranteed by the dual-motor synchronous drive, and the anti-backlash strategy is only enabled when the load is approaching the reference position. The challenge is that the motion system structure is transforming during this transition. The model predictive control combined with the extended state observer is applied to get adaptive in this scenario. The expected effect is that the servo can track the desired position with high precision and high response. The scheme is comprehensively validated and evaluated in an experimental servo setup with a dual-motor drive. [ABSTRACT FROM AUTHOR]

Humans can simultaneously exert force and maintain motion states on both arms with synchronous attributes to accomplish interactive cooperation tasks. To endow dual robots with the same remarkable capability, a multi-objective synchronization control scheme is investigated in this article. The feedback laws for each robot are twofold. Specifically, the integral of the past synchronization force errors is adopted to design an impedance-based force feedback law. Moreover, the integral of the future predicted synchronization motion errors is used to establish a motion feedback law based on the nonlinear model predictive policy. The stability of the closed-loop system is theoretically proven. The practical performance of the proposed method is verified by a dual-robot mirror grinding experiment, where each robot exhibits high-accuracy motion capability and force compliance behavior. The experiment results show that the synchronization accuracy of position, velocity, and force is within 1 mm, 0.1 mm/s, and 1 N, respectively. [ABSTRACT FROM AUTHOR]

In this article presents a trajectory planning approach toward safe and smooth robot motion in dynamic environments. We develop a hierarchical planning framework with a global planner generating the shortest path between the robot and the navigation target. Specially, a virtual target (VT) is set to run on the global path with a designed velocity. At the local level, the robot chases the VT and tracks the global path when traveling through the dynamic environment. We employ the model predictive control (MPC) framework for the local path generation. In particular, the prediction horizon of the MPC is adaptively changed concerning the distance between the robot and the VT. It implicitly reflects the crowdedness of the environment, which helps reduce the environmental uncertainty. Besides, we develop an event-triggered mechanism that executes the local plan aperiodically to release the computational burden. Based on the local chasing and tracking performance, we develop a global path replanning scheme in response to the untraversable area emerging in the dense environment. The developed framework is validated through extensive experiments in dynamic environments, demonstrating that the robot can reach the target faster and showcase a safer and smoother trajectory in the navigation. [ABSTRACT FROM AUTHOR]

A finite-set model predictive control (FS-MPC) with coupling capacitor voltage observer scheme is proposed for thyristor-controlled LC-coupling hybrid active power filter (TCLC-HAPF) in this article. It ensures the fast transient response, low steady-state error, and good robustness under both inductive and capacitive load situations. Generally, the implementation of FS-MPC for TCLC-HAPF requires sensing of coupling capacitor voltage and thyristor-controlled reactor current in addition to sensing load voltage, load current, and compensation current, which increases the system complexity and cost. To relax this problem, the simplified predictive model is first proposed, thus reducing the required sensing signals and system order. Then, a coupling capacitor voltage observer is utilized to reduce the hardware costs, which can obtain a comparable performance with directly sensed capacitor voltage. The delay compensation is also considered for the control delay in practical realization. Finally, the effectiveness and performance of the FS-MPC with coupling capacitor voltage observer for TCLC-HAPF are verified by simulated and experimental results. [ABSTRACT FROM AUTHOR]

In this letter, a neural predictor-based dynamic surface predictive control framework, endowed with the merits of adaptive dynamic surface control and finite control-set model predictive control, is proposed where the estimation of neural predictor is incorporated to identify the system dynamics and lumped unknown uncertainties. The key features of the proposal are that, first, the issue of “explosion of complexity” inherent in the classical back-stepping control is avoided, second, the model uncertainties and disturbances are explicitly dealt with, and, third, the tedious determination procedure of weighting factors is removed. These features lead to a much simpler adaptive predictive control solution, which is convenient to implement in applications. Furthermore, a Lyapunov function is constructed, and the stability analysis is given. It demonstrates that all signals in the closed-loop system are uniformly ultimately bounded. Finally, this proposal is experimentally assessed, where the performance evaluation of steady-state and transient-state confirms the availability of the proposed solution for modular multilevel converter. [ABSTRACT FROM AUTHOR]

