Advanced Control for Applications: Engineering and Industrial Systems

ABSTRACT The time constant is often an important indicator parameter to evaluate the response speed of thermocouples. However, in practical applications, the output signal suffers from interference and deformation due to the excitation signal not satisfying the desired step size, data transmission delay, and the effect of mechanical vibrations, which do not match the output response of the system of the same order. In addition, the measurement results are not reproducible due to the subjective influence of the manual measurement method. In this paper, we propose a method to compute the time constant. The output signal is modified by filtering and fitting to enable the automatic calculation of the time constant, and the parameters of the algorithm are discussed. We developed a thermocouple time constant measurement device using a water bath and laser excitation conditions, obtaining response signals from thermocouples with different wire diameters and laser powers. The results demonstrate the effectiveness of the proposed time constant calculation methods in accurately capturing the changing trend of thermocouples with different wire diameters and laser powers. These methods successfully meet the requirements for processing the dynamic response signals of thermocouples under various excitation conditions.

Yunhong Liu

Xianming Liu

Yuanrong Zhao

Chongwen Wang

Fang Ge

Advanced Control for Applications

Research on Measurement Algorithm of Thermocouple Time Constant Under Multiple Incentive Experimental Conditions

多激励实验条件下热电偶时间常数测量算法研究

ABSTRACT Extraordinary properties emerge from subsystems' interactions. Hybrid energy systems (HESs) are a promising concept that could change the renewable energy landscape. By co‐designing generation, storage, and conversion technologies, HESs can provide new electrical power services, increase grid stability and control authority, and generate energy and/or nonenergy products such as electricity, hydrogen, ammonia, heat, digital data, or fresh water. This article discusses some conditions the co‐design of HESs should follow to optimize the combined system ( synergy ), avoiding deterioration ( dysfunction ). It introduces some technoeconomic synergy conditions , develops a synergy margin , and analyses several case studies, exploring also the control co‐design methodology to optimize synergistically the hybrid system.

Mario Garcia‐Sanz

Hybrid Energy Systems: Synergy Margin and Control Co‐Design

混合能源系统：协同裕度与控制协同设计

ABSTRACT Switched reluctance motors (SRMs) have gained popularity in various industrial applications due to their advantages, including structural simplicity, high reliability, low cost, and operational stability over a wide speed range without relying on rare‐earth permanent magnet materials. However, these motors exhibit drawbacks such as weak torque density, low efficiency, and significant torque ripple, particularly in high‐speed operation. An efficient converter‐based control approach is proposed to manage speed and torque variations in SRM motors, addressing these issues. A multilevel converter (MC) is introduced as a fundamental component. The novel multilevel converter (NMC) accommodates SRMs with varying numbers of phases and exhibits quick demagnetization and excitation behaviors, enabling independent operation of each phase even during conduction overlaps. Subsequently, an effective controller is developed for the SRM motor, combining proportional integral derivative (PID) control with enhanced fire hawks optimization (EFHO). The EFHO optimizes the PID gain values to enhance controller performance. The converter operation minimizes torque ripple and facilitates speed management. The EFHO technique is a fusion of fire hawks optimization (FHO) and the sine cosine algorithm (SCA). This amalgamation improves the updating process of FHO by incorporating SCA. The proposed methodology is implemented in MATLAB and evaluated through various metrics, including SRM motor current, voltage, speed, and torque, under electric vehicle (EV) load conditions. Performance comparisons are conducted with traditional optimization algorithms such as the whale optimization algorithm (WOA) and ant colony optimization (ACO). The results validate the effectiveness of the proposed methodology in achieving improved SRM motor control and performance.

Guntuku Ravi Kiran

Subba Rao Kotam Raju

Malligunta Kiran Kumar

A Novel Enhanced Fire Hawks Optimization Approach for Improving the Efficiency of Converter‐Based Controllers in Switched Reluctance Motors

一种用于提高开关磁阻电机中基于转换器控制器效率的新型增强火鹰优化方法

ABSTRACT This paper primarily focuses on the distribution of axial loads in railway networks. The analysis assesses the advantages and disadvantages of articulated trains. In addition, the perspectives and proposed solutions of academicians are analyzed. Simpack accurately models all aspects of this articulated train model. Subsequently, the program emulates the control system of the train. This study demonstrates a control system that utilizes a hydraulic actuator. The proposed model and control system actuator's dynamic response are evaluated according to EN14363 and UIC518 standards to validate the functionality of the component. The stability of a railway train, particularly when making turns, relies on the proper allocation of weight on the wheels and axles. This research investigates the hazards associated with the uneven distribution of loads in train wagons, particularly when navigating curving tracks. A train designed for intercity travel was constructed utilizing a specific control system. The discrepancy in axle load is mitigated by implementing a straightforward control method. The train's carriage, which is suspended without bogies, resulted in fluctuating axle load. A disparity in axle load was established by an actuator positioned between the wagons. The control mechanism minimized load disparity and regulated axle load.

