To improve the observation accuracy and robustness of the sensorless control of an interior permanent magnet synchronous motor (IPMSM), a sliding mode observer based on the super twisting algorithm (STA-SMO) with adaptive parameters estimation control is proposed, as parameter mismatches are considered. First, the conventional sliding mode observer (CSMO) is analyzed. The conventional exponential approach law produces a large chattering phenomenon in the back EMF estimation, which causes a large observation error when filtering the chattering through the low-pass filter. Second, a high-order approach law of the super twisting algorithm is introduced to observe the rotor position and speed estimation, which uses the integral function to eliminate the chattering of the sliding mode. Third, an adaptive parameter estimation control (APEC) is presented to enhance the observation accuracy caused by parameter mismatches; the motor parameter adaptive law of the APEC is designed by Lyapunov’s stability law. Finally, the proposed method not only reduces both the chattering and the low-pass filter, but it also enhances accuracy and robustness against parameter mismatches, as discussed through simulations and experiments.

Currently, numerous intelligent maximum power point tracking (MPPT) algorithms are capable of tackling the global optimization challenge of multi-peak photovoltaic output power under partial shading conditions, yet they often face issues such as slow convergence, low tracking precision, and substantial power fluctuations. To address these challenges, this paper introduces a hybrid algorithm that integrates an improved salp swarm algorithm (SSA) with the perturb and observe (P&O) method. Initially, the SSA is augmented with a dynamic spiral evolution mechanism and a Lévy flight strategy, expanding the search space and bolstering global search capabilities, which in turn enhances the tracking precision. Subsequently, the application of a Gaussian operator for distribution calculations allows for the adaptive adjustment of step sizes in each iteration, quickening convergence and diminishing power oscillations. Finally, the integration with P&O facilitates a meticulous search with a small step size, ensuring swift convergence and further mitigating post-convergence power oscillations. Both the simulations and the experimental results indicate that the proposed algorithm outperforms particle swarm optimization (PSO) and grey wolf optimization (GWO) in terms of convergence velocity, tracking precision, and the reduction in iteration power oscillation magnitude.

In this letter, a robust feedback active noise control (FBANC) system with an online secondary-path modeling (OSPM) subsystem is proposed for suppressing sinusoidal noise signals. First, a decoupling scheme is proposed by using of an adaptive linear prediction filter (LPF) that takes the FBANC residual noise as its input and separates the remaining sinusoidal noise from the broadband noise that includes an inherent additive noise within the primary noise as well as a broadband component due to an auxiliary white Gaussian noise (AWGN). The remaining sinusoidal noise (LPF output) is used effectively not only to update the FIR controller but also to scale the AWGN, while the broadband noise (LPF error) serves as a desired signal for the OSPM. In this way, the hostile coupling between the FIR controller and the OSPM subsystem is significantly mitigated, leading to both faster system convergence and improved noise reduction performance (NRP). Second, a simple and practical scheme for refreshing the proposed FBANC system is also developed to enhance its robustness against even abrupt changes with the SP and/or the primary noise signal. Extensive simulations with synthetic and real secondary paths and noise signals are conducted to demonstrate the effectiveness of the proposed FBANC system.

High-precision aligned wafer bonding is essential to heterogeneous integration, with the device dimension reduced continuously. To get the alignment more accurately and conveniently, we propose a moiré-based alignment method using centrosymmetric grating marks. This method enables both coarse and fine alignment steps without requiring additional conventional cross-and-box alignment marks. Combined with an aligned wafer bonding system, alignment accuracy better than 300 nm (3σ) was achieved after bonding. Furthermore, the working principle of the moiré-based alignment for the backside alignment system was proposed to overcome the difficulty in bonding of opaque wafers. We believe this higher alignment accuracy is feasible to satisfy more demanding requirements in wafer-level stacking technologies.

To address the stability of the power supply to agricultural facilities and greenhouses in remote areas, this paper proposes a solution based on the bus voltage fluctuation issue in an islanded photovoltaic-storage DC microgrid. Traditional power supply methods often struggle to meet demand due to significant fluctuations in solar irradiance and load. To resolve this, an improved sliding-mode linear active disturbance rejection control (ISMLADRC) strategy is designed, significantly enhancing the response speed of the microgrid control system while improving its adaptability in complex agricultural environments. The system integrates a hybrid energy storage system and photovoltaic power generation to optimize microgrid power compensation, ensuring the stability of the power supply to agricultural facilities and greenhouses. Simulation results demonstrate that the proposed control scheme enhances the robustness and efficiency of the original system, ensuring a reliable power supply for crop production in remote areas, advancing smart agriculture, and promoting the sustainable development of green agriculture.