Complex Control Systems Research Articles

Bistable Mechanisms 3D Printing for Mechanically Programmable Vibration Control

Bistable mechanisms, characterized by their ability to maintain two distinct stable states under external stimuli, offer a versatile solution to engineering challenges, particularly in vibration suppression systems. While tuned mass dampers (TMDs) effectively mitigate vibrations at specific frequencies, their efficacy falls short across a broader spectrum. This work proposes enhanced adaptability for passive vibration reduction without dependence on complex control systems or external power sources by integrating bistable mechanisms into TMD systems. A bistable mechanism is designed based on numerical simulations and is 3D‐printed via fused filament fabrication. The bistable mechanism provides more adaptive characteristics by displacing the tuned mass on a flexible link. Following experimental testing, the optimized design for bistable mechanisms has been determined. Then, a chain of bistable mechanisms is introduced to achieve the desired displacement for carrying the TMD payload. The stroke length for the TMD vibration test has been customized to reduce system vibrations by carefully optimizing geometric configurations of bistable mechanisms.

A Reusable Capillary Flow-Driven Microfluidic System for Abscisic Acid Detection Using a Competitive Immunoassay

Point-of-care (PoC) devices offer a promising solution for fast, portable, and easy-to-use diagnostics. These characteristics are particularly relevant in agrifood fields like viticulture where the early detection of plant stresses is crucial to crop yield. Microfluidics, with its low reagent volume requirements, is well-suited for such applications. Self-driven microfluidic devices, which rely on capillary forces for fluid motion, offer an attractive alternative by eliminating the need for external pumps and complex fluid control systems. However, traditional microfluidic prototyping materials like polydimethylsiloxane (PDMS) present challenges due to their hydrophobic nature. This paper presents the development of a reusable, portable, capillary-driven microfluidic platform based on a PDMS-PEG (polyethylene glycol) copolymer designed for the rapid low-cost detection of abscisic acid (ABA), a key biomarker for the onset of ripening of non-climacteric fruits and drought stress in vines. By employing passive fluid transport mechanisms, such as capillary-driven sequential flow, this platform enables precise biological and chemical screenings while maintaining portability and ease of use. A simplified field-ready sample processing method is used to prepare the grapes for analysis.

Research Progress on Gene Regulation of Plant Floral Organogenesis

Flowers, serving as the reproductive structures of angiosperms, perform an integral role in plant biology and are fundamental to understanding plant evolution and taxonomy. The growth and organogenesis of flowers are driven by numerous factors, such as external environmental conditions and internal physiological processes, resulting in diverse traits across species or even within the same species. Among these factors, genes play a central role, governing the entire developmental process. The regulation of floral genesis by these genes has become a significant focus of research. In the AE model of floral development, the five structural whorls (calyx, corolla, stamens, pistils, and ovules) are controlled by five groups of genes: A, B, C, D, and E. These genes interact to give rise to a complex control system that governs the floral organsgenesis. The activation or suppression of specific gene categories results in structural modifications to floral organs, with variations observed across different species. The present article examines the regulatory roles of key genes, including genes within the MADS-box and AP2/ERF gene clusters, such as AP1, AP2, AP3, AG, STK, SHP, SEP, PI, and AGL6, as well as other genes, like NAP, SPL, TGA, PAN, and WOX, in shaping floral organ genesis. In addition, it analyzes the molecular-level effects of these genes on floral organ formation. The findings offer a deeper understanding of the genetic governance of floral organ genesis across plant species.

