Control Method Research Articles

Cyanophage Engineering for Algal Blooms Control

Cyanobacteria represent a prevalent category of photosynthetic autotrophs capable of generating deleterious algal blooms, commonly known as cyanobacteria harmful algal blooms (cyanoHABs). These blooms often produce cyanotoxins, which pose risks to public health and ecosystems by contaminating surface waters and drinking water sources. Traditional treatment methods have limited effectiveness. Therefore, there is an urgent need for a new approach to effectively manage cyanoHABs. One promising approach is the use of cyanophages, which are viruses that specifically target cyanobacteria. Cyanophages serve as an effective biological control method for reducing cyanoHABs in aquatic systems. By engineering cyanophages, it is possible to develop a highly specific control strategy that minimally impacts non-target species and their propagation in the environment. This review explores the potential application of cyanophages as a strategy for controlling cyanoHABs. It includes the identification and isolation of broad-spectrum and novel cyanophages, with a specific focus on freshwater Microcystis cyanophages, highlighting their broad spectrum and high efficiency. Additionally, recent advancements in cyanophage engineering are discussed, including genome modification, functional gene identification, and the construction of artificial cyanophages. Furthermore, the current state of application is addressed. Cyanophage is a promising control strategy for effectively managing cyanoHABs in aquatic environments.

Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning

The acceleration of urbanization has led to increasingly severe traffic congestion, creating an urgent need for effective traffic signal control strategies to improve road efficiency. This paper proposes an adaptive traffic signal control method based on offline reinforcement learning (Offline RL) to address the limitations of traditional fixed-time signal control methods. By monitoring key parameters such as real-time traffic flow and queue length, the proposed method dynamically adjusts signal phases and durations in response to rapidly changing traffic conditions. At the core of this research is the design of a model named SD3-Light, which leverages advanced offline reinforcement learning to predict the optimal signal phase sequences and their durations based on real-time intersection state features. Additionally, this paper constructs a comprehensive offline dataset, which enables the model to be trained without relying on real-time traffic data, thereby reducing costs and improving the model’s generalization ability. Experiments conducted on real-world traffic datasets demonstrate the effectiveness of the proposed method in reducing the average travel time. Comparisons with several existing methods highlight the clear advantages of our approach in enhancing traffic management efficiency.

Phytophthora heterospora is the causal agent of black rot disease on the orchid Cattleya leopoldii in Brazil.

Widely produced and marketed worldwide, orchids suffers from several diseases that have a negative effect on production. Black rot disease is among the most common and severe disease, characterized by black spots of rot on leaves, pseudobulbs and roots, which usually lead to the plant death. The world literature lists some Phytophthora species as causal agents of the disease, but there is no advanced study on the etiology of this disease in Brazil, which makes it difficult to determine an efficient control method. This work aims to contribute to the study of the etiology of black rot on Cattleya leopoldii in Brazil. Severely diseased C. leopoldii plants with typical symptoms of black rot, collected from a commercial orchid farm in Brazil, were taken to the laboratory and used to isolate the pathogen. Based on morphological characters analysis combined with molecular data, the isolates were identified as belonging to the species Phytophthora heterospora. This is the first worldwide report of P. heterospora causing black rot disease on orchids.

Study on improved control strategy of virtual synchronous generator

Virtual synchronous generator (VSG) control technology for photovoltaic, energy storage, wind power, and other new energy to provide flexibility in the grid interface characteristics, is conducive to improving the stability of the power system, and has been widely considered by many scholars. Firstly, an improved VSG control method is proposed through simulation and analysis, which realizes the complete decoupling of the frequency response time constant and the inertia quantity of the active power control loop and reduces the complexity of the parameter design of the VSG system. Secondly, to avoid the frequent action of the VSG system caused by small-scale frequency change perturbation, this study proposes a VSG frequency optimization control method for VSG frequency control with rated angular velocity ωset feedforward composed of multivariate factors when considering a primary frequency regulation dead zone. Thirdly, the impact of VSG parameter design on the system is investigated through the system response characteristics of power scheduling, primary frequency regulation at the grid connection, and the small-signal dynamic characterization of the improved VSG. Finally, Simulation and experimental verification yielded an active power overshoot of 7% and a maximum frequency deviation of 0.17 Hz for the improved system. The improved control method resulted in an improvement of 0.1 s in the frequency response time and a reduction of 0.15 Hz in the oscillation amplitude. The response speed of the improved control method is much better, while the oscillation amplitude is reduced to meet the grid's regulation requirements. The simulation and experimental analysis verify the feasibility of the improved VSG control method.

