Control System Research Articles

Justifying the structure of the improved mechanism for manual control of motor vehicles’ pedals

Due to the challenging military situation in Ukraine, the demand for tailored and flexible pedal control systems for individuals with physical disabilities is especially important. Standard pedal configurations often fail to meet the specific needs of drivers with limited mobility, making vehicle operation both difficult and potentially unsafe. The development of specialized manual control devices, such as hand-operated pedals or adjustable foot controls, is crucial for ensuring that these drivers can manage their vehicles with accuracy and safety. Such innovations not only improve accessibility but also foster greater autonomy and inclusion, empowering disabled drivers to navigate the roads with confidence and ease. This article aims to review existing prototypes of manual control mechanisms for vehicle pedals and to develop an improved device that enables simultaneous control of three pedals – accelerator, brake, and clutch – with one hand. As a result of a patent review of several existing prototypes of control mechanisms, it was concluded that the vast majority of them provide control for only two pedals – the accelerator and brake. This means they can only be used in motor vehicles with automatic transmissions or electric cars. Only a few mechanisms were designed to control three pedals, but they required using both the driver’s hands, directly affecting driving safety. Therefore, improving existing designs of the mechanisms for controlling three pedals by transferring all control functions to one hand of a driver remains relevant. The research methodology involves the use of classical methods from the theory of mechanisms and machines to conduct the structural synthesis of an improved multi-link hinge-lever mechanism and its kinematic analysis, aimed at determining the main parameters of pedals’ movements in a vehicle under various control inputs from the driver’s hand. The results obtained can be utilized by researchers and engineers to enhance manual control mechanisms for vehicle pedals and in the practical implementation processes. The prospects for future research on this topic are in developing an experimental prototype of the control mechanism and its testing and adjustment for different vehicle modifications to improve running smoothness and driving comfort and safety.

Fuel recycling feedback control via real-time boron powder injection in EAST with full metal wall

Abstract A feedback control of fuel recycling via real-time boron powder injection, addressing the issue of continuously increasing recycling in long-pulse plasma discharges, has been successfully developed and implemented on EAST tokamak. The feedback control system includes four main parts: the impurity powder dropper (IPD), a diagnostic system measuring fuel recycling level represented by Dα emission, a plasma control system (PCS) implementing the Proportional Integral Derivative (PID) algorithm, and a signal converter connecting the IPD and PCS. Based on this control system, both active control and feedback control experiments have recently been performed on EAST with a full metal wall. The experimental results show that the fuel recycling can be gradually reduced to lower level as PCS control voltage increases. In the feedback control experiments, it is also observed that the Dα emission is reduced to the level below the target Dα value by adjusting boron injection flow rate, indicating successful implementation of the fuel recycling feedback control on EAST. This technique provides a new method for fuel recycling control of long pulse and high parameter plasma operations in future fusion devices.

Development of a prioritized traffic light control system for emergency vehicles

This research presents a model for an adaptive traffic signal control system aimed at improving urban traffic regulation. It dynamically adjusts signal timing based on vehicle volume at intersections, prioritizing emergency vehicles by allowing them immediate passage. Utilizing Arduino coding, the system controls traffic light intensity according to the traffic flow, enhancing road safety and efficiency. This innovative approach not only facilitates faster clearance for emergency services without human intervention but also reduces congestion and accident rates. This research creates a model for a prioritized traffic signal control system. When the vehicular volume at the intersection varies, the signal time alters autonomously. It identifies the ambulance/emergency vehicles and allows the green light for emergency vehicles like ambulances, and fire engines. This approach may be used to detect traffic accidents and infractions of automobile spiral motions. When erected on the road, the entire system allows for quick traffic clearing for rescue vehicles without requiring a policeman. The system's design eliminates the need for sensors or radio frequency identification (RFID) tags, simplifying traffic management. Simulations validate that emergency vehicle travel time is significantly reduced, proving the system's effectiveness in streamlining urban traffic flows.

