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The purpose of the paper is to propose a new implementation of the PID (proportional–integral–derivative) algorithm in digital hardware. The proposed structure is optimized for cost. It follows a serialized, rather than parallel, scheme. It uses only one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequentially cyclic manner. The proposed circuit operates on standard single-precision (32-bit) floating-point numbers. It implements an extended PID formula, containing a non-ideal derivative component, and weighting coefficients, which enable reducing the influence of setpoint changes in the proportional and derivative components. The circuit was implemented in a Cyclone V FPGA (Field-Programmable Gate Array) device from Intel, Santa Clara, CA, USA. The proper operation of the circuit was verified in a simulation. For the specific implementation, which is reported in the paper, the sampling period of 516 ns was obtained, which means that the proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on single-precision floating-point numbers. However, the presented solution is much more efficient in terms of cost. It uses 1173 LUT (Look-up Table) blocks, 1026 registers, and 1 DSP (Digital Signal Processing) block, i.e., about 30% of logic resources required by comparable solutions. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive control for a class of uncertain nonlinear systems under denial-of-service (DoS) attacks. We analyze the closed-loop system stability under DoS attacks in terms of attack duration, attack frequency and resting time duration respectively. Three scenarios of DoS attacks against the system stability are considered. Firstly, it is shown that if the duration of each attack is less than a given constant, asymptotical convergence of system output is still preserved. Secondly, if the bounds on the frequency and duration of attacks with respect to overall intervals meet certain conditions, the proposed event-triggered control scheme guarantees that all the closed-loop signals are globally bounded and the stabilization error converges to a ball with a radius arbitrarily small. Thirdly, if resting time duration meets certain conditions after an arbitrarily long attack, closed-loop boundedness is still preserved. Simulation results are shown to illustrate the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

Hardware-in-the-Loop (HiL) testing has emerged as a pivotal technique for validating and enhancing the performance of control systems across various industries, notably in automotive and embedded manufacturing sectors. This paper provides an extensive exploration of HiL testing methodologies and their application in the Internet of Things (IoT) domain. HiL testing involves simulating real-world conditions by integrating specialized simulation software and hardware components, facilitating realistic evaluation of controllers' functionality and behaviour. Different variants of HiL, such as Model-in-the-Loop (MiL), Software-in-the-Loop (SiL), Processor-in-the-Loop (PiL), and Hardware-in-the-Loop (HiL), are discussed within the broader context of XiL (Anything-in-the-Loop) models. The paper underscores the significance of HiL testing in optimizing the development process, reducing costs, and minimizing hazards. It elaborates on the utilization of HiL testing to validate intricate control algorithms, simulate fault scenarios, and assess system behaviour under diverse conditions. The integration of HiL testing with IoT applications is examined, highlighting the potential benefits in areas like smart cities, industrial IoT, healthcare, agriculture, and energy management. The study presents a novel fault injection framework within HiL testing, allowing real-time analysis of complex systems' responses to simulated faults. This framework aids in identifying vulnerabilities and evaluating system reliability, thereby enhancing safety and performance. The authors introduce the concept of sensor schema, Observations & Measurements (O&M), and Sensor Web Enablement (SWE) as key components for efficient sensor data integration, sharing, and interoperability. The paper concludes by emphasizing the indispensable role of Hardware-in-the-Loop testing in fostering innovation, safety, and efficiency in the IoT landscape. By offering a comprehensive overview, analysis, and practical insights into the application of HiL testing methodologies, this research contributes to the advancement of robust and reliable IoT systems. [ABSTRACT FROM AUTHOR]