Improved adaptive robust control of direct-drive pump-valve cooperative brake-by-wire unit with disturbance compensation

Abstract Aiming at the needs of high-level autonomous driving, a direct-drive pump-valve coordinated brake-by-wire unit was designed. To improve the response speed, control accuracy, and robustness of the brake-by-wire unit, an improved adaptive integral robust control method (AIRC-AD) based on a novel adaptive law and disturbance compensation was proposed. By integrating pressure tracking error information and overall parameter estimation error information, a novel parameter adaptive law with fast convergence speed was designed. A finite-time disturbance observer was designed to observe the overall system disturbance, improving the system’s anti-disturbance performance. Finally, the stability of the control method was proven using the Lyapunov stability proof method. The results show that under step target signals, sinusoidal target signals and triangular wave target signals, AIRC-AD has better response speed and control accuracy. In conditions such as increased permanent magnet temperature, increased coil resistance, or reduced flow area due to hydraulic pipeline blockage, AIRC-AD has better adaptability. The unit and the method can meet the demand for precise braking force control in high-level autonomous driving.

Study of the effect of forced precipitation of working fluid on a vineyard sprayer for weed control

BACKGROUND: A distinctive feature of the sprayer presented is the possibility of more efficient penetration of the working solution into the middle and lower tiers of weeds, as well as on the lower (abaxial) side of the leaf. This is ensured by the use of an air-liquid jet, which significantly increases the effectiveness of the applied chemical method of weed control. AIM: Analysis of the effect of air jets on the deposition of chemicals on the surface of leaves of weeds. The penetration of the drug into the middle and lower tiers of plants, as well as deposition on the adaxial and abaxial sides of the leaves, is taken into account. METHODS: To combat unwanted vegetation, a special device was applied, which was used in field research both with and without air support. With the help of the developed method, spraying was carried out on indicator cards fixed on the upper, middle and lower tiers of the plant RESULTS: For the first time, an innovative technology based on the use of a flexible air distribution sleeve as part of a herbicide sprayer was proposed to combat weeds in vineyards. This sleeve ensures the precise supply of airflow from the fan to the working fluid spraying area. The result of such an air-liquid flow is improved efficiency and monodispersity of herbicide droplets, which, in turn, ensures uniform coverage of the lower tiers and the abaxial surface of the leaves of weeds. This innovative solution opens up new prospects in the fight against unwanted vegetation in the vineyards. CONCLUSION: The practical value of the proposed weed control technology lies is increasing the quality of the technological operation due to the forced precipitation of the working solution.

Lying To Our Children

Most parents lie to their children. They do it for fun, as a method of behaviour control, and to protect children from what they consider to be dangerous truths. At the same time, most parents bring their children up with the message that honesty is a virtue and that lying is usually wrong. How should our practice and our preaching be reconciled? In this paper, I examine the ethics of parental lies. Most philosophers who have written on the ethics of deception have focused on deception of and by autonomous adults. I therefore begin by surveying this literature. Contemporary philosophers have given three types of reason to explain what makes lying wrong (when it is wrong): negative consequences, breaches of trust, and interference with autonomy. I briefly analyze what constitutes a breach of trust and identify four factors that affect how bad a breach is. I then explicate how lying can constitute a wrongful interference with autonomy. A long-running debate concerns whether lying is ethically different than other forms of deception. I argue—briefly—that we do not need to resolve this debate in order to evaluate parental deception. Armed with a framework for what makes lying to autonomous adults wrongful, I turn to the special case of parental lying. Since the parent-child relationship is typically very close, lying to one’s child is a relatively serious breach of trust. This is exacerbated in the case of serious lies that implicate the parent-child relationship or the child’s identity. On the other hand, at least for young children, concerns about autonomy are less significant than for autonomous adults. I close by applying my analysis, along with data on the consequences of parental deception, to different types of parental lie. I argue that lying to one’s child is more rarely justified than is commonly thought, and delineate the circumstances in which it can be justified.

