Control Approach Research Articles

Contribution of the Strategic Economic Plan to Singapore’s Long-term Growth: A Synthetic Control Approach

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">We assess the impact of the Strategic Economic Plan on Singapore’s long-term GDP per capita. To our knowledge, we are the first to evaluate the effect of the 1991 plan on Singapore’s successful growth trajectory using methodologies aimed at causal identification. In other words, this article applies the Synthetic Control method and World Bank data from 1970 to 2018. We estimate an average increase of 6,641.71 US$ in Singapore’s GDP per capita. Results remain robust to placebo and leave-one-out tests and the Synthetic Difference-in-Differences method.</span></p>

Parameters online identification‐based data‐driven backstepping control of hypersonic vehicles

SummaryThe control problem of the hypersonic vehicles is studied in this article. A new control approach is presented. This approach consists of a data‐driven dynamic model established by multiple neural networks, an online identification method for system parameters, and a basic backstepping controller. The implementation of this approach requires a dynamic model and system parameters including the moment of inertia and aerodynamic parameters of the hypersonic vehicles. The parameter identification problem is regarded as a dynamic optimization process. The loss function is designed by the Lagrange criterion, and its constraints are determined by the physical and the numerical values. In the case of model mutation, the system parameters identified online are used as the nominal values of the output of the neural network in the data‐driven model to adjust the controller through its gradient descent. Simulation comparisons are given to show the effectiveness of the proposed data‐driven approach.

Boosting photocatalytic activity in ternary lamellar hierarchical structured ZnFe2O4/polymeric carbon nitride by constructing an electronic bridge

Photocatalytic technology is one of the ideal approaches for clean energy production and environmental pollution control in the future. However, the rapid recombination of photo-generated charge carriers is a bottleneck problem of low solar energy conversion efficiency. Herein, a magnetic ZnFe2O4/Pt/polymeric carbon nitride (PCN) semiconductor catalyst is designed and prepared for high-performance photocatalytic hydrogen production reaction and photocatalytic degradation rate of RhB. It exhibits 339.31 μmol·g−1·h−1 hydrogen production rate and 96.08 % photocatalytic degradation rate of RhB within 120 min under visible light. The introduction of Pt, constructing an electronic bridge, accelerated the transfer of photo-generated carriers, meanwhile, the smaller band gap of the ternary composites enabled the generation of more photo-generated electrons. The internal electric field accelerated the accumulation of electrons and holes in the conduction band of ZnFe2O4 and valence band of PCN, thus photocatalytic activity is greatly enhanced. The electron-hole separation is also improved and the transport of photo-generated electrons is facilitated by the special structure of the heterojunction and the unique morphology of PCN. In addition, the used catalyst can be recovered through magnetic fields, and this magnetic catalytic system has great application prospects in the fields of photocatalytic hydrogen production and pollutant degradation.

Intelligent IoT-based Control System of the UAV for Meteorological Measurements

This study focuses on the creation and examination of an intelligent automated control system for UAVs utilized in meteorological measurements based on the Internet of Things (IoT) and mobile technologies. The proposed system enables the achievement of commendable flight control standards for UAVs during meteorological data gathering, thereby markedly enhancing the overall effectiveness of meteorological stations. Notably, this system is constructed on the foundation of three integrated principles: (a) a hierarchical two-level approach for control and data collection based on IoT and mobile technologies, (b) a straightforward and dependable fuzzy logic control characterized by high performance, and (c) the effective optimization of fuzzy control components through the application of bio-inspired multi-agent computing techniques. To assess the performance of the suggested intelligent system, this study involves the creation and bioinspired optimization of the climb speed fuzzy controller. Subsequent simulation experiments are conducted to evaluate the automatic control of UAV’s flight processes under different modes. The analysis of the simulation results indicates that the developed system, utilizing fuzzy control, exhibits superior efficiency and higher quality metrics when compared to existing counterparts, especially in diverse flight scenarios such as uniform climbing, gradual approach to designated altitude levels, and smooth landing during meteorological measurements.

