Intelligent Control Research Articles

Intelligent control strategies for grid-connected photovoltaic wind hybrid energy systems using ANFIS

Intelligent control strategies for grid-connected photovoltaic wind hybrid energy systems using ANFIS

Modified Higher Order Sliding Control Based Active Harmonic Current Compensator in Induction Furnace Using Emotional Tuned Intelligent Controller

Modified Higher Order Sliding Control Based Active Harmonic Current Compensator in Induction Furnace Using Emotional Tuned Intelligent Controller

Speed Regulating of Wind Turbine Unit using Iterative Learning Control

Conventional control methods, such as Proportional Integral Derivative (PID) control, have been extensively utilized for speed regulation in renewable energy wind turbine units. However, the limitations and disadvantages of PID, including difficulties in handling nonlinearities and uncertainties inherent in wind turbine dynamics, necessitate the exploration of alternative approaches. This research paper proposes the adoption of Iterative Learning Control (ILC) as an intelligent controller for addressing the shortcomings of PID. By exploiting the repetitive nature of wind turbine operation, ILC offers the potential to enhance speed regulation performance by iteratively refining control actions based on past experiences for advancing renewable energy. Through simulation studies, the effectiveness of ILC in improving the transient response and tracking accuracy of wind turbine units is demonstrated.

Improving primary frequency response in photovoltaic systems using hybrid inertia and intelligent control techniques

Abstract Low inertia poses a significant challenge to the widespread integration of photovoltaic (PV) systems into the grid, particularly affecting frequency stability. This paper addresses this challenge by proposing an advanced hybrid inertia (AHI) approach, characterized by the integration of real and virtual inertia, combined with the application of intelligent control techniques, to enhance the primary frequency response of a PV system. Internal inertia is provided by a synchronous generator (SG) acting as a compensator, while virtual inertia is generated through a real-time deloading strategy in the PV plant. The AHI is implemented on an isolated grid, utilizing various intelligent control techniques to improve frequency stability. The intelligent techniques employed include genetic algorithms (GA) and fuzzy logic controllers (FLC), which do not require mathematical modeling. This allows them to overcome the nonlinear dynamics of the PV plant and the uncertainties associated with climatic changes. These techniques are applied to the nonlinear components on the generator side, including the governor loop, DC-DC converter loop, and power reserve injection management loop. A frequency fluctuation scenario, induced by generating power perturbations in the proposed system, demonstrates and validates the improvement in frequency stability achieved by the HI and intelligent techniques. All tests are conducted using MATLAB Simulink. Various simulation results show a significant improvement in frequency stability through the incorporation of HI and intelligent control techniques.

Software defined network-based controller system in intelligent transportation system

<span lang="EN-US">Intelligent transportation system (ITS) is meant for redefining the conventional transport by incorporating various analytical features that not only offers safety but also enriches traffic data quality extensively. Review of existing literature shows that there is a significant gap towards utilizing vehicular adhoc network (VANET) for optimal performance in ITS environment. Therefore, this paper contributes towards a simplified and yet intelligent controller system harnessing potential of software defined network (SDN) towards effective directional management of complex transportation system. The novelty of this model is dual-fold. The first novelty is about the usage of locally and globally processed traffic information for undertaking decision towards clearing the waiting vehicles in observation point in specific route segment. The second novelty is associated with relaying of distinct traffic clearance signal to the distinct vehicles unlike any of teh existing transportation management scheme.</span>

Advancing Electric Vehicle Charging Ecosystems With Intelligent Control of DC Microgrid Stability

Advancing Electric Vehicle Charging Ecosystems With Intelligent Control of DC Microgrid Stability

Controlling of Solar Based Electric Vehicle Charging Station Through Intelligence Controller for G2V and V2G Modes

Abstract: Solar based electric vehicle (EV) charging station was proposed in this paper. The maximum power of a PV module varies due to changing temperature, solar radiation, and load. In order to extract the Maximum power point in this work, an MPPT system consisting of a PV module, a DC-DC converter, and a fuzzy logic controller is designed and simulated in Simulink. The EV battery SOC characteristic is observed to be fully charged within short period. In this paper single Phase grid and the PV system with the Fuzzy controller MPPT is connected to the DC link, which is connected to the EV. Also in the absence of solar PV energy, electric vehicle is charged from the grid. A battery of rating 100AH is charged with the solar PV panel using a boost converter which generates output voltage of 400V. Then the voltage is stepped down for buck operation according to 220 V battery requirement. A Bidirectional AC-DC rectifier is connected to the Grid, and the DC-DC boost converter for the MPPT and the DC-DC bidirectional Converter is connected to the EV for the Charging and Discharging of the EV. During charging and discharging modes the battery voltage and current is presented. It is clear that the grid voltage and current are in phase during charging. During discharging they are said to be out of phase indicating the reverse power flow.

Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.

Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.

Research on wheat broken rate and impurity rate detection method based on DeepLab-EDA model and system construction

The broken rate and impurity rate of wheat are important indicators for assessing the quality of combine harvester operations. In view of the overlapping, occlusion and dense adhesion between the scattered grains during the operation of the combine harvester, it is difficult to obtain the grain crushing characteristics and impurity mass, which leads to low detection accuracy. In this paper, a method for detecting wheat broken rate and impurity rate based on DeepLab-EDA semantic segmentation model was proposed, and a detection system was built. In the detection system, an image acquisition device was designed and developed based on the principle of electromagnetic vibration, and the deep learning model was deployed in the embedded processor. Through the human–computer interaction interface design, the online processing and analysis of wheat image data and the display of the detection results of broken rate and impurity rate were realized. Comparative experiments with traditional semantic segmentation models showed that the MIoU, MP and MR of the DeepLab-EDA model were 89.41 %, 95.97 % and 94.83 %, respectively, representing improvements of 9.94%, 7.41%, and 7.52% over the baseline model, and indicating a significant enhancement in the accurate identification and segmentation of broken grain and impurities. Based on this, indoor group matching experiments were conducted with three groups of broken rate and impurity rate levels set at 0.5%, 1.5%, and 2.5%, showing the average errors of 7.54% and 6.30% for broken rate and impurity rate detection systems, respectively. Furthermore, the detection device was installed under the grain outlet of the GM80 combine harvester for field experiments, which showed average errors of 13.32% and 9.77% for wheat broken rate and impurity rate, respectively. The effectiveness and accuracy of the wheat broken rate and impurity rate detection system meet the requirements, which can provide a data basis for intelligent control of combine harvester operation parameters by the operator.

An IoT-Based Smart Street Lighting System to Lower Energy Consumption

Smart Lighting System is an automated and intelligent lighting control system that can be deployed across multiple locations or in one location, utilizing various IoT connection protocols, devices, and sensors. The system is able to intelligently adjust to changes in ambient light conditions, traffic patterns, and pedestrian activity through the integration of Internet of Things (IoT) technologies. IoT-enabled sensors are installed in every street light, creating a network that connects to a central control unit. Using real-time data, this system dynamically modifies street light brightness to maximize energy efficiency and improve safety. It provides a simulation and optimization tool for street light placement, considering factors such as street length, light distance, and the number of lights ahead. This tool aids in visualizing the power consumption implications of different configurations, promoting efficient urban planning. Demonstrates the energy-saving potential of the proposed system. As the street light intervals and the number of lights ahead decreases, the optimal street light power significantly reduces, leading to substantial energy savings. The system's ability to dim or turn off lights during low-traffic or well-lit periods contributes to reduced operational costs and enhanced sustainability.

High environmentally adaptable phase control with reinforcement learning for coherent beam combination

In contemporary times, the utilization of high-power lasers is progressively expanding. However, the potential of a single laser to generate power confronts inherent physical constraints, leading to limitations in its incremental power augmentation. To address this challenge, coherent beam combining technology has been introduced as a means to achieve amplified power output. In this paper, an intelligent phase control method for coherent beam combining is proposed, which introduces reinforcement learning algorithm to provide assistance for the control problem, designs algorithmic variables based on the physical model of coherent beam combining, deploys it in the environment for multiple interactions, and updates the neural network in real time using the feedback from the environment. The efficacy of the proposed algorithm is validated in a 7-channel coherent beam combining simulation system, and the experiment results show that the synthesized beam is strong and stable under the control of the algorithm, and the convergence of the algorithm as well as the control effect is discussed and validated in different environments, and its performance also exceeds that of the existing mainstream phase control algorithm SPGD.