Data‐Driven Adaptive Cooperative Output Regulation for Completely Unknown Linear Multi‐Agent Systems Based on Finite Length Data

ABSTRACTWith respect to complex control systems, traditional model‐dependent methods are increasingly challenged, particularly when system models are unknown or intractable. Moreover, most past research has focused on systems that are partially or fully known. In this technical paper, a data‐driven paradigm is employed to investigate the cooperative output regulation problem (CORP) for completely unknown linear heterogeneous discrete multi‐agent systems (MASs). Input and state information are utilized to design effective control strategies and a novel data‐based algorithm is proposed with finite length data. An adaptive observer is designed to estimate the exosystem state, with only the leader's children having access to the unknown leader's system matrix. To address the challenge of unknown dynamics, the CORP is transformed into a linear quadratic regulation (LQR) problem by solving the regulation equation. Compared with the reinforcement learning method, the closed‐form optimal control gain is obtained directly from the relevant data without the need for an initial stabilization controller or iterative calculation. Simulation results validate the proposed scheme's effectiveness.

Research on reachable set boundary of neutral system with various types of disturbances.

This study delves into neutral-type systems (NTSs), emphasizing the critical role of defining precise reachable set (RS) boundaries for safe and efficient system design and operation. The investigation notably addresses the challenges posed by time-varying delays, applying Lyapunov's direct method alongside advanced matrix inequality techniques to identify minimized and more accurate ellipsoidal boundaries of the RS in NTSs influenced by bounded and nonlinear disturbances. Our findings, verified through numerical simulations and comparisons with existing literature, demonstrate enhanced control and management capabilities for complex systems, thus underscoring the substantial theoretical and practical value of incorporating delay elements in NTSs.

Optimized fuzzy logic and sliding mode control for stability and disturbance rejection in rotary inverted pendulum

This paper presents a novel and comprehensive control framework for the Rotary Inverted Pendulum (RIP), focusing on a hybrid control strategy that addresses the limitations of conventional methods in nonlinear and complex systems. The proposed controller synergistically combines an Optimized Fuzzy Logic Controller (OFLC) with Sliding Mode Control (SMC), leveraging the strengths of both techniques to achieve superior performance. The integration of Particle Swarm Optimization (PSO) into the OFLC significantly enhances its adaptability and precision, while the SMC law provides robust disturbance rejection and system stability. Another key innovation in this framework is the incorporation of an Extended State Observer (ESO), which ensures accurate state estimation and reduces sensor dependency. The most significant physical outcome of this work is the demonstrated improvement in the system’s stability and robustness, even under external disturbances and uncertainties, showcasing the potential of the proposed control framework to achieve precise, stability control in nonlinear systems like the RIP. Extensive simulations validate the effectiveness of the proposed controller, demonstrating significant improvements in stability, disturbance rejection, and control precision, even under disturbance. The results highlight the potential of this approach as a robust solution for complex control systems, offering a significant advancement in the field of nonlinear system control with wide-ranging applications.

COMPARISON OF 3D-PRINTED PARTS’ QUALITY USING PRINTERS WITH “COREXY” AND “DELTA” KINEMATICS

Additive manufacturing, or 3D printing, describes technologies that manufacture parts by depositing thin layers of molten material on top of each other and creating the final part layer by layer. Each layer is built on the basis of geometry designed in CAD systems. Additive manufacturing technology opens up new design approaches: "design manufacturing" versus the traditional "design for manufacturing" approach. Geometric freedom allows you to design products as they are visualized, without manufacturing restrictions. Recently, 3D printers with Delta-type kinematics have gained popularity, which is an alternative to standard, cartesian 3D printers. These models use a more complex control system due to differences in the generation of print paths, but may have some advantages over a cartesian configuration. In order to expand the knowledge of additive manufacturing, a comparative study was conducted with cartesian and Delta printers to evaluate the printing performance of the test part. This article examines 3D printers with CoreXY and Delta kinematics, compares their characteristics, and identifies key differences in printing processes. As an example, for quality comparison, arbitrary parts printed in batches of three units from the same material with the same settings inside the slicer program were considered. Parts from both 3D printers were scanned using a LIDAR scanner, and the resulting scan models were transferred to a CAD environment for comparison. The results of the comparison were obtained by the shape and quality of the surface, the production time of one part and batch of parts, mass and dimensional characteristics. Looking at the results, it can be seen that the parts printed by the 3D printer with Delta kinematics have a better surface quality without post-processing, while the parts printed by the 3D printer with kinematics CoreXY correspond as closely as possible to the dimensions specified in the CAD model.