In this article, we present the exact transient solution to the unbalanced and balanced faults in the doubly fed induction machine (DFIM). Stator currents, rotor currents, and stator fluxes have been validated using simulation and experiment. The work is meant to strengthen and fasten the predictability of large DFIMs in the design stage to comply with mechanical constraints or grid fault issues. Moreover, the analytical approach reduces the computational costs of large-scale stability studies and is especially suited to the initial phase, where plethora design computations must be carried out for the DFIM before it is checked for its transient interaction with the power system. The possibility to dynamically estimate the DFIM performance is simplified by original equations derived from first principles. First, case studies of two large $265 \,{\rm MVA}$ DFIMs are used to verify the analytical approach, and to justify the proposed “large machine approximation” using simulation with an exact match. Finally, laboratory measurements were conducted on a 10.96 and a $1.94 \,{\rm kVA}$ DFIM to validate the transient current peaks predicted in the proposed analytic expressions for two-phase and three-phase faults, respectively. [ABSTRACT FROM AUTHOR]

With the computing power of High-Performance Computing (HPC) systems having stepped into the exascale era, more complex problems can be solved with scientific applications on a large scale. However, due to the significant performance gap between computing nodes and storage subsystems, suboptimal design for the Input/Output (I/O) module will significantly impede the efficiency of scientific applications, especially for the ubiquitous atmosphere applications. Two-phase I/O implemented in N-to-1 mode creates a serious bottleneck that hinders the scalability for the Model for Prediction Across Scales-Atmosphere (MPAS-A) on the new generation Sunway supercomputer. To address the I/O problem, we apply a custom data reorganization method to enable N-to-M I/O mode to exploit the parallel file system's performance and limit the data transfer among MPI ranks to a restricted scope to alleviate communication overhead. Moreover, we have conducted several methods to accelerate the computations, including the redesign for tracer transport, a hybrid buffering scheme, and a three-level parallelization scheme, which allows MPAS-A to use all heterogeneous computing resources efficiently. Experimental results show admirable scalability and efficiency of our I/O method, which achieves speedups of 41× and 58.9× for input and output compared with the raw I/O method on 30,000 MPI ranks. By scaling MPAS-A to 39 million heterogeneous cores, we demonstrate the necessity of a well-constructed I/O module for a real-world atmosphere application. Speed tests show that our optimization methods obtain good results for computations, and MPAS-A achieves a speed of 0.82 Simulated Day per Hour (SDPH) and 0.76 parallel efficiency of strong scaling with 600,000 MPI ranks. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have the powerful ability to automatically extract efficient features, which makes them prominent in electroencephalogram (EEG) based seizure prediction tasks. However, current research in this field cannot take the model uncertainty into account, causing the prediction less credible. To this end, we introduce a novel end-to-end patient-specific seizure prediction framework via model uncertainty learning. Specifically, we propose a reparameterized EEG-based lightweight CNN architecture and a modified Monte Carlo dropout (RepNet-MMCD) strategy to improve the reliability of the DNNs-based model. In RepNet, we obtain multi-scale feature representations by applying depthwise separable convolutions of different kernels. After training, depthwise convolutions with different scales are equivalently converted into a single convolution layer, which can greatly reduce computational budgets without losing model performance. In addition, we propose a modified Monte Carlo (MMCD) strategy, leveraging the samples-based temporal information in EEG signals to simulate the Monte Carlo dropout sampling. Sensitivity, false-positive rate (FPR), and area under curve (AUC) of the proposed RepNet-MMCD achieve 93.1%, 0.033/h, 0.950 and 81.6%, 0.056/h, 0.903 on two public datasets, respectively. We further extend the MMCD strategy to the other baseline methods, which can improve the performance of seizure prediction by a clear margin. [ABSTRACT FROM AUTHOR]

