Search Results (48)

By applying the Non-Perturbative Approach (NPA), the corresponding linear differential equation is obtained. Aimed at organizational investigation, the resulting linear equation is used. Strong agreement between numerical calculations and the precise frequency is demonstrated, and the reliability of the results acquired is established by the correlation with the numerical solution. Additionally, this study explores a new control process to affect the stability and behavior of dynamic motorcycle systems that vibrate nonlinearly. A multiple time-scale method (MTSM) is applied to examine the analytical solution of the nonlinear differential equations describing the aforementioned system. Every instance of resonance was taken out of the second-order approximations. The simultaneous primary and 1:1 internal resonance case ($\mathsf{\Omega }\approx {\omega }_{eq},$${\omega }_2\approx {\omega }_{eq}$) is recorded as the worst resonance case caused while working on the model. We investigated stability with frequency–response equations and bifurcation. Numerical solutions for the system are covered. The effects of the majority of the system parameters were examined. In order to mitigate harmful vibrations, the controller under investigation uses (PD) proportional derivatives with (PPF) positive position feedback as a new control technique. This creates a new active control technique called PDPPF. A comparison between the PD, PPF, and PDPPF controllers demonstrates the effectiveness of the PDPPF controller in reducing amplitude and suppressing vibrations. Unwanted consequences like chaotic dynamics, limit cycles, or loss of stability can result from bifurcation, which is the abrupt qualitative change in a system’s behavior as a parameter. The outcomes showed how effective the suggested controller is at reducing vibrations. According to the findings, bifurcation analysis and a control are crucial for designing vibrating dynamic motorcycle systems for a range of engineering applications. The MATLAB software is utilized to match the analytical and numerical solutions at time–history and frequency–response curves (FRCs) to confirm their comparability. Additionally, case studies and numerical simulations are presented to show how well these strategies work to control bifurcations and guarantee the desired system behaviors. An analytical and numerical solution comparison was prepared. Full article

This study investigates the nonlinear dynamics and control of an underactuated hybrid system consisting of a Duffing oscillator, a pendulum, and a piezoelectric energy harvester. A novel Piezoelectric Harvester Proportional–Derivative (PHPD) control scheme is introduced, which integrates the harvester’s electrical output directly into the feedback loop to achieve simultaneous vibration suppression and energy utilization. The nonlinear governing equations are derived and analyzed using the Multiple-Scale Perturbation Technique (MSPT) to obtain reduced-order dynamics. Bifurcation analysis is employed to identify stability boundaries and critical parameter transitions, while numerical simulations based on the fourth-order Runge–Kutta method validate the analytical predictions. Furthermore, frequency response curves (FRCs) and an ideal system are evaluated under multiple controller and system parameter configurations. Bifurcation classification is performed on the analyzed figure to detect various bifurcations within the system, along with the computation of the Largest Lyapunov Exponent (LLE). The results demonstrate that PHPD control significantly reduces vibration amplitude and accelerates convergence, offering a new pathway for energy-efficient, high-performance control in nonlinear electromechanical systems. Full article

This paper investigates the system architecture and circuit topology of grid-connected inverters with embedded energy storage (EES), encompassing their modulation strategies and control methodologies. A mathematical model for an EES grid-connected inverter is derived based on capacitor current feedback control, from which the expression for the inverter’s output impedance is obtained. Building on this foundation, this study analyzes the influence of control parameters—such as the proportional coefficient, resonant coefficient, and switching frequency—on the inverter’s output impedance. Subsequently, the stability of single and multiple inverter grid-connected systems under various operating conditions is assessed using impedance analysis and the Nyquist criterion. Finally, the validity of the stability analysis based on the established mathematical model is verified through simulations conducted on the Matlab/Simulink platform, where models for both a single inverter and a two-inverter grid-connected system are constructed. Full article

