Artificial Intelligence Control Research Articles

Comparison of Artificial Intelligence Control Strategies for a Peristaltically Pumped Low-Pressure Driven Membrane Process.

Peristaltic pumping is used in membrane applications where high and sterile sealing is required. However, control is difficult due to the pulsating pump characteristics and the time-varying properties of the system. In this work, three artificial intelligence control strategies (artificial neural networks (ANN), fuzzy logic expert systems, and fuzzy-integrated local models) were used to regulate transmembrane pressure and crossflow velocity in a microfiltration system under high fouling conditions. A pilot plant was used to obtain the necessary data to identify the AI models and to test the controllers. Humic acid was employed as a foulant, and cleaning-in-place with NaOH was used to restore the membrane state. Several starting operating points were studied and setpoint changes were performed to study the plant dynamics under different control strategies. The results showed that the control approaches were able to control the membrane system, but significant differences in the dynamics were observed. The ANN control was able to achieve the specifications but showed poor dynamics. Expert control was fast but showed problems in different working areas. Local models required less data than ANN, achieving high accuracy and robustness. Therefore, the technique to be used will depend on the available information and the application dynamics requirements.

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Wind-driven turbines utilizing the doubly-fed induction generators aligned with the progressed IEC 61400 series standards have engrossed specific consideration as of their benefits, such as adjustable speed, consistent frequency mode of operation, self-governing competencies for voltage and frequency control, active and reactive power controls, and maximum power point tracking approach at the place of shared connection. Such resource combinations into the existing smart grid system cause open-ended problems regarding the security and reliability of power system dynamics, which needs attention. There is a prospect of advancing the art of wind turbine-operated doubly-fed induction generator control systems. This section assesses the smart grid-integrated power system dynamics, characteristics, and causes of instabilities. These instabilities are unclear in the wind and nonlinear load predictions, leading to a provisional load-rejection response. Here, machine learning computations and transfer functions measure physical inertia and control system design’s association with power, voltage, and frequency response. The finding of the review in the paper indicates that artificial intelligence-based machine and deep learning predictive diagnosis fields have gained prominence because of their low cost, less infrastructure, reduced diagnostic time, and high level of accuracy. The machine and deep learning methodologies studied in this paper can be utilized and extended to the smart grid-integrated power context to create a framework for developing practical and accurate diagnostic tools to enhance the power system’s accuracy and stability, software requirements, and deployment strategies.

Design of a Customizable Test Bench of an Electric Vehicle Powertrain for Learning Purposes Using Model-Based System Engineering

Nowadays, electric vehicles attract significant attention because of the increasingly stringent exhaust emission policies all over the world. Moreover, with the fast expansion of the sustainable economy, the demand for electric vehicles is expanding. In the recent age, maintenance has seriously hampered the marketing and use of electric automobiles. As a result, the technique for maintaining electric vehicles is regarded as vital since it directly affects the security and availability for the end user and the passengers. Another key aspect of electric mobility is the integration of artificial intelligence in control, diagnostics, and prognostics. Meanwhile, a lot of research efforts are still devoted to developing and innovating electric traction systems, especially for diagnostic and prognostic purposes. Furthermore, topics covering important, current, and sustainability challenges should contain more than theoretical knowledge in high-quality education, particularly in engineering education. The purpose is to bridge the gap between the new technology and the learner’s circumstances through giving practical technical expertise and training in the sphere of overall engineering competences, to avoid non-standard, unskilled maintenance work. This article presents the first phase towards designing and developing a test bench of an electric vehicle’s powertrain used for research, learning and e-learning purposes, employing model-based systems engineering (MBSE) and systems modeling language (SysML) through the CESAM architecting and modeling framework. The aforementioned approach is used on our case study to build and present an operational viewpoint layout of the control, energy management, diagnostic, and prognostic test bench as part of the system’s initial phase of designing the system; the test bench layout proposed in this paper represents a flexible, low-cost, multidisciplinary downsized laboratory providing basic experiments related to e-mobility and covering numerous branches and study fields.