IT-2 Fuzzy Control and Behavioral Approach Navigation System for Holonomic 4WD/4WS Agricultural Robot

In contemporary agriculture, researchers are focusing on precision farming and environmentally conscious techniques, leading to the advancement of autonomous agricultural mobile robots. Consequently, A navigation system using TSK Interval Type-2 fuzzy logic has been developed and, for the first time, applied to a holonomic mobile agricultural robot equipped with four-wheel drive and steer. This system employs a behavioral approach to accomplish the robot’s primary objective in unfamiliar environments. It comprises four controllers: one exclusively dedicated to goal-seeking behavior, with the remaining controllers responsible for obstacle avoidance. Supervision of these controllers is conducted through the Mandani fuzzy logic approach, which combines the outputs of all four controllers, robot wheel velocities and steering angles, using a proposed equations. Various case studies were examined and simulated utilizing MATLAB and VREP software platform to assess the navigator system’s performance. The simulation outcomes showcase the efficiency of the proposed equations and the system’s navigation capabilities. The stability of the entire system was thoroughly examined and confirmed, employing the Lyapunov function, in environments with and without obstacles.

Improving Power Quality in a Grid-Connected Hybrid Power System through the Implementation of a Fuzzy Logic Controller

The examination of modeling and designing grid-connected hybrid power systems with efficient control strategies is the focus of this study. Integration of wind and photovoltaic systems into the ac-bus bar system is a common practice in enhancing operational performance. Utilization of Matlab Simulink software is employed for the design of this hybrid system, incorporating efficient MPPT control algorithms to optimize power generation from solar and wind sources under varying climatic conditions. Analysis of the maximum power point tracking mechanism and control methods of the hybrid power system is conducted, particularly in response to changes in weather conditions such as wind speed and solar radiation. Maintaining grid voltages and Unity Power Factor (UPF) while feeding currents into the grid is essential for the hybrid system. The employment of Proportional Integral (PI) control instead of Fuzzy Logic Controller (FLC) in the voltage-oriented control approach is explored to enhance power quality.

Adaptive reinforcement learning-based control using proximal policy optimization and slime mould algorithm with experimental tower crane system validation

This paper presents a novel optimal reference tracking control approach resulted from the combination of a popular policy gradient Reinforcement Learning (RL) algorithm, namely Proximal Policy Optimization (PPO), and a metaheuristic Slime Mould Algorithm (SMA). One of the most important parameters in the PPO-based RL process is the learning rate, which has a big impact on how the parameters of the actor neural network (NN) are iteratively updated. In every episode of the RL process, the weights and the biases of the actor NN are multiplied with the learning rate, determining how much the learning agent will step into a certain direction computed based on previous experiences. The classical PPO algorithm usually relies on fixed values for the learning rates which rarely change, or not at all, during the learning process. However, its main drawback is that the learning agent cannot take advantage of positive momentum in the learning process by accelerating towards good learning experiences or slow down and quickly change the direction in the case of consecutive negative learning experiences. The main objective of the combination proposed in this paper is to create an adaptive SMA-based PPO approach applied to control systems, which instead of using fixed learning rate values, it uses the SMA to compute optimal values of the learning rates in each time step of the learning process based on the progress of the learning agent. This paper investigates if the adaptive SMA-based PPO control approach can be considered as an alternative to the classical PPO version, which employs fixed values of the learning rate. A comparison is carried out using control system performance indices gathered while performing an optimal reference tracking control task on tower crane system laboratory equipment.

Adopting the euro: A synthetic control approach

We investigate whether joining the European Monetary Union and losing the ability to set monetary policy affected the economic growth of Eurozone countries. We use the synthetic control approach to create a counterfactual scenario for how each Eurozone country would have evolved without adopting the euro. We let this matching algorithm determine which combination of other developed economies best resembles the pre-euro path of twelve Eurozone economies. Our estimates suggest that most countries’ economic growth was not significantly affected. There were some mild losers (France, Germany, Italy, and Portugal) and a clear winner (Ireland). The drivers of these economic gains and losses are heterogeneous. First, we find that Ireland’s economic gains are more modest when excluding profits and income earned by foreigners. Second, our results show that adopting the euro spurred government consumption and trade and deterred private consumption and investment, on average.