Iterative learning control (ILC) typically requires strict repeatability in initial states, trajectory length, external disturbances, and system dynamics. However, these assumptions are often difficult to fully satisfy in practical applications. While most existing studies have achieved limited progress in relaxing either one or two of these constraints simultaneously, this work aims to eliminate the restrictions imposed by all four strict repeatability conditions in ILC. For general finite-duration multi-input multi-output (MIMO) linear discrete-time systems subject to multiple non-repetitive uncertainties—including variations in initial states, external disturbances, trajectory lengths, and system dynamics—an innovative open-closed loop robust iterative learning control law is proposed. The feedforward component is used to make sure the tracking error converges as expected mathematically, while the feedback control part compensates for missing tracking data from previous iterations by utilizing real-time tracking information from the current iteration. The convergence analysis employs an input-to-state stability (ISS) theory for discrete parameterized systems. Detailed explanations are provided on adjusting key parameters to satisfy the derived convergence conditions, thereby ensuring that the anticipated tracking error will eventually settle into a compact neighborhood that meets the required standards for robustness and convergence speed. To thoroughly assess the viability of the proposed ILC framework, computer simulations effectively illustrate the strategy’s effectiveness. Further simulation on a real system, a piezoelectric motor system, verifies that the ILC tracking error converges to a small neighborhood in the sense of mathematical expectation. Extending the ILC to complex real-world applications provides new insights and approaches. Full article

The complex nature of immune system behavior in both autoimmune diseases and transplant rejection can be understood through the lens of avalanche dynamics in critical-point systems. This paper introduces the concept of the “immunological avalanche” as a framework for understanding unpredictable patterns of immune activity in both contexts. Just as avalanches represent sudden releases of accumulated potential energy, immune responses exhibit periods of apparent stability followed by explosive flares triggered by seemingly minor stimuli. The model presented here draws parallels between immune system behavior and other complex systems such as earthquakes, forest fires, and neuronal activity, where localized events can propagate into large-scale disruptions. In autoimmune conditions like systemic lupus erythematosus (SLE), which affects multiple organ systems including the kidneys in approximately 50% of patients, these dynamics manifest as alternating periods of remission and flares. Similarly, in transplant recipients, the immune system exhibits metastable behavior under constant allograft stimulation. This critical-point dynamics framework is characterized by threshold-dependent activation, positive feedback loops, and dynamic non-linearity. In autoimmune diseases, triggers such as UV light exposure, infections, or stress can initiate cascading immune responses. In transplant patients, longitudinal analysis reveals how monitoring oscillatory patterns in blood parameters and biological age markers can predict rejection risk. In a preliminary study on kidney transplant, all measured variables showed temporal instability. Proteinuria exhibited precise log–log linearity in power law analysis, confirming near-critical-point system behavior. Two distinct dynamic patterns emerged: large oscillations in eGFR, proteinuria, or biological age predicted declining function, while small oscillations indicated stability. During avalanche events, biological age increased dramatically, with partial reversal leaving persistent elevation after acute episodes. Understanding these dynamics has important implications for therapeutic approaches in both contexts. Key findings suggest that monitoring parameter oscillations, rather than absolute values, better indicates system instability and potential avalanche events. Additionally, biological age calculations provide valuable prognostic information, while proteinuria measurements offer efficient sampling for system dynamics assessment. This conceptual model provides a unifying framework for understanding the pathogenesis of both autoimmune and transplant-related immune responses, potentially leading to new perspectives in disease management and rejection prediction. Full article

We present a flexible mathematical method that extends the Rosenstark method and enables the analysis of any electrical network with feedback and all its key parameters such as gain, frequency response, input/output impedances, and noise. Unlike the original Rosenstark method, the proposed approach provides a unified procedure that can be universally applied without the need of additional steps (like Blackman’s theorem) to fully characterize the network. Our approach inherits the same advantages of the Rosenstark method, eliminating the need for approximate sub-topologies or circuit simplifications, and can be seamlessly implemented across various circuit configurations. It is thus particularly effective for analyzing amplifiers with multiple feedback loops or experimental situations involving parasitic elements that impact stability or noise. The method is useful both for experienced designers and for individuals with limited experience in circuit analysis, such as physics and engineering undergraduate students, as it relies on a minimal set of procedures to evaluate all network parameters. Full article