How How to Use Graph Theory Benefiting the Neighborhoods Environment

Graph Theory is a popular field with wide applications. It also has the reputation of being the type of math that is more up-to-date with modern times. However, a quick search of the math curriculum at the secondary level will show a striking absence. This paper hopes to reverse the trend and bring to light both a way to introduce graph theory and also to show the application to the real world all in one unified story. Within this article, the reader will move from little to no knowledge toward a fundamental understanding of one component of graph theory. The reader will also be able to walk away with the ability to analyze efficient routes in real-world networks. This work is a crafted message, a student-to-student message, on building intuition for theories and then using them directly. This article is primarily an argument for the importance of engaging with graph theory early in mathematics study. It argues that graph theory is more than a fashionable buzzword. It is the methodical study of interconnection, and it applies to most of the biggest critical global problems we face today: Environmental Protection, Nuclear Safety, Global Food Supply, and Artificial Intelligence Control. In the end, this article may focus on building one application, resource conservation, and logistic optimization, though the reach will be much further. The tools of graph theory are easily adapted to any problem involving systems and their related parts.

High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Nowadays, water heater is a common household appliance. Water heater can be divided into three types, based on fuel sources: gas, diesel, and electric. Electric water heater is the most common due to its ease of use. The problems that often occur on electric water heater are over-temperature due to user error in setting up the thermostat and inaccurate readings caused by a conventional system control. These problems will cause a surge in power consumption. Over-temperature and conventional control inaccuracies can be overcome using the Artificial Intelligence (AI) control algorithm in the form of an adaptive neuro-fuzzy inference system (ANFIS). The proposed algorithm acts as a control by maintaining the stability of the temperature to obtain more accurate results. An accurate temperature reading can lower power consumption in electric water heater. This study tries to simulate Electric Water Heater temperature control using the ANFIS algorithm until stable readings can be achieved in all temperature settings. Results from disturbance tests in the form of external condition that causes sudden temperature change show that the system can maintain stability with an average error margin of 0.045% and the rate of accuracy of 99.955%.

Experimental validation and intelligent control of a stand-alone solar energy conversion system using dSPACE platform

This paper presents the performances of an artificial intelligent fuzzy logic controller (FLC) based maximum power point tracking (MPPT) and a conventional perturb and observe (P&O) based MPPT controller is presented for a stand-alone PV system and tested in an emulated test bench experimentation. The studied system is composed of a DC power supply emulating the PV panel, a DC/DC boost converter, a variable resistive load and a real-time MPPT controller implemented in the dSPACE DS1104 controller. To verify the performance of the FLC proposed, several simulations have been performed in Matlab/Simulink environment. The proposed method outperforms the P&O method in terms of global search capability and dynamic performance, according to the comparison with the P&O method. To verify the practical implementation of the proposed method, the control of the emulated PV source and the MPPT algorithms are designed using the simulink/Matlab environment and implemented on dSPACE DS1104 controller. Experimental results confirm the efficiency of the proposed method and its high accuracy to handle the resistance varying.

Sensor Vegetable Greenhouse and Agricultural Product Supply Chain Management Based on Improved Neural Network

In recent years, people have begun to collect environmental data in vegetable greenhouses. Therefore, this article studies the current internal control technology of vegetable greenhouses, and improves the sensor application in vegetable greenhouses, in a targeted manner by combining with the improved neural network algorithm. The model has more accurate prediction accuracy than traditional BP. By using the Android client and ZigBee artificial intelligence control technology it creates the most suitable living conditions for the vegetables, fruits, and other crops in the vegetable greenhouse by controlling the various valves, sun visors, and light supplementary switches inside the vegetable greenhouse. The simulation results show that the model proposed in this article has higher convergence accuracy. In addition, this article applies the improved neural network to the management of the agricultural product supply chain, starting from the agricultural product supply chain and agricultural product supply chain management. Based on this, the analytic hierarchy process is used to explore the influencing factors of agricultural product supply chain management, and the analysis shows that consumers are targeting different consumer demand raised by the agricultural product supply chain. The improved neural network provides new ideas for the research of sensor vegetable greenhouses and agricultural product supply chain management.

The Sustainable Effect of Artificial Intelligence and Parental Control on Children’s Behavior While Using Smart Devices’ Apps: The Case of Saudi Arabia

