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Summary: Fast battery charging units act as non‐linear loads, which induce a more harmonic effect in the utility grid. Hence, maintaining the stable voltage, current ripple, and total harmonic distortions within the permissible IEEE std level during the charging time of grid to electric vehicle (G2V) has become an important task for researchers in the future. In order to mitigate power fluctuations during every charging cycle, it will enhance the battery life cycle in electric vehicles (EVs). Three phase–three level–three switch Vienna rectifier with fuzzy logic controller tuned PID controller is used here for AC/DC conversion with minimum switching loss. A fuzzy intelligent control strategy has been employed with the DC link voltage regulator in order to sustain the DC link voltage as constant and progress the voltage/current profile by injecting active power at the point of common coupling. Unit vector template (UVT)‐based control strategy is employed here to identify the fundamental components from the load side and also estimate the reference phase current. This scheme enhances the performance and reduces the mathematical system complexity to diminish the harmonics in the distribution grid. The adaptability and feasibility of this proposed control method have been verified by the MATLAB simulation tool. To validate the response of the Vienna Rectifier with UVT controller are simulated with different unbalanced load conditions and also verified with 30 kW laboratory prototype experimental results using dsPIC30F4011 controller and the switch is IRFP260 with power diode RHRG30120. [ABSTRACT FROM AUTHOR]

Summary: 5G and beyond (5GB) systems show a flexible characteristic to meet different user communications requirements in parallel. Multi‐numerology waveform design is an important part of this human centric characteristic, however, it is necessary to make multi‐numerology planning. In this paper, the integrated sensing and communications (ISAC) concept is used with machine learning (ML) techniques to develop an intelligent numerology control mechanism for 5GB. Thanks to the ISAC capabilities, an environment‐aware system is designed, and several useful sensing information (USI) that affect the wireless channel characteristic is utilized. It is assumed that the USI is obtained through 5GB smart city networks. At that point, synthetic data generation is performed to form a new dataset with USI‐based features. Additionally, a novel feature control algorithm is proposed in the paper. Lastly, simulation results are presented for different type of ML models including several ensemble methods. [ABSTRACT FROM AUTHOR]

Fuel management is an important issue in aviation for safety and efficiency reasons. This work develops an alternative solution based on an artificial intelligent technique to the fuel distribution management problem in commercial aircrafts. The fuel flow control amongst tanks during the flight is addressed. A fuzzy management system is implemented that decides the best fuel distribution based on safety criteria (keeping engines fed) and dynamic stability (placing the centre of gravity at the appropriate position), amongst other specifications. Expert knowledge is used to define the rules of the fuel fuzzy control, taking into account that the dynamics of the system changes, whilst fuel is being consumed. It has been simulated on a real long‐range‐type commercial aircraft with satisfactory results regarding stability, even in the case of internal malfunction (in pipes, pumps, or valves), and with external disturbances (engine failure). The knowledge‐based fuzzy control is able to maintain the centre of gravity position within the stability and manoeuvrability margins along the flight. Besides, the intelligent control strategy minimizes the action of the actuators, providing some advantages to the control solution. [ABSTRACT FROM AUTHOR]

Addressing the dual enhancement of circular polarization (glum) and luminescence quantum yield (QY) in circularly polarized luminescence (CPL) systems poses a significant challenge. In this study, we present an innovative strategy utilizing the entropically driven self‐assembly of amphiphilic phosphorescent platinum(II) complexes (L−Pt) with tetraethylene glycol chains, resulting in unique temperature dependencies. The entropically driven self‐assembly of L−Pt leads to a synergistic improvement in phosphorescence emission efficiency (QY was amplified from 15 % at 25 °C to 53 % at 60 °C) and chirality, both in the ground state and the excited state (glum value has been magnified from 0.04×10−2 to 0.06) with increasing temperature. Notably, we observed reversible modulation of phosphorescence and chirality observed over at least 10 cycles through successive heating and cooling, highlighting the intelligent control of luminescence and chiroptical properties by regulating intermolecular interactions among neighboring L−Pt molecules. Importantly, the QY and glum of the L−Pt assembly in solid state were measured as 69 % and 0.16 respectively, representing relatively high values compared to most self‐assembled CPL systems. This study marks the pioneering demonstration of dual thermo‐enhancement of phosphorescence and CPL and provides valuable insights into the thermal effects on high‐temperature and switchable CPL materials. [ABSTRACT FROM AUTHOR]