Neural networks, mimicking the structural and functional aspects of the human brain, have found widespread applications in diverse fields such as pattern recognition, control systems, and information processing. A critical phenomenon in these systems is synchronization, where multiple neurons or neural networks harmonize their dynamic behaviors to a common rhythm, contributing significantly to their efficient operation. However, the inherent complexity and nonlinearity of neural networks pose significant challenges in understanding and controlling this synchronization process. In this paper, we focus on the synchronization of a class of fractional-order, delayed, and non-autonomous neural networks. Fractional-order dynamics, characterized by their ability to capture memory effects and non-local interactions, introduce additional layers of complexity to the synchronization problem. Time delays, which are ubiquitous in real-world systems, further complicate the analysis by introducing temporal asynchrony among the neurons. To address these challenges, we propose a straightforward yet powerful global synchronization framework. Our approach leverages novel state feedback control to derive an analytical formula for the synchronization controller. This controller is designed to adjust the states of the neural networks in such a way that they converge to a common trajectory, achieving synchronization. To establish the asymptotic stability of the error system, which measures the deviation between the states of the neural networks, we construct a Lyapunov function. This function provides a scalar measure of the system’s energy, and by showing that this measure decreases over time, we demonstrate the stability of the synchronized state. Our analysis yields sufficient conditions that guarantee global synchronization in fractional-order neural networks with time delays and Caputo derivatives. These conditions provide a clear roadmap for designing neural networks that exhibit robust and stable synchronization properties. To validate our theoretical findings, we present numerical simulations that demonstrate the effectiveness of our proposed approach. The simulations show that, under the derived conditions, the neural networks successfully synchronize, confirming the practical applicability of our framework. Full article

Soil environmental monitoring is crucial for ensuring the sustainability and productivity of agriculture. This study aims to develop a wireless soil monitoring system that utilizes Narrowband Internet of Things (NB-IoT), solar energy, and Global Positioning System (GPS) technologies to address the issues of high labor demand, high costs, and delayed feedback in traditional soil monitoring methods. This system can collect soil temperature, humidity, and meteorological data in real time, transmit them to a cloud platform for analysis and visualization, and predict future soil data. It employs multiple learning algorithms to build models and uses the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization. Field stability experiments were conducted on the system, and the performance of the soil moisture prediction model was evaluated. During the 84-day stability experiment, the system operated stably for 80 days, with a data collection success rate of 95.87%. In the performance evaluation of the soil moisture model, the GBDT model achieved a coefficient of determination (R²) of 0.9838 on the validation set and a root-mean-square error (RMSE) of 0.0013, with an RMSE of 0.0013 on the test set as well. The experimental results demonstrate that the system is stable and reliable, featuring low power consumption, wide coverage, and high accuracy. It can effectively predict soil moisture, providing timely and accurate support for irrigation and farming decisions. Full article

A split-phase three-level LCL grid-connected inverter is proposed to match the single-phase three-wire split-phase output power grids in countries such as those in North America. However, influencing factors such as grid impedance and background harmonics in non-ideal power grids may lead to distortion and even instability of the output waveform of the grid-connected inverter. To address the aforementioned issues, through a stability analysis of the dual-feedback system of inverter-side current control and capacitor current active damping, a composite active damping strategy is put forward to enhance the stability of the LCL grid-connected inverter. This composite active damping strategy encompasses a standardized method for designing the robust capacitor current feedback coefficient and a method of embedding leading-phase correction to improve system stability. The strategy proposed in this paper is more streamlined and standardized when calculating the capacitor current feedback coefficient, enabling the system to operate stably under a wide range of grid impedance variations. Moreover, an analysis of the mechanism by which grid background harmonics affect the grid-connected current waveform is conducted, and the PR controller is replaced with a multiple proportional-resonant (MPR) controller. The MPR controller can achieve infinite gain at specific harmonics, thereby suppressing specific low-order harmonics in the grid and reducing the total harmonic distortion (THD) of the grid-connected current. Finally, the effectiveness of the proposed control strategy is verified on a 12 kW experimental platform. The results indicate that, compared with the inverter-side current feedback active damping (ICFAD) method, the composite active damping strategy proposed in this paper exhibits stronger robustness, and the added MPR controller significantly enhances the anti-interference ability of the grid-connected inverter against grid harmonics. Full article

