In this article, a fuzzy adaptive fractional-order proportional plus derivative controller for total power control of an Advanced Heavy Water Reactor is designed in the presence of thermal hydraulics feedback. In addition to a conventional Fuzzy Logic Control layer (FLC), the proposed controller uses a self-tuning FLC layer for run-time adaption of the controller output. A genetic algorithm (GA) based optimization method is used for tuning the controller parameters. Transient Simulation studies are carried out to demonstrate the efficacy of the proposed controller for set-point changes and feedwater temperature disturbances. Results of the proposed controller are compared with those of the traditional proportional plus derivative (PD) and fractional-order proportional plus derivative (FOPD) controllers using standard performance indices. It is observed that the AFOPD controller has 13.99% performance improvement over the FOPD controller and 20.53% improvement over the PD controller for considered objective function. At various operating conditions also, the AFOPD controller shows similar superiority over FOPD and PD controllers.

For sustainable hospitality and tourism, the validity of online evaluations is crucial at a time when they influence travelers’ choices. Understanding the facts and conducting a thorough investigation to distinguish between truthful and deceptive hotel reviews are crucial. The urgent need to discern between truthful and deceptive hotel reviews is addressed by the current study. This misleading “opinion spam” is common in the hospitality sector, misleading potential customers and harming the standing of hotel review websites. This data science project aims to create a reliable detection system that correctly recognizes and classifies hotel reviews as either true or misleading. When it comes to natural language processing, sentiment analysis is essential for determining the text’s emotional tone. With an 800-instance dataset comprising true and false reviews, this study investigates the sentiment analysis performance of three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Among the training, testing, and validation sets, the CNN model yielded the highest accuracy rates, measuring 98%, 77%, and 80%, respectively. Despite showing balanced precision and recall, the LSTM model was not as accurate as the CNN model, with an accuracy of 60%. There were difficulties in capturing sequential relationships, for which the RNN model further trailed, with accuracy rates of 57%, 57%, and 58%. A thorough assessment of every model’s performance was conducted using ROC curves and classification reports.

In this research article, a fractional-order nonlinear Proportional plus Integral plus Derivative (FONPID) controller is incorporated into a complex surge tank system where the gains of the controller are tuned through a machine learning control approach to control the nonlinear variations in level setpoints. Obtaining a proper output response from a non-linear system is challenging and demanding for researchers. In this case, nonlinear Proportional plus Integral plus Derivative (NPID) and conventional Proportional plus Integral plus Derivative (PID) controllers are not sufficient for obtaining desired output robustness in the system performances. Hence, to fulfill the need for an adaptive controller for a spherical surge tank system, FONPID can be a better choice. The machine learning control is applied to the gains of the FONPID controller with the Cuckoo Search Optimization Algorithm (CSA), a swarm-intelligence algorithm mostly known for its levy flights and searching pattern for best quality eggs. The whole idea of using machine learning control is to tune the gains to make the controller adaptive towards parametric variation and uncertainties. The machine learning control uses the Integral of Absolute Error (IAE) performance index criteria as the minimum objective function of CSA for tuning of gain constraints of the controllers. The proposed FONPID controller is then compared with NPID and conventional PID controllers to stabilize level setpoint variations. The results demonstrate that the FONPID controller gives better, robust, and optimum results over NPID and PID controllers. In comparison to NPID and PID controllers, the FONPID controller performs significantly better, with gains ranging from 11.68% to 215.31% across various operational modes and system parametric variations.

The advanced heavy water reactor (AHWR) is the advanced type of nuclear reactor which has nonlinear coupled multi-input multi-output (MIMO) dynamical characteristics, making it difficult for researchers to manage the worldwide demand power. In order to control the demand power efficiently, an intelligent controller, i.e. a fractional-order fuzzy proportional plus derivative (FOFPD) controller is suggested along with the control rod dynamics in this work. The suggested FOFPD controller is utilized in the normalized point kinetics (NPKs) model of the AHWR system. To make the FOFPD controller more stable and robust, the gains of the controller are tuned with the help of a meta-heuristic genetic algorithm (GA)-based optimization approach that uses the integral of absolute error (IAE) as the performance criteria. To validate the simulation findings, the recommended controller's performance is compared to that of fractional-order PD (FOPD) and conventional PD for the trajectory tracking of demand power. It can be stated that the FOFPD controller’s response outperforms among all the investigated controllers.