Single observer speed sensorless field weakening control of two parallel motors

Two induction motors with parallel connected stator windings supplied by a single inverter (single observer) with a field weakening control system. The objective of this study is to propose a torque production speed sensorless vector control technique for each parallel connected rotor by adding field weakening control to improve the performance of each motor and reducing the number of adaptive observers on each motor to a single observer. Design of a rotor flux-oriented control (RFOC) system. Simulation results with the C-MEX S-function of MATLAB/Simulink 2015b show that the proposed method works better and can be operated at speeds that exceed the nominal speed of the motor.

Energy-saving and carbon reduction analysis of series/parallel combination for heat pump and chiller in a hotel building

Taiwan is known for its hot and humid climate, where buildings rely on air conditioning systems to provide comfortable conditions. Specifically in commercial buildings such as hotels, apart from being required to provide comfortable indoor conditions, they need to provide hot water 24 h for guests, leading to the common application of chiller and heat pump systems in hotels. With the rise of the Taiwanese tourism industry, many hotels have been newly built or renovated. However, during the planning and construction stages, considerations may be limited by personnel experience, operational difficulties, installation costs, or the desire to simplify operations. This often results in the separate consideration of heat pump systems and chiller systems, leading to unnecessary resource and energy waste. Therefore, this study takes place at a hotel building in Taichung located in Central Taiwan. Designing a series/parallel system between the heat pump and chiller systems. Through a control system and a real-time recording system, controllable factors were analyzed, such as system equipment supply/return water temperature, outdoor relative and enthalpy, and occupancy rate change. Additionally, energy-saving potential was evaluated from the collected data through linear regression. The results show that when the outdoor air temperature, outdoor enthalpy, and occupancy rate are low, the parallel system is more energy-efficient due to the higher energy consumption of the chiller under low load conditions, which is suitable for winter. Conversely, the series system is more energy-efficient than under hot weather and high indoor load conditions, making it suitable for use in the summer.

Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems

Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems

IEEE Control Systems Society Awards: Please Nominate Worthy Candidates! [Member Activities

IEEE Control Systems Society Awards: Please Nominate Worthy Candidates! [Member Activities

Graduation-inspired manufacturing system and synchronization mechanism for hybrid assembly cell lines

This paper presents an innovative flexible assembly system – hybrid assembly cell lines (HACL) to adapt to small batch, high product variety and dynamic demands. HACL provides specially designed trolleys for assembly operations, materials and tools to form the workstations flexibly and quickly at low reconfiguration cost. To conduct accurate and effective control for this complex assembly system, this paper presents a modified Graduation-inspired Manufacturing System (GMS), a recently proposed new card-based control system. GMS for HACL designs three kinds of tickets and the ticketing mechanism to absorb the uncertainties of HACL. Job tickets are for workload control and synchronization of part deliveries; setup tickets are for cell line formation and synchronization of resources of tools and machines; operation tickets are for production execution and synchronization of workers and operations in cell lines. GMS for HACL develops Internet of Things-enabled (IoT-enabled) architecture for managing the smart tickets to achieve automatic and intelligent interoperability between GMS and the physical workstations of HACL. This work designs synchronization mechanism based on specific model and genetic algorithm to synchronize manufacturing resources under GMS for HACL

Robust Control System Tuner and Cascaded Adaptive Vectorial Filter-Based PV-Battery System With a Dual-Loop ANF-PLL

Robust Control System Tuner and Cascaded Adaptive Vectorial Filter-Based PV-Battery System With a Dual-Loop ANF-PLL

CFD numerical simulation of tunnel dust pollution control using a negative pressure double extraction ventilation system

