Advanced Control for Applications最新文献

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.

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.

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 L²-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.

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.

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.

Renewable energy-based electric vehicle (EV) charging systems have become increasingly popular in recent years, particularly in commercial and industrial environments. This study looks at a broad-spectrum bidirectional buck boost DC to DC converter employing solar photovoltaic (PV) technology. This combination is intended for usage in vehicle to grid (V2G) and grid to vehicle (G2V) modes for electric car applications. In both operating modes, the converter attains a high-voltage transfer gain ratio (VTGR) without any theoretical output voltage restrictions. It provides evidence on suitable nature for EV applications with versatility, redundancy, and practicality. The theoretical evaluation was verified by the development and testing of a 500 W experimental model. A peak efficiency of 97.8% and 97.4% in V2G and G2V modes is indicated by the data, confirming the usefulness and efficacy of this integrated strategy. These results show that the bidirectional buck boost DC to DC converter, when combined with the suggested integration of PV and EV, improves V2G-G2V operations in EV charging systems and is both practicable and feasible.

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.