Advanced heavy water reactor (AHWR) is a type of advanced nuclear reactor which is the most efficient nuclear fission reactor for power generation due to the large thorium reserve. Its safe, stable, and efficient operations are critical for the revival of the fission energy sector. Incorporation of a robust control scheme into the nuclear reactor model is required for proper trajectory tracking of demand power. The AHWRs are coupled, significantly nonlinear, and multi-input multi-output (MIMO) systems. External disruptions and time-varying characteristics harm the systems' performance. As a result, the controller built for these systems must be able to deal with the complexity, which is most challenging for control engineers. A fractional-order nonlinear proportional, integral, and derivative (FONPID) control method is presented in this study for normalized power distribution management of the AHWR using normalized point kinetic equations (NPKEs) for trajectory tracking, disturbance rejection, and noise suppression tasks to improve the output power. All controller settings are fine-tuned using a genetic algorithm (GA) with the sum integral of time and square error (ITSE). The suggested FONPID controllers' performance is compared to its integer-order control structure, i.e., NPID and classical PID control structure. External disturbances at controller output and random noise at the sensor output are tested for resilience to establish the usefulness of the suggested control methods. The simulation results showcased that the proposed FONPID controller outperforms its integer-order (IO) counterpart as well as the traditional PID controller.

The main objective of this work is to showcase how a Nonlinear Proportional plus Integral plus Derivative (NPID) controller can be utilized to analyze the output of a highly coupled multi-input multi-output (MIMO) nonlinear system called the Three-Link Robotic Manipulator System for setpoint monitoring or trajectory tracking performance. In general, the three-link planar robots are employed in the production sector and are exposed to a variety of disturbances, such as heavy equipment movements, manipulator's arm variations, a loud industrial climate, and so on. In order to cater for these limitations, A proposed NPID controller is incorporated into the three-link revolute joint robotic manipulator system and its gain parameters are tuned using the GA (Genetic Algorithm) to minimize the weighted sum of the integral of absolute error (IAE) signal. Based on this chosen objective function, the proposed NPID controller is then compared to NPI, PID, and PI controllers for testing trajectory tracking accuracy in order to conduct a complete comparative performance analysis.

Nonlinear dynamics are critical in nuclear power reactors such as Advanced Heavy Water Reactor (AHWR). The core of AHWR must be governed and controlled by a suitable and reliable controller to deliver the demand electricity properly. In this paper, a fractional-order nonlinear controller (FO-NPD) comprises of fractional-order (FO), nonlinear (N), and proportional plus derivative (PD) terms are proposed and incorporated into the normalized point kinetics (NPKs) model of AHWR core for trajectory tracking performance, disturbance rejection, and noise suppression analysis. The FO- NPD controller gains are modified at run-time using the cuckoo search algorithm (CSA) based on a performance metric index termed as the sum of the integral of square error (ISE). The tuned gains get modified in real-time which makes the recommended controller more resilient and robust. To validate the obtained simulation results, a comparative performance study is carried out between the proportional plus derivative (PD), the fractional-order proportional plus derivative (FO- PD), and the nonlinear proportional plus derivative (NPD) with the proposed FO-NPD controller. And it is observed that the proposed controller demonstrates the superiority over other three controllers.

The first and foremost goal of this research article is to explore the efficient trajectory tracking performance of the integrated power system (IPS) using a fractional-order nonlinear proportional plus integral plus derivative (FONPID) control strategy for fulfilling the demand power. The various energy-generating components such as wind, solar, and diesel and storage components such as batteries, flywheels, and ultracapacitors are just a few of the components that make up the IPS. Due to the integration of such stochastic energy components, the grid frequency varies continuously affecting the fulfillment of power demand. To mitigate the variations in grid frequency, a FONPID controller is proposed which has a greater ability to withstand the changes that occur in the system parameters and the nonlinearity of the rate limitation. The gains of the proposed controller are modified using the gray wolf optimization (GWO) technique based on the sum of the integral of the square in frequency deviation (ISFD) for greater performance. The trajectory tracking for the demand power is showcased for the proposed controller. To validate the obtained simulation results, the suggested controller, i.e., FONPID, is compared with PID and FOPID controller whereas it has been observed that the FONPID controller outperforms the classical PID and the FOPID controller.

Networked control systems (NCSs) are significant and foremost multidisciplinary research areas for many decades. This paper is mainly oriented toward recent developments and challenges of network-induced delays due to inclusion of data network in NCSs. Network delays deteriorate the control performance and stability of the NCSs. The time-varying delays can be measured in real time by calculating the time difference of sending and receiving control packets. Various compensation techniques are reviewed to mitigate the effect of constant, time-varying, and stochastic delay. Lastly, some conclusions are drawn and the future research scope is directed.

A comparative study of fractional order proportional and integral (FOPI), proportional integral and derivative (PID) as well as fractional order PID (FOPID) is carried out in this paper for integrated power system (IPS) with subsystems consisting energy storage and generation block. Due to non-linear behavior of individual energy components like Diesel Energy Generator (DEG), Solar Thermal Power Generator (STPG), Battery Energy Storage System (BESS), Flywheel Energy Storage System (FESS), Wind Turbine Generator (WTG), Fuel Cells (FCs) and Ultra-Capacitor (UC) etc. or sudden change in generation and load, the output of an IPS system deviate from its nominal desired value. To control the IPS system properly these control techniques have been successfully implemented. Gains of the controllers are tuned with Genetic algorithm (GA). The simulation results of these control techniques demonstrate that FOPID controller has best robustness and lesser grid frequency deviation among them.