To effectively control the dust pollution problem in tunneling, we studied the dust control and removal effect of a novel negative pressure double extraction ventilation system. We first investigated the spatial and temporal evolution law of wind flow and dust under single-pressure entry ventilation conditions, and found that the dust diffused to the tunnel exit within 150s. The average dust concentrations at the driver’s location, the front of the tunnel, and the back of the tunnel were 339.26, 278.39, and 232.43mg/m3, respectively, and the average dust concentration in the tunnel was 243.74mg/m3. Subsequently, based on the principle of air extraction and dust control, a negative pressure double extraction dust control system was constructed. The spatial and temporal evolution of wind flow and dust at different locations of the exhaust duct was investigated. When the exhaust duct A was 3.0m away from the floor of the roadway, CA=3.0m, and when the exhaust duct B was 4.0m away from the left wall of the roadway, CB=4.0m, the average dust concentrations at the driver’s location, the front of the tunnel, and the back of the tunnel were 45.62, 164.58, and 5.21mg/m3, respectively. Compared with single pressure-entry type ventilation, the dust prevention effect of the system improved by 86.55%, 40.88%, and 97.76%, respectively, and this installation position was determined to be the optimal position for dust removal.

Deep one-class classification model assisted by radius constraint for anomaly detection of industrial control systems

Anomaly detection of industrial control systems (ICS) based on sensor data analytic is of utmost importance because ICS may suffer from various attacks leading to anomaly behaviors and even equipment failures. As an emerging deep learning technique, deep support vector data description (DeSVDD) has been successfully applied to ICS anomaly detection. Its advantage lies in only requiring normal data to train one-class classifier, which is suitable for actual industrial scenarios with extremely data imbalance of abundant normal data and scare abnormal data. However, this also results in its shortcoming of ignoring the valuable classification information hidden in the abnormal data. In order to overcome this issue, this paper proposes an improved DeSVDD method, called radius constraint DeSVDD (RC-DeSVDD). The proposed model constructs an anomaly detection model framework of abnormal data assisted DeSVDD, where a radius constraint is designed by considering the difference between normal and abnormal data. Further, considering the limited amount of abnormal data, a bi-directional generative adversarial network (BiGAN) is introduced to generate abnormal data. Experimental results on three benchmark datasets demonstrate the superiority of the proposed RC-DeSVDD anomaly detection method.

Three Stage Power Electronic Transformer Based MVAC Collection System and Its Control System Design for Offshore Wind Power Generation Mills

Three Stage Power Electronic Transformer Based MVAC Collection System and Its Control System Design for Offshore Wind Power Generation Mills

A comprehensive review on power quality issues and disturbances mitigation through shunt active power filters

In contemporary power systems, power quality (PQ) has become a matter of paramount concern. The challenges associated with PQ extend beyond conventional three-phase systems, encompassing the integration of various distributed generation (DG) sources like renewable energy installations, storage systems, and diverse power generation technologies such as diesel generators and fuel cells. The prevalent adoption of rapid-switching devices within the utility infrastructure has resulted in a surge of harmonics and reactive power disturbances. The increased use of harmonics-producing loads has led to several power quality issues, particularly harmonics. The distortions caused by these power quality issues must adhere to the limits established by international standards. Mitigation of these concerns is critical, and active power filters are a realistic option. However, passive filters have problems such as bulkiness, and resonance with either/both load and utility impedance, and the source impedance affects filtering properties. As a result, active power filters (APF) are designed to address the shortcomings of passive filters. Active power filters (APF) offer several advantages over passive filters, including compact size, enhanced filtering characteristics, dynamic performance, and flexible operation. The control strategy of APF strongly influences the APF performance, efficiency, and reliability. This paper presents a detailed assessment of current active filter control systems, highlighting their key features. The characteristics, performance, applicability, and implementation of various control techniques are explored and investigated.

