Search Results (7,107)

This paper, based on differential game theory, examines governance models and cooperative strategies for managing cross-border water pollution in regions with uneven economic development. To address cross-regional water pollution, three differential game models are constructed under different scenarios: the Nash noncooperative mechanism, the pollution control cost compensation mechanism, and the collaborative cooperation mechanism. This study analyzes the dynamic changes in pollution emissions, governance investments, and economic returns within each model. The results indicate that the collaborative cooperation mechanism is the most effective, as it significantly reduces pollution emissions, maximizes overall regional benefits, and achieves Pareto optimality. In comparison, the pollution control cost compensation mechanism is suboptimal under certain conditions, while the Nash noncooperative mechanism is the least efficient, resulting in the highest pollution emissions. Furthermore, the research explores the influence of cooperation costs on the selection of governance models. It finds that high cooperation costs reduce local governments’ willingness to engage in collaborative cooperation. However, an appropriate compensation mechanism can effectively encourage less-developed regions to participate. Numerical analysis confirms the dynamic evolution of pollution stocks and economic returns under different models, and provides corresponding policy recommendations. This paper offers theoretical insights and practical guidance for cross-regional water pollution management, highlighting the importance of regional cooperation and cost-sharing in environmental governance. Full article

This study introduces a novel method for controlling an autonomous photovoltaic pumping system by integrating a Maximum Power Point Tracking (MPPT) control scheme with variable structure Sliding Mode Control (SMC) alongside Perturb and Observe (P&O) algorithms. The stability of the proposed SMC method is rigorously analyzed using Lyapunov’s theory. Through simulation-based comparisons, the efficacy of the SMC controller is demonstrated against traditional P&O methods. Additionally, the SMC-based system is experimentally implemented in real time using dSPACE DSP1104, showcasing its robustness in the presence of internal and external disturbances. Robustness tests reveal that the SMC controller effectively tracks Maximum Power Points (MMPs) despite significant variations in load and solar irradiation, maintaining optimal performance even under challenging conditions. The results indicate that the SMC system can achieve up to a 70% increase in water flow rates compared with systems without MPPT controllers. Furthermore, SMC demonstrated high sensitivity to sudden changes in environmental conditions, ensuring efficient power extraction from the photovoltaic panels. This study highlights the advantages of integrating SMC into Photovoltaic Water Pumping Systems (PV-WPSs), providing enhanced control capabilities and optimizing system performance. The findings contribute to the development of sustainable water supply solutions, particularly in remote areas with limited access to the electrical grid. Full article

In this study, a finite-time disturbance observer (FTDOB) with a new structure is originally put forward for the motion tracking problem of a class of nonlinear systems subject to model uncertainties and exogenous disturbances. Compared to existing disturbance estimator designs in the literature, in which the estimation error only converges to the origin asymptotically under assumptions that the first and/or second derivatives are vanishing, the suggested DOB is able to estimate the disturbance exactly in finite time. Firstly, uncertainties (parametric and unstructured uncertainties), unknown dynamics, and external disturbances in system dynamics are lumped into a generalized disturbance term that is subsequently estimated by the proposed DOB. Based on this, a DOB-based backstepping controller is synthesized to ensure high-accuracy tracking performance under various working conditions. The stability analysis of not only the DOB but also the overall closed-loop system is theoretically confirmed by the Lyapunov stability theory. Finally, the advantages of the proposed FTDOB and the FTDOB-based controller over other DOBs and existing DOB-based controllers are explicitly simultaneously demonstrated by a series of numerical simulations on a second-order mechanical system and comparative experiments on an actual DC motor system. Full article

In the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon measurement across all operational aspects of distribution networks, reduce their carbon emissions through controlling unit operations, and ensure stable and safe operation. First, we propose a method for measuring carbon emission intensity on the source and network sides of active distribution networks with network losses, allowing for the calculation of total carbon emissions throughout the operation of networks and their equipment. Next, based on the carbon flow distribution of distribution networks, we construct a low-carbon dispatch model and formulate its optimization problem within a Markov Decision Process framework. We improve the Soft Actor–Critic (SAC) algorithm by adopting a Gaussian-distribution-based reward function to train and deploy agents for optimal low-carbon dispatch. Finally, the effectiveness of the proposed model and the superiority of the improved algorithm are demonstrated using a modified IEEE 33-bus distribution network test case. Full article

