Control Approach Research Articles

Performance Improvement of Predictive Voltage Control for Interlinking Converters of Integrated Microgrid

Voltage quality in Renewable Energy System may be harmed by varying power demand and fluctuating energy source outputs. The model predictive voltage (MPV) control approach is suggested in this study. The proposed control strategy enables appropriate power interactions between the AC and DC subgrids while sharing power fluctuations in a coordinated way. Both the AC and DC subgrids support each other in accordance with their normalized relative changes in AC frequency and DC voltage, respectively. A typical photovoltaic (PV) wind-battery-based hybrid AC/DC microgrid is modelled and investigated, and the usefulness of the proposed scheme is validated under the renewable energy variations and load perturbations. MATLAB/Simulink has validated the efficacy of the suggested strategy. A set of comparative tests was conducted between conventional Proportional-Integral (PI) control, Sliding Mode Control (SMC) strategies, and a novel Model Predictive Voltage Control (MPV) approach under diverse conditions. The aim was to comprehensively assess the advantages of the proposed MPV method. Results demonstrate that, in contrast to traditional PI and SMC control strategies, the MPV technique exhibits distinct benefits in terms of superior control performance and achieves exceptionally low Total Harmonic Distortion (THD) values. Ultimately, the proposed solution not only raises the performance standard but also ensures the stable operation of the Renewable Energy System (RES).

Virtual voltage control to redistribute reactive power of generators in a microgrid

The paper examines a strategy for managing voltage control in a microgrid by redistributing reactive power among its distributed generators. Unlike traditional droop control, the new control approach can provide a more accurate reactive power response based on a virtual impedance that helps calculate a virtual voltage. In addition, this virtual impedance is employed for the current controller inverter to improve the results. The adaptive virtual voltage control works well to provide active and reactive power. The proposed control works effectively by balancing the active and reactive power of the grid and maintains the fundamental frequency. The control technique assists the new microgrid (MG) in adapting the operation effectively and redistributing the active and reactive power.

Bidirectional DC-DC Converter and Improved Electrical Vehicle Dynamic Response Control

Background: In automotive applications where bidirectional power flow is necessary to lighten the power system, dual active bridge (DAB) converters are frequently employed. Variations in the required output voltage, erratic input voltage, and shifting loads all have an impact on this converter. As a result, converter performance has to be improved. To increase efficiency, the current stress of the DC-DC converter must be optimised. This paper proposes a control scheme for the coupled inductor bidirectional DC-DC (CIB DC-DC) converter utilising both model predictive control (MPC) and a proportional-integral (PI) controller. The integration of these control techniques aims to enhance the performance and efficiency of the converter. Methods: The MPC algorithm is employed to predict the converter's future behaviour based on a dynamic model, taking into account system constraints and performance criteria. By optimising the control action over a finite time horizon, the MPC algorithm ensures an optimal response, considering the current state and anticipated changes. Additionally, a PI controller is incorporated to augment the control strategy. The proportional component of the PI controller enables a fast initial response to the error between the desired and actual converter outputs. The integral component eliminates steady-state errors and provides robustness against disturbances, resulting in improved overall system performance. Results: The proposed control scheme is implemented and evaluated through simulations and experimental tests on a prototype converter. The results demonstrate the effectiveness of the combined MPC and PI controller approach. Conclusion: The coupled inductor bidirectional DC-DC (CIB DC-DC) converter using MPC can provide precise control of power flow between two voltage domains, enabling efficient bidirectional power transfer. The predictive capabilities of MPC allow it to adapt to varying load conditions and respond quickly to changes, ensuring stable operation and accurate regulation of voltage and current. Overall, the coupled inductor bidirectional DC-DC converter controlled using MPC over PI offers improved performance, efficiency, and flexibility compared to traditional control methods. MPC can handle the complex dynamics response and non-linear characteristics of the converter, making it suitable for bi-directional vehicle charging applications, where precise control and high efficiency can be achieved.