With the rapid development of new energy sources, distributed power supplies are widely used in DC microgrid systems. The DC–DC converter, as the hub for transmitting energy between the distributed power supply and the DC bus, plays an important role in the stability of the whole system performance. Due to the complexity of the actual working environment, the DC bus voltage is often affected by uncertainties such as fluctuations of distributed power supply and random changes in load, so the reliability of DC–DC converters is increasingly required, and it is difficult for traditional linear controllers to ensure that a DC–DC converter operates stably over a wide range. In order to solve this problem, this paper takes the Single-Inductor Triple-Output (SITO) Buck converter, which represents the multi-input and multi-output system, as the research object, analyzes the respective operating characteristics and control difficulties, and proposes Objective Holographic Feedback Nonlinear Control (OHFNC) to improve the stability of the research system. The optimal control based on objective holographic feedback is proposed to address the cross-influence factors between the multiple output branches of the Buck converter and the inability of accurate feedback linearization. Finally, the validity of the model and theoretical analysis is verified by simulation and experimental results. Full article

Amyotrophic lateral sclerosis (ALS) has an interactive, multifactorial etiology that makes treatment success elusive. This study evaluates how regulatory dynamics impact disease progression and treatment. Computational models of wild-type (WT) and transgenic SOD1-G93A mouse physiology dynamics were built using the first-principles-based first-order feedback framework of dynamic meta-analysis with parameter optimization. Two in silico models were developed: a WT mouse model to simulate normal homeostasis and a SOD1-G93A ALS model to simulate ALS pathology dynamics and their response to in silico treatments. The model simulates functional molecular mechanisms for apoptosis, metal chelation, energetics, excitotoxicity, inflammation, oxidative stress, and proteomics using curated data from published SOD1-G93A mouse experiments. Temporal disease progression measures (rotarod, grip strength, body weight) were used for validation. Results illustrate that untreated SOD1-G93A ALS dynamics cannot maintain homeostasis due to a mathematical oscillating instability as determined by eigenvalue analysis. The onset and magnitude of homeostatic instability corresponded to disease onset and progression. Oscillations were associated with high feedback gain due to hypervigilant regulation. Multiple combination treatments stabilized the SOD1-G93A ALS mouse dynamics to near-normal WT homeostasis. However, treatment timing and effect size were critical to stabilization corresponding to therapeutic success. The dynamics-based approach redefines therapeutic strategies by emphasizing the restoration of homeostasis through precisely timed and stabilizing combination therapies, presenting a promising framework for application to other multifactorial neurodegenerative diseases. Full article

Separate DC-DC converters for each energy source are typically configured in fuel-cell hybrid vehicles. This results in a complex control structure of the powertrain system, low energy density of the converter, and high cost due to the large number of components. Conducting research on DC-DC converters with good energy flow management and high integration is a trend to solve such problems. Based on the analysis of the basic functional structure of the converter, this paper designs a buffering unit circuit with energy collection and distribution functions and appropriately connects it with the pulse unit circuit of the converter. Through device optimization reuse and power transmission path integration, a class of non-isolated four-port DC-DC converters is constructed, which consists of an auxiliary energy charging module, input energy source control module, braking energy feedback module and forward bootstrap boost circuit. This converter has two bi-directional ports, a uni-directional input and a bi-directional output, for separate connection to the power batteries, supercapacitors, fuel cells and DC bus. It can adapt to the fluctuation of the vehicle’s driving condition while achieving dynamic and flexible regulation of power flow and can flexibly allocate power according to the load current and voltage level of energy. It can realize a total of 14 operation modes, including six output power supply operation modes, five auxiliary power charging operation modes, and three braking energy regeneration operation modes. Furthermore, the mathematical model of this converter is constructed using the state-average method and the small-signal modeling method in order to achieve the responsiveness and stability of switching multiple operating modalities. The PI control parameters are optimized using the particle swarm optimization algorithm to achieve optimized control of the converter. The simulation system is set up using MATLAB R2024a to verify that the proposed converter topology and algorithm can dynamically allocate appropriate current paths to manipulate the power flow under various operating conditions, effectively improving the utilization rate and efficiency of energy. The converter has the characteristics of high gain and high power density, which is suitable for three-energy fuel cell hybrid electric vehicles. Full article