This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R 2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R 2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831. [ABSTRACT FROM AUTHOR]

With the development of technology, future tractors will become more intelligent. More complex interfaces and several control components tend to cause drivers' cognitive load to be easily overloaded. Therefore, an evaluation method of the control interface should be proposed from the cognitive load perspective. To realise this concept, first, the influence of the design elements of the interface on behaviour time was investigated and a behaviour time model was established. Consequently, the experimental data could be well fitted by the model (R2 > 0.9), and the behaviour time of the operating interfaces could be effectively predicted. Second, an evaluation index of the interface was proposed based on behaviour time, which could evaluate the interface's design level. Finally, the validity of the evaluation method was verified. The results provide theoretical guidance for human–machine interfaces for tractors from the perspective of drivers' cognitive load. [ABSTRACT FROM AUTHOR]

Cyclic air braking is a key factor affecting the safe operation of trains on long downhill sections. However, a train's cycle braking strategy is constrained by multiple factors such as driving environment, speed, and air-refilling time. A Q-learning algorithm-based cyclic braking strategy for a heavy haul train on long downhill sections is proposed to address this challenge. First, the operating environment of a heavy haul train on long downhill sections is designed, considering various constraint parameters, such as the characteristics of special operating routes, allowable operating speeds, and train tube air-refilling time. Second, the operating status and braking operation of a heavy haul train on long downhill sections are discretized in order to establish a Q-table based on state–action pairs. The training of algorithm performance is achieved by continuously updating Q-tables. Finally, taking the heavy haul train formation as the study object, actual line data from the Shuozhou–Huanghua Railway are used for experimental simulation, and different hyperparameters and entry speed conditions are considered. The results show that the safe and stable cyclic braking of a heavy haul train on long downhill sections is achieved. The effectiveness of the Q-learning control strategy is verified. [ABSTRACT FROM AUTHOR]

Although the probability of a rollover accident is lower than that of other forms of collision, rollover is a serious accident that can break the symmetry of the vehicle and cause serious loss of life and property. There are many factors affecting rollovers, such as the environment, the vehicle, and the driving control. A coach comprises a complex dynamic system; as such, the accuracy and rationality of the used mathematical model are decisive in the study of coach rollover warning and control. By analogy with the modeling method of an automobile collision accident, the general process of a coach rollover accident is analyzed in this study in combination with the contact form and freedom of motion characteristic of the coach body and external environment. According to the principle of conservation of energy, the mathematical models of critical rollover impact force in a collision between vehicles and obstacles and in a collision between two vehicles are established, allowing for analysis of the relationships between the critical tripped rollover impact forces required for a 90° rollover and the continuous action time and collision point height. During the collision between the vehicle and the obstacle, the occurrence of a vehicle rollover is related not only to the impact force in the collision process but also to the collision duration time. Even if the impact force is relatively small, the collision lasts long enough that a second collision may occur until the vehicle rolls over. In the process of a two-vehicle collision, the critical rollover impact force is not only related to the vehicle mass but also to the vehicle wheelbase and the height of the collision point. Based on the law of conservation of momentum, the mathematic models of 90-degree rollover and 180-degree rollover are established, and the critical rollover velocities are calculated. The purpose of this study is to provide reference and guidance for the research methods of vehicle rollover stability and anti-rollover control in the intelligent vehicle era. [ABSTRACT FROM AUTHOR]

This document is a review of advanced digital technologies in modeling and control of technological processes. It highlights the importance of international collaboration in scientific research and the sharing of knowledge and resources. The special issue includes studies in various fields such as mining, mineral processing, vision and image processing systems. The papers cover topics such as power quality data collection, energy generation estimation, predictive models for fault detection, virtual soft sensors, autonomous transportation control systems, machine learning for the oil and gas industry, optical inspection systems, image encryption, critical velocity prediction, computational fluid dynamics, and steel defect recognition. The contributions provide valuable insights and potential solutions for improving efficiency and sustainability in different industries. [Extracted from the article]