In partial label learning, a multi-class classifier is learned from the ambiguous supervision where each training example is associated with a set of candidate labels among which only one is valid. An intuitive way to deal with this problem is label disambiguation, i.e., differentiating the labeling confidences of different candidate labels so as to try to recover ground-truth labeling information. Recently, feature-aware label disambiguation has been proposed which utilizes the graph structure of feature space to generate labeling confidences over candidate labels. Nevertheless, the existence of noises and outliers in training data makes the graph structure derived from original feature space less reliable. In this paper, a novel partial label learning approach based on adaptive graph guided disambiguation is proposed, which is shown to be more effective in revealing the intrinsic manifold structure among training examples. Other than the sequential disambiguation-then-induction learning strategy, the proposed approach jointly performs adaptive graph construction, candidate label disambiguation and predictive model induction with alternating optimization. Furthermore, we consider the particular human-in-the-loop framework in which a learner is allowed to actively query some ambiguously labeled examples for manual disambiguation. Extensive experiments clearly validate the effectiveness of adaptive graph guided disambiguation for learning from partial label examples. [ABSTRACT FROM AUTHOR]

In this letter, a simplified single current loop control scheme for single-phase dual-boost inverter has been developed, combining half cycle modulation and virtual-vector (VV) based model predictive control (MPC). As the control reference can be slipped and allocated into two side boost, only a unified grid current reference is relied on in this proposed control method. Furthermore, with the concept of feedforward control, the global optimal solution space in MPC can be compressed, taking the duty ratio as boundary for the precompacted solution space. Thereby, the improved MPC can select the optimal VV in a smaller and closer subspace. Consequently, much finer control effect can be obtained. Experimental tests are provided to validate the proposed solution and its merits. [ABSTRACT FROM AUTHOR]

In this article, a robust cascaded deadbeat predictive speed control method (PSC) is proposed with speed and disturbance observer for the dual three-phase variable-flux permanent-magnet synchronous motor (VF-PMSM) drives. Considering the intrinsic delay in speed loop, an improved predictive speed model has been derived in discrete domain. In turn, a speed observer is proposed for the cascaded PSC to mitigate the issue of this intrinsic delay. In order to improve the system robustness, the torque disturbance observer including the generalized proportional integral observer and the sliding-mode observer has been proposed for parameter variation and load torque perturbation. Furthermore, the stability analysis indicates that the margin of parameter mismatch is determined by the coefficient in the speed observer, which can be expressed as the equivalent gain in discrete domain. The performance comparison between the conventional PSC method and the proposed PSC method has been investigated for parameter robustness. The experimental results are presented to verify the improvements of the proposed PSC method for the dual three-phase VF-PMSM drives under model uncertainty and magnetization manipulation conditions. [ABSTRACT FROM AUTHOR]

Conventional predictive position control (PPC) method balances the position, speed and current of permanent magnet synchronous machine with different weighting factors. However, tedious tuning work of weighting factors is needed. To simplify the tuning process, a cost function that includes the system position, and current information is constructed based on deadbeat control method. A novel method based voltage boundary is proposed to limit the current and speed simultaneously, the voltage regions corresponding to current and speed constraints are analyzed and the voltage duty cycle is further modified according to the voltage regions, the complex weighting factor turning work is avoided, the current and speed limits are easy to implement with proposed method and less position error can be achieved. Finally, the proposed method is experimentally compared with a conventional finite-control-set PPC method and a direct speed model predictive control method. [ABSTRACT FROM AUTHOR]

