Adjustment Of Control Parameters Research Articles

Load-frequency and voltage control for power quality enhancement in a SPV/Wind utility-tied system using GA & PSO optimization

Load Frequency Control (LFC) and Voltage Control (VC) are critical aspects of hybrid generation systems. In this work, the performance comparison of three different control approaches for LFC and VC: Genetics Algorithm (GA)-tuned Proportional Integral Differentiator (PID), Particle Swarm Optimization (PSO)-PID, and a conventional PID controller is presented. Especially, the performance is assessed and analyzed for convergence speed and computational complexity for each approach. Mathematical framework for each approach is discussed, including the required equations for hybrid generation system. It is reported that the traditional PID controller exhibits fast convergence due to its direct adjustment of control parameters. Simulation results reveal that it requires manual tuning and has low computational complexity. In contrast, the GA-PID utilizes a GA optimization process which automatically tunes the PID gains. Although, it may require multiple generations to converge to the optimal solution, however, it offers better control performance. Moreso, it comes at the cost of higher computational complexity compared to the traditional PID controller. In contrast, the PSO-PID employs an algorithm for parameter optimization. It converges faster than the GA-PID but still requires more iterations than the traditional PID controller. Similar to the GA-PID, it has higher computational complexity due to fitness function evaluation and particle updates. The optimization results provide insights into the convergence speed and computational complexity trade-offs between the three control approaches. Practitioners in the field of hybrid energy systems can utilize the outcomes to make informed decisions based on their specific requirements and available computational resources.

Multi-subswarm cooperative particle swarm optimization algorithm and its application

Particle swarm optimization (PSO) is a classical evolutionary algorithm and has been widely used to solve continuous optimization problems. However, the performance of the PSO algorithm is highly sensitive to its control parameters and learning strategies. To address this problem, a multi-subswarm cooperative particle swarm optimization algorithm (MSC-PSO) is proposed in this paper. In MSC-PSO, firstly, a population muti-subswarm division method is designed to enhance the global exploration capability. Secondly, a level-based particle adaptive inertia weight strategy is introduced to adjust control parameters. Finally, a level-based update learning mechanism is used for particles adaptive selection learning strategy. The performance of the MSC-PSO is evaluated by the CEC2020 test suite, and five heuristic algorithms are compared with the MSC-PSO in the experiments. The experimental results demonstrate that the proposed MSC-PSO algorithm has significant advantages in terms of convergence speed and solving optimal solutions. Furthermore, the proposed MSC-PSO algorithm is used to solve the plant protection unmanned aerial vehicles (UAV) path planning problem. The experimental results proved that the proposed MSC-PSO algorithm effectively solves the real-world UAV path planning problem, and achieves at most a 34 % reduction in round-trip distance and a 24.1 % reduction in total non-spraying time compared to classical PSO.

Prior-guided adaptive generative adversarial network method for various types of borehole image inpainting

During geophysical exploration, inpainting defective logging images caused by mismatches between logging tools and borehole sizes can affect fracture and hole identification, petrographic analysis, and stratigraphic studies. However, existing methods do not describe sufficient stratigraphic continuity. In addition, they ignore the completeness of characterization in terms of fractures, gravel structures, and fine-grained textures in the logging images. To address these issues, we develop a deep-learning method for inpainting stratigraphic features. First, to enhance the continuity of image inpainting, we build a generative adversarial network and train it on numerous natural images to extract relevant features that guide the recovery of continuity characteristics. Second, to ensure that complete structural and textural features are found in geologic formations, we introduce a feature-extraction-fusion module with a cooccurrence mechanism consisting of channel attention (CA) and self-attention (SA). CA improves texture effects by adaptively adjusting control parameters based on highly correlated prior features from electrical logging images. SA captures long-range contextual associations across preinpainted gaps to improve completeness in fractures and gravel structure representation. Our method has been tested on various borehole images demonstrating its reliability and robustness.