A novel adaptive fast sliding mode control method based on fuzzy algorithm for the air management system of fuel cell stack

Proton exchange membrane fuel cells (PEMFCs) offer advantages, such as, cleanliness, high efficiency, and environmental friendliness. To achieve optimal control of the PEMFC stack's air supply and management system under complex operating conditions, a novel non-singular terminal sliding mode controller based on fuzzy algorithm is proposed. Compared with the conventional sliding mode control, the terminal sliding mode control exhibits further performance improvements. However, it still has limitations. The proposed innovative sliding mode structure would ensure finite-time convergence and avoid the issue of singularity associated with terminal sliding mode control. Moreover, fuzzy logic makes sliding mode gain adapt to the system and further enhances the performance and stability, which is benefited from a stronger sliding surface. Results indicate that, compared with the other controllers, the designed controller could reduce delay further and provide faster response to sudden changes. The overshoot of sliding surface has decreased by 70% compared to its previous level. This novel control approach significantly enhances the efficiency and reliability of the PEMFC system.

Personalized Path-Tracking Approach Based on Reference Vector Field for Four-Wheel Driving and Steering Wire-Controlled Chassis

It is essential and forward-thinking to investigate the personalized use of four-wheel driving and steering wire-controlled unmanned chassis. This paper introduces a personalized path-tracking approach designed to adapt the vehicle’s control system to human-like characteristics, enhancing the fit and maximizing the potential of the chassis’ multi-directional driving and steering capabilities. By modifying the classic vehicle motion controller design, this approach aligns with individual driving habits, significantly improving upon traditional path-tracking control methods that rely solely on reference vector fields. First, the classic reference vector field’s logic was expanded upon, and it is shown that a personalized upgrade is feasible. Then, driving behavior data from multiple drivers were collected using a driving simulator. The fuzzy c-means clustering method was used to categorize drivers based on typical states that match vehicle path-tracking performance. Additionally, the random forest algorithm was used as the method for recognizing driving style. Subsequently, a personalized path-tracking control strategy based on the reference vector field was developed and a distributed execution architecture for four-wheel driving and steering wire-controlled unmanned chassis was established. Finally, the proposed personalized path-tracking approach was validated using a driving simulator. The results of the experimental tests demonstrated that the personalized path-tracking control approach not only fits well with various driving styles but also delivers high accuracy in driving style identification, making it highly suitable for application in four-wheel driving and steering wire-controlled chassis.

Reliable optimal controls for SEIR models in epidemiology

We present and compare two different optimal control approaches applied to SEIR models in epidemiology, which allow us to obtain some policies for controlling the spread of an epidemic. The first approach uses Dynamic Programming to characterise the value function of the problem as the solution of a partial differential equation, the Hamilton–Jacobi–Bellman equation, and derive the optimal policy in feedback form. The second is based on Pontryagin’s maximum principle and directly gives open-loop controls, via the solution of an optimality system of ordinary differential equations. This method, however, may not converge to the optimal solution. We propose a combination of the two methods in order to obtain high-quality and reliable solutions. Several simulations are presented and discussed, also checking first and second order necessary optimality conditions for the corresponding numerical solutions.

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Light-Responsive Pt(IV) Prodrugs with Controlled Photoactivation and Low Dark Toxicity.

Light-induced release of cisplatin from Pt(IV) prodrugs represents a promising approach for precise control over the antiproliferative activity of Pt-based chemotherapeutic drugs. This method has the potential to overcome crucial drawbacks of conventional cisplatin therapy, such as high general toxicity toward healthy organs and tissues. Herein, we report two Pt(IV) prodrugs with BODIPY-based photoactive ligands Pt-1 and Pt-2, which were designed using carbamate and triazole linkers, respectively. Both prodrugs demonstrated the ability to release cisplatin under blue light irradiation without the requirement of an external reducing agent. Dicarboxylated Pt-2 prodrug turned out to be more stable in the dark and more sensitive to light than its monocarbamate Pt-1 counterpart; these observations were explained using DFT calculations. The investigation of the photoreduction mechanism of Pt-1 and Pt-2 prodrugs using DFT modeling and ΔG0 PET estimation suggests that the photoinduced electron transfer from the singlet excited state of the BODIPY axial ligand to the Pt(IV) center is the key step in the light-induced release of cisplatin from the complexes. Cytotoxicity studies demonstrated that both prodrugs were nontoxic in the dark and toxic to MCF-7 cells under low-dose irradiation with blue light, and the observed effect was solely due to the cisplatin release from the Pt(IV) prodrugs. Our research presents an elegant synthetic approach to light-activated Pt(IV) prodrugs and presents findings that may contribute to the future rational design of photoactivatable Pt(IV) prodrugs.