Robust optimal control for uncertain wheeled mobile robot based on reinforcement learning: ADP approach

This paper presents a robust optimal control approach for the wheel mobile robot system, which considers the effects of external disturbances, uncertainties, and wheel slipping. The proposed method utilizes an adaptive dynamic programming (ADP) technique in conjunction with a disturbance observer. Initially, the system's state space model is formulated through the utilization of kinematic and dynamic models. Subsequently, the ADP method is employed to establish an online adaptive optimal controller, which solely relies on a single neural network for the purpose of function approximation. The utilization of the disturbance observer in conjunction with the compensation controller serves to alleviate the effects of disturbances. The Lyapunov theorem establishes the stability of the complete closed-loop system and the convergence of the weights of the neural network. The proposed approach has been shown to be effective through simulation under the effect of the disturbances and the change of the desired trajectory.

Control of elongated plasmas in superconductive tokamaks in the absence of in-vessel coils

The roadmap for the commissioning and first operations of superconductive tokamaks envisages the possibility of running discharges with fairly elongated plasmas before the complete installation of the in-vessel components, including vertical stabilization coils, or any other specific sets of coils to be used for the magnetic control of fast transients. In the absence of dedicated actuators, the magnetic control system shall perform the essential fast control actions by using the out-vessel superconductive coils, if needed. These are typically less efficient in reacting to fast transients, due to the shielding effect of the vessel and imply a coupling with other control tasks relying on the same actuators, such as plasma current, position, and shape control. Hence, effective actuator-sharing strategies must be put in place. This paper presents an architecture and a possible control strategy that is able to cope with vertically unstable elongated plasmas subject to fast varying disturbances, in the absence of dedicated in-vessel coils. The architecture exploits a model-based actuator-sharing approach to effectively accomplish the main magnetic control objectives while minimizing the cross-couplings among the various tasks. The effectiveness of the approach is demonstrated by means of nonlinear simulations of realistic JT-60SA scenarios. In particular, an isoflux plasma shape controller is integrated with plasma current control and vertical stabilization. The proposed control approach proves to control vertical displacement events and plasma deformations due to fast variations of poloidal beta with satisfactory performance.

Distributed finite-time adaptive consensus output-feedback control of multi-agent systems under switching topologies

The paper investigates the finite-time adaptive consensus control of nonlinear multi-agent systems (MASs) by output-feedback. During the process of control design, the fuzzy logic system (FLS) is employed to approximate nonlinear function, as well as constructing a fuzzy state observer to estimate unmeasured states. Combining the dynamic surface control (DSC) with adaptive backstepping method conquers the complexity explosion issue. Then, semi-global practical finite-time stability (SGPFS) for closed-loop system is obtained under arbitrary switching communication topologies. All the signals of the overall system are uniformly ultimately bounded (UUB). Finally, the availability of developed control approach will be demonstrated through a simulation.

Crack Catcher AI – Enabling smart fracture mechanics approaches for damage control of thin silicon cells or wafers

Silicon has been one among a few key technologically important materials especially in our modern world. Silicon, especially in its current most useful forms (thin layers), is a brittle material by nature. Thin silicon cells crack easily during manufacturing assembly. Every crack is a defect in any PV manufacturing lines, and if let to grow/propagate it will lead to reliability and quality issues with time. Nevertheless, the global silicon solar PV industry is aggressively reducing the cost of solar PV systems through cutting down the thickness of silicon solar cells. Cell cracks immediately lower PV module efficiency and can lead to premature aging of the entire module. The “Crack Catcher AI” was our research joint collaboration's entry in the Department of Energy (DOE)'s American-Made Solar Innovation competition in 2022 (Round 6) which later was selected as the national semifinalists and won an award announced by DOE in December 2022. It uses smart stress sensing and smart fracture prediction approaches utilizing fundamental fracture mechanics and big data analytics to reduce crack defects and enhance long-term durability/reliability of PV products/devices. Smart Stress Sensing uses a novel laser-based curvature methodology for fast in-line stress metrology in manufacturing lines. Smart Fracture Prediction uses Artificial Intelligence (AI) for defect control metrology and yield enhancement in PV production lines. It is based on monocrystalline silicon used in solar PV industries, but all scientific principles utilized and technological innovations developed in this article would be applicable as well for single crystal silicon semiconductor wafers.