While using multirotor UAVs for transport of suspended payloads, there is a need for stability along the desired path, in addition to avoidance of any excessive payload oscillations, and a good level of precision in maintaining the desired path of the vehicle. However, due to the nonlinear and underactuated nature of the system, in addition to the presence of mismatched uncertainties, the development of a control system for this application poses an interesting research problem. This paper proposes a control architecture for a multirotor slung load system by integrating a Multi-Surface Sliding Mode Control, aided by a Radial Basis Function Neural Network, with a Deep Q-Network Reinforcement Learning agent. The former will be used to ensure asymptotic tracking stability, while the latter will be used to suppress payload oscillations. First, we will present the dynamics of a multirotor slung load system, represented here as a quadrotor with a single pendulum load suspended from it. We will then propose a control method in which a multi-surface sliding mode controller, based on an adaptive RBF Neural Network for trajectory tracking of the quadrotor, works in tandem with a Deep Q-Network Reinforcement Learning agent whose reward function aims to suppress the oscillations of the single pendulum slung load. Simulation results demonstrate the effectiveness and potential of the proposed approach in achieving precise and reliable control of multirotor slung load systems. [ABSTRACT FROM AUTHOR]

As an integral part of the European strategy for advancing fusion-generated electricity, IFMIF-DONES represents a high-intensity neutron irradiation plant with the main purpose of assessing the suitability of materials for fusion reactor applications. Its primary mission is to examine how materials respond to irradiation within a neutron flux that mimics the conditions expected in the first wall of the proposed DEMO reactor, which is intended to succeed ITER. Consequently, IFMIF-DONES, whose construction is slated to commence shortly, plays a pivotal role in aiding the development, approval, and safe operation of DEMO, as well as future fusion power plants. This paper provides a quick overview of the current development of the IFMIF-DONES neutron source with a particular snapshot of the present engineering design status for what concerns the instrumentation and control systems together with its complex diagnostics, that guarantees the safe monitoring, supervision and regulation of all operations. The current status of design, after the completion of the preliminary design phase is presented, as well as the existing and future plans for their integration also using some of the new capabilities offered by Artificial Intelligence tools. [ABSTRACT FROM AUTHOR]

This paper proposes a two-level control strategy based on a super-twisting sliding-mode algorithm (STA) to optimally allocate power imbalances in shipboard microgrids (SMGs) while achieving frequency regulation. The strategy employs an STA observer to estimate the unknown power load demand imbalances in finite time. This estimate is then passed to an online high-level optimal control framework to periodically determine the optimal sequence of power reference values for each energy storage device (ESS), minimising the operational cost of the SMG. The online optimised power reference values are interpolated and passed to the low-level STA control strategy to control the output power of each ESS. The efficacy of the proposed methods is demonstrated through numerical simulations conducted on a prototypical model of an SMG equipped with two ESSs, namely batteries and fuel cells with associated hydrogen storage. [ABSTRACT FROM AUTHOR]

The field of human-computer interaction has progressed very rapidly over the last few decades, creating another area of research - human-robot interaction (HRI). This paper describes the implementation of HRI using developed Lithuanian language synthesis and recognition tools. Lithuanian language has a very small population of native speakers, which makes training the synthesizers and automated speech recognition tools very complex. Different Lithuanian language dialects and non-native speakers also experience severely deteriorated language recognition performance. There are still many issues concerning the lower level of the HRI field. The current automated Lithuanian speech recognition does not take into account the speaker's emotions. The issues of HRI are being considered. The task of developing HRI control system is discussed. HRI with the NAO V6 robot, a control system for verbal interaction, is discussed. The contribution of our research is related to the application of HRI to the pedagogical problem of teaching and motivating school children in the classroom. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the controllability conditions of linear control systems involving distinct local fractional derivatives. Sufficient conditions for controllability using Kalman rank conditions and the Gramian matrix are presented. We show that the controllability of the local fractional system can be determined by the invertibility of the Gramian matrix and the full rank of the Kalman matrix. We also show that the local fractional system involving distinct orders is controllable if and only if the Gramian matrix is invertible. Illustrative examples and an application are provided to demonstrate the validity of the theoretical findings. [ABSTRACT FROM AUTHOR]

Reachable sets of nonlinear control systems can in general only be approximated numerically, and these approximations are typically very expensive to compute. In this paper, we explore a strategy for choosing the temporal and spatial discretizations of Euler's method for reachable set computation in a non-uniform way to improve the performance of the method. [ABSTRACT FROM AUTHOR]