In the present study, we introduce the two-dimensional Chlodovsky-type Bernstein operators based on the $(p,q)$-integer. By leveraging the inherent symmetry properties of $(p,q)$-integers, we examine the approximation properties of our new operator with the help of a Korovkin-type theorem. Further, we present the local approximation properties and establish the rates of convergence utilizing the modulus of continuity and the Lipschitz-type maximal function. Additionally, a Voronovskaja-type theorem is provided for these operators. We also investigate the weighted approximation properties and estimate the rate of convergence in the same space. Finally, illustrative graphics generated with Maple demonstrate the convergence rate of these operators to certain functions. The optimization of approximation speeds by these symmetric operators during system control provides significant improvements in stability and performance. Consequently, the control and modeling of dynamic systems become more efficient and effective through these symmetry-oriented innovative methods. These advancements in the fields of modeling fractional differential equations and control theory offer substantial benefits to both modeling and optimization processes, expanding the range of applications within these areas. Full article

This paper evaluates the open- and closed-loop DC–DC converter operation within a DC coupling multilevel inverter architecture to obtain an infinite-level stepped sinusoidal voltage. Adding a cascade controller to the DC–DC converter should reduce the settling time and increase the number of levels in the output voltage waveform; it could decrease the speed error and phase shift concerning the sinusoidal reference signal. The proposed methodology consists of implementing an experimental multilevel inverter with DC coupling through a single-phase bridge inverter energized from a BUCK converter. Trigger signals for the two converters are obtained from a control circuit based in an ATMEGA644P microcontroller to explore its capabilities in power electronics applications. A digital controller is also implemented to evaluate the operation of the BUCK converter in open and closed loop and observe its influence in the stepped sinusoidal output voltage. The evaluation is performed to energize a resistive load with common output voltage in multilevel inverters, i.e., 3, 5, 7, 11, and infinity levels. Results show that during the design stage, fast dynamic elements, like the storage capacitor, can be used to obtain a minimum THD because the settling time is sufficiently fast, the speed error remains small, and there is no need for a controller. A digital controller requires processing time, and although in theory it can reduce the settling time to a minimum, the processor introduces latency in the control signals generation, producing the opposite effect. Controller complexity of the digital controller must be considered because it increases processing time and influences the efficiency of the closed-loop operation. Full article

Aiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperatively intercepting a maneuvering target, this paper proposes a cooperative guidance law with less energy consumption and a newly accurate time-to-go estimation algorithm in the two-dimensional (2D) plane. Firstly, based on the relative motion equations between UAVs and the target on the 2D plane, the line-of-sight (LOS) direction and the LOS normal direction models are established. Then, based on the distributed model predictive control (DMPC) theory, DMPC cooperative guidance laws are designed in two directions. This guidance law can ensure that all UAVs intercept the maneuvering target at the expected LOS angle at the same time and reduce the energy consumption during the guidance process. Then, a new time-to-go estimation algorithm is designed, which can reduce the time-to-go estimation error and improve the cooperative accuracy. Finally, the simulation results show that the DMPC cooperative guidance law reduces energy consumption by more than 50% compared to other guidance laws and the proposed time-to-go estimation algorithm improves the accuracy by 200% compared to traditional methods. Full article