The parallel operation of multiple LCL-type converters will result in a deviation of the resonant frequency and resonance phenomena. The occurrence of harmonic resonance can cause problems such as an increase in harmonic voltage and current. This can lead to the malfunction of relay protection and automatic devices, causing damage to system equipment. In severe cases, it can cause accidents and threaten the safe operation of the power system. A hybrid damping active disturbance rejection control (HD-ADRC) method is proposed in this paper to suppress the harmonic resonance of parallel LCL-type converters. First, a third-order linear disturbance rejection controller (LADRC) including the linear extended-state observer and the error-feedback control rate is designed based on LCL-type converter model analysis. The proposed method considers the resonance couplings caused by both internal and external disturbances as the total disturbance, thus improving the anti-disturbance capabilities as well as the operational stability of converters in parallel. Then, a hybrid damping control is proposed to reconstruct the damping characteristics of converters to suppress the parallel resonance spike and reduce the resonance frequency offset. And the parameter selection of the control system is optimized through a stability analysis of the tracking performance and anti-disturbance performance of the HD-ADRC controller. Finally, all the theoretical considerations are verified by simulation and experimental results based on the Matlab/Simulink 2018B and dSpace platform. The simulation and experimental results show that the PI controller gives a THD of 5.33%, which is reduced to 4.66% by employing the HD-LADRC, indicating an improved decoupling between the converters working in parallel with the proposed control scheme. Full article

The thick ore bodies in the Xianglushan tungsten mine have been irregularly mined, forming a super large, connected irregular goaf group and tall, isolated irregular pillars inside. At the same time, there is a production capacity task of recovering residual and dangerous ore bodies. This poses the potential for serious ground-pressure disasters, such as roof caving, pillar collapse, and large-scale goaf collapse during mining. Based on the actual needs of the site, we established a microseismic monitoring system. After analyzing the mining and filling processes and their relationships, and, combined with the distribution characteristics of microseismic multiple parameters, we constructed a ground-pressure disaster warning mode and mechanism. We analyzed the stability of the goaf, further formed a warning system, and achieved disaster warning. In response to the current situation of the difficulty of early warning of ground pressure in the Xianglushan tungsten mine, continuous on-site monitoring of existing goaves, point pillars, and strip pillars, as well as analysis of stress changes during dynamic mining and filling processes, we explored scientific and reasonable early warning mechanisms and models, understanding the relationship between the changes in microseismic parameters during dynamic mining and filling processes and ground pressure, studying and improving the reliability of underground microseismic monitoring and early warning, and achieved the internal connection between building early warning systems and the prevention of ground-pressure disasters. The results indicate that the mining and filling process of the ore body is the main factor in maintaining a stable and balanced distribution of underground ground pressure in mining engineering. Microseismic monitoring can invert the evolution of ground pressure and form a feedback system with ground-pressure warning, achieving mine safety management. Full article

In response to the challenge of existing wireless communication anti-jamming methods in effectively handling unknown jamming, this paper proposes a multi-parameter control anti-jamming algorithm for wireless communication systems based on the Linear–Quadratic Regulator (LQR). First, the proposed algorithm models the wireless communication system as a linear switched system based on the modulation and coding scheme. Subsequently, a feedback controller design method based on the LQR is introduced. By utilizing the multiple Lyapunov function method combined with linear matrix inequalities, sufficient criteria for the asymptotic stability of the system under unknown jamming conditions are provided. Finally, theoretical analysis and simulation results indicate that the proposed algorithm can rapidly adjust modulation and coding schemes as well as transmission power in complex jamming environments, thereby maintaining bit error rate (BER) stability and enhancing the reliability of the communication system. Full article