The study was necessitated by the unprecedented consumption of smart devices by children in Saudi Arabia, which has been a concern to parents and other stakeholders. It investigated the way that game apps, social media apps, and video-streaming apps impact child development. It also examined the roles played by artificial intelligence control and parental control in enhancing the sustainability of children’s behavior amidst smart technologies. The theories underpinning this research were the theory of reasoned action and the theory of planned behavior. The population of the study was 1,616,755 households in the eastern, western, and central regions. The researcher used an online survey to capture the sentiments of a sample of 415 parents who had given their children at least one smart device. The primary questionnaire focused on game apps, social media apps, video-streaming apps, artificial intelligence control, parental control, and the sustainability of child behavior. On the other hand, a separate questionnaire designed specifically to capture demographic information was also drafted. The structural equation model (SEM) was preferred, as it depicted the moderating roles of artificial intelligence control and parental control by using SPSS AMOS software. Findings established that games, social media, and video-streaming applications negatively affected the sustainability of child behavior. The findings presented in this paper show that the moderating effect of artificial intelligence control was more statistically significant than parental controls in influencing the sustainability of child behavior. Moreover, the results show that the greatest effect on children’s behavior were social media, video-streaming, and games apps. respectively. Nevertheless, both approaches resulted in positive child behavior. Hence, the study concluded that using artificial intelligence control is more effective than relying on parental controls to enhance the behavioral sustainability of children with smart device applications in Saudi Arabia.

Design of Multi-Objective-Based Artificial Intelligence Controller for Wind/Battery-Connected Shunt Active Power Filter

Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work presents a hybrid controller for the self-tuning filter (STF)-based Shunt active power filter (SHAPF), integrated with a wind power generation system (WPGS) and a battery storage system (BS). The SHAPF comprises a three-phase voltage source inverter, coupled via a DC-Link. The proposed neuro-fuzzy inference hybrid controller (NFIHC) utilizes both the properties of Fuzzy Logic (FL) and artificial neural network (ANN) controllers and maintains constant DC-Link voltage. The phase synchronization was generated by a self-tuning filter (STF) for the effective working of SHAPF during unbalanced and distorted supply voltages. In addition, STF also does the work of low-pass filters (LPFs) and HPFs (high-pass filters) for splitting the Fundamental component (FC) and Harmonic component (HC) of the current. The control of SHAPF works on d-q theory with the advantage of eliminating low-pass filters (LPFs) and phase-locked loop (PLL). The prime objective of the projected work is to regulate the DC-Link voltage during wind uncertainties and load variations, and minimize the total harmonic distortion (THD) in the current waveforms, thereby improving the power factor (PF).Test studies with various combinations of balanced/unbalanced loads, wind velocity variations, and supply voltage were used to evaluate the suggested method’s superior performance. In addition, the comparative analysis was carried out with those of the existing controllers such as conventional proportional-integral (PI), ANN, and FL.

Application and Thinking of Artificial Intelligence in Electrical Automation

With the vigorous development of China’s market economy, a new generation of artificial intelligence technology has also developed rapidly, which has strongly promoted the optimization and improvement of China’s electrical automation control system. The use of artificial intelligence technology in power automation systems mainly has the following two advantages: it can reduce the manufacturing time of power products, thereby increasing the number of workers manufacturing power products and promoting enterprises to conduct larger-scale machine manufacturing, thereby improving the comprehensive economic benefits of enterprise machine manufacturing, but also improve the quality of manufactured products of electric power products. Since the machine control of artificial intelligence is more efficient than manual manufacturing, it reduces the manufacturing problems caused by the subjective factors of manufacturing workers to a certain extent and thus improves the qualification rate of machine production. Electrical engineering plays an important role in China’s urban development, and automatic control is an important part of electrical engineering, which can have a direct impact on the quality of electrical engineering. Therefore, in order to promote the healthy development of cities in China, this paper studies the application of artificial intelligence technology in electrical engineering automation by analyzing relevant data, in order to provide a reliable basis for relevant personnel to carry out their work.

Application of artificial intelligence (AI) control system on chiller plant at MTR station

Chillers account for up to 40% of total station energy consumption in the Hong Kong Mass Transit Railway (MTR) system. As part of green railway initiatives, a site trial was conducted to apply a fully automated AI system to control a chiller plant in order to optimise energy performance in real time while maintaining a level of passenger comfort that suits each station’s environment. Through the predictive power of the AI system, the plant power’s consumption and cooling demands can be forecasted based on actual chiller, station, and weather conditions, all of which vary over time. The optimal operational settings can then be determined using an optimisation model for real-time chiller plant control, including staging, sequencing, chilled water supply temperature set-point, etc. This paper presents the formulation of an AI system using data-driven machine learning models and numerical optimisation, and the comparison of the actual energy performance of the proposed system against rule-based control optimisation in a conventional building management system (BMS) through the site trial. The results revealed the proposed AI system achieves better energy efficiency with annual energy savings of approximately 8.7%.