Attitude determination and control systems (ADCS) represent a critical facet of CubeSat missions, orchestrating the precise orientation and stabilization of these small satellites in the space environment. This paper presents a comprehensive design and development of an ADCS tailored for CubeSats, harnessing a reaction wheel system to deliver a cost-effective and dependable solution for small satellite applications. The research begins by elucidating the requisites and specifications for the ADCS and then delves into the design phase, complemented by intricate modelling and simulation employing MATLAB Simulink and the Webots Simulator. The results of this study underscore the exceptional performance of the proposed ADCS configuration, leveraging the reaction wheel model. This system demonstrates an unparalleled capacity to achieve precise and controlled attitude adjustments, well within the defined parameters. Furthermore, this research underscores the pivotal role played by efficient system design, meticulous simulation, and rigorous testing in the triumphant implementation of ADCS, greatly enhancing CubeSat missions and their contributions to the realm of space exploration and technology innovation. This comprehensive approach to the design and testing of an ADCS for CubeSats ensures that these diminutive satellites continue to make significant strides in space missions, paving the way for an exciting future of space research and technology development. [ABSTRACT FROM AUTHOR]

This paper focuses on the design and use of a control system for a renewable energy production plant based on hydrogen. The proposed control system aims at ensuring the stability and smooth functionality of the plant, which consists of a (i) photovoltaic system connected to an electrolyzer through a battery, (ii) a DC/DC step down transformer, and (iii) an electrolyzer heat exchange system. In this study, solar irradiance is the main system input, and hydrogen production the main output. Since the system utilizes solar energy as input, it depends on the random input of solar irradiance, ambient temperature, and wind flow. Furthermore, the electrolyzer's functionality is subject to several operational variables including cell voltage, current, temperature, and pressure. The electrolyzer heat exchange system operates at specified water temperatures and flow rates. The DC/DC output voltage, and therefore the voltage supplied to the electrolyzer, is regulated by changing its duty cycle. To regulate hydrogen production in the renewable energy production plant, an efficient control system is required. Accordingly, in this work, a control system is designed accounting for three different electrolyzer technologies, alkaline, PEM (proton exchange membrane), and E-TAC (electrochemical - thermally activated chemical water splitting). Subsequently, the effectiveness of the control system is analyzed using Matlab and Simulink models. The main results indicate that the battery is a crucial element in the whole system as it supplies the necessary energy to the electrolyzer. By regulating the appropriate components, the proposed control system proved capable of minimizing power fluctuations and increasing system efficiency up to 20% depending on ambient conditions. Additionally, the results indicate E-TAC and PEM efficiencies 13% and 7% higher than alkaline, respectively. [Display omitted] • Design of a control system for three different electrolyzer technologies. • Use of control systems for electrolyzer voltage and heat exchange. • Photovoltaic system connected to an electrolytic one through a battery. • Proposed control system increases system efficiency up to 20%. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) face the critical challenge of reducing node energy consumption to extend network lifetimes in the presence of uncertainties and external disturbances. This paper proposes an event-based PID controller to minimize power consumption while maintaining required data rates in WSNs. Initially, the system is stabilized using a simple controller. Two structures are considered for this purpose: one based on state feedback control and the other utilizing a PID controller. The event-based PID controller is then developed to improve the WSN system's performance and reduce power consumption. The performance of the event-based PID-controlled system is evaluated for both structures. Results with the first structure show moderate improvements in settling time, overshoot, and error, with values of 9.78 sec, 0, and 9.715, respectively. Subsequently, significant enhancements are observed with the second structure of the event-based PID controller, incorporating a stabilizing PID controller. The results demonstrate a settling time of 0.0924 sec, overshoot of 3.58, and error of 0.01486. To further enhance the controlled system's performance, the Gray Wolf Optimization (GWO) method is applied to fine-tune the parameters (P, I, and D) of the implemented controllers. The application of the GWO algorithm maintains desirable characteristics, with settling time, overshoot, and error values of 7.7527 sec, 0.0358, and 6.4217, respectively. This research offers valuable insights for reducing power consumption and improving the performance of WSNs using an event-based PID controller and GWO optimization. [ABSTRACT FROM AUTHOR]