Model‐based adaptive control of modular DAB converter for EV chargers

AbstractThis paper presents the discrete‐time modelling and control of modular input‐parallel–output‐parallel (IPOP) dual‐active‐bridge (DAB) converters for electric vehicle (EV) charging. The proposed adaptive control system ensures adequate current‐sharing among parallel modules while minimizing DAB current stress by adopting dual phase‐shift modulation. Driven by the growing need for fast EV charging options, the paper highlights the importance of achieving top‐notch control performance, especially with varying load conditions. Specifically, it introduces a discrete‐time model for adjusting controller parameters adaptively, which simplifies the typically cumbersome manual tuning process associated with these systems. The proposed PI formulae are derived to satisfy specifications on the frequency domain as phase margin and the gain crossover frequency of the open loop gain transfer function, ensuring stability and robustness in operation. Moreover, the implementation of these formulae in discrete microcontrollers facilitates seamless PI autotuning for precise current, voltage, or power control. Notably, the proposed control strategy effectively mitigates current overshot issues commonly encountered during module engagement and shedding operations in modular EV chargers. To validate its efficacy, the proposed controller is evaluated through extensive testing and comparisons within the PLECS environment, particularly focusing on a two‐module IPOP‐DAB converter scenario, and including comparisons with classical offline model‐based pole placement methodology. Furthermore, real‐time hardware‐in‐the‐loop experiments are conducted to confirm the feasibility and performance of the proposed controller under realistic EV charging profiles.

Using LQR controller for vertical position control on EAST

Vertical position control is essential for stabilizing plasma with elongated configurations. The EAST tokamak is equipped with a set of in-vessel control (IC) coils dedicated to this purpose. Currently, a PD controller with fixed parameters is used for the vertical position control of EAST plasma. However, the response of the plasma in the vertical position changes with changes in plasma configuration, which can result in different control parameter requirements. It is essential to develop a model-based fast-tuning control algorithm for ensuring stability in the vertical position under different configurations. In this study, a model-based vertical position controller tuning method based on a linear quadratic regulator algorithm (LQR) is proposed. Compared with contemporary PD controllers, the proposed model-based LQR controller can enable adjusting controller parameters based on the response of the system, achieving stable control under different vertical position responses. In the EAST experiment, the model-based LQR controller achieved stable control under a shot with a continuously increasing growth rate and reached a maximum controllable vertical displacement growth rate of 968 s−1. The robustness of the system was also demonstrated in a free drift experiment. The new vertical displacement control method can be adapted to different system states and plasma configurations and improve the controllability and safety of future devices.

Locomotion Control of Cyborg Insects by Charge-Balanced Biphasic Electrical Stimulation.

The integration of electronic stimulation devices with insects in the context of cyborg insect systems has great application potential, particularly in the fields of environmental monitoring, urban surveillance, and rescue missions. Despite considerable advantages compared to the current robot technology, including flexibility, durability, and low energy consumption, this integration faces certain challenges related to the potential risk of charge accumulation caused by prolonged and repetitive electrical stimulations. To address these challenges, this study proposes a universal system for remote signal output control using infrared signals. The proposed system integrates high-precision digital-to-analog converters capable of generating customized waveform electrical stimulation signals within defined ranges. This enhances the accuracy of locomotion control in cyborg insects while maintaining real-time control and dynamic parameter adjustment. The proposed system is verified by experiments. The experimental results show that the signals generated by the proposed system have a success rate of over 76.25% in controlling the turning locomotion of cyborg insects, which is higher than previously reported results. In addition, the charge-balanced characteristics of these signals can minimize muscle tissue damage, thus substantially enhancing control repeatability. This study provides a comprehensive solution for the remote control and monitoring of cyborg insects, whose flexibility and adaptability can meet various application and experimental requirements. The results presented in this study lay a robust foundation for further advancement of various technologies, particularly those related to cyborg insect locomotion control systems and wireless control mechanisms for cyborg insects.