Design and Implementation of an L1 Adaptive Proportional Output Feedback Controller

A new approach for output feedback L1 adaptive control based on a proportional adaptation law is presented. The effectiveness of this design is assessed in simulation and validated through real-time testing on an airfoil pitch control wind tunnel experimental rig. Experimental evaluation of the robustness of the controllers, assessed by introducing various disturbances into the control signals, shows that the adaptive control has a better performance compared to PID control, particularly in scenarios with reduced control effectiveness and time-varying disturbances. The experimental results demonstrate the efficacy of the proposed method in practical applications.

Modified Tilt-integral active disturbance rejection controller with inertia emulated direct current link for frequency control of marine microgrid

The increasing integration of intermittent renewable sources (RSs) poses a dynamic frequency stability challenge for modern marine vessel microgrids. To address this issue, this paper proposes a novel control approach, specifically targeting frequency and tie-line power stabilization in a diverse source marine microgrid (MμG) with two intertied areas featuring renewable (wind-wave) sources. The suggested approach introduces a modified tilt-integral active disturbance rejection (TI-ADRC) controller designed to ensure effective damping of power frequency oscillations. As the control scheme depends on the optimal setting of the proposed controller, a recently developed marine predator technique (MPT) has been adopted. The performance of the proposed controller is compared with other recent controllers viz. PID, tilt-integral derivative (TID), two-degree-of-freedom (2DOF)-PID, fuzzy-PI, and ADRC to validate its superiority. To further enhance the system dynamics, a precise modeling of inertia emulated direct current (IEPDC) tie link is incorporated in microgrid system. The impact assessments, considering time delays with pre/post IEPDC link, demonstrate a substantial 57.79% and 81.53% reduction in peak frequency overshoot compared to DC link (conventional model) and AC link, respectively. The analysis of the eigen plot confirms the stability of the control system. Sensitivity assessments of the controller against ± 30% parametric variations and load fluctuations are conducted, affirming its robustness. Finally, the result from OPAL-RT confirm the practicality of the proposed method. It is asserted that the suggested controller is reliable and functions well in n the presence of diverse disruptions and parametric variations.

LVRT Enhancement of DFIG-based WECS using SVPWM-based Inverter Control

Abstract: Across many countries, wind turbine generation systems (WTGS) have been established over the past few decades. In this paper, we augment the low voltage ride-through (LVRT) enrichment facility of driving a DFIG-based wind energy conversion system (WECS) using space vector pulse width modulation (SVPWM)-based inverter control. The proposed technique employs an SVPWM-based control algorithm to regulate the voltage and frequency of the output power during grid faults, thereby enhancing the WECS's ability to remain connected to the grid and provide power. The study focuses on decreasing transient current throughout the instant of fault. Modeling and control approaches were also discussed in this study. The performance of the proposed technique is evaluated using MATLAB/Simulink simulations, and the results demonstrate that the technique effectively improves the LVRT capability of the DFIG-based WECS. Background: Due to the variation in wind speed, the power generated by wind turbines is inconsistent. The power generated and the losses in wind turbines change correspondingly with changes in wind speed. The only type of machine that can generate power at speeds below the fixed speed is the doubly-fed induction generator (DFIG). But DFIG is oversensitive to network faults, which makes the bidirectional converters and DC link capacitor fail due to high inrush current and over-voltage. Methods: The converters connected to DFIG consist of an AC-to-DC converter, a boost converter, and a space vector pulse width modulation (SVPWM)-based DC-AC converter. The performance of the SVPWM controller is analyzed during symmetrical and unsymmetrical fault conditions. Results: The anticipated control provides adequate reactive power support to the network through the time of the fault and improves voltage and current waveform. The reactive power flow is also analyzed, and the effectiveness of the proposed controller is verified using MATLAB and Simulink. Conclusion: SVPWM (Space Vector Pulse Width Modulation)-based inverter control is an effective technique for wind energy conversion systems (WECS). The use of SVPWM can provide accurate and precise control of the AC voltage generated from the DC voltage source, resulting in improved system efficiency and reduced harmonic distortion in the output waveform. : The comparative analysis of THD suggests that SVPWM is a superior technique compared to other inverter control techniques such as sine-triangle pulse width modulation (SPWM) and carrier-based pulse width modulation (CPWM). SVPWM can help to reduce the distortion in the output waveform, leading to improved system efficiency, reduced wear on the system components, and overall better performance of the WECS. Furthermore, SVPWM offers several advantages over other inverter control techniques, including better utilization of DC voltage, improved voltage control, and better utilization of switching devices. These advantages make SVPWM a valuable tool for optimizing the operation of WECS and improving the reliability and performance of renewable energy systems. The value of THD for SVPWM inverter control in WECS is 1.53 under symmetrical fault and 1.34 for unsymmetrical fault, respectively. In summary, the use of SVPWM-based inverter control for WECS is an effective way to improve the efficiency and performance of the system while reducing the distortion in the output waveform and providing adequate reactive power support. The advantages of SVPWM over other inverter control techniques make it a valuable tool for the development and optimization of renewable energy systems.