Human-Inspired Control (HIC) holds promise for endowing machines with human-like cognition, decision-making, and adaptability. In this study, we employ a fusion of cognitive modeling, machine learning, and control theory as the foundational architecture and empirically validate its suitability for robot control within the realm of HIC. Fuzzy logic stands out as a viable approach for HIC control, wherein control rules can be devised drawing from human intuitive inspiration. Specifically, this study explores similarity inference control systems in robotics, with the objective of enhancing line-following control as an alternative to fuzzy systems. The experimental optimization results provide insights into the advantages and limitations of the similarity inference control system. Despite achieving performance comparable to that of traditional two-stage fuzzy control systems, careful consideration of noise sensitivity is paramount. While the similarity inference approach streamlines implementation and obviates the necessity for expert-designed fuzzy rules, its susceptibility to noise can compromise performance, particularly in noisy environments. These considerations are pivotal for the development of control systems aimed at mitigating noise sensitivity, enhancing task-specific performance, and ensuring the adaptability and robustness of line-following robots. To tackle this challenge, we both discuss and experimentally evaluate potential solutions and their applicability in this paper. • The study focuses on integrating cognitive modeling, machine learning, and control theory in HIC, exploring methods like fuzzy logic. • It delves into robotics' similarity inference control systems, optimizing line-following, tackling noise susceptibility, and suggesting enhancements via sensor data and machine learning. [ABSTRACT FROM AUTHOR]

The LAPARA System, a Philippine-made robotic surgical system, tested its control system in this paper. Three types of tests are then done to the system: PID Optimization Test, Position Checking, and Data Transfer Rate and Memory Bandwidth Testing. Results from the PID resulted in the values 2.32 for the P, 0.4 for the I, and 1.5 for the D to be chosen to ensure the system runs smoothly. The system was also able to run properly during Position Checking, though movement in the Pitch and Yaw required refinement due to the constraints. Also, the data transfer rate for the PC to Arduino Due connection yielded a 128kb/s speed, slower than the 480 Mbps rating, while the memory bandwidth testing yielded results that allowed for storage of 23,040 32_bit values. In conclusion, although minor adjustments were needed to refine the system, the LAPARA system was able to perform as intended.

The integration of Digital Twin (DT) technology into the photovoltaic (PV) sector represents a significant advancement in energy management, optimization, servicing, and maintenance. This comprehensive literature review aims to enhance understanding, categorization, and adoption of DT and data fusion technologies within the PV industry to guide future research endeavors. The review categorizes PV models into three types: digital models, digital shadows, and digital twins, based on their data connection and integration attributes. It recognizes data fusion as the critical enabling technology for the development of complex DT models and proposes a framework for integrating data fusion with DT systems. A detailed examination of prevalent PV modeling methodologies is conducted to delineate their advantages and limitations, serving as a valuable resource for industry practitioners. The paper concludes that digital models and digital shadows are effective for initial PV system forecast and monitoring, while fully integrated DT models offer significant advantages, including real-time analysis, predictive capabilities, and active system optimization. However, implementing and maintaining DT models require advanced data analytics, high computational costs, and robust system security, presenting important challenges to be addressed in future research endeavors.

Alternative power generators form the basis for reliability, affordability, and sustainability in accessing rural and urban communities from developing or developed countries (with minimized emission of gases) in connection and isolation from the utility grid system have obviously illustrated important roles in power system. Econometrics and energy assessment of a hybrid renewable energy-power reserve network were analyzed with grid connection in Ko-Kut Island (Thailand) from this paper. Hybrid sources (solar photovoltaic energy, utility grid, biomass resources fueling biogas energy system, wind energy turbine and high energy flywheel) and a unified power reserve unit (vanadium redox flow: VRXFB, sodium sulphur: NaS, Li+: lithium-ion, ZnBr: zinc bromide flow batteries) were designed to effectively manage the energy flow linking the unified generators and the needed energy from the island. A combined dispatch energy controller was used as an interactive controller system across the generators, batteries, and load to enhance the unified energy system’s flexible operation into four different configurations in order to investigate their energy cost, net present service cost, energy sales/energy purchases from the utility grid, and estimated operational cost performances, respectively. In addition, an optimized power management control algorithm was designed with microgrid power analysis software (HOMER) to determine the most economic and efficient generating system architecture. It was observed from the optimization result that the architecture of VRXFB energy system had the least energy tariff ( ${\$}$ 0.1013/kWh), net current ( ${\$}$ 2,502,038) and operational ( ${\$}$ 58,830.93) service costs with the highest renewable energy fractional contribution of 75.0 % to the grid network, technically.