Adaptive prediction for effluent quality of wastewater treatment plant: Improvement with a dual-stage attention-based LSTM network

The accurate effluent prediction plays a crucial role in providing early warning for abnormal effluent and achieving the adjustment of feedforward control parameters during wastewater treatment. This study applied a dual-staged attention mechanism based on long short-term memory network (DA-LSTM) to improve the accuracy of effluent quality prediction. The results showed that input attention (IA) and temporal attention (TA) significantly enhanced the prediction performance of LSTM. Specially, IA could adaptively adjust feature weights to enhance the robustness against input noise, with R2 increased by 13.18%. To promote its long-term memory ability, TA was used to increase the memory span from 96 h to 168 h. Compared to a single LSTM model, the DA-LSTM model showed an improvement in prediction accuracy by 5.10%, 2.11%, 14.47% for COD, TP, and TN. Additionally, DA-LSTM demonstrated excellent generalization performance in new scenarios, with the R2 values for COD, TP, and TN increasing by 22.67%, 20.06%, and 17.14% respectively, while the MAPE values decreased by 56.46%, 63.08%, and 42.79%. In conclusion, the DA-LSTM model demonstrated excellent prediction performance and generalization ability due to its advantages of feature-adaptive weighting and long-term memory focusing. This has forward-looking significance for achieving efficient early warning of abnormal operating conditions and timely management of control parameters.

Prediction and control of profile for silicon steel strip in the whole tandem cold rolling based on PSO-BP algorithm

In the rolling process of silicon steel strip of the five-stand UCMW tandem cold rolling line, the quality of finished profile is the result of cooperative control of five stands, the exit profiles of the first four stands jointly decide the exit profile of the fifth stand, so it is necessary to accurately control the strip profile in the rolling process. However, the shapemeter is only typically equipped at the exit of fifth stand, making it impossible to obtain the strip profile of the first four stands. A profile prediction model for the whole tandem cold rolling based on PSO-BP algorithm is established, compared with the finite element method, this model can satisfy the demand for fast prediction of strip profile and quickly analyzing the profile change characteristics for five stands according to the adjustment of profile control parameters in the industrial field. Firstly, a 3D elastic-plastic finite element model of rolls and strip for five stands is built, and a large number of profile calculation data are as the training sample sets to build the PSO-BP model. Through the transfer calculation of strip profile between adjacent stands, the PSO-BP model of five stands is established, and the validity is verified by comparing with the actual measured data. Secondly, to improve the quality of product profile, focusing on the unreasonable setting of production parameters, many improvement schemes of WRB and WRS for five stands are designed, and the PSO-BP model is used to quickly calculate the strip profile, then comparing the result of each scheme, the optimum improvement scheme is selected, which is furtherly carried out rolling experiments in the industrial field, and the quality of strip profile is well improved. At present, the new scheme has been successfully applied to industrial production.

Analysis of Hydrogen Combustion in a Spark Ignition Research Engine with a Barrier Discharge Igniter

Hydrogen fuel is gaining particular attention in internal combustion engines. In addition to zero-carbon emissions, major advantages relate to its combustion characteristics, which allow a significant increase in thermal efficiency under ultra-lean operation and with very low NOx levels. The ignition system is one of the main technology enablers, as it determines the capability to control ultra-lean operations, avoid backfire phenomena, and/or reduce the risks of abnormal combustions. The latter results from hydrogen’s low ignition energy and it is associated with factors like high-temperature residuals, hot spots, and irregular spark plug discharge. The ACIS gen 2-Barrier Discharge Igniter excels in accelerating the initial flame growth speed by the generation of non-equilibrium low-temperature plasma, a strong ignition promoter for the combined action of kinetic and thermal effects. Moreover, its volumetric discharge facilitates combustion initiation on a wide region, in contrast to the localized ignition of traditional spark systems. In this work we present for the first time, to the best of our knowledge, experimental results showing the performance of a hydrogen engine with a low-temperature plasma discharge. Tests were conducted on a single-cylinder research engine, achieving ultra-lean conditions with cycle-to-cycle variability results below 2.5%. The analysis indicates that the H2-BDI combined solution is capable of accelerating the evolution of the flame front compared to traditional spark plugs, leading to a significant reduction in the cycle-to-cycle variability. A meticulous adjustment of the BDI control parameters further enhances igniter performance and contributes to a deeper understanding of the innovative approach proposed in this study.