A broadband acoustic metamaterial lens for sub-wavelength imaging based on bandwidth enhancement

Acoustic metamaterials manufactured from natural materials can exhibit exotic characteristics that cannot be found in nature materials through the design of geometric structures and resonance effects, and acoustic metamaterials can be used to achieve sub-wavelength acoustic imaging as the acoustic metamaterial lens (AML). Currently, the AMLs based on the Fabry–Pérot (FP) resonance principle are all with single effective length hole, which is directly related to the thickness of its structure. They can only achieve acoustic imaging in a relatively narrow frequency range, and there is no AML which can achieve broadband sub-wavelength acoustic imaging. In this paper, a kind of broadband acoustic metamaterial lens (BAML) is introduced. While maintaining a constant overall thickness of the AML, a design which is composed of the supercell consisting of a cross distribution of different effective length holes is proposed. This design enables the AML formed by these supercells to encompass multiple operating frequencies corresponding to various holes, thereby broadening the frequency range of acoustic imaging. Finite element simulation and imaging experiments were conducted, and results demonstrated that the BAML consisting of multiple effective length holes has the ability to enhance bandwidth of acoustic imaging. Compared to AML with single effective length hole, it offers a broader range of imaging frequencies, this design offers a general method of bandwidth enhancement. BAML has potential application value in ultrasonic imaging, sound absorption, intelligent thermal control and medical diagnosis.

Conductive hydrogels‐based self‐sensing soft robot state perception and trajectory tracking

AbstractSoft robots face significant challenges in proprioceptive sensing and precise control due to their highly deformable and compliant nature. This paper addresses these challenges by developing a conductive hydrogel sensor and integrating it into a soft robot for bending angle measurement and motion control. A quantitative mapping between the hydrogel resistance and the robot's bending gesture is formulated. Furthermore, a nonlinear differentiator is proposed to estimate the angular velocity for closed‐loop control, eliminating the reliance on conventional sensors. Meanwhile, a controller is designed to track both structural and nonstructural trajectories. The proposed approach integrates advanced soft sensing materials and intelligent control algorithms, significantly improving the proprioception and motion accuracy of soft robots. This work bridges the gap between novel material design and practical control applications, opening up new possibilities for soft robots to perform delicate tasks in various fields. The experimental results demonstrate the effectiveness of the proposed sensing and control approach in achieving precise and robust motion control of the soft robot.

Fringing‐Effect‐Based Capacitive Proximity Sensors

AbstractProximity sensing technology, which can obtain information without physical contact, has become an ideal choice in scenarios where physical contact is not feasible. Despite substantial advancements in tactile sensing, proximity sensing technology still holds great potential and has yet to be fully developed. Among numerous proximity sensing technologies, the fringing‐effect‐based capacitive proximity sensor (FE‐CPS) has garnered considerable attention due to its low cost, low power consumption, wide sensing range, and flexible and versatile structural design. However, research on FE‐CPS has not yet formed a complete system, and its development and intellectualization are still in their infancy, urgently requiring a systematic review to advance its development. This paper systematically summarizes the recent advances in FE‐CPS, from basic theory to practical applications. The working principle and typical structure of FE‐CPS are first introduced, followed by a discussion of methods for optimizing device performance. Furthermore, the application scenarios of FE‐CPS in intelligent pre‐alarm systems, intelligent control systems, and intelligent material perception systems are reviewed. Finally, the future development and challenges faced by FE‐CPS are prospected.

Research on Optimization Strategies of Curriculum System in the New Quality Teaching Reform of Intelligent Control Technology Specialty

This research focuses on the optimization strategies of the curriculum system of the intelligent control technology major under the background of the new quality teaching reform. Through a comprehensive and in-depth analysis of the numerous problems existing in the current curriculum system, and in close combination with the latest demands of industry development and cutting-edge educational and teaching theories, a series of highly targeted and practically operable optimization strategies are proposed. The fundamental purpose is to significantly improve the teaching quality of the intelligent control technology major and spare no effort to cultivate high-quality professionals who can fully adapt to the requirements of the times.