This article presents a novel Lyapunov-based model predictive control strategy for squirrel-cage induction motor fed by a voltage source inverter. The model predictive control method has received enormous attention thanks to its rapid response to load perturbations. However, the traditional model predictive control method may suffer from instability due to the poor choice of the objective function, weighting factors, or other design parameters. In particular, the selection of the objective function may not be sufficient to ensure global stability. Since the performance of the model predictive control method highly relies on the explicit model of the system, a simple penalization term in the objective function can lead to system instability. As a result, the transient and steady-state performances are negatively affected. The objective function is reformulated as the Lyapunov energy function to deal with this ambiguous situation in this article. The control input constraints are defined in the formulated optimal control problem, and the optimal solution is explored by assessing the Lyapunov stability criterion. The proposed method ensures asymptotic stability since the control action that satisfies the Lyapunov stability condition is selected. The experimental work proves the theoretical concepts. Moreover, the proposed method does not require the weighting factors to control the multiple control goals. The experimental results demonstrate that the proposed control strategy improves the steady-state performance. The machine torque and speed are well regulated, and the quality of the stator current is improved. [ABSTRACT FROM AUTHOR]

In this work, we develop a zero-sequence model to analyze the zero-sequence current, i.e., circulating current, in multiple paralleled power converters. On this basis, we propose and verify a novel and simple solution: predictive zero-sequence current control (PZSC $^2$) to freely mitigate the zero-sequence current. The proposed technique is implemented in a direct model predictive control framework via a synthesized voltage vector, which consists of an optimal shrunken voltage vector and a zero voltage vector. The former is selected to minimize the tracking error of the $\alpha \beta$ -axis current, while the latter is properly selected to mitigate the circulating current. Notably, the zero voltage vector is solely determined by the zero-sequence current direction, without using other converters’ operating knowledge. This permits the proposed PZSC $^2$ to operate in a self-governing manner, fitting the spatially distributed applications. We validate the proposed solution through a lab-constructed hardware test bench. Experimental results verify its effectiveness considering both steady-state and transient performance. [ABSTRACT FROM AUTHOR]

Three-phase voltage source inverters (VSIs) with output $ LC$ filter are preferred topologies to provide voltage and frequency support for islanded ac microgrids. This article proposes a finite-set quasi-sliding model predictive control (FS-QSMPC) scheme for $LC$ -filtered VSIs to improve the output-voltage quality. By explicitly including a predictive sliding-mode function into the cost function, reduced steady-state ripple and enhanced robustness are achieved compared to typical FS-MPC. Besides, theoretical analysis of stability and robustness for FS-QSMPC is given. Analytical tuning of the weighting factor is also derived to decrease the tuning effort. Comparative simulations and experiments verify the presented approach. [ABSTRACT FROM AUTHOR]

The stability of power electronic devices is prone to instability when supplying constant power loads (CPL) due to their negative impedance specifications. This necessitates an efficient control methodology to overcome the destabilization issue and regulate the response of the output voltage. In particular, this article proposes a novel interval type-III fuzzy neural network-based model predictive control (IT3FNNMPC) to mitigate the destructive effects of CPL in dc/dc boost converters implemented in the microgrid (MG) and power system distribution. In a typical MGs configuration, the IT3FNNMPC controller is formulated and designed in a constraint control problem to achieve a fast-transient response of system voltage. The feasibility and applicability of the designed IT3FNNMPC controller have been validated by hardware-in-the-loop real-time simulations. [ABSTRACT FROM AUTHOR]

Discrete space vector modulation (DSVM) is employed to synthesize large number of voltage vectors (VVs) to improve the steady-state performance of predictive torque control (PTC). However, enumerating all the VVs in the prediction process increases the computation load of the motor drive. To address this issue, this article proposes a unidirectional VV preselection (UVVP) method for optimizing the performance of DSVM-based PTC of ac motors. The proposed UVVP method divides both voltage space vector diagram (VSVD) and flux space trajectory into twelve 30° based sectors to reduce the impact of candidate VVs with negative effects. At each flux position, the UVVP strategy can restrict the nearest candidate VVs in the 30° based VSVD region within the circular flux trajectory. This is achieved by using the speed direction to avoid the candidate VVs, which are in the same flux sector for reverse flux rotation, hence resulting in significant flux and torque ripple reduction. The main benefit of the proposed method is its simplicity since it only requires the flux sector and speed information as well as it can generate the VV enumerations online while ensuring the reduction of computation burden. [ABSTRACT FROM AUTHOR]