Intelligent sliding mode fault-tolerant control for aircraft engines with actuator dynamics and faults based on adaptive dynamic programming

Abstract The fault-tolerant control issue of aircraft engines with actuator dynamics and faults is investigated in this paper. By proposing a novel intelligent sliding mode fault-tolerant control (ISMFTC) method, which combines an adaptive dynamic programming (ADP) sliding surface with Grey Wolf Optimizer (GWO) for controller parameter optimisation, the goal is to achieve quality steady-state and dynamic performance in aircraft engines while maintaining strong fault-tolerance properties. Firstly, by considering not only actuator dynamics but also actuator faults, an uncertain nonlinear cascaded model of aircraft engines is developed according to characteristic of aircraft engines and their actuators. Secondly, an ADP-based sliding surface is proposed for considered aircraft engine uncertain nonlinear cascaded system. It can obtain a certain sense of optimised performance, and could be solved by ADP strategy off-line as well. Thirdly, fault-tolerant controller is obtained on the basis of sliding mode theory and adaptive fault estimation law, namely, ADP-based ISMFTC controller. Meanwhile, GWO is integrated into the investigation of ADP-based ISMFTC controller, optimised designable control parameters are obtained subsequently. Besides, robustness analysis is elaborated according to Lyapunov theory, fault estimation error is bounded and states of closed-loop system are uniformly ultimately bounded. Simulation on a twin-shaft turbofan aircraft engine, indicates the effect of proposed ADP-based ISMFTC method.

IMPACTO AMBIENTAL POR USO DE PLAGUICIDAS Y SUSTENTABILIDAD DEL SISTEMA PRODUCTIVO DEL MAÍZ AMARILLO DURO EN HUAURA, PERÚ

<p><strong>Background:</strong> Hard yellow maize (MAD) is one of the main crops in economic, social and environmental terms worldwide, however, it is managed through conventional agriculture which entails a high dependence on agrochemical inputs, which cause a negative impact on the agroecosystem and through this study it will be possible to identify the critical points and establish solutions for them. <strong>Objective: </strong>To determine the coefficient of environmental impact of pesticides in the field and find the general sustainability index of the MAD production units. <strong>Objective:</strong> Determine the coefficient of environmental impact of pesticides in the field and find the general sustainability index of the MAD production units. <strong>Methodology:</strong> The environmental impact (EI ha<sup>-1</sup>) of pesticides in the field was evaluated by calculating the environmental impact coefficient (EIQ), concentration of the active ingredient, dose and number of applications. In addition, sustainability was determined through multicriteria analysis by applying 85 questionnaires with structured questions to farmers whose main activity is MAD, with economic, social and environmental indicators standardized on a scale of 1 to 5. <strong>Results:</strong> The study showed that the EI ha<sup>-1</sup> of MAD was 105.11 rated as high, which generates a negative impact on the environment and a high risk for farmers who apply these pesticides. It was also found that the sustainability index of the social and economic dimensions was 3.56 and 3.18 respectively, while the environmental index (2.64) obtained a value below the minimum sustainable threshold, therefore the MAD production system in Huaura is not sustainable. The analysis found the critical points: levels and types of fertilization (2.74), incorporation of organic matter (2.33), crop residue management (1.51), living barriers (1.36), pest control method (2.49), frequency of pesticide applications (2.53), irrigation system (2.54), level of education (2.96), quality of technical assistance and training (2.8), income from other activities (2.9) and marketing channel (1.82). <strong>Implications:</strong> Timely intervention on critical points allows establishing solution options and improving pesticide management.<strong> Conclusion: </strong>The study showed that the IE ha<sup>-1</sup> of MAD was high, indicating that the pesticides used for chemical pest and disease control generate a negative impact on the environment and a high risk for farmers who apply these pesticides. Likewise, it was found that the MAD production system is not sustainable. Therefore, the identification of the critical points allows the establishment of strategies to strengthen the MAD agroecosystem in Huaura, Peru.</p>

An Optimized Multi-Level Control Method for Wireless Power Transfer System Using the Particle Swarm Optimization Algorithm