This paper presents a dual-layer approach for managing and controlling an AC Microgrid (MG). The MG integrates a Photovoltaic System (PVS), Wind Turbine System (WTS), a Battery Storage System (BSS), all interconnected with the utility grid. The dual-layer is structured into a Control Layer (CL) and an Energy Management System Layer (EMS-L). The CL proposes an efficient model coupled with a system control, ensuring high power quality for the AC microgrid. This system employs a Particle Swarm Optimization (PSO) algorithm to optimize the set point tracking performance. Simultaneously, the EMS-L utilizes a Sparrow Search Algorithm (SSA) with the objective of minimizing the total operating costs of the AC microgrid. This is achieved by employing a 15-minutes step time over a 24-hours period, enhancing both precision and rapidity of the system’s operation. This enhanced temporal resolution effectively models and responds to fluctuations in energy demand, supply variability and environmental factors. This synergistic integration allows for nuanced and efficient energy flow management in order to optimise the MG performance. A significant aspect of the research is the comparative analysis of the proposed SSA-EMS with PSO and Genetic Algorithm (GA), alongside an unoptimized system. This analysis highlights the hybrid methodology’s superior efficiency and effectiveness. The robust framework, combining SSA at the EMS layer with PSO at the control layer, has been empirically tested using a real world data to confirm its effectiveness and resilience in practical scenarios. These tests provide solid evidence of the methodology’s potential in boosting the sustainable and reliable operation of microgrids. Significantly, the SSA-EMS showcases notable cost efficiency, achieving reductions of 33.34% and 41.18% compared to PSO and GA. Impressively, compared to an unoptimized system, the SSA-EMS demonstrates an even more remarkable cost reduction of 49.43% and 59.10% while considering the impact on battery health constraints.

Ultra-stable room-sized environmental chambers may involve significant power variations leading to hysteresis and nonlinearities of the stabilization system. This paper describes utilization of a pulse code modulation control which significantly reduces observed nonlinearity of the chamber actuation system. Stability of environmental conditions for temperature Δ T = ± 25 mK (15–35 °C dynamic range), relative humidity Δ R h = ± 0. 1 % (3%–95% dynamic range), and pressure Δ P = ± 12 Pa (± 500 Pa dynamic range) was achieved within 27 m 3 chamber volume, which is two orders of magnitude better than the corresponding stabilization achievable using a standard linear proportional–integral–derivative (PID) controller. Utilizing this chamber we have demonstrated two orders of magnitude improvement of the operational relative frequency stability of ultrastable quartz oscillators (USO), reaching 2 × 1 0 − 13 stability at 1- 1 0 5 s averaging time. • The chamber allow the development of instruments before the environment is available. • The chambers simulate conditions to test the failure rates of the novel instruments. • Control of the environmental factors is the key to qualify the source of failure. • The chamber is tuned to qualify atomic clocks, masers, oscillators, frequency units. • A pulse code modulation control reduces nonlinearity of the chamber actuation system. [ABSTRACT FROM AUTHOR]

Indonesia, a tropical country with a growing population, has significant potential for food production but faces challenges in meeting this demand. Factors such as generation change, industrialization, food production monopolies, climate change, food security measures, and a lack of technological progress affect productivity. Governments must address these problems by implementing cost efficiency, supply chain management, minimum labor consumption, and adequate food distribution. Food security is vital to the health and well-being of the population, and food is a vital food source to consume. Vegetables, a popular food source, are vital for health and growth. Salad, a plant used for food production, is beneficial to food production and is the leading food in the modern market. Technologically speaking, food security is vital to the health and well-being of the population. Governments should focus on improving food security and ensuring that food is accessible to all. The proposed system consists of five sensors: the DHT11 sensor, the TDS meter, the humidity sensor (DS18B20), the water height sensor, and the pH meter. Data from the sensor will be stored in a cloud database via the LoRa communication network, allowing users to access data through Android applications. [ABSTRACT FROM AUTHOR]