In order to improve the control performance of conventional finite-control-set model predictive control (FCS-MPC) method and keep the balance between the steady-state performance and switching frequency of the system, this article proposes an improved MPC method based on the conventional MPC method. First, the reference voltage vector of the next control instant is predicted to determine the candidate voltage vectors of the next two control instants. Then, an optimal vector selection way is proposed, in which candidate voltage vectors selected by the reference voltage vector and designed cost function structure in series. Moreover, the selection process of optimal voltage vector from the candidate voltage vectors is analyzed. Finally, the comparison experiments between the conventional FCS-MPC method and the proposed MPC method are conducted. And experimental results show the validity of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Finite control set model predictive control (FCS-MPC) has been widely studied and applied in power converters and machine drives. It takes advantage of the nonlinear finite switching states of the power converters, and a discrete model is used to predict the behavior of the system. The main advantages of FCS-MPC lie in easy inclusion of nonlinear constraints, simple structure and intuitive concept. However, FCS-MPC applied in multiphase machine drives will face with the challenge of heavy computation cost. In this article, a computationally efficient FCS-MPC method is proposed for multiphase drives. The concept of virtual voltage vector is introduced to eliminate low-order harmonic currents in open-loop mode, resulting in simplified prediction model and cost function. To further simplify the enumerative optimization process, the redundant vectors are eliminated, and only five evaluations are required. By this way, the computation complexity is considerably reduced and is approximately irrespective of phase number. Thus, it can be easily generalized to multiphase drives. Moreover, the merit of simple structure is kept, which is the most important characteristic of FCS-MPC. The proposed method is compared with the existing FCS-MPC methods to illustrate its effectiveness. The experimental results have verified that the proposed method can reduce computation complexity, achieve superior steady-state and dynamic performance. [ABSTRACT FROM AUTHOR]

This article proposes a fixed switching frequency (FSF)-based improved model predictive control (MPC) for a five-level active neutral-point clamped (5L-ANPC) converter. Compared to the conventional MPC for 5L-ANPC converter, the proposed MPC significantly reduces the computational burden while ensuring the balance of dc-link and flying capacitor voltages. The proposed MPC optimally selects the control voltage vectors to reduce common mode voltage (CMV), leading to a small CMV of the converter. Using three voltage vectors action per control cycle in MPC, FSF is achieved. Steady-state and dynamic performances of the proposed MPC have been evaluated by experiments. The results verify the feasibility and effectiveness of the proposed MPC method. [ABSTRACT FROM AUTHOR]

In this article, a min-max model predictive control (MPC) method of planar motors is proposed for the first time to achieve robust precision position tracking, which has a low computational burden and strong capability to deal with the problems of stability, robustness, optimization, and input constraints. A state-space model with a homogeneous state equation is built to describe the dynamics of the time-varying reference trajectory. Combining the state-space model of the reference trajectory and that of the planar motor, an augmented state-space model is established to obtain an error state formulation. Then, using the error state formulation, a min-max optimal control problem subject to the constraints on bounded uncertainty, stability, and control input is developed. Moreover, applying the theory of asymptotically stable invariant ellipsoids and employing the nested invariant ellipsoids, the explicitly linear state-feedback control laws are obtained using a linear-matrix-inequalities based offline control algorithm. Finally, the min-max MPC is applied to a planar motor system developed in the laboratory for an experimentally comparative study. The results demonstrate the effectiveness of the proposed min-max MPC of planar motor for robust precision position tracking applications. [ABSTRACT FROM AUTHOR]