A Wireless Power Transfer (WPT) system, known for its contactless power delivery, is extensively used for power supply in spacecraft applications. Achieving efficient and stable power transfer necessitates the integration of DC/DC converters on both the primary and secondary sides of WPT systems for power conversion and control. Traditional efficiency optimization methods primarily focus on impedance matching within the wireless power resonance network, often neglecting the overall efficiency optimization of multi-stage DC-DC and WPT systems. This oversight results in suboptimal overall system efficiency despite optimal efficiency in the wireless transmission segment. Additionally, the time-varying nature of mutual inductance and load parameters during power transmission in WPT systems presents challenges for maximum efficiency tracking and power control. This paper introduces a multi-level coordinated control efficiency optimization method for WPT systems utilizing the particle swarm optimization (PSO) algorithm. This method takes into account the transmission losses across all power conversion units within the WPT system, establishing a mathematical model for the joint optimization of overall system transmission efficiency and power. The PSO algorithm is then employed to solve this optimization model using estimated mutual inductance and load values. By adjusting the DC/DC converters on both sides, the method ensures optimal overall system efficiency and consistent power transmission. Experimental results indicate that under varying load and mutual inductance conditions, a Series–Series (SS) compensated WPT system using this method achieves a 200 W power output with maximum efficiency tracking, a power output error of 0.63%, and an average transmission efficiency of 86.2%. This demonstrates superior power transmission stability and higher efficiency compared to traditional impedance matching methods.

Active noise control of refrigerator based on cascaded notch feedback algorithm

Refrigerators bring convenience to people, but the noise they produce can also affect people’s lives. Refrigerator noise is dominated by low-frequency noise, and the active noise control method is more effective for this kind of noise. However, the existing active noise control methods for refrigerators do not take into account factors such as the limited space and complex structure of the refrigerator compressor room, and external interference signals will be introduced to affect the noise reduction performance during the error signal collection. Given that, this paper proposes the cascade notch feedback algorithm, which can well reduce the influence of external interference signals to better achieve the refrigerator noise reduction. The algorithm consists of the cascaded notch filtering algorithm and the feedback algorithm. The cascade notch filtering algorithm is formed by cascading the adaptive notch filtering algorithm, which is used to deal with the main frequency and harmonic noise generated by the refrigerator. The feedback algorithm consists of a robust algorithm for dealing with external interference signals introduced during error signal collection. The simulation experiment proves that the algorithm has advantages in terms of noise reduction and computational cost compared with the adaptive notch filtering algorithm and the improved algorithm. The experimental test platform is set up to carry out the actual refrigerator noise reduction experiments under different conditions. The algorithm has the effect of noise reduction under different robust algorithms.

Reinforcement learning-adaptive fault-tolerant IGC method for a class of aircraft with non-affine characteristics and multiple uncertainties

Abstract In this paper, a brand-new adaptive fault-tolerant non-affine integrated guidance and control method based on reinforcement learning is proposed for a class of skid-to-turn (STT) missile. Firstly, considering the non-affine characteristics of the missile, a new non-affine integrated guidance and control (NAIGC) design model is constructed. For the NAIGC system, an adaptive expansion integral system is introduced to address the issue of challenging control brought on by the non-affine form of the control signal. Subsequently, the hyperbolic tangent function and adaptive boundary estimation are utilised to lessen the jitter due to disturbances in the control system and the deviation caused by actuator failures while taking into account the uncertainty in the NAIGC system. Importantly, actor-critic is introduced into the control framework, where the actor network aims to deal with the multiple uncertainties of the subsystem and generate the control input based on the critic results. Eventually, not only is the stability of the NAIGC closed-loop system demonstrated using Lyapunov theory, but also the validity and superiority of the method are verified by numerical simulations.

Piecewise Time Polynomials-Based Control Methods for Obstacle Avoidance and Precision Positioning of Tower Crane Systems with Varying Cable Lengths

During the hoisting and lowering operations of a tower crane, dynamic variations in cable lengths significantly influence the oscillation frequency and amplitude of the load. These variations complicate the oscillation characteristics, heightening the challenge of balancing obstacle avoidance with precise positioning. To tackle this issue, we propose a trajectory planning and tracking control method that integrates hoisting control to reduce the impact of varying cable lengths on load swinging and achieve accurate positioning during obstacle navigation. A novel definition of swing angle is introduced to model the crane’s rigid and swinging components separately, enhancing model accuracy while simplifying complexity. A piecewise polynomial constructs the load trajectory in a low-dimensional flat space, which is then mapped to a high-dimensional generalized state space through a homeomorphic transformation, ensuring trajectory smoothness and traceability. A fractional-order sliding mode controller is employed to facilitate rapid and precise tracking of the actuated degrees of freedom, suppressing load oscillation while maintaining positioning accuracy. Experimental validation on a tower crane platform shows that the proposed strategy enables smooth obstacle avoidance and precise target point reaching, even with varying cable lengths.