The safety and efficiency of underground coal mining are threatened by thick hard roofs characterized by large overhang areas, problematic spontaneous caving, and high dynamic load upon their breakage. In this study, a mechanical model of the bearing capacity of thick hard roofs in upward mining coalfaces associated with mining activities is built based on bending theories for beams with single generalized displacement and the elastic foundation beam theory. Using this method, we analyze the deformation and fracture mechanisms of a thick hard roof during upward mining. We further derive the mechanical equations of rotational angle, bending moment, shear force, and deflection of the free overhang and coal-bearing zone in the thick hard roof and an equation for calculating the limiting span. The mechanical behaviors of the thick hard roof bearing state are analyzed under different parameters. The results show that the foundation coefficient, roof thickness, and angle of upward mining have little influence on the roof bending moment but are positively correlated to the limiting span. Roof load and overhang length have a significant influence on the roof bending moment. They are negatively and positively correlated with the limiting span, respectively. Finally, a case study is performed on the Ш601 upward mining coalface in the Zhuzhuang Coal Mine. The distribution characteristics of the bending moment of the thick hard roof at different extraction stages are analyzed. At each stage, the limiting spans of the thick hard roof upon breaking were calculated as 13.18, 18.82, and 22.50 m, respectively, being close to the on-site measured periodic weighting lengths of 13.33, 19.33 m, and 22.67 m. This close fit proves the feasibility and accuracy of the developed mechanical model. The present study offers theoretical guidance for estimating the weighting length of thick hard roofs in coalfaces and for engineering technology control in similar scenarios. Full article

For a forward-bending biped robot with 10 degrees of freedom on its legs, a new control framework of MPC-DCM based on force and moment is proposed in this paper. Specifically, the Diverging Component of Motion (DCM) is a stability criterion for biped robots based on linear inverted pendulum, and Model Predictive Control (MPC) is an optimization solution strategy using rolling optimization. In this paper, DCM theory is applied to the state transition matrix of the system, combined with simplified rigid body dynamics, the mathematical description of the biped robot system is established, the classical MPC method is used to optimize the control input, and DCM constraints are added to the constraints of MPC, making the real-time DCM approximate to a straight line in the walking single gait. At the same time, the linear angle and friction cone constraints are considered to enhance the stability of the robot during walking. In this paper, MATLAB/Simulink is used to simulate the robot. Under the control of this algorithm, the robot can reach a walking speed of 0.75 m/s and has a certain anti-disturbance ability and ground adaptability. In this paper, the Model-H16 robot is used to deploy the physical algorithm, and the linear walking and obstacle walking of the physical robot are realized. Full article

To address the common issues of accuracy and stability in trajectory tracking tasks for autonomous vehicles, this study proposes an innovative composite control strategy that skillfully integrates lateral and longitudinal dynamic control. For lateral control, model predictive control (MPC) theory is introduced to compute the front wheel steering angle that ensures optimal trajectory following. On the longitudinal control level, the vehicle’s acceleration and deceleration logic are finely tuned to ensure precise adherence to the preset speed trajectory. More importantly, by deeply integrating these two control methods, the comprehensive coordination of the vehicle’s lateral and longitudinal movements is achieved. To validate the effectiveness of the proposed control strategy, simulations were conducted using the CarSim and MATLAB/Simulink platforms. The analysis of the simulation results confirms that the proposed method effectively improves speed tracking stability and significantly enhances path tracking accuracy and overall driving stability. Full article

This article introduces a novel perspective on designing a stepping controller for bipedal robots. Typically, designing a state-feedback controller to stabilize a bipedal robot to a periodic orbit of step-to-step (S2S) dynamics based on a reduced-order model (ROM) can achieve stable walking. However, the model discrepancies between the ROM and the full-order dynamic system are often ignored. We introduce the latest results from behavioral systems theory by directly constructing a robust stepping controller using input-state data collected during flat-ground walking with a nominal controller in the simulation. The model uncertainty discrepancies are equivalently represented as bounded noise and over-approximated by bounded energy ellipsoids. We conducted extensive walking experiments in a simulation on a 22-degrees-of-freedom small humanoid robot, verifying that it demonstrates superior robustness in handling uncertain loads, various sloped terrains, and push recovery compared to the nominal S2S controller. Full article