Smartload with series-connected dc electric spring (ES) and noncritical load (NCL) structure can compensate for load voltage and improve the power quality. In this article, a distributed virtual inertial control framework based on model predictive control is proposed for low inertia in the dc microgrid. First, the current prediction model of the ES bidirectional full-bridge dc/dc converter is presented. Based on the virtual inertia control, model predictive control is used to optimize virtual capacitance during the operation. When the system suffers from disturbance, virtual capacitance increases to slow down the change of the dc bus voltage. When disturbance happens, the system can be stabilized quickly by reducing the virtual capacitance. Then, the consensus algorithm based on distributed control is established after defining the NCL voltage deviation rate, which can achieve the balance of NCL voltage deviations, and effectively avoid the more significant voltage deviation in one of NCLs. Furthermore, the small-signal model of the proposed control method is developed, and the influence of the proposed controller on the small-signal stability is described by employing the eigenvalue analysis method. Finally, based on the RT-Lab hardware-in-the-loop simulation system, comparisons and analysis are made to verify the effectiveness of the proposed control method under power step conditions. [ABSTRACT FROM AUTHOR]

In this article, a low-ripple continuous control set (CCS) model predictive torque control (MPTC) method for switched reluctance machines (SRMs) is proposed. The inherent high nonlinearity of the SRMs makes it difficult to solve the optimization problem in the CCS MPTC algorithm analytically. To address this issue, an equivalent linear SRM model is adopted, and the cost function is also properly modified. Then, with the torque boundary values provided by executing the voltage vectors of an improved switching table, the optimization problem in the CCS MPTC method becomes simple and analytically solvable. The Lagrange multiplier method is employed to solve this optimization problem analytically and generate the optimum torque reference values for the active phases. Based on the estimated torque variation rates, the duty cycles for each phase are calculated. Extensive simulation and experimental tests are carried out in a four-phase 8/6 SRM setup. These testing results reveal that the proposed CCS MPTC method shows much lower torque ripples and current ripples in a wide speed range with a low computational burden than the existing finite control set MPTC methods. [ABSTRACT FROM AUTHOR]

A virtual vector based modulated model predictive control (VV-M2PC) strategy is proposed for the indirect matrix converters (IMCs) in this article. Compared with the conventional M2PC, the VV-M2PC constructs and utilizes new virtual vectors to reduce the current control errors. Besides, the implementation and feasibility for the proposed strategy is discussed in detail. The comparative simulation and experimental results based on an experimental IMC prototype verify that the steady-state control performance can be improved under the VV-M2PC without affecting the dynamic-state control performance and increasing the computational burden. [ABSTRACT FROM AUTHOR]

In order to extend the speed range and reduce power electronics devices of the permanent-magnet linear motor traction system, a half-centralized open-end winding topology is studied in this article, in which two movers are supplied by three voltage-source-inverters (VSIs). All these VSIs share a common dc-bus voltage, and the zero-sequence current becomes a challenge. In order to solve cope with this challenge, two phase model predictive voltage controls (PMPVCs) are proposed. Referring to the traditional finite-control-set MPVC (FCS-MPVC), an FCS-PMPVC scheme is proposed and the optimal phase voltage group is determined by minimizing three independent phase cost functions. For FCS-PMPVC, 21 phase voltage group are evaluated while the evaluation number of the traditional FCS-MPVC is 512. In order to reduce the computation burden furthermore, a new concept phase voltage vector (PVV) is defined, and the optimal PVV is directly determined by the nearest distance principle instead of the time-consuming evaluation. The second PMPVC is named as geometrical-location PMPVC (GL-PMPVC). Both PMPVCs have same performances while the computation burden of GL-PMPVC is nearly 47% of that of FCS-PMPVC. The effectiveness of the proposed PMPVCs have been verified by experimental results. [ABSTRACT FROM AUTHOR]