Learning-driven load frequency control for islanded microgrid using graph networks-based deep reinforcement learning

As the complexity of microgrid systems, the randomness of load disturbances, and the data dimensionality increase, traditional load frequency control methods for microgrids are no longer capable of handling such highly complex and nonlinear control systems. This can result in this can result in significant frequency fluctuations and oscillations, potentially leading to blackouts in microgrids. To address the random power disturbances introduced by a large amount of renewable energy, this paper proposes a Learning-Driven Load Frequency Control (LD-LFC) method. Additionally, a Graph Convolution Neural Networks -Proximal Policy Optimization (GCNN -PPO) algorithm is introduced, which enhances the random power disturbances introduced by a large amount of renewable energy. Algorithm is introduced, which enhances the perception ability of the reinforcement learning agent regarding grid state data by embedding a graph convolutional network. The effectiveness of this approach is validated through simulations on the isolated microgrid Load Frequency Control (LFC) model of China Southern Grid (CSG).

Decentralized Control of Complex Systems: Lyapunov Function Approach

In this contribution, we generalize existing methods for decentralized control design, providing a unified methodological framework that applies to linear continuous and discrete-time complex systems, as well as certain classes of nonlinear complex systems. Our approach leverages the direct connection between the stability properties of the overall complex system and those of its individual subsystems. By conducting the entire controller design process at the subsystem level, we circumvent the need for explicit interconnection values. Through numerical examples, we demonstrate that the proposed method ensures asymptotic stability of the full complex system within a specified region, and also guarantees stability of the isolated subsystems. In particular, we obtain quantifiable stability margins (e.g., γc bounds) and closed-loop eigenvalue placements that verify the effectiveness of the design. These results highlight not only the method’s theoretical robustness, but also its practical significance in simplifying the design process, reducing computational overheads, and enhancing scalability for large and interconnected engineering systems.

Hopf bifurcation analysis in cai system with fractional order

This work focuses on examining the dynamic behavior of a fractional-order hyperchaotic Cai system, a complex mathematical model with potential applications in various fields. The investigation begins by using the Routh-Hurwitz criteria, specifically adapted for fractional-order systems, to evaluate the local asymptotic stability of the equilibrium point. Additionally, the necessary conditions for the occurrence of Hopf bifurcation are derived, highlighting the system's transition from stable equilibrium to oscillatory dynamics. To further illustrate the system's complex behavior, phase portraits and chaotic attractors are generated for various parameter values. These visual representations, supported by detailed numerical simulations, provide a comprehensive depiction of the system's behavior under different conditions. The findings offer valuable insights into the stability and chaotic nature of the fractional-order hyperchaotic Cai system, enriching the understanding of its potential applications in modeling and control of complex systems. Through this analysis, the study contributes to the growing field of fractional-order dynamical systems, emphasizing their ability to describe and analyze phenomena with memory and hereditary properties.

Toward Damage-Less Robotic Fragile Fruit Grasping: A Closed-Loop Force Control Method for Pneumatic-Driven Soft Gripper.

Fragile fruit uploading and packaging are labor-intensive and time-consuming steps in postharvest industry. With the aging of the global population, it is supposed to develop robotic grasping systems to replace manual labor. However, damage-less grasping of fragile fruit is the key problem in robotization. Inappropriate grasping force will result in damage, early-stage bruise, or slip. Benefits from the advantages of softness and compliance of a pneumatic-driven soft gripper have been widely adopted for agricultural product and food manipulation. Nevertheless, pneumatic gripper is a complex, multivariable, nonlinear, and long time-delay control system, which is difficult to achieve robust closed-loop grasping force control. In this study, we aim to solve this problem and developed a robotic grasping force control system with pneumatic gripper and matrix force sensor. The force distribution condition was explored to tackle the problem in changing of the main contact point. A double closed-loop control method was proposed based on Kalman filter (KF) and proportion integration differentiation controller with dead band. The external and internal control loops were force controller and air pressure of the pump controller, respectively. The double closed-loop controller with dead band achieved robust grasping force control through air pressure. The experimental results validated the effectiveness of the KF method for denoising and the matrix force visualization method for exploring grasping mechanism. Ablation studies were carried out to demonstrate the effectiveness of the multiple grasping force sensing units in matrix form and the dead band in the controller. The maximum steady-state error was 0.07 N. In addition, the generalization performance and the antidisturbance ability of the grasping force control system was also validated. In summary, the problem in closed-loop control of the grasping force for pneumatic gripper has been solved in our study, and the method in this research is potential to be deployed in fruit postharvest industry.