Development of a universal plug tray seeder for small seeds based on electrostatic adsorption

Addressing the limitations of the traditional air suction plug tray seeder regarding versatility, clogging, noise, and energy consumption, a novel plug tray seeding method suitable for a broader range of small seed sizes has been proposed. A universal plug tray seeder has also been designed based on electrostatic adsorption for small seeds. Key factors affecting seed electrostatic adsorption were analyzed through electrostatic simulation, determining the optimal manufacturing method for the suction needle and the best range for the electrostatic voltage. Leveraging the theory of granular dynamics, a seed vibration box was designed using the principle of microphone vibration to enhance seed flowability and reduce the multiple seeding rate. Furthermore, the control system achieved seed recognition based on YOLOv8n and adaptive matching of seeding parameters, enhancing the universality of the seeder. The seeder was optimized and validated through practical experiments, with a comparative analysis of energy consumption and sound intensity conducted. The results indicated that the electrostatic suction needle, made with a single copper electrode of 1 mm diameter and coated with a 1 mm thick planar epoxy resin adsorption layer, along with an electrostatic voltage of 5 ∼ 10 kV, could effectively adsorb seeds. The vibration box significantly improved the seeding effect by vibrating seeds of tomato, pepper, and muskmelon at frequencies of 10 ∼ 25 Hz, and seeds of broccoli, cabbage, and eggplant at frequencies of 30 ∼ 50 Hz. The combined action of the electrostatic suction needle and the vibrating seed box resulted in an 83.20 % reduction in energy consumption and a significant decrease in sound intensity. Although the single seeding rate for muskmelon and cabbage seeds slightly decreased due to higher rates of leakage seeding and multiple seeding, the single seeding rate for other seeds remained around 90 %. This study provides a theoretical foundation for the universal seeding method of small seeds and offers significant reference value for the design of low-energy, low-noise plug tray seeders.

A monochrome pipelined HMI system for foodborne microorganisms testing

Hyperspectral microscopy imaging (HMI) is an efficient and non-destructive method to detect microbial contaminants in food, as it can provide both spatial morphology and spectral signature. Aims at reducing thermal effect, low cost, and improving spectral resolution in testing, a pipeline-operated LEDs monochromatic illumination mode is proposed, which integrates the design concepts of both grating-based and LED-based HMI systems. By design of the LED set, shared grating monochromatic optical path, and coordinated control system, an HMI system has been developed that could obtain the hyperspectral data cube with 101 bands in 400–700 nm. Hyperspectral datasets of three species of Aspergillus are prepared using the prototype, and efficient results have been achieved in the training and testing of classical classification algorithms (1D-CNN (97.33 %), k-NN (96.33 %), SVM (97.67 %) and ResNet-18 (95.67 %)). The results demonstrate that the proposed monochromatic illumination mode and associated system are potential detection solutions for foodborne microbial contaminants with low-cost and high-accurate.

Development of an advanced current mode charging control strategy system for electric vehicle batteries

Electric vehicles (EVs) face customer hesitancy due to challenges in locating fast charging stations, lengthy recharging times, and incompatible charging ports. This research addresses these issues by proposing a novel current mode control strategy for EV battery charging. Traditional charging methods often result in suboptimal rates, battery degradation, and safety risks. The primary objective is to enhance charging efficiency, safety, and battery lifespan by optimizing parameters such as voltage and current. Control mode charging offers significant advantages over plug-in charging by minimizing stress factors that contribute to degradation, such as high temperatures and excessive charging cycles. This approach aims to extend the lifespan of EV batteries while ensuring safe, efficient, and fast charging. The control system offers three charging modes: slow (0.49 A, 6.31 W, 264 mins), medium (2.74 A, 34.85 W, 50 mins), and fast (4.62 A, 50.80 W, 30 mins) using a 12 V single-phase supply. This advanced strategy significantly improves EV charging system efficiency, with fast charging achieving 80% higher efficiency than slow charging in both simulations and experimental testing. The key contribution of this research is the development of a tailored current mode charging strategy that optimizes charging efficiency while ensuring battery longevity and safety.