In this article, a fault-tolerant model-free predictive controller (FT-MFPC) for cascaded H-bridge multilevel inverters (CHMLIs) is proposed. FT-MFPC is based on an association of model-free predictive controller (MFPC) and a proportional--integral (PI) like structure known as current variation controller (CVC). As in MFPC, the dynamic structure (DS) that is used for the prediction of the system output is obtained from past measurements of current variations, but CVC modifies the dynamics before they are stored for prediction. As a consequence, even if the converter operates with bypassed modules, FT-MFPC adapts its DS to represent the real dynamics of the system and enables a closed-loop response with balanced load currents in unbalanced CHMLIs. Simulation and experimental results validate the adaptability of the proposed strategy to operate a seven-level inverter under various configurations of bypassed H-bridge modules. The experimental results show that, for different postfault operations, the maximal current total harmonic distortion and imbalance factor at steady state in the worst case scenario are below 5% and 2%, respectively. [ABSTRACT FROM AUTHOR]

For multiobjective model predictive current control (MPCC) method with penalties, the penalty weights are introduced into the cost function to determine the relative importance of control targets. However, the dimensions and value ranges of each control target are different. Thus, the penalty coefficient is difficult to determine. To avoid the difficulty of dimensionless parameter design, this article proposes a multiobjective control method called MPCC with multiple current bounds. In this article, the switching frequency and the common-mode voltage (CMV) are selected as the control target. Their control is converted into the limitation of the current ripple so that the control parameters have a clear physical meaning. On the one hand, the proposed method can realize the cooperative control of reference current tracking, switching frequency suppression, and CMV suppression. On the other hand, the key control parameters have the same dimension and strong correlation with others. Therefore, appropriate parameter design can be realized. Experimental investigations for permanent magnet synchronous motor prove the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

Obstacle avoidance is a sizable challenge for robots working in a multiple dynamic obstacle environment. Conventional obstacle avoidance methods often require complex calculations to process all dynamic obstacles detected in the scene. Avoiding dangerous moving objects in time is often difficult when obstacles are many. In this article, we propose a robot obstacle avoidance method based on motion saliency for dynamic environments. First, we use segmented dynamic objects to calculate the saliency of dynamic objects and segment dangerous dynamic objects. Then, we use the B-spline curve to predict the movement of dangerous dynamic objects and combine it with the nonlinear model predictive control method to avoid dangerous obstacles in the dynamic environment of the robot. Considering the motion behavior of different dynamic objects, our obstacle avoidance strategy is to generate obstacle-free paths by adjusting the speed of the robot or connecting the centers of the rolling variable-size circles. Finally, we conduct a series of experiments to verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The article proposes a new vector analysis based model predictive control (VAMPC) method for multilevel converters with four different switch configurations. It is based on track of an error vector using fundamental state vectors, and can achieve deadbeat control of four switching states multilevel converters with multiple control objectives. Compared with conventional finite control set model predictive control (FCS-MPC) method, VAMPC can achieve excellent spectrum characteristics in the case of a slight increase in computational burden. The VAMPC method is experimentally validated on a 100-kHz GaN-based three-level flying capacitor (FC) converter. Compared with the FCS-MPC, VAMPC leads to significant reduction in steady-state control errors. For example, the inductor current total harmonic distortion of the FC converter is now reduced by more than 60%. [ABSTRACT FROM AUTHOR]