A Study on Cyclical Learning Rates in Reinforcement Learning and Its Application to Temperature and Power Consumption Control of Refrigeration System

In recent years, with the advancement of computer hardware technology, an increasing number of complex control systems have begun employing reinforcement learning over traditional PID controls to address the challenge of managing multiple outputs simultaneously. In this study, we have for the first time adopted the cyclical learning rate method, which is widely used in deep learning, and applied it to deep reinforcement learning. Utilizing MATLAB Simulink, a detailed simulation model was developed with the RSD-4TFK5J model refrigeration storage as the reference object. We precisely evaluated the effects of various cyclical learning rate strategies on the training process of the model. The simulation results demonstrate the effectiveness of the cyclical learning rate method during the training phase, showcasing its potential to enhance learning efficiency and system performance in complex control environments.

Advanced technologies for the study of neuronal cross-organ regulation: a narrative review

The nervous system plays an integral role in the homeostasis of living organisms through the regulation of multiple organ systems. Research has highlighted the extensive role of the nervous system in regulating organ function, including key aspects such as metabolic processes, respiratory, cardiovascular, and immune responses. These findings are inseparable from the development of new technologies such as viral tracing, optogenetics, whole-tissue imaging, and neural activity recording. As technology continues to advance, our understanding of the regulatory role of the nervous system in other organs has expanded to more complex cognitive and emotional control systems, such as the cerebral cortex and subcortical areas. Recent studies have also shown the bidirectional cross-organ regulatory mechanisms between the gut microbiota and the brain. In addition, the body–brain axis also monitors inflammatory responses to ensure a balance between proinflammatory and anti-inflammatory responses. This review delves into the intricate regulatory functions of the nervous system as they pertain to cross-organ communication, emphasizing the broader implications that extend beyond mere metabolic regulation. It employs cutting-edge technologies such as viral tracing, whole-tissue clearing, optogenetics, and in vivo neuronal activity recording to dissect the influence of the nervous system on various organs, including but not limited to the heart, liver, and spleen. These advanced methodologies have substantially broadened our comprehension of the fundamental operations of the nervous system within diverse physiological systems, revealing the complex neural networks that orchestrate organ-specific functions. Our review highlights the significant potential of advanced technologies in neuronal cross-organ regulation to pave the way for therapeutic strategies aimed at addressing a wide array of conditions that impact organ health.

A cloud-edge framework for energy-efficient event-driven control: an integration of online supervised learning, spiking neural networks and local plasticity rules

Abstract This paper presents a novel cloud-edge framework for addressing energy constraints in complex control systems. Our approach centers around a learning-based controller using Spiking Neural Networks (SNN) on physical plants. By integrating a biologically plausible learning method with local plasticity rules, we harness the energy efficiency, scalability of the newtwork, and low latency of SNNs. This design replicates control signals from a cloud-based controller directly on the plant, reducing the need for constant plant-cloud communication. The plant updates weights only when errors surpass predefined thresholds, ensuring efficiency and robustness in various conditions. Applied to linear workbench systems and satellite rendezvous scenarios, including obstacle avoidance, our architecture dramatically lowers normalized tracking error by 96% with increased network size. The event-driven nature of SNNs minimizes energy consumption, utilizing only about 11.1 × 104 pJ (0.3% of conventional computing requirements). The results demonstrate the system’s adjustment to changing work environments and its efficient use of energy resources, with a moderate increase in energy consumption of 37% for dynamic obstacles, compared to non-obstacle scenarios.