Cyclic air braking is a key element for ensuring safe train operation when running on a long and steep downhill railway section. In reality, the cyclic braking performance of a train is affected by its operating environment, speed and air-refilling time. Existing optimization algorithms have the problem of low learning efficiency. To solve this problem, an intelligent control method based on the deep Q-network (DQN) algorithm for heavy-haul trains running on long and steep downhill railway sections is proposed. Firstly, the environment of heavy-haul train operation is designed by considering the line characteristics, speed limits and constraints of the train pipe's air-refilling time. Secondly, the control process of heavy-haul trains running on long and steep downhill sections is described as the reinforcement learning (RL) of a Markov decision process. By designing the critical elements of RL, a cyclic braking strategy for heavy-haul trains is established based on the reinforcement learning algorithm. Thirdly, the deep neural network and Q-learning are combined to design a neural network for approximating the action value function so that the algorithm can achieve the optimal action value function faster. Finally, simulation experiments are conducted on the actual track data pertaining to the Shuozhou–Huanghua line in China to compare the performance of the Q-learning algorithm against the DQN algorithm. Our findings revealed that the DQN-based intelligent control strategy decreased the air braking distance by 2.1% and enhanced the overall average speed by more than 7%. These experiments unequivocally demonstrate the efficacy and superiority of the DQN-based intelligent control strategy. [ABSTRACT FROM AUTHOR]

The pre-chamber pressure is an important control parameter that affects the coke dry quenching process. It often fluctuates violently and is detrimental for the safe operation of the coke dry quenching process. This study proposes an intelligent control method for the pre-chamber pressure based on working condition identification for the coke dry quenching process to realize stable control of the pre-chamber pressure. First, by describing the coke dry quenching process and analyzing the factors affecting the pre-chamber pressure, an intelligent control strategy was developed. Then, the K-means clustering algorithm was used to identify the working conditions of pre-chamber, and the working conditions were divided into two categories: stable and fluctuating. For stable conditions, a fuzzy proportional-integral-derivative controller was designed to improve the pressure control accuracy. For fluctuating conditions, an expert controller was designed to rapidly adjust the pressure. Finally, experiments based on actual data were performed and the results showed that the proposed method can effectively improve the control accuracy of pressure under different conditions. This satisfies the requirements for a continuous coke dry quenching process. [ABSTRACT FROM AUTHOR]

Azobenzene photoswitches are fundamental components in contemporary approaches aimed at light-driven control of intelligent materials. Significant endeavors are directed towards enhancing the light-triggered reactivity of azobenzenes for such applications and obtaining water-soluble molecules able to act as crosslinkers in a hydrogel. Here, we report the rational design and the synthesis of azobenzene/alginate photoresponsive hydrogels endowed with fast reversible sol–gel transition. We started with the synthesis of three cationic azobenzenes (AZOs A, B, and C) and then incorporated them in sodium alginate (SA) to obtain photoresponsive supramolecular hydrogels (SMHGs). The photoresponsive properties of the azobenzenes were investigated by UV–Vis and 1H NMR spectroscopy. Upon irradiation with 365 nm UV light, the azobenzenes demonstrated efficient trans-to-cis isomerization, with complete isomerization occurring within seconds. The return to the trans form took several hours, with AZO C exhibiting the fastest return, possibly due to higher trans isomer stability. In the photoresponsive SMHGs, the minimum gelation concentration (MGC) of azobenzenes was determined for different compositions, indicating that small amounts of azobenzenes could induce gel formation, particularly in 5 wt% SA. Upon exposure to 365 nm UV light, the SMHGs exhibited reversible gel–sol transitions, underscoring their photoresponsive nature. This research offers valuable insights into the synthesis and photoresponsive properties of cationic, water-soluble azobenzenes, as well as their potential application in the development of photoresponsive hydrogels. [ABSTRACT FROM AUTHOR]

Wind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card. [ABSTRACT FROM AUTHOR]

Electric vehicles with proper battery charging mechanism are essential to achieve superior performance with good dynamic response and high efficiency. This paper comes up with the analysis of interval type-2 fuzzy logic controller (IT2FLC) for interleaved Cuk converter for battery charging in electric vehicles. The key intention of this paper is to obtain excellent controller parameters such as improved accuracy and stability with good power quality attributes of less harmonic distortion and unity power factor at the supply side using a robust and intelligent control approach. IT2FLC is developed effectively to acquire the optimal proportional integral (PI) parameters for the constant current and constant voltage charging controllers to enrich the operation of the battery charging system. This in turn achieves excellent transient parameters with less settling time of 0.01 s, reduced overshoot of 1% and efficiency of about 93.85%. The so-called interval type-2 (IT2) controller is therefore accomplished to alleviate uncertainties and improvise the dynamic stability of the charging solution. The behavioural traits of the intelligent controller are examined and compared with Ziegler–Nicholas tuned PI and T1FL-based PI using MATLAB/Simulink. A hardware prototype of 350 W, 48 V/5 A charger is built and verified with dsPIC33F to evaluate the working principle of the converter using IT2. [ABSTRACT FROM AUTHOR]

