Abstract This paper innovatively proposes the Black Kite Algorithm (BKA), a meta-heuristic optimization algorithm inspired by migratory and predatory behavior of black kite. The BKA integrates Cauchy mutation strategy Leader to enhance global search capability convergence speed algorithm. novel combination achieves good balance between exploring solutions utilizing local information. Against standard test function sets CEC-2022 CEC-2017, as well other complex functions, attained best...

Digital nucleic acid amplification provides unprecedented opportunities for absolute quantification by counting of single molecules. This technique is useful molecular genetic analysis in cancer, stem cell, bacterial, non-invasive prenatal diagnosis which many biologists are interested. paper describes a self-priming compartmentalization (SPC) microfluidic chip platform performing digital loop-mediated (LAMP). The energy the pumping pre-stored degassed bulk PDMS exploiting high gas...

A more practical, user-friendly digital PCR microchip based on integrated self-priming compartmentalization and dehydration control is first developed.

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this are to enhance the robustness and remain high performance finite control-set model (FCS-MPC) under unmodeled dynamics parameter mismatch conditions. More specifically, an dynamic linearization technique is utilized equivalently reformulate nonlinear converter system at each operating point. Based on this, model-free adaptive scheme presented iteratively...

The focus of this article is to introduce the concept an online reinforcement learning (RL) solution and propose a novel finite control-set model predictive control framework subject system uncertainties, which possesses excellent applicative potential for power converter systems with unknown perturbations. In framework, task performed by incorporating adaptive neural network approximation-based RL predictor-based current solution. To be more precise, critic responsible <italic...

This letter concentrates on introducing a learning methodology that extends and improves classical finite control-set model predictive control approach, which is able to significantly mitigate the inherent limitations of system uncertainties unknown perturbations subject robustness characteristics. To this end, in our work, architecture, addressed as an unsupervised technique, presented. In task, we define single neural network learn tracking part online, robustifying term embedded into...

A scalable self-priming fractal branching microchannel net chip for digital PCR is developed the first time.

Based on conventional particle swarm optimization (PSO), this paper presents an efficient and reliable heuristic approach using PSO with adaptive random inertia weight (ARIW) strategy, referred to as the ARIW-PSO algorithm, build a multi-objective model for reservoir operation. Using triangular probability density function, is randomly generated, function automatically adjusted make generally greater in initial stage of evolution, which suitable global searches. In evolution process,...

An event-triggered control technique has been developed recently. This explicitly reduced the signal transmission by introducing a flexible design of threshold inequalities. It was later extended to model-predictive for power converter systems. In this letter, incorporating into an state-observer-based finite-control-set framework, we have new architecture systems with parametric uncertainties. Meanwhile, novel cost function respect angle minimization term is embedded proposal. The novelty...

Standard model predictive control is an optimization-based strategy that can handle multiple objectives and system nonlinear constraints. However, it typically suffers from the limitation of uncertainties in practical systems, such as external unknown disturbances parametric uncertainties. Motivated by aforementioned limitation, this article, a novel robust framework, endowed with merits fuzzy logic finite control-set solution, proposed. The main objective article to enhance robustness while...

This article proposes a novel data-driven neural predictors-based robust finite control-set model predictive control (FCS-MPC) methodology for power converters, which aims to enhance the robustness of system and flexibility multiple objectives. To be specific, network solution, has good potential estimate unknown nonlinear dynamics by deploying real-time historical data, is incorporated into proposed design with cost function derived intuitively from Lyapunov's theory. The key feature this...

This letter investigates the possibility of deploying a novel finite control-set model predictive control solution for solving ongoing research challenges in regulated modular multilevel converter, i.e., parameter sensitiveness and excessive computational burden as well weighting factors selection. Specifically, it is realized by cascading predictor-based neural network design, which enables smooth fast identification system dynamics, computationally efficient finite-set control, responsible...

Traditional predictive control schemes dominated the research field of numerous power electronic applications over past few years, since they usually lead to solutions with good dynamic and steady results. However, these strongly depend on available knowledge system (e.g., accurate modeling information), which often results in lack robustness presence parametric uncertainties. Furthermore, unnecessary energy loss heavily correlates high switching frequency, directly affects efficiency....

This article investigates a data-driven-based predictive current control (DD-PCC) approach for modular multilevel converter (MMC) to circumvent the sensitiveness parameter variation and unmodeled dynamics of finite control-set model (FCS-MPC) method. By integrating model-free adaptive (MFAC)-based data-driven solution into FCS-MPC framework, performance deterioration caused by uncertainties is suppressed. The design suggested controller only based on input–output measurement data, where...

This article aims to first focus on an improvement of finite control-set model predictive control strategy for power converters that is based reinforcement learning event-triggered architecture with the help adaptive dynamic programming technique and mechanism subject system uncertainties. Our development, endowed merits as well a solution, able alleviate issues parametric uncertainties high switching frequency inherent in existing scheme, while retaining control. Finally, this proposal...

In pursuit of accurate and fast trajectory tracking power converters, an explicit model is commonly used in the finite control-set predictive control (FCS-MPC) framework to predict precise behaviors controlled variables. reality, however, mismatch inevitable, which causes inherent challenges parameter sensitivity uncertainties FCS-MPC method. This article proposes a dynamic-linearization-based architecture circumvent such dependence while keeping attractive features conventional By...