Automatic mass balancing of spacecraft attitude simulators by optimal feedback control

There is an increasing interest in development of spacecraft attitude simulators. A necessary and challenging task in preparation for any attitude control experiment is to remove the gravitational disturbances on the simulator by a mass balancing system which relocates the center of mass to coincide with the center of rotation. This research is aimed at filling a substantial gap in the literature by presenting a methodical framework for analysis and design of closed-loop automatic mass balancing systems. In this regard, a state-space representation for imbalanced dynamics of attitude simulators is derived to enable the application of modern control techniques to the problem which will be shown to be very effective in achieving an accurate balancing. The model is further enhanced by state augmentation and incremental control input to address the practical requirements for an efficient balancing strategy. Next, an optimal feedback control is designed on the enhanced dynamics model to minimize the rotational kinetic energy and leveling accuracy as key performance measures of the problem. The simulation results and comparative evaluations with a previously validated method verified that the proposed modeling and control approach is effective in achieving an accurate mass balancing while providing a convenient and methodical analysis and design procedure.

Transmission dynamics, complications and mitigation strategies of the current mpox outbreak: A comprehensive review with bibliometric study.

As the mankind counters the ongoing COVID-19 pandemic by the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), it simultaneously witnesses the emergence of mpox virus (MPXV) that signals at global spread and could potentially lead to another pandemic. Although MPXV has existed for more than 50years now with most of the human cases being reported from the endemic West and Central African regions, the disease is recently being reported in non-endemic regions too that affect more than 50 countries. Controlling the spread of MPXV is important due to its potential danger of a global spread, causing severe morbidity and mortality. The article highlights the transmission dynamics, zoonosis potential, complication and mitigation strategies for MPXV infection, and concludes with suggested 'one health' approach for better management, control and prevention. Bibliometric analyses of the data extend the understanding and provide leads on the research trends, the global spread, and the need to revamp the critical research and healthcare interventions. Globally published mpox-related literature does not align well with endemic areas/regions of occurrence which should ideally have been the scenario. Such demographic and geographic gaps between the location of the research work and the endemic epicentres of the disease need to be bridged for greater and effective translation of the research outputs to pubic healthcare systems, it is suggested.

Human-inspired similarity control system: Enhancing line-following robot perception

Human-Inspired Control (HIC) holds promise for endowing machines with human-like cognition, decision-making, and adaptability. In this study, we employ a fusion of cognitive modeling, machine learning, and control theory as the foundational architecture and empirically validate its suitability for robot control within the realm of HIC. Fuzzy logic stands out as a viable approach for HIC control, wherein control rules can be devised drawing from human intuitive inspiration. Specifically, this study explores similarity inference control systems in robotics, with the objective of enhancing line-following control as an alternative to fuzzy systems. The experimental optimization results provide insights into the advantages and limitations of the similarity inference control system. Despite achieving performance comparable to that of traditional two-stage fuzzy control systems, careful consideration of noise sensitivity is paramount. While the similarity inference approach streamlines implementation and obviates the necessity for expert-designed fuzzy rules, its susceptibility to noise can compromise performance, particularly in noisy environments. These considerations are pivotal for the development of control systems aimed at mitigating noise sensitivity, enhancing task-specific performance, and ensuring the adaptability and robustness of line-following robots. To tackle this challenge, we both discuss and experimentally evaluate potential solutions and their applicability in this paper.

Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a set of available heat sources, and couple it with dispersion modelling of corresponding emissions to provide automatic insights into air quality implications of various heat sourcing strategies. We achieve cross-domain insights in the nexus of energy and air quality via a set of developed ontologies and autonomous software agents, which can be chained together via the World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we have integrated the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and generation potential to foster strategic analyses and scenario planning. The calculations use building and weather data from the knowledge graph in place of the inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.