Ocular inserts offer distinct advantages, including a preservative-free drug delivery system, the ability to provide tailored drug release, and ease of administration. The present research paper delves into the development of an innovative ocular insert using CaliCut technology. Complementing the hot melt extrusion (HME) process, CaliCut, an advanced technology in ocular insert development, employs precision laser gauging to achieve impeccable cutting of inserts with desired dimensions. Its intelligent control over the stretching process through auto feedback-based belt speed adjustment ensures unparalleled accuracy and consistency in dosage form manufacturing. Dry eye disease (DED) poses a significant challenge to ocular health, necessitating innovative approaches to alleviate its symptoms. In this pursuit, castor oil has emerged as a promising therapeutic agent, offering beneficial effects by increasing the thickness of the lipid layer in the tear film, thus improving tear film stability, and reducing tear evaporation. To harness these advantages, this study focuses on the development and comprehensive characterization of castor oil-based ocular inserts. Additionally, in-vivo irritancy evaluation in rabbits has been undertaken to assess the inserts' safety and biocompatibility. By harnessing the HME and CaliCut techniques in the formulation process, the study demonstrates their instrumental role in facilitating the successful development of ocular inserts. Development of ocular inserts using HME and CaliCut and their characterization. [Display omitted] [ABSTRACT FROM AUTHOR]

The development of intelligent prostheses has effectively improved the life of amputees. However, the current prosthetics mainly focus on restoring the basic mobility of amputees, without considering the use habits of the wearer and the diversity of arm movements, which makes the unknown interference and complex control requirements in daily life an obstacle to the use of prosthetics. To solve this problem, this paper proposes a combination method of adaptive control algorithms of bionic arm shoulder joint based on DDPG to realise intelligent control of the shoulder joint of upper limb prosthesis. Based on using adaptive control to reduce the interference of external variables, the accuracy of the joint module system is improved through reinforcement learning. The results show that the controller has a good effect on improving the dynamic performance of the mechanical system and can be widely used in bionic mechanical control. [ABSTRACT FROM AUTHOR]

This research investigates the role of various energy storage systems (ESS) in improving the power system resiliency. Different ESS configurations are analyzed individually and in combination to assess their impact on system performance. Additionally, a fuzzy-based controller (PIλDN + FFOPI) is proposed as a secondary controller for load frequency control, with control parameters optimized using an improved Volleyball Premier League algorithm (IVPL). This algorithm leverages the powerful synergy of opposition-based learning and the versatility of the VPL algorithm, resulting in a highly effective and innovative approach. To validate the proposed approach, the modified Kundur's 2-area test system under deregulation is first utilized, and its effectiveness is confirmed through testing with both step and random load disturbances. Sensitivity analysis is conducted to evaluate system performance across a wide range of parameter variations. Furthermore, the modified IEEE 39 bus system is utilized to assess compatibility and scalability. Results demonstrate the effectiveness of combined ESS configurations and the fuzzy-based controller in enhancing system stability and reliability. This research contributes to power system engineering by offering insights into the benefits of energy storage systems for dynamic response enhancement. The proposed fuzzy-based control strategy, tuned by the IVPL algorithm, presents a promising approach for improving power system performance and stability. [ABSTRACT FROM AUTHOR]

In recent years, the concept of pipeline intelligence, or smart pipelines, has emerged as a promising approach to address various pipeline transmission issues. Intelligent control system requires the variation and thickness of wax deposition inside the crude oil transmission pipeline to make decisions to minimize wax deposition. This study aimed to investigate wax deposition in both polyethylene (PE) and stainless steel (SS) pipes under different water contents, utilizing a custom-designed flow loop apparatus. It was found that the wax-deposit thickness on the surface of the PE test section was thicker (water content lower than 50 vol. %) and then thinner (water content higher than 50 vol. %) than that on the surface of the SS test section. The radial diffusivity and wax mass flux were calculated and proved to be responsible for the behavior variation