As is known, cable plays a significant role in electric power transmission of the ship. From the safety perspective, this paper aims to predict the ship cable life more accurately and analyze its reliability under small sample conditions. To achieve this goal, based on the non-equidistant grey model (NEGM (1, 1)), an optimized equal-dimension dynamic NEGM (1, 1) is proposed in this paper. In the improved model, the raw data smoothness and the background value are optimized to apply to grey model parameters. Equal-dimension dynamic prediction algorithm (EDDPA) is adopted to predict the lifetime of ship CXF type cable. Compared with the aging model from literature and NEGM (1, 1), the prediction effectiveness and reliability of the proposed model are more considerable. Interestingly, with the increase of recurrence times, the numerical results show this model has higher prediction performance. Finally, the reliability and confidence interval of the life evaluation of ship CXF cable are given to provide more valuable information for the cable management. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) strategy can provide significant benefits for controlling nonlinear systems over classical cascade field-oriented control (FOC). However, the MPC is still in the development stage for a high-performance predictive model. Therefore, the proposed MPC in this article updates the internal linear predictive model at each time step to accurately represent the nonlinear plant of a permanent-magnet synchronous motor (PMSM) plant over different speed regions. In other words, the adaptive discrete linear plant model (ADLPM) is designed to update the current operating conditions of the machine parameters and the equilibrium points of the measured stator currents, speed, and load torque. For the operation in the flux-weakening region, the proposed MPC depends on a performance control algorithm (PCA) to obtain high dynamic performance. In this PCA algorithm, the proposed MPC depends on the modified reference velocity rather than the original reference velocity, which can calculate the required d-axis current directly. Moreover, the proposed cost function is designed directly in terms of the error values of the velocity and d-axis current, which fits the motor performance based on the further constraint of the maximum magnitude of the drawn stator current provided to control the acceleration of the rotor. Finally, comprehensive simulations and experiments have fully demonstrated that the proposed MPC can reduce the speed drop, and torque ripple in response to those of the FOC and traditional MPC strategies. [ABSTRACT FROM AUTHOR]

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Accurately predicting the state of health (SoH) of lithium-ion batteries remains one of the top challenges, especially in uncertain environments where feature availability is not guaranteed. The main contribution of this article is to propose a highly available feature fault-tolerant prediction framework capable of predicting the SoH of batteries with a very high accuracy under sensor data unavailability. Previously published studies mitigated the feature incompleteness issue using models that deeply depend on a specific set of features; if one of the features becomes unavailable, the prediction model fails. Unlike the existing methods, the proposed feature fault-tolerant prediction framework proposes a novel agile approach so that when one of the features is missing, the model considers the next available feature to make the prediction. Our fault-tolerant prediction framework is highly available for SoH prediction and always predicts at a very high accuracy level. A hybrid deep learning model combining long short-term memory and deep neural networks along with a meticulous feature engineering design is used to train the NASA dataset and predict the SoH of batteries with a high precision. In order to prove the efficiency of the model, the feature fault-tolerant prediction framework configuration has been validated against two other datasets: Toyota Research Institute in partnership with MIT and Stanford dataset and NASA dynamic profile datasets. The performance is evaluated using the error indicators: mean absolute error, root-mean-square error, and the $R$ square. [ABSTRACT FROM AUTHOR]

This article proposes a modified deadbeat predictive current control (M-DPCC) method to improve the dynamic performance of permanent magnet synchronous motor (PMSM). Classical DPCC only predicts one control cycle of the PMSM, which restricts the dynamic performance since multiple control cycles are required for the transient state under heavy load and rated speed working condition. The proposed M-DPCC extends the single-step prediction to the entire dynamic interval in the transient state, by which the comprehensive optimization on the angle of the stator voltage vector can be implemented. Moreover, M-DPCC performed multi-step deadbeat calculation to keep the computation modest. To verify the effectiveness of the proposed M-DPCC, transient-state performance of PMSM using the M-DPCC method, the modulated model predictive control and classical DPCC are explored under a wide range of working condition. Comparative simulation and experimental results indicate that dynamic performance of the PMSM can be improved, especially under rated speed and heavy load conditions. Besides, the parameter sensitivity to inductance mismatch of the three methods is carried out. The stable operation range of the M-DPCC under inductance mismatch is larger than the other two methods. Moreover, the computation complexity of the three methods were investigated. The M-DPCC acquired less computation time than the modulated model predictive control. [ABSTRACT FROM AUTHOR]