Microstructure control of ultra-high-strength AlZnMgCu alloys fabricated by wire arc directed energy deposition: the role of variable polarity electric pulse

ABSTRACT In the manufacturing of large-scale ultra-high strength AlZnMgCu alloy components, wire arc directed energy deposition can be an efficient and flexible candidate. However, the fabricated AlZnMgCu alloys are faced with problems like coarse microstructures and high sensitivity to defects. Although metallurgical methods like micro-alloying and ceramic particle addition have shown considerable effects in microstructural control, pure process control methods that don’t affect the composition still lack investigation. This work proposed an effective control method by simply adding electric pulses during manufacturing. Results show that the application of pulses significantly reduces pore defects. A 2 Hz pulse can reduce the porosity by 65.7% to 0.122%. Low-frequency pulses effectively promote grain refinements. At 2 Hz, the deposited metal exhibited a mix of short columnar and fine equiaxed grains with a near-random orientation. Applying electric pulses leads to at least a 4.8% increase in UTS and a 225.0% increase in elongation.

Dynamic Control of Piezoelectric Sandwich Beams with Multi-Frequency Excitations

In this work, we present a non-linear analysis of the dynamics of a sandwich beam with piezoelectric patches under multi-frequency excitations. The model takes into account geometrical non-linearities, piezoelectric non-linearities, and inertia. Hamilton’s principle is used to determine the electromechanical equation of motion, and Garlekin’s technique is used to determine the equivalent model of the piezoelectric generator. A mathematical methodology based on the multi-scale method for vibration control and stability is reviewed in this work to determine a system of amplitude and phase equations. The control of primary and secondary mode resonances is developed, and feedback effects are analyzed for small and large amplitudes of vibrations of sandwich beams. Frequency response curves are presented and discussed for various gain parameters.

Adaptive Integral Sliding Mode Control with Chattering Elimination Considering the Actuator Faults and External Disturbances for Trajectory Tracking of 4Y Octocopter Aircraft

This paper presents a control strategy for a 4Y octocopter aircraft that is influenced by multiple actuator faults and external disturbances. The approach relies on a disturbance observer, adaptive type-2 fuzzy sliding mode control scheme, and type-1 fuzzy inference system. The proposed control approach is distinct from other tactics for controlling unmanned aerial vehicles because it can simultaneously compensate for actuator faults and external disturbances. The suggested control technique incorporates adaptive control parameters in both continuous and discontinuous control components. This enables the production of appropriate control signals to manage actuator faults and parametric uncertainties without relying only on the robust discontinuous control approach of sliding mode control. Additionally, a type-1 fuzzy logic system is used to build a fuzzy hitting control law to eliminate the occurrence of chattering phenomena on the integral sliding mode control. In addition, in order to keep the discontinuous control gain in sliding mode control at a small value, a nonlinear disturbance observer is constructed and integrated to mitigate the influence of external disturbances. Moreover, stability analysis of the proposed control method using Lyapunov theory showcases its potential to uphold system tracking performance and minimize tracking errors under specified conditions. The simulation results demonstrate that the proposed control strategy can significantly reduce the chattering effect and provide accurate trajectory tracking in the presence of actuator faults. Furthermore, the efficacy of the recommended control strategy is shown by comparative simulation results of 4Y octocopter under different failing and uncertain settings.