A new hybrid control strategy for improving ride comfort on lateral suspension system of railway vehicle

The ride comfort is a key research area for the quality improvement of railway vehicles. Several control methods have been developed to improve the ride comfort, in which the semi-active control approach inherits the advantages of passive and active control approaches. However, the semi-active control may perform ineffectively in vibration attenuation when the carbody load changes. To address this issue, we proposed a novel hybrid control method that combines the skyhook (SH) and displacement velocity (DV) control methods to reduce vibrations of the carbody in the lateral direction under carbody load changes. First, a hybrid control model built in Matlab software was applied to a quarter railway vehicle model for a preliminary assessment of the ride comfort. Second, to provide a more comprehensive evaluation, the proposed model was applied to a whole railway vehicle model using co-simulation (Matlab-Simpack). In the quarter model, under the sinusoidal excitation, the hybrid control showed a lower acceleration by 48.5% and 42.8% when the carbody load reduced to a half and a quarter, respectively, compared to the passive control. This finding was confirmed with the whole model. Moreover, in the whole model, under the track irregular excitation, the hybrid control showed a lower acceleration by 26.6% and 17.4% when the carbody load reduced to a half and a quarter, respectively. Further, the proposed method showed an acceptable safety level, measured by the derailment coefficient (0.0743 at a half load and 0.089 at a quarter load). In conclusions, the proposed hybrid SH-DV control exhibits high robustness, adaptability, effectiveness, and acceptable safety level in response to variations in body load.

Do No Harm? Unintended Consequences of Pharmaceutical Price Regulation in India

The Drugs (Prices Control) Order of 2013 in India regulated the prices of certain essential and life-saving drugs to ensure their affordability and availability, expecting an increase in sales of those drugs. The authors study the effects of the regulation on sales volumes of each regulated drug using a synthetic control approach with sales data from a comparable country that did not experience a regulatory change. They assess the robustness of the results via multiple empirical approaches to triangulate the findings. Contrary to the order's objectives, they find that sales volumes declined for regulated drugs. Since the order placed restrictions on production levels and on drugs exiting the market, the lowered margins of regulated drugs could have pushed pharmaceutical firms to reduce their marketing expenditures on them. The authors provide evidence of such a reduction using detailing data from a large pharmaceutical firm. The findings illustrate that this shift in detailing adversely affected prescriptions from physicians without formal medical degrees who treat poor and disadvantaged people in India—patients that the order was intended to help the most. A survey the authors conducted shows that these physicians rely on detailing more than medically trained doctors. Taken together, the results provide insights into the strategic actions of firms when faced with regulations, and highlight their unintended consequences. The generalizable nature of the study's findings across a broad set of medications has implications for governmental agencies in terms of the need to account for the entire ecosystem when implementing such regulations.

Influence of usage and model inaccuracies on the performance of smart hot water heaters: lessons learned from a demand response field test

Domestic hot water heaters are considered to be easily integrated as flexible loads for demand response. While literature grows on reproducible simulation and lab tests, real-world implementation in field tests considering state estimation and demand prediction-based model predictive control approaches is rare. This work reports the findings of a field test with 16 autonomous smart domestic hot water heaters. The heaters were equipped with a retrofittable sensor/actuator setup and a real-time price-driven model predictive control unit, which covers state estimation, demand prediction, and optimization of switching times. With the introduction of generic performance indicators (specific costs and thermal efficiency), the results achieved in the field are compared by simulations to standard control modes (instantaneous heating, hysteresis, night-only switching). To evaluate how model predictive control performance depends on the user demand prediction and state estimation accuracy, simulations assuming perfect predictions and state estimations are conducted based on the data measured in the field. Results prove the feasible benefit of RTP-based model predictive control in the field compared to a hysteresis-based standard control regarding cost reduction and efficiency increase but show a strong dependency on the degree of utilization.

LADRC-based grid-connected control strategy for single-phase LCL-type inverters.