INFORMATION SYSTEM OF STREET LIGHTING CONTROL IN A SMART CITY

Context. In the context of the rapid development of technologies and the implementation of the concept of smart cities, smart lighting becomes a key element of a sustainable and efficient urban environment. The research covers the analysis of aspects of the use of sensors, intelligent lighting control systems with the help of modern information technologies, in particular such as the Internet of Things. The use of such technologies makes it possible to automate the regulation of lighting intensity depending on external conditions, the movement of people or the time of a day. This contributes to the efficient use of electricity and the reduction of emissions into the atmosphere. Objective. The purpose of the paper is to analyze the procedures for creating an information system as a tool for monitoring and evaluating the level of illumination in a smart city with the aim of improving energy efficiency, safety, comfort and effective lighting management. The implementation of a smart lighting system for Lviv will help improve energy efficiency and community safety. Method. A content analysis of scientific publications was carried out, in which the results of research on the creation of street lighting monitoring systems in real urban environments were presented. The collection and analysis of data on street lighting in the city, such as energy consumption, illumination level, lamp operation schedules, and others, was carried out. Machine learning methods were used to analyze data and predict lighting needs. Using the UML methodology, the conceptual model of the street lighting monitoring information system was developed based on the identified needs and requirements. Results. The role of data processing technologies in creating effective lighting management strategies for optimal use of resources and meeting the needs of citizens is highlighted. The study draws attention to the challenges and opportunities of implementing smart lighting in cities, maximizing the positive impact of smart lighting on modern urban environments. The peculiarities of the development and use of an information system for controlling street lighting in a smart city are analyzed. The potential advantages and limitations of using the developed system are determined. Conclusions. The project on the creation of an information system designed to provide an energy-efficient lighting system in a smart city will contribute to increasing security, particularly, ensuring the safety of the community through integration with security systems, reducing energy consumption, through minimizing the electricity usage in periods when the need for lighting is not necessary. It has been determined that to implement an information system for remote monitoring and lighting control in a smart city, it is advisable to consider the possibility of using a complex lighting control system. Calculations were made on the example of Lviv for the city’s lighting needs. The use of motion sensors to determine the need to turn on lighting was analyzed. A conceptual model of the information system was developed using the object-oriented methodology of the UML notation. The main functionality of the information system is defined.

An Intelligent Optimized Digital Twins Framework for Fault Diagnosis in Complex Control Systems

An Intelligent Optimized Digital Twins Framework for Fault Diagnosis in Complex Control Systems

Rancang Bangun Miniatur Plant Pusat Simulator Pompa Air Kotor Menggunakan Panel Kontrol Relay Kontaktor

Water pumps play an important role in the management of wastewater, particularly in maintaining environmental cleanliness and health. This research aims to design and create a miniature plant simulator for wastewater pumps using a relay contactor control panel. This miniature plant is designed to simulate the operation of the wastewater pump system automatically, replacing the often inefficient manual methods. The research process begins by determining the main components needed, such as a three-phase induction motor, contactors, thermal overload relays, and related sensors. Subsequently, the design and construction of the simulator and its control panel are carried out. Implementation includes testing the functionality of each component and the integrity of the overall system, including individual component testing and testing under system failure conditions. The research results show that the miniature plant simulator built functions well as expected. The automatic control system using relay contactors has proven effective in operating the water pump efficiently and safely. This miniature plant can be used not only as a learning tool but also as a prototype for developing larger and more complex wastewater pump control systems