The aim of this work is to study the causes of accidents in chemical processes, develop a methodology for accident prevention via control, and illustrat its realization by examples using a variety of strategies. The general concept of critical situations was introduced systematically covering both emergency and pre-emergency situations. In large-scale chemical plants, examples of accidents are presented. Accident causes as a result of disturbances and control faults in technological processes are analyzed. Approaches for preventing accidents are considered. The revealing of critical situations is presented as a problem of pattern recognition, and the subtasks of the recognition are analyzed. An emergency scale based on the assessment of various states of the chemico-technological process is introduced and applied for distinguishing the different levels of accident. The real obstacles in the prevention of accidents via control are shown and analyzed. Matrices of critical situations with corresponding characteristics are given. The main tasks for the prevention and elimination of critical situations are highlighted and characterized, and our methodology for the realization of these tasks is presented. Practical examples of the prevention of accidents in the industrial ammonia synthesis processes (including approaches and strategies) are demonstrated based on the real-time control of autothermal reactors. [ABSTRACT FROM AUTHOR]

The zero-trust framework is a potential solution to address complex dynamic behaviors, information interactions, complex network topologies, and environmental security threats to groups, such as the safety of the intelligent connected vehicle (ICV) platoon. In addition, it addresses the challenges faced due to increased traffic of intelligent vehicles, communication in critical infrastructure, and malicious attacks between vehicles. Motivated by these observations, we have investigated the zero-trust theory and framework based on the authorization and confirm processes among connected nodes in a group. Furthermore, we have analyzed and summarized the information perception, full information chain communication, and fault-tolerant control mechanism of ICV systems under a zero-trust framework, in addition to the methods of trust value calculation and heterogeneous vehicle platoons. The results indicated that zero-trust theory has a strong potential in group reliability research. [ABSTRACT FROM AUTHOR]

This paper proposes a two-level control strategy based on a super-twisting sliding-mode algorithm (STA) to optimally allocate power imbalances in shipboard microgrids (SMGs) while achieving frequency regulation. The strategy employs an STA observer to estimate the unknown power load demand imbalances in finite time. This estimate is then passed to an online high-level optimal control framework to periodically determine the optimal sequence of power reference values for each energy storage device (ESS), minimising the operational cost of the SMG. The online optimised power reference values are interpolated and passed to the low-level STA control strategy to control the output power of each ESS. The efficacy of the proposed methods is demonstrated through numerical simulations conducted on a prototypical model of an SMG equipped with two ESSs, namely batteries and fuel cells with associated hydrogen storage.

In this paper, we investigate the controllability conditions of linear control systems involving distinct local fractional derivatives. Sufficient conditions for controllability using Kalman rank conditions and the Gramian matrix are presented. We show that the controllability of the local fractional system can be determined by the invertibility of the Gramian matrix and the full rank of the Kalman matrix. We also show that the local fractional system involving distinct orders is controllable if and only if the Gramian matrix is invertible. Illustrative examples and an application are provided to demonstrate the validity of the theoretical findings.

In the field of traffic management and control systems, we are witnessing a symbiotic evolution, where intelligent infrastructure is progressively collaborating with smart vehicles to produce benefits for traffic monitoring and security, by rapidly identifying hazardous behaviours. This exponential growth is due to the rapid development of deep learning in recent years, as well as the improvements in computer vision models. These technologies allow for monitoring tasks without the need to install numerous sensors or stop the traffic, using the extensive camera network of surveillance cameras already present in worldwide roads. This study proposes a computer vision-based solution that allows for real-time processing of video streams through edge computing devices, eliminating the need for Internet connectivity or dedicated sensors. The proposed system employs deep learning algorithms and vision techniques that perform vehicle detection, classification, tracking, speed estimation, and vehicle geolocation., Author(s): Jaime Villa (corresponding author) [1]; Franz García [1]; Rubén Jover [2]; Ventura Martínez [2]; José M. Armingol [1] 1. Introduction In the 21st century, where mobility is at the [...]