Predefined-time control design for tracking chaotic trajectories around a contour with an UAV

The surveillance or monitoring of places is crucial to ensuring security, protecting people and assets, preventing crimes, and detecting emergencies, to mention some. Unmanned Aerial Vehicles (UAVs) play a vital role in these applications, offering versatility, agility, and aerial vision. A crucial step for such tasks is to protect the UAV path ahead. This paper focuses on a methodology harnessing the unpredictable nature of chaotic systems to generate trajectories around a closed area or contour. However, although a vast quantity of research papers mention the use of chaotic path generation, they have yet to learn about the control system and the dynamics affecting the UAV, where developing the control theory is challenging. In this paper, we design controllers based on predetermined-time stability, ensuring the achievement of the desired trajectory before a specified time. Additionally, adjusting control parameters is a crucial step during the control design, impacting the control performance. Hence, we present a method to optimize and adapt controller parameters through evolutionary optimization, demonstrating precision enhancement. We validate the proposed system’s performance and the controllers through numerical simulations, indicating that the UAV effectively and accurately follows some types of chaotic trajectories like a square contour, aiming at the feasibility of this methodology in real UAV surveillance applications.

Integrated application of synergetic approach for enhancing intelligent steam generator control systems

This article focuses on the integrated application of the synergetic approach to enhance the quality of intelligent steam generator control systems.By combining various techniques such as model-based control, adaptive control, and artificial intelligence, an efficient and flexible control system can be developed. Model-based control utilizes mathematical models of steam generators to formulate control algorithms and predict system behavior. Adaptive control enables the system to adapt to changing conditions by adjusting control parameters based on real-time measurements. Artificial intelligence techniques, including neural networks and genetic algorithms, facilitate learning, optimization, and data-driven decision-making processes. The objectives of this research are to investigate the benefits of the synergetic approach in steam generator control, including improved steam generation efficiency, optimized energy consumption, enhanced system stability and reliability, and adaptability to varying operating conditions and disturbances. The findings and conclusions of this study are expected to provide valuable insights for engineers, researchers, and professionals involved in the design and implementation of intelligent steam generator control systems. By integrating the synergetic approach, substantial enhancements in control quality can be achieved, leading to optimal operation and maximum efficiency of power plants.

Adaptive T-S fuzzy synchronization for uncertain fractional-order chaotic systems with input saturation and disturbance

This paper proposes an adaptive Takagi-Sugeno (T-S) fuzzy control scheme to synchronize two uncertain fractional-order (FO) chaotic systems with input saturations and external disturbances. Under the FO Lyapunov criterion, a novel adaptive T-S FO controller with a simple architecture is developed. System constraints are handled by modeling a saturator as a fraction of unknown functions. Due to the presence of system uncertainties, FO adaptive laws are established to adjust controller parameters. The asymptotic convergence of tracking errors and the boundedness of all closed-loop signals can be ensured. Especially, the feedback control is achieved in T-S FO chaotic systems and the posed method avoids solving linear matrix inequalities, which relieves the calculation burden. The feasibility of the presented method is confirmed through numerical simulations.

Research on dynamic PI parameter tuning of double-fed wind turbine based on ant colony algorithm

Considering the drawbacks of flexible adjustment of PI controller control parameters in the constant pressure control system of doubly fed fans, a PI controller control parameter flexible adjustment method considering ant colony optimization algorithm (ACO) is proposed. This method uses the proportional coefficient and integration time parameters in the PI controller as the ants in the ant colony optimization algorithm, and the ultimate optimization goal is to control the absolute error integration function. During the control process, the parameters of the PI controller are dynamically adjusted. When the electrified wire netting IR-drop and wind velocity changes, a comparative analysis of the dynamic tuning PI controller parameters and the non-dynamic tuning method was conducted. At last, the simulation studies have shown that the constant voltage control system using the PI parameter Flexible adjustment control method found on ACO can rapidly improve the grid voltage stability of double-fed wind turbines and reduce response time.