To ensure that grid-connected currents are of high quality, it is crucial to optimize the dynamic performance of grid-connected inverters and their control. This study suggests using a combination of reduced-order linear active disturbance rejection control (LADRC) and a Proportional-Integral (PI) controller. By applying this control strategy to a single-phase photovoltaic grid-connected system, the system's ability to suppress grid harmonics is significantly improved. The validity and effectiveness of this control approach have been confirmed through simulations and experiments. The results show that the LADRC-based control system is robust and capable of rejecting disturbances, resulting in a significant reduction in the Total Harmonic Distortion (THD) of grid-connected currents. Comparative analysis with traditional control methods demonstrates the superior performance of the proposed approach.

Locomotor Activity of Adult Olive Fruit Flies Recorded under Conditions of Food or Water Deprivation

The olive fruit fly, known as Bactrocera oleae (Rossi) (Diptera: Tephritidae), is causing substantial economic losses in olive crops worldwide. Studying the activity patterns of the insect may expand our knowledge to eventually adopt more sustainable and effective pest control approaches. In the present study, we investigated the impact of food and water deprivation on the mobility of olive fruit flies using a modified version of the LAM25 system (locomotor activity monitor)—Trikinetics, an automated locomotor activity electronic device. Both male and female flies at four different age groups, reared on olives in the laboratory, were individually placed in glass tubes. Their locomotor activity was recorded every minute by three monitors within the digital device over a three-day period. Our observations revealed that adults exhibited significantly reduced movement during nighttime compared to daytime. The greatest mobility was observed during the period of 15:00 to 20:59. Additionally, younger flies demonstrated higher levels of mobility compared to older ones. Flies subjected to both food and water deprivation exhibited higher mobility compared to the control group. These insights offer valuable insights for enhancing pest management strategies aimed at controlling olive fruit flies adopting a more sustainable approach.

Enhancing the solar water pumping efficiency through Beta MPPT method-controlled drive

This paper presents an innovative approach to achieve efficient solar water pumping through the integration of a Photovoltaic (PV) array and a Brushless Direct Current (BLDC) motor water pumping system. The system incorporates a Voltage Source Converter (VSC) with six switches, utilized to facilitate commutation. The inherent solar radiation is harnessed by the PV array, capitalizing on its renewable nature to generate electricity. By dynamically adjusting the switching states of the six VSC switches, the speed of the BLDC motor is modulated in response to the varying levels of available solar radiation. The BLDC motor's hall sensor signals play a crucial for determining the rotor's position and they are employed to generate precise commutation signals. The control strategy integrates the Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) algorithm, which initially governs the commutation signals. To enhance adaptability to rapidly changing solar irradiation conditions, the control strategy dynamically updates the commutation signals using the innovative Beta MPPT algorithm. To assess the efficiency of the proposed control strategy, a comprehensive comparison between the INC and Beta MPPT algorithms is conducted using MATLAB Simulink. The performance of the BLDC motor under these algorithms was evaluated in terms of its ability to optimize energy extraction. The graphical analysis of these algorithms, considering the temporal aspect, substantiates the identification of the superior MPPT algorithm for BLDC motor control in solar water pumping applications. This study contributes to the advancement of solar water pumping systems by introducing a novel control approach that combines PV array utilization, VSC-based commutation, and a dual-step MPPT algorithm. The results demonstrate the effectiveness of the Beta MPPT algorithm by enabling the system to respond promptly to fluctuating solar irradiation conditions, thereby enhancing the overall efficiency of the solar water pumping process.

Accelerated Gradient Approach For Deep Neural Network-Based Adaptive Control of Unknown Nonlinear Systems.

Recent connections in the adaptive control literature to continuous-time analogs of Nesterov's accelerated gradient method have led to the development of new real-time adaptation laws based on accelerated gradient methods. However, previous results assume that the system's uncertainties are linear-in-the-parameters (LIP). To compensate for non-LIP uncertainties, our preliminary results developed a neural network (NN)-based accelerated gradient adaptive controller to achieve trajectory tracking for nonlinear systems; however, the development and analysis only considered single-hidden-layer NNs. In this article, a generalized deep NN (DNN) architecture with an arbitrary number of hidden layers is considered, and a new DNN-based accelerated gradient adaptation scheme is developed to generate estimates of all the DNN weights in real-time. A nonsmooth Lyapunov-based analysis is used to guarantee the developed accelerated gradient-based DNN adaptation design achieves global asymptotic tracking error convergence for general nonlinear control affine systems subject to unknown (non-LIP) drift dynamics and exogenous disturbances. A comprehensive set of simulation studies are conducted on a two-state nonlinear system, a robotic manipulator, and a complex 20-D nonlinear system to demonstrate the improved performance of the developed method. Our simulation studies demonstrate enhanced tracking and function approximation performance from both DNN architectures and accelerated gradient adaptation.

