The four-switch three-phase (FSTP) inverters are known for their cost-effective advantages and minimal switching losses. However, such inverter topology's progress is lagging due to control constraints and requirements, including voltage vector limitations and parameter perturbations. To overcome the issue, this paper proposes a triple-voltage-vector model-free predictive current control (TVV-MFPCC) for FSTP inverter-fed surface permanent magnet synchronous motor (SPMSM) drives. The proposed TVV-MFPCC uses the principle of discrete-space-vector modulation (DSVM) to increase the voltage vector selections. Three primary voltage vectors, either the same or distinct, are linearly combined to yield the synthesized voltage vectors. A redundant voltage vector reduction scheme is also introduced to lessen calculations by optimally reducing the candidate voltage vectors to sixteen equivalent hybrid switching modes. To improve prediction accuracy, the TVV-MFPCC performs three different current readings and three current difference calculations in each sampling period. Experiments using a TMS320F28379D microcontroller are conducted to compare the performance of the proposed TVV-MFPCC against conventional MFPCC (C-MFPCC) and validate the scheme.

Model-free predictive current control (MFPCC) is a promising substitute for model predictive current control (MPCC). However, the performance of the MFPCC, to a large extent, hinges on the update frequency of its lookup table. Conventionally, the update is only performed when two successive switching states applied by the controller are identical, causing a stagnation problem to the current difference of those switching states that are not applied. To address the stagnation problem, this paper proposes a novel mechanism called synchronized current difference update for model-free predictive current control (SCDU-MFPCC). The presented scheme uses the model of permanent-magnet synchronous motor (PMSM) to construct equivalent differential stator currents corresponding to seven basic voltage vectors. To that end, current slope will be defined from the current difference of two successively applied voltage vectors. An updating factor associated with the current slope is then introduced into the prediction scheme to correct the enforced response of all switching states. This scheme is applied on every current measurement to update the stored information regardless of the successive switching states applied are distinct or not. Finally, experiments are conducted to assess the performance of the new approach using a TMS320F28379D microcontroller. Experimental results demonstrate that the proposed method substantially reduces the stagnation effect under steady-state and dynamic operations.

Cardiopulmonary monitoring is important and useful for diagnosing and managing multiple conditions, such as stress and sleep disorders. Wearable ambulatory systems can provide continuous, comfortable, and inexpensive means for monitoring; it always has been a research subject in recent years. Being simple and cost-effective, electrocardiogram-based commercial products can be found in the market that provides cardiac diagnostic information for assessment, including heart rate measurement and atrial fibrillation identification. Based on a data-driven and self-adaptive approach, this study aims to estimate heart rate and respiratory rate simultaneously from one lead electrocardiogram signal. In contrast to ensemble empirical mode decomposition with principle component analysis, performed in the time domain, our method uses spectral data fusion, together with intrinsic mode functions using ensemble empirical mode decomposition obtains a more accurate heart rate and respiratory rate. Equipped with a rule-based selection of defined frequency levels for respiratory rate (RR) estimation, the proposed method obtains (0.92, 1.32) beat per minute for the heart rate and (2.20, 2.92) breath per minute for the respiratory rate as their mean absolute error and root mean square error, respectively outperforming other existing methods.

A model-free predictive current control for synchronous reluctance motor drives based on triple-voltage-vector with optimized duty ratio modulation is presented in this paper. The proposed scheme introduces an effective and low complexity strategy of selecting candidate voltage vectors to largely reduce computational loadings. First, the basic voltage vectors are organized to apportion six regions, representing six composite switching modes. Each region comprises two active voltage vectors and a zero voltage vector modulated by distinct duty ratios. The scheme starts from the selection of an initial basic voltage vector corresponding to a predefined cost function. The second candidate voltage vector then follows, which is chosen from the region adjacent to the initial one. The third vector is a default zero voltage vector. As a result, voltage vectors outside the selected regions are excluded from the process. Moreover, the adjustable feature makes the optimization of duty ratios adaptive. Finally, the proposed method is experimentally put to tests under various operating conditions utilizing the synchronous reluctance motor drives. Experimental results are presented to demonstrate the effectiveness of the proposal.

For current control in power conversion and motor drive systems, there exist three classic methods in the literature and they are the hysteresis current control (HCC), the sine pulse-width modulation (SPWM), and the space vector pulse width modulation (SVPWM). HCC is easy to implement, but has relatively large current harmonic distortion as the disadvantage. On the other hand, the SPWM and SVPWM use modulation technique, commonly together with at least one proportional-integral (PI) regulator to reduce load current ripples, and hence demanding more computation time. This paper aims to improve the performance of a recently proposed new current control method—the single-voltage-vector model predictive current control (SVV-MPCC), for synchronous reluctance motor (SynRMs) drives. To that end, a dual-voltage-vector model-free predictive current control (DVV-MFPCC) for SynRMs is proposed. Unlike the SVV-MPCC that applies only a single voltage vector per sampling period, the proposed DVV-MFPCC is capable of providing two successive segmentary current predictions in the next sampling period through all possible combinations from any two candidate switching states increasing the number of applicable switching modes from seven to nineteen and reducing the prediction error effectively. Moreover, the new control does not utilize any parameters of the SynRM nor its mathematical model. The performance is effectively enhanced compared to that of SVV-MPCC. The working principle of the DVV-MFPCC will be detailed in this paper. Finally, the SVV-MPCC, the single-voltage-vector model-free predictive current control (SVV-MFPCC), the dual-voltage-vector model predictive current control (DVV-MPCC), and the DVV-MFPCC are realized to control the stator currents of SynRM through a 32-bit microcontroller TMS320F28377S. Experimental results are provided to validate the new method and verify that the DVV-MFPCC performs better than do the SVV-MPCC, the SVV-MFPCC, and the DVV-MPCC.

This paper proposes a two-phased problem reformulation approach to solve the quadrotor UAV attitude control problem. Given an initial attitude, the objective is to design a control to drive it toward a desired value. The first phase begins with the definition of three types of errors pertaining to attitude discrepancy, followed by the utilization of virtual control with which the dynamics of quadrotor's attitude and angular velocity are then unified under the framework of SO(3), rendering the original control problem much more transparent to tackle. Through cancellation of certain unwanted terms emerging from the unification process, entry-wise treatment of the remaining matrix dynamics is then employed in the second phase to transform it further into the stabilization problem of a 3-dimensional linear time-invariant system that is fairly easy to solve since to which standard feedback designs such as linear quadratic regulator and pole placement are readily applicable. In addition to the classical designs, a specialized and structurally simple controller is provided to reduce the number of gains. To account for system parametric uncertainties, external disturbances, and sensor measurement errors and noises, a second controller based on classical H _∞ theory is presented to enhance the robustness of the design. A numerical example is given, and computer simulations are conducted to generate error and control trajectories to assess and compare the effectiveness of the presented designs.