Nonlinear Robust Adaptive Sliding Mode Control Strategies Involve a Fractional Ordered Approach to Reducing Dengue Vectors

This study presents an innovative approach by integrating adaptive sliding mode control strategies with fractional order modeling to address the challenge of reducing Aedes aegypti mosquito populations, the primary vector of Dengue - a widespread and debilitating disease. By employing the Atangana-Baleanu-Caputo fractional operator to model the dynamics of the mosquito population, we achieve a more precise representation of complex and non-linear behaviors. The motivation behind adopting the Adaptive Sliding Mode Control (ASMC) approach lies in the critical need to efficiently control Aedes aegypti mosquito populations, a key step in combating the prevalence of dengue. The ASMC method dynamically adjusts control parameters based on evolving conditions, enhancing its adaptability to the changing dynamics of mosquito populations.The Lyapunov stability theorem ensures the reliability of tracking convergence and control structure. Additionally, we implement the Toufik Atangana method to solve both state and adjoint fractional differential equations using the ABC derivative operator. This incorporation adds a novel dimension to the study, providing a comprehensive framework for addressing the intricate dynamics inherent in the Aedes aegypti mosquito population. To assess the effectiveness of the proposed strategy, a numerical performance index is introduced at the end of the abstract. This index justifies the controller’s efficacy by comparing it to other conventional controllers. The inclusion of this quantitative measure reinforces the significance of the proposed strategy in the context of dengue prevention and control efforts.

Design of Human-Machine Integration System to Meet Diverse Interactive Tasks

A flexible production model in which humans and robots collaborate has become an urgent need for industrial production. Therefore, research on human-machine integration systems has received more and more attention in recent years. Human-computer interaction is widely used in industrial production and entertainment life, such as industrial control, VR games, etc. In this article, we designed and implemented a human-machine fusion system that meets different interactive tasks. Human movement estimation and robot interactive control are the key technologies. First, we propose a human posture solution method based on information fusion of multiple cameras and inertial measurement units. This method constructs an optimization problem, fuses the 2D joint detection information under multiple cameras and the measurement information of the worn inertial measurement unit, and optimizes the estimation of human kinematic posture, which improves the problems of incomplete posture information and sensitivity to noise under a single sensor. Therefore, this method improves the accuracy of pose estimation. Secondly, combining the kinematic characteristics of the robot and the characteristics of human-computer interaction, we designed a robot control strategy based on target point tracking and model predictive control, so that the robot can adapt to dynamic environments and different interaction needs by adjusting control parameters. At the same time, our design ensures the safety of robots and operators. Finally, we conducted human-computer interaction experiments such as action following, item delivery, and active obstacle avoidance. The experimental results show the effectiveness and reliability of the designed robot interaction system in a human-machine fusion environment.

Rapid Stabilization of Timoshenko Beam by PDE Backstepping

In this paper, we present rapid boundary stabilization of a Timoshenko beam with anti-damping and anti-stiffness at the uncontrolled boundary, by using infinite-dimensional backstepping. We introduce a Riemann transformation to map the Timoshenko beam states into a set of coordinates that verify a 1-D hyperbolic PIDE-ODE system. Then backstepping is applied to obtain a control law guaranteeing closed-loop stability of the origin in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L^{2}$</tex-math></inline-formula> sense. Arbitrarily rapid stabilization can be achieved by adjusting control parameters, and has not been achieved in previous results. Finally, a numerical simulation shows the effectiveness of the proposed controller. This result extends a previous work which considered a slender Timoshenko beam with Kelvin-Voigt damping, by allowing destabilizing boundary conditions at the uncontrolled boundary and attaining an arbitrarily rapid convergence rate.