This article presents information about harmonic distortion and resonance in distribution networks. The theory behind harmonics and resonance is presented. Examples from the literature and the results of power quality measurements, as well as the authors’ experiences connected with significant changes in harmonic distortions, are presented. The harmonic resonance phenomenon is explained, and the risk of resonance in a distribution system network is highlighted. Attention is paid to the connection of a new power plant to the network; however, other risks, e.g., those connected to network reconfiguration, are also highlighted. Further simulation case studies are presented to show interactions between volt/VAR control and harmonic distortion. Simulations consider a few scenarios: impact of voltage change on impedance characteristics and resulting harmonics amplitudes, the impact of a capacitor on impedance characteristics, and the impact of network expansion on harmonic distortion. The final part presents alternative, low-cost harmonics mitigation methods. The concept of the utilization of phase-shifting transformers for two twin-type 1 MW plants located next to each other is verified by on-site measurement. The concept of adapting the harmonics spectrum of new devices to impedance characteristics is presented. Finally, an alternative concept for active mitigation of harmonics under resonance conditions is provided. The concept is based on the reactive power correction in order to change the harmonics phase shift. A comparison of harmonic mitigation methods and general recommendations are provided. Further research is outlined. Full article

Addressing gaps in the existing research, this study investigates how event image affects attitudes, subjective norms, and perceived behavioral control (PBC); how attitudes, subjective norms, and PBC impact behavioral intention, applying the theory of planned behavior (TPB) framework; and how social support moderates these variables among older participants in park golf. We gathered data from senior park golf tournament participants and utilized confirmatory factor analysis to validate the measurement scale, assessing factor loadings, average variance extracted (AVE), and construct reliability (CR), while our analysis of Cronbach’s alpha ensured scale reliability. We also employed structural equation modeling with maximum likelihood estimation to analyze the positive relationships and conducted a moderation analysis using Jamovi software. The results indicate the significant effect of event image on attitudes, subjective norms, and PBC, as well as the significant effect of attitudes, subjective norms, and PBC on behavioral intention. Moreover, social support moderates the relationship between subjective norms and behavioral intention. Full article

The interplay between socio-psychological factors and biological systems is pivotal in defining human health and disease, particularly in chronic non-communicable diseases. Recent advancements in psychoneuroimmunology and mitochondrial psychobiology have emphasized the significance of psychological factors as critical determinants of disease onset, progression, recurrence, and severity. These insights align with evolutionary biology, psychology, and psychiatry, highlighting the inherent social nature of humans. This study proposes a theory that expands insulin’s role beyond traditional metabolic functions, incorporating it into the Mitochondrial Information Processing System (MIPS) and exploring it from an evolutionary medicine perspective to explore its function in processing psychological and social factors into biological responses. This narrative review comprises data from preclinical animal studies, longitudinal cohort studies, cross-sectional studies, machine learning analyses, and randomized controlled trials, and investigates the role of insulin in health and disease. The result is a proposal for a theoretical framework of insulin as a social substance within the socio-psycho-biological framework, emphasizing its extensive roles in health and disease. Type 2 Diabetes Mellitus (T2DM) with musculoskeletal disorders and neurodegeneration exemplifies this narrative. We suggest further research towards a comprehensive treatment protocol meeting evolutionary expectations, where incorporating psychosocial interventions plays an essential role. By supporting the concept of ‘insulin resilience’ and suggesting the use of heart rate variability to assess insulin resilience, we aim to provide an integrative approach to managing insulin levels and monitoring the effectiveness of interventions. This integrative strategy addresses broader socio-psychological factors, ultimately improving health outcomes for individuals with T2DM and musculoskeletal complications and neurodegeneration while providing new insights into the interplay between socio-psychological factors and biological systems in chronic diseases. Full article

Monte Carlo Model Predictive Control (MCMPC) is a kind of non-linear Model Predictive Control (MPC) that determines control inputs using the Monte Carlo method. The Monte Carlo method used in MCMPC can handle discontinuous phenomena, and there have been reports of its application in control systems for quadrotors, where the objective is to maintain the stability of the attitude during collisions. However, as with conventional MPC, concerns remain about control performance degradation due to modeling errors and external disturbances such as unexpected wind gusts. In this study, we propose a novel Robust Monte Carlo Model Predictive Control (RMCMPC), which improves the robustness of MCMPC by considering the hypersurface known from the Sliding Mode Control (SMC) theory. The proposed RMCMPC is applied to a quadrotor attitude control system, and its effectiveness is validated through numerical simulations. Full article