Self-Organizing Type-2 Fuzzy Double Loop Recurrent Neural Network for Uncertain Nonlinear System Control.

Nonlinear systems, such as robotic systems, play an increasingly important role in our modern daily life and have become more dominant in many industries; however, robotic control still faces various challenges due to diverse and unstructured work environments. This article proposes a double-loop recurrent neural network (DLRNN) with the support of a Type-2 fuzzy system and a self-organizing mechanism for improved performance in nonlinear dynamic robot control. The proposed network has a double-loop recurrent structure, which enables better dynamic mapping. In addition, the network combines a Type-2 fuzzy system with a double-loop recurrent structure to improve the ability to deal with uncertain environments. To achieve an efficient system response, a self-organizing mechanism is proposed to adaptively adjust the number of layers in a DLRNN. This work integrates the proposed network into a conventional sliding mode control (SMC) system to theoretically and empirically prove its stability. The proposed system is applied to a three-joint robot manipulator, leading to a comparative study that considers several existing control approaches. The experimental results confirm the superiority of the proposed system and its effectiveness and robustness in response to various external system disturbances.

Comparative Study of the MPPT Control for the Photovoltaic Water Pumping System between FSS-P&O and VSS-P&O

This paper describes the implementation of a renewable energy system that operates independently. It comprises a photovoltaic generator (PV) that supplies power to a solar pumping system, driven by a permanent magnet direct current motor (PMDC) via a DC-DC Buck converter. Consequently, the objective is to maintain steady operation with continuous power supply despite changes in two environmental parameters, including solar irradiation and absolute temperature. The maximal power extraction of the PV panel using the usual perturbation and observation (P&O) technique achieves this objective. This method must provide appropriate duty cycle control for the DC-DC buck converter when the user-selected Fixed-Step Size (FSS) is used, unfortunately, selecting an insufficient fixed-step size led to a power ripple issue with the PV panel. Incorporating a new Variable Step-Size (VSS) into the traditional P&O algorithm shows the occurrence of the enhanced P&O-MPPT control approach. The proposed technique is validated by utilizing the PROTEUS/ISIS software. For various climatic situations, the results demonstrate that the proposed control technique is preferable to the one based on the standard P&O-MPPT.

Exploring the spectroscopic and I‑V‑T characteristics advancements of cadmium zinc tungsten phosphate diode

This research accomplished the growth of cadmium zinc tungsten phosphate (CZWP) thin films on both glass and p-Si substrates, employing the sol–gel spin coating method. The sol–gel technique offers a versatile and controlled approach for fabricating nanomaterials with tailored properties. The structural and morphological analyses, conducted through XRD and FE-SEM, provided comprehensive insights into the nature of the films. The optical properties, absorbance behavior, energy gap, refractive indices, dielectric, conductivity, and electronegativity, underwent meticulous examination through UV–Vis spectroscopy. The X-ray diffraction analysis of the zinc cadmium tungsten phosphate diode reveals diffraction lines indicative of a nanostructure featuring a monoclinic-phase Zn2P2O7 and Cd3P6O28. Furthermore, SEM analysis confirms a nanoporous morphology with a nanograpes-like structure in the successful crystalline structure of the cadmium zinc tungsten phosphate nanostructure. The optical absorption studies, covering a wavelength range from 190 to 1500 nm, unveiled both direct and indirect energy band gaps, measuring 4.14 and 3.77 eV, respectively. A rigorous analysis of the I-V-T characteristics for the CZNP/p-Si junction in dark mode led to the identification of key parameters, including the transport ideality factor, barrier height, and series resistance.