Forouzan Ghalebzade

Habibollah Molatefi

Rahim Zahedi

Sareh Daneshgar

Analyzing Operational Dynamics: Centralized Load Distribution and Suspended Wagons in Self‐Propelled Trains

分析运营动态：自驱动列车中的集中负荷分配与悬挂车厢

ABSTRACT There is an increasing demand for air‐conditioners (ACs) to maintain a comfortable indoor environment for all types and sizes of buildings including commercial, industrial, and office spaces. Such ACs are mostly operated by traditional ON–OFF controllers to maintain a temperature setpoint. Extensive control engineering knowledge resulting from experiments on actual buildings is needed before the wide application of an advanced control methods, such as model predictive control (MPC), which are more effective and energy‐efficient than the traditional controllers. Simulation studies on the application of control may provide promising results, but the corresponding experimental validations may prove otherwise. User‐friendly experimental setups to investigate the performance of real‐time advanced control on an actual building and its HVAC system is scarce. This paper details the design, implementation and testing of a user‐friendly, remote and autonomous test bed to acquire measured data through an IoT platform, and to control ACs in real time through MATLAB Thingspeak. Measurement and data acquisition equipment are installed in a two‐zone concrete building in Mauritius. MPC of the indoor air temperature achieved an AC temperature setpoint tracking RMSE which was 0.7°C lower than that achieved by the built‐in ON/OFF AC control. The test bed also revealed that portable air‐conditioners are not very efficient, given that the maximum cooling efficiency achieved in this work was only 60%. It also provided valuable insights based on the experiments carried out, in terms of improvements to sensing and data acquisition. Control engineers can implement such a test bed for the development and application of advanced controllers as per their needs and applications.

H. Shamachurn

M. Seebaruth

N. S. Kowlessur

S. Z. Sayed Hassen

Real‐Time Model Predictive Control of Air‐Conditioners Through <scp>IoT</scp>—Results From an Experimental Setup in a Tropical Climate

通过物联网实现空调的实时模型预测控制——热带气候下实验装置的结果

ABSTRACT The Internet of Things (IoT) is rapidly evolving, and this has supported the adoption of a new computing paradigm that moves processing power to the network's edge. The job must be assigned to the computer nodes, where their associated data is available, to minimize overheads generated by data transmissions in the network, because the edge nodes have limited processing power and are vulnerable to security. Hence, the paper introduces a novel security‐enhanced scheduling model for IoT‐based smart cities utilizing machine learning techniques. Initially, nodes are initialized using the LHK‐Means algorithm. Subsequently, tasks, representing requests from multiple users to access IoT data, are scheduled. Anomaly detection tasks are then identified using an L 2 ‐Norm‐based fuzzy model. Normal tasks are processed by the BN‐CNN model, which schedules data collection tasks through the initialized IoT device nodes. Comparative analysis with existing models illustrates the effectiveness of the proposed approach in terms of accuracy, precision, recall, sensitivity, specificity, and f‐measure.

Anuj Kumar Dwivedi

Sanjeev Kumar Prasad

Security Enhancement Scheduling Model for <scp>IoT</scp>‐Based Smart Cities Through Machine Learning Method

基于机器学习方法的物联网智能城市安全增强调度模型

Publication Information	Publisher: John Wiley and Sons Inc，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2019，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate:

Advanced Control for Applications: Engineering and Industrial Systems

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Research on Measurement Algorithm of Thermocouple Time Constant Under Multiple Incentive Experimental Conditions

Hybrid Energy Systems: Synergy Margin and Control Co‐Design

A Novel Enhanced Fire Hawks Optimization Approach for Improving the Efficiency of Converter‐Based Controllers in Switched Reluctance Motors

Analyzing Operational Dynamics: Centralized Load Distribution and Suspended Wagons in Self‐Propelled Trains

Real‐Time Model Predictive Control of Air‐Conditioners Through IoT—Results From an Experimental Setup in a Tropical Climate

Hybrid Energy Systems: Synergy Margin and Control Co‐Design

Research on Measurement Algorithm of Thermocouple Time Constant Under Multiple Incentive Experimental Conditions

A Novel Enhanced Fire Hawks Optimization Approach for Improving the Efficiency of Converter‐Based Controllers in Switched Reluctance Motors

Analyzing Operational Dynamics: Centralized Load Distribution and Suspended Wagons in Self‐Propelled Trains

Security Enhancement Scheduling Model for IoT‐Based Smart Cities Through Machine Learning Method

ISSN	2578-0727
h-index	7