Design and analysis of a load frequency control system based on improved artificial intelligence control algorithm

In actual industrial control, the performance of the control system may be undermined or destabilized by the various complex nonlinear control processes. To ensure the stability of the control system, this paper improves the generalized active disturbance rejection control (GADRC). Firstly, the dead zone of the governor was converted to obtain the total transfer function of the control system, and the system stability was analyzed, considering the applicability of GADRC to non-minimum phase processes. Then, an error compensation strategy was proposed to control the system performance. Simulation results show that the improved GADRC can fully utilize the known information of the plant model, and obtain proper gains of the controller and the observer. In addition, the load frequency control (LFC) system can work effectively, when the dead zone of the governor has nonlinear processes. Finally, the proposed error compensation strategy can ensure the control performance of the system.

Design and Simulation of DFIG based Wind Energy System using AI EMS

Abstract: Renewable energy sources are considered to be an unpredictable resource especially in wind turbines; rapid variation in wind speeds cause power fluctuations leads to interrupt the power system stability during the high-level power operation. To overcome these issue energy storage systems such as Battery storage is added to balance the grid and helps to balance the generated power and the power demand. The battery system is also used to mitigate the power fluctuations and helps to give more power to balance the load requirement. The proposed (EMS) Energy Management System integrates with the Artificial Neural Network for operation of both battery and wind system depending upon the demand need. In the case of the battery management system, the intelligent controllers help to control the State of Charge (SoC) limits of the battery to make sure the required time of charging and discharging. The proposed Artificial Intelligent Controller acts as an Energy Management System and Battery Management System as well as it is used for power balance generation and load also efficiently mitigating the voltage fluctuation during variation in the load. MATLAB is used for the simulation process since it contains the features of Artificial Intelligence which can in turn function as switching device. Keywords: Renewable energy sources, Battery Storage, Energy management system, State of charge, Artificial neural network etc.

Hieroglyphically Supervised Bioinspired Visioneural Controller

Unlike an intelligent microcontroller, biologic neural networks maintain active control throughout their operation, while the former usually needs to be disengaged during maintenance or software updates. Ideally, artificial intelligent control systems incorporate an incremental learning capability, without having the need to be retrained offline. Moreover, some hardware‐based neural networks (HNNs) rely on complementary software simulators during training. Such approaches decrease their potential to implement real‐time adaption, in a similar manner to their biologic counterparts. Herein, an independent, bioinspired robotic vision system with an incremental learning ability is presented. The system is trained and operated by associating instructions with hieroglyphic symbols and used to maneuver a robotic vehicle. A neural processor, capable of incremental online learning, forms the basis for the decision‐making flow. The entire range of functionality, starting from training, data storage, and up to controlling, is demonstrated herein.

A hybrid artificial intelligence control of a turbulent jet: Reynolds number effect and scaling

This work aims to investigate experimentally the effect of the Reynolds numberRe,based on the nozzle diameterD, on jet mixing manipulation using an unsteady radial minijet. A novel artificial intelligence (AI) control system has been developed to manipulate the jet overRe = 5800–40 000. The system may optimize simultaneously the control law and a time-independent parameter, which dictate the actuation ON/OFF states and amplitude, respectively. The control parameters include the mass flow rate, excitation frequency and diameter ratios (Cm,fe/f0andd/D) of the minijet to the main jet as well as the duty cycle (α) of minijet injection. Jet mixing is quantified usingKeandK0, whereKis the decay rate of the jet centreline mean velocity, and subscriptseand 0 denote the manipulated and unforced jets, respectively. It has been found that the maximumKeachievable does not vary withRe.Scaling analysis of the huge volume of experimental data obtained from the AI system reveals that the relationshipKe = g1(Cm,fe/f0,α,d/D, Re, K0) may be reduced toKe/K0 = g2$(\zeta )$, whereg1andg2are different functions and the scaling factor$\zeta = ({C_m}/\alpha ){(D/d)^{1 - n}}(1/Re){({f_e}/{f_{e,opt}})^m}$,mand 1 − nare the power indices, and subscriptoptdenotes the value at whichKeis maximum. The scaling law is discussed in detail, along with the physical meanings of the dimensionless parametersKe/K0,ζ,$({C_m}/\alpha ){(D/d)^{1 - n}}(1/Re)$and${({f_e}/{f_{e,opt}})^m}$.

Meaningful human control: actionable properties for AI system development

How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.