The impact of a large-scale natural disaster on local economic activity: evidence from the 2003 Bam earthquake in Iran

AbstractThis study provides new causal evidence for the impact of a large-scale natural disaster on local economic activity in Iran using nighttime light intensity. We apply the synthetic control method (SCM) to nighttime light (NTL) data from 1992 to 2013 to study the impact of the 2003 Bam earthquake on Bam County in Iran and its neighboring counties. According to the results and statistical inference tests for the SCM, Bam County and its neighboring counties experienced a statistically significant boost in economic activity in the years following the earthquake. Bam’s GDP economic activity increases by an accumulated US$620 million in the post-earthquake period. We find that the average economic gain in Bam following the 2003 earthquake is approximately 5% of Bam’s GDP. We also discuss possible contributing factors to the post-disaster economic boom in Bam.

DFT + U Study of Plutonium Hydrides with Occupation Matrix Control.

The magnetic order of plutonium hydrides (PuHx) has long been a subject of controversy, both experimentally and theoretically. The magnetic, structural, electronic, and thermodynamic properties of PuHx are investigated using Hubbard-corrected density functional theory (DFT + U), with U derived from linear response calculations. To address the issue of electronic metastable states within DFT + U, we employ an occupation matrix control method as well as allowing 5f orbitals to break the structural symmetry. This advanced computational approach establishes that the ground-state magnetic order for PuH2 is antiferromagnetic and that for PuH3 is ferromagnetic, consistent with Faraday and nuclear magnetic resonance experiments. Using the conventional hydrogen-vacancy model for PuHx, the hydrogen-content-induced magnetic transition and anomalous variation of the magnetic moment are successfully reproduced. In particular, the calculated transition point of x matches the experimental findings. Furthermore, the electronic configuration of the Pu atom in PuHx is determined to be 5f5, which is consistent with observations from X-ray photoemission spectroscopy. Our calculated values for enthalpy of formation, heat capacity, and entropy are in good agreement with experimental data, thereby validating the robustness of our theoretical framework.

Intelligent Model-Free Control for Power Line Inspection Robots: Tackling Input Time Delays with Data-Driven Solutions

This article presents an innovative approach to model-free adaptive control designed for power line inspection robots facing challenges with input time delays. The strategy begins by employing a compact-form dynamic linearization technique to transform the original system into a data-driven model. Subsequently, utilizing real-time input and output information, the system’s pseudo-partial derivatives are assessed online. Leveraging these assessment parameters, a weighted one-step prediction control mechanism is designed, and a compact-form dynamic linearization model-free adaptive control framework is established. Moreover, the research incorporates compression mapping to thoroughly confirm the convergence of the algorithm, thereby ensuring its stability. Ultimately, the effectiveness and practicality of this control method are substantiated through a series of simulation experiments, demonstrating its robust performance.

Adaptive Weighted Particle Swarm Optimization for Controlling Multiple Switched Reluctance Motors with Enhanced Deviatoric Coupling Control

Switched reluctance motors (SRMs) are widely used in industrial applications due to their advantages. Multi-motor synchronous control systems are crucial in modern industry, as their control strategies significantly impact synchronization performance. Traditional deviation coupling control structures face limitations during the startup phase, leading to excessive tracking errors and exacerbated by uneven load distribution, resulting in desynchronized motor acceleration and increased speed synchronization errors. This study proposes a modified deviation coupling control method based on an adaptive weighted particle swarm optimization (PSO) algorithm to enhance multi-motor synchronization performance. Traditional deviation coupling control applies equal reference torque inputs to each motor’s current loop, failing to address uneven load distribution and causing inconsistent accelerations. To resolve this, a gain equation based on speed deviation is introduced, incorporating self-tracking error and gain coefficients for dynamic synchronization error compensation. The gain coefficients are optimized using the adaptive weighted PSO algorithm to improve system adaptability. A simulation model of a synchronization control system for three SRMs was developed in the Matlab/Simulink R2023b environment. This model compares the synchronization performance of traditional deviation coupling, Fuzzy-PID improved structure, and adaptive PSO improved structure during motor startup, sudden speed increases, and load disturbances. The validated deviation coupling control structure achieved the initial set speed in approximately 0.236 s, demonstrating faster convergence and a 6.35% reduction in settling time. In both the motor startup and sudden speed increase phases, the two optimized methods outperformed the traditional structure in dynamic performance and synchronization accuracy, with the adaptive PSO structure improving synchronization accuracy by 54% and 37.17% over the Fuzzy-PID structure, respectively. Therefore, the PSO-optimized control system demonstrates faster convergence, improved stability, and enhanced synchronization performance.