Advanced Control for Applications: Engineering and Industrial Systems

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

Advanced Control for Applications

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 Fuzzy logic control is the most common method utilized to tune proportional integral derivative (PID) controller parameters online. However, proportional integral derivative controllers often perform poorly in the control of nonlinear and/or complicated systems, such as direct current motors, where the model parameters are not exactly known if the scaling factors are not properly selected besides the membership function and rule sets in a fuzzy logic controller design. Finding the most suitable scaling factors for complex systems where the model parameters are not exactly known or nonlinear systems is a challenging task. Furthermore, traditional trial and error techniques of determining appropriate scaling factors are experience based, time consuming, and may not always provide optimal response. In this paper, a particle swarm optimization algorithm is suggested for optimizing the input and output gains of the fuzzy PID controller. The robustness and effectiveness of the suggested controller was validated using MATLAB/Simulink. The performance of the suggested controller is compared with the Ziegler Nichols and Particle Swarm Optimization Algorithm tuned PIDs, and fuzzy PID controllers. The simulation result show that the fuzzy PID controller whose scaling factor was tuned using particle swarm optimization outperforms the other controllers in avoiding disturbance and has a better trajectory tracking capability.

Dessale Akele Wubu

Ayodeji Olalekan Salau

Girma Kassa Alitasb

Particle Swarm Optimization Algorithm Based Fuzzy <scp>PID</scp> Controller Design for Speed Tracking Control of Separately Excited <scp>DC</scp> Motor

基于粒子群优化算法的模糊<scp>PID</scp>控制器设计用于他励<scp>直流</scp>电机速度跟踪控制

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

ABSTRACT This research article instigates a seminal approach for optimizing reactive power in renewable energy sources (RES) and electric vehicles (EVs) coalescing distribution systems, using the innovative Hunter–Prey Optimization (HPO) algorithm in conjunction with DSTATCOM as a reactive power compensator. The proposed methodology aims to minimize losses, enhance voltage stability, and improve overall system performance by simultaneously optimizing reactive power flows in photovoltaic RES (PV_DG), EV charging stations (EVCS), and DSTATCOMs within the distribution system. Simulations carried on IEEE‐33, IEEE‐69, and IEEE‐118 test bus systems in MATLAB environment demonstrate that the HPO‐based approach achieves a 91.47% and 96.61% reduction in real power losses and an improvement in voltage profile with a minimum voltage value of 0.991 and 0.994 p.u. (respectively for IEEE‐33 and 69 bus systems), compared to traditional algorithms. These results highlight the lofty performance of the HPO method, effectively addressing the challenges posed by the integration of RES and EVs along with DSTATCOM.

Soundarya Lahari Pappu

Varaprasad Janamala

A. S. Veerendra

Hunter–Prey Optimization Algorithm for Optimal Allocation of <scp>PV</scp>, <scp>DSTATCOM</scp>, and <scp>EVCS</scp> in Radial Distribution Systems

猎物优化算法在径向配电系统中对<scp>光伏</scp>、<scp>DSTATCOM</scp>和<scp>EVCS</scp>的最优配置

出版信息	出版商: John Wiley and Sons Inc，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2019，原创研究文献占比: ，自引率:， Gold OA占比:

Advanced Control for Applications: Engineering and Industrial Systems

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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

Particle Swarm Optimization Algorithm Based Fuzzy PID Controller Design for Speed Tracking Control of Separately Excited DC Motor

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

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

Hunter–Prey Optimization Algorithm for Optimal Allocation of PV, DSTATCOM, and EVCS in Radial Distribution Systems

Particle Swarm Optimization Algorithm Based Fuzzy PID Controller Design for Speed Tracking Control of Separately Excited DC Motor

ISSN	2578-0727
h-index	7