Bio inspired technique for controlling angle of attack of aircraft

<p>This paper deals with the design of a proportional–integral (PI) controller for controlling the angle of attack of flight control system. For the first time teaching learning-based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed PI controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the PI controller. The superiority of proposed approach is demonstrated by comparing the results with that of the conventional methods like genetic algorithm (GA) and particle swarm optimization (PSO). It is observed that TLBO optimized PI controller gives better dynamic performance in terms of settling time, overshoot, and undershoot as compared to GA and PSO based PI controllers. The various performance indices like mean square error (MSE), integral absolute error (IAE), and integral time absolute error (ITAE) are improved by using the TLBO soft computing techniques. Further, robustness of the system is studied by varying all the system parameters from −50% to +50% in step of 25%. Analysis also reveals that TLBO optimized PI controller gains are quite robust and need not be reset for wide variation in system parameters.</p>

Plant factory with artificial light design, realization, and economic analysis

It has been challenging to construct an autonomously controlled plant factory with artificial light (PFAL). It is also useful in engineering and bioscience research and education. The purpose of this research is to design and construct a micro-scale PFAL (µPFAL) with automatic environment control for a university project. Then, analyze the effectiveness of managing temperature, humidity, pH, EC, and CO2 on crop production, as well as the cost, and benefit of µPFAL. The µPFAL is made up of LED lighting, air condition, vertical cultivation, EC-pH regulation, a CO2 supply unit, and environmental control and monitoring. Control was provided via Arduino with PC monitor. For economic evaluation, cost-benefit analysis was used. The results of the control environment in µPFAL were achieved with a deviation of less than 2.5%. An Arduino-based environmental control system with a computer for monitoring was suited for university’s PFAL.Our µPFAL could produce 80.45 g/head fresh weight of green oak lettuce, the lettuce’s yield of 19 kg/m2/y. The payback period of µPFAL is 3.28 years, net present value of 82,543.30 THB, an internal rate of return of 24% and the B/C ratio of 1.22. Future research should include solar energy to assist µPFAL in meeting its sustainable goal.

Order Degree and Centrality Analysis of Advanced Fighter Environmental Control System and Thermal Management System

As fighter aircraft become more advanced, traditional air cycle systems have fallen short of meeting the complex environmental control requirements. Advanced fighters such as the F-22 Raptor have responded to these challenges by adopting integrated thermal management systems. These systems feature complex thermodynamic processes and intricate information transmission pathways among various components, creating a sophisticated network structure. To unravel the complexity of such systems, this paper employs a blend of thermodynamics and information theory. We conduct multi-level analysis using structure entropy method and centrality algorithms to explore the information transmission characteristics within these thermodynamic systems. At the system level, we find that variation in environmental parameters have only a 1% impact on order degree, whereas the influence from the system's own structure is more pronounced. At the component level, within the whole system, the varies components in AFT PAO and fuel loops emerge as critical hubs for information transmission. Among these components, the PAO/Fuel stands out as the most important, with closeness and betweenness centrality exceeding that of all other components by at least 11.5% and 29.1%. This study offers a theoretical foundation for the optimization of thermodynamic system structure and layout, viewed through the lens of information theory.

Modelling of Harris Hawks optimization with deep learning-assisted microgrid energy management approach

Microgrid (MG) is a potential decentralized energy distribution and generation technology that is resilient, reliable, and efficient. This small-scale power system is reliable and resilient since it connects to the grid or runs independently. Renewable energy is difficult to integrate into MG due to variable load and unreliable electricity. MG operation relies on an energy management system (EMS) to balance electricity demand and supply, reduce operational costs, and maximize renewable energy use. Intelligent control systems, optimization methods, and machine learning algorithms were used for MG EMS. The Harris Hawks optimization with deep learning-assisted microgrid energy management (HHODL-MGEM) technique is developed in this work. HHODL-MGEM comprises two main stages. In the first step, the HHODL-MGEM approach uses the Harris Hawks optimization or HHO algorithm to meet load power demands at a low cost while maintaining DC bus voltage and protecting the battery from overcharging and depletion. In the second step, long short-term memory (LSTM) networks can predict power costs. The HHODL-MGEM approach is evaluated using multiple methods. The experimental results showed that HHODL-MGEM outperforms other methods.