Artificial intelligence techniques for dynamic security assessments - a survey

The increasing uptake of converter-interfaced generation (CIG) is changing power system dynamics, rendering them extremely dependent on fast and complex control systems. Regularly assessing the stability of these systems across a wide range of operating conditions is thus a critical task for ensuring secure operation. However, the simultaneous simulation of both fast and slow (electromechanical) phenomena, along with an increased number of critical operating conditions, pushes traditional dynamic security assessments (DSA) to their limits. While DSA has served its purpose well, it will not be tenable in future electricity systems with thousands of power electronic devices at different voltage levels on the grid. Therefore, reducing both human and computational efforts required for stability studies is more critical than ever. In response to these challenges, several advanced simulation techniques leveraging artificial intelligence (AI) have been proposed in recent years. AI techniques can handle the increased uncertainty and complexity of power systems by capturing the non-linear relationships between the system’s operational conditions and their stability without solving the set of algebraic-differential equations that model the system. Once these relationships are established, system stability can be promptly and accurately evaluated for a wide range of scenarios. While hundreds of research articles confirm that AI techniques are paving the way for fast stability assessments, many questions and issues must still be addressed, especially regarding the pertinence of studying specific types of stability with the existing AI-based methods and their application in real-world scenarios. In this context, this article presents a comprehensive review of AI-based techniques for stability assessments in power systems. Different AI technical implementations, such as learning algorithms and the generation and treatment of input data, are widely discussed and contextualized. Their practical applications, considering the type of stability, system under study, and type of applications, are also addressed. We review the ongoing research efforts and the AI-based techniques put forward thus far for DSA, contextualizing and interrelating them. We also discuss the advantages, limitations, challenges, and future trends of AI techniques for stability studies.

Analysis of multi-term arbitrary order implicit differential equations with variable type delay

Due to their capacity to simulate intricate dynamic systems containing memory effects and non-local interactions, fractional differential equations have attracted a great deal of attention lately. This study examines multi-term fractional differential equations with variable type delay with the goal of illuminating their complex dynamics and analytical characteristics. The introduction to fractional calculus and the justification for its use in many scientific and technical domains sets the stage for the remainder of the essay. It describes the importance of including variable type delay in differential equations and then applying it to model more sophisticated and realistic behaviours of real-world phenomena. The research study then presents the mathematical formulation of variable type delay and multi-term fractional differential equations. The system’s novelty stems from its unique combination of variable delay, generalized multi terms fractional differential operators (n and m), and integral implicit parameters, and studying the stability of the the newly formulated system as compared to the work in the existing literature. While the variable type delay is introduced as a function of time to describe instances where the delay is not constant, the fractional order derivatives are generated using the Caputo approach. The existence, uniqueness, and stability of solutions are the main topics of the theoretical analysis of the suggested differential equations. In order to establish important mathematical features, the inquiry makes use of spectral techniques, and fixed-point theorems. The study finishes by summarizing the major discoveries and outlining potential future research avenues in this developing field. It highlights the potential contribution of multi-term fractional differential equations with variable type delay to improving the control and design of complex systems. Overall, this study adds to the growing body of knowledge in the field of fractional calculus and provides insightful information about the investigation of multi-term fractional differential equations with variable type delay, making it pertinent for academics and practitioners from a variety of fields.

An artificial neural network based approach for harmonic component prediction in a distribution line

With the increasing use of nonlinear devices in both generation and consumption of power, it is essential that we develop accurate and quick control for active filters to suppress harmonics. Time delays between input and output are catastrophic for such filters which rely on real-time operation. Artificial Neural Networks (ANNs) are capable of modeling complex nonlinear systems through adjustments in their learned parameters. Once properly trained, they can produce highly accurate predictions at an instantaneous time frame. Leveraging these qualities, various complex control systems may be replaced or aided by neural networks to provide quick and precise responses. This paper proposes an ANN-based approach for the prediction of individual harmonic components using minimal inputs. By extracting and analyzing the nature of harmonic component magnitudes obtained from the survey of a particular area through real-time measurements, a sequential pattern in their occurrence is observed. Various neural network architectures are trained using the collected data and their performances are evaluated. The best-performing model, whose losses are minimal, is then used to observe the harmonic cancellation for multiple unseen cases through a simplified simulation in hardware-in-the-loop. These neural network structures, which produce instantaneous and accurate outputs, are effective in harmonic filtering.