A Review of Real-Time Implementable Cooperative Aerial Manipulation Systems

This review paper focuses on quadrotor- and multirotor-based cooperative aerial manipulation. Emphasis is first given to comparing and evaluating prototype systems that have been implemented and tested in real-time in diverse application environments. The underlying modeling and control approaches are also discussed and compared. The outcome of this review allows for understanding the motivation and rationale to develop such systems, their applicability and implementability in diverse applications and also challenges that need to be addressed and overcome. Moreover, this paper provides a guide to develop the next generation of prototype systems based on preferred characteristics, functionality, operability, and application domain.

Transcranial alternating current stimulation over frontal eye fields mimics attentional modulation of visual processing.

Attentional control over sensory processing has been linked to neural alpha oscillations and related inhibition of cerebral cortex. Despite the wide consensus on the functional relevance of alpha oscillations for attention, precise neural mechanisms of how alpha oscillations shape perception and how this top-down modulation is implemented in cortical networks remain unclear. Here, we tested the hypothesis that alpha oscillations in frontal eye fields (FEF) are causally involved in the top-down regulation of visual processing in humans (male and female). We applied sham-controlled, intermittent transcranial alternating current stimulation (tACS) over bilateral FEF at either 10 Hz (alpha) or 40 Hz (gamma) to manipulate attentional preparation in a visual discrimination task. Under each stimulation condition, we measured psychometric functions for contrast perception and introduced a novel linear mixed modeling approach for statistical control of neurosensory side effects of the electric stimulation. TACS at alpha frequency reduced the slope of the psychometric function, resulting in improved sub-threshold and impaired super-threshold contrast perception. Side effects on the psychometric functions were complex and showed large interindividual variability. Controlling for the impact of side effects on the psychometric parameters by using covariates in the linear mixed model analysis reduced this variability and strengthened the perceptual effect. We propose that alpha tACS over FEF mimicked a state of endogenous attention by strengthening a fronto-occipitoparietal network in the alpha band. We speculate that this network modulation enhanced phasic gating in occipitoparietal cortex leading to increased variability of single-trial psychometric thresholds, measurable as a reduction of psychometric slope.Significance statement Attention is fundamental to the voluntary control of perception and behavior. Yet, precise underlying neural mechanisms remain unclear. Here, we provide evidence for a vital role of frontal alpha oscillations in the regulation of gating of visual information by using intermittent transcranial alternating current stimulation (tACS). We show that modulation of frontal alpha oscillations affected the slope of psychometric functions of visual contrast perception, leading to contrast-dependent improvement and impairment of perception. Our data adds to work on alpha oscillations in spatial attention and studies on the psychometrics of attention. Furthermore, we introduce a novel approach for the statistical control of tACS side effects and thereby contribute to the ongoing debate on outcome variability in studies using transcranial neurostimulation methods.

Investigating the Dynamics of Bayoud Disease in Date Palm Trees and Optimal Control Analysis

The fungus Fusarium oxysporum (f.sp. albedinis) causes Bayoud disease. It is one of the epiphytotic diseases that affects a wide range of palm species and has no known cure at present. However, preventive measures can be taken to reduce the effects of the disease. Bayoud disease has caused enormous economic losses due to decreased crop yield and quality. Therefore, it is essential to develop a mathematical model for the dynamics of the disease to propose some affordable methods for disease management. In this study, we propose a novel mathematical model that describes the transmission dynamics of the disease in date palm trees. The model incorporates various factors such as the contact rate of the fungi with date palm trees, the utilization of fungicides, and the introduction of a quarantine compartment to prevent disease dissemination. We first prove a few key properties of the proposed model to ensure that the model is well-posed and suitable for numerical investigations. We establish that the model has a unique positive solution that is bounded and stable over time. We use sensitivity analysis to identify the parameters that have the greatest effect on the reproduction number R0 and illustrate this effect graphically. We then formulate an optimal control problem to identify the most suitable and cost-effective disease control approaches. As a first approach, we solely focus on the application of fungicide to susceptible trees and determine the best spray rates for a greater decrease in exposed and infected trees. Secondly, we emphasize quarantining exposed and infected trees at optimal quarantine rates. Finally, we explore the combined effect of fungicide spraying and isolating infected trees on disease control. The findings of the last approach turn out to be the most rewarding and cost-effective for minimizing infections in date palm trees.