A system for visualization of power-quality and optimization of the charging behavior for electric vehicles

Optimization of the dynamic behavior of the charging process for electric vehicles requires a centralized adjustment of control parameters. The development of suitable algorithms for the adjustment needs a tool to analyze and to visualize measured data from the environment of the charger. In this paper, we present that Power-Quality Map, a system to collect data and to show them on a map, which offers methods for analyzing those measured data. This paper describes the structure of the system, the software architecture and the functionality to visualize maps.

Improved Proportional Integral (PI) controller for water level control in open channel systems: A case study of the Middle Route Project for South-to-North Water Transfer

Study regionThe three pools near the terminal of the main canal of the Middle Route Project for South-to-North Water Transfer in China. Study focusThe conventional Proportional Integral (PI) controller neglects the potential for improved control performance by utilizing a PI controller with adaptable control parameters based on different water level conditions, particularly in large-scale open channels with significant water level variations. To address this limitation, this study proposes a novel hierarchical Proportional Integral (H-PI) control approach. The H-PI approach incorporates a weight coefficient into the control logic of the PI controller, allowing flexible adjustment of control parameters. These parameters are tuned using a hydrodynamic model integrated with a genetic algorithm for optimization. New hydrological insights for the regionThe simulation results of the study area manifest the efficacy of the proposed method in substantively diminishing gate control frequencies in contrast to other methods, specifically for gate deadbands of 3 cm and 6 cm, respectively. Moreover, the proposed method exhibits higher sensitivity to significant water level deviation changes. The robustness of the proposed method is evaluated under different disturbance scenarios, confirming its resilience and practical feasibility for implementation in the study area. In summary, the proposed H-PI control approach offers distinct advantages in minimizing water level deviation and gate control frequency. Furthermore, its robustness under different disturbance scenarios further supports its practical utility.

Landing control of a returned spacecraft under conditions of uncertainty

The nonlinear model of a spacecraft requires high robustness, and the classical control method, as one of the earliest developed control methods, has simple algorithms and high robustness. A control system structure that ensures retention attack angle and overload by combining fuzzy control with classical control for adaptive optimization and adjustment of control parameters and tracking of the attack angle step signals is proposed. The simulation results show that the performance of the improved system is better than that of the original system. Improved algorithms are used to simulate the attack angle and overload retention sections, also simulated aerodynamic uncertainties is carried out. The results show that the developed control law meets the requirements of a certain level of robustness. Keywords aerospace vehicles, position control, adaptive fuzzy control, PID adjusting, uncertainty

Coexisting superhydrophobicity and superadhesion features of Ziziphus mauritiana abaxial leaf surface with possibility of biomimicking using electrospun microfibers

The coexistence of superhydrophobicity and superadhesion features is being reported for the abaxial leaf of Indian jujube (Ziziphus mauritiana) possessing hairy, matted surface texture with fiber dia ranging ∼ 5.6–7.1 μm. Very high-water contact angle (WCA &amp;gt; 143°) and high contact angle hysteresis (CAH ∼ 30°–46°) were observed and compared for the tender, mature, and senescent leaf states. The tender leaf exhibits a maximal superhydrophobicity with WCA as high as ∼151° and an increased roll-off angle from ∼21° to 33°. Conversely, next two leaf states are characterized by excellent adhesion even up to a base tilting of 90° without any tendency to roll-off. An attempt has also been made for biomimicking the leaf's hairy fiber microstructure with polyvinylidene fluoride (PVDF) by employing an electrospinning setup, with adjustable control parameters. The fabricated ∼4.3-μm-dia PVDF-based nonwoven fibers were seen to be replicated at par with excellent superhydrophobicity and high adhesion features. The WCA and CAH of artificially grown fibers were estimated to be ∼145.7° and 49.4°. The nonwoven, yarn-like surface construct of microfibers fits well in worm-like chain model, which considers a normal distribution of segments described through discrete jointed length, persistent length, and bending angle between successive segments.