Online Safe Flight Control Method Based on Constraint Reinforcement Learning

UAVs are increasingly prominent in the competition for space due to their multiple characteristics, such as strong maneuverability, long flight distance, and high survivability. A new online safe flight control method based on constrained reinforcement learning is proposed for the intelligent safety control of UAVs. This method adopts constrained policy optimization as the main reinforcement learning framework and develops a constrained policy optimization algorithm with extra safety budget, which introduces Lyapunov stability requirements and limits rudder deflection loss to ensure flight safety and improves the robustness of the controller. By efficiently interacting with the constructed simulation environment, a control law model for UAVs is trained. Subsequently, a condition-triggered meta-learning online learning method is used to adjust the control raw online ensuring successful attitude angle tracking. Simulation experimental results show that using online control laws to perform aircraft attitude angle control tasks has an overall score of 100 points. After introducing online learning, the adaptability of attitude control to comprehensive errors such as aerodynamic parameters and wind improved by 21% compared to offline learning. The control law can be learned online to adjust the control policy of UAVs, ensuring their safety and stability during flight.

Temperature tunability of topological phase transitions and edge states in two-dimensional acoustic topological insulators

Topological insulators are characterized by exhibiting an internal insulating state but a surface conductor state, which makes them advantageous for applications in novel devices. However, for most given acoustic topological metamaterials, the operating frequency is relatively fixed and the effect of temperature on their topological properties is rarely considered. Therefore, a temperature-tunable acoustic topological insulator is constructed in this paper. The quadruple degenerate Dirac cone formed at Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Gamma$$\\end{document} point can be opened by adjusting the temperature, causing topological band inversion between the doubly degenerate dipolar and quadrupole states, and achieving topological phase transition. The evolution of its topological state with temperature is numerically investigated and a novel topological acoustic waveguide is constructed. The switching effect of temperature on the waveguide device is verified by numerical simulation and experiment. Non-contact active modulation of edge states in the structure is achieved by temperature-controlled topological phases, exhibiting acoustic switching effects. This study can provide corresponding references for the intelligent control of acoustic topology in noise, vibration, and other aspects.

Intelligent controller design of an autonomous system using a social spider optimizer for path navigation and obstacle avoidance

This research paper proposes a hybrid fuzzy logic controller for achieving autonomous path navigation and obstacle avoidance through the use of the Social Spider Optimizer algorithm. The proposed controller employs kinematic modelling to determine the mobile robot’s path navigation and utilizes a fuzzy logic system for effective control. The Social Spider Optimizer algorithm optimizes the parameters of the fuzzy controller, while the FLC is responsible for obstacle avoidance. The effectiveness of the proposed controller has been analyzed, and a comparative study has been carried out with optimization techniques like particle swarm optimization (PSO) and cuckoo search optimization (CSO) controllers. The study aims to propose a hybrid fuzzy logic controller, that provides efficient navigation and obstacle avoidance for mobile robots. In a simulation, the starting point is considered as (0,0) and the destination point is set as Xk = 1.1 and Yk = 1.2. The performance of the proposed method is compared with FLC and methods like PSO and CSO. With the SSO-based FLC, the proposed mobile robot identifies the obstacle distance and travels towards the destination with smooth navigation. The results show the efficacy of the proposed controller in comparison to other controllers for mobile robots in terms of path navigation and obstacle avoidance.

Enhancing Traffic Control with AI Blockchain and Dynamic Computation Techniques

The rapid urbanization and increasing vehicular density in modern cities have led to significant challenges in traffic management and control. As urban areas continue to expand, the demand for more efficient and intelligent traffic control systems has become increasingly critical. This paper presents a novel approach to enhancing traffic management by integrating Artificial Intelligence (AI), Blockchain technology, and Dynamic Computation Techniques. AI is utilized to analyze and predict traffic patterns, enabling real-time adjustments to traffic signals and flow management. Blockchain provides a secure, transparent, and decentralized platform for data sharing and coordination among various stakeholders, ensuring data integrity and trust. The incorporation of Dynamic Computation Techniques allows for flexible and scalable processing of complex traffic data, facilitating rapid decision-making and adaptation to changing conditions. This multidisciplinary approach not only improves traffic efficiency and reduces congestion but also paves the way for more resilient and sustainable urban transportation systems. The findings highlight the transformative potential of combining AI, Blockchain, and advanced computation methods in the field of traffic control.