Comparative Analysis of Artificial Intelligent Controllers Based Intentional Islanding Algorithm for Distributed Energy Resources (DERs) in Disaster Management

Consuming alternate power from distributed energy resources (DERs) like diesel generators (DG), renewable energy sources (RES), batteries etc., during grid unavailability is increasing day by day. Unavailability of grid is caused due to many reasons like typical faults (LG, LL, etc.), damage to network equipment due to terrorist attacks and natural disasters and many more. Amongst all, natural disasters lead to grid failures of longer durations. Facilitating power to the loads like hospitals, in the aftermath of natural disasters is of major concern. An attempt is made in this article to provide power to distribution system loads considering the effects of natural disasters while creating islands. This article proposes intentional islanding algorithm based on Artificial Intelligence (AI) controllers to create islands in post-disaster situations. An IEEE recommended practice system for interconnecting DERs to the secondary network is considered as a base system for implementing the proposed algorithms.

A New Hope in the Fight Against Antimicrobial Resistance with Artificial Intelligence.

Recent years have witnessed the rise of artificial intelligence (AI) in antimicrobial resistance (AMR) management, implying a positive signal in the fight against antibiotic-resistant microbes. The impact of AI starts with data collection and preparation for deploying AI-driven systems, which can lay the foundation for some effective infection control strategies. Primary applications of AI include identifying potential antimicrobial molecules, rapidly testing antimicrobial susceptibility, and optimizing antibiotic combinations. Aside from their outstanding effectiveness, these applications also express high potential in narrowing the burden gap of AMR among different settings around the world. Despite these benefits, the interpretability of AI-based systems or models remains vague. Attempts to address this issue had led to two novel explanation techniques, but none have shown enough robustness or comprehensiveness to be widely applied in AI and AMR control. A multidisciplinary collaboration between the medical field and advanced technology is therefore needed to partially manage this situation and improve the AI systems’ performance and their effectiveness against drug-resistant pathogens, in addition to multiple equity actions for mitigating the failure risks of AI due to a global-scale equity gap.

Does AI control or support? Power shifts after AI system implementation in customer relationship management

ABSTRACT Many companies are currently investing in artificial intelligence (AI) because of its potential to increase customer satisfaction or financial performance. However, the success rates in implementing AI systems are low, partly due to technology-centric approaches that neglect work practices. This study draws on Bourdieu’s theory of practice to highlight the potential power shift related to AI in customer relationship management, based on the concepts field, capital, and habitus. Two longitudinal case studies were conducted to understand the power shift related to AI implementation. These two AI systems were designed with the objective to support employees. However, subsequently, their implementation changed the balance of power with a significant shift towards more management control, resulting in a devaluation of employees’ work practices. The paper discusses implications for theory and practice in terms of the discrepancies and power shifts following the introduction of AI systems to support customer relationship management.

Comparison of Deep Reinforcement Learning and PID Controllers for Automatic Cold Shutdown Operation

Many industries apply traditional controllers to automate manual control. In recent years, artificial intelligence controllers applied with deep-learning techniques have been suggested as advanced controllers that can achieve goals from many industrial domains, such as humans. Deep reinforcement learning (DRL) is a powerful method for these controllers to learn how to achieve their specific operational goals. As DRL controllers learn through sampling from a target system, they can overcome the limitations of traditional controllers, such as proportional-integral-derivative (PID) controllers. In nuclear power plants (NPPs), automatic systems can manage components during full-power operation. In contrast, startup and shutdown operations are less automated and are typically performed by operators. This study suggests DRL-based and PID-based controllers for cold shutdown operations, which are a part of startup operations. By comparing the suggested controllers, this study aims to verify that learning-based controllers can overcome the limitations of traditional controllers and achieve operational goals with minimal manipulation. First, to identify the required components, operational goals, and inputs/outputs of operations, this study analyzed the general operating procedures for cold shutdown operations. Then, PID- and DRL-based controllers are designed. The PID-based controller consists of PID controllers that are well-tuned using the Ziegler–Nichols rule. The DRL-based controller with long short-term memory (LSTM) is trained with a soft actor-critic algorithm that can reduce the training time by using distributed prioritized experience replay and distributed learning. The LSTM can process a plant time-series data to generate control signals. Subsequently, the suggested controllers were validated using an NPP simulator during the cold shutdown operation. Finally, this study discusses the operational performance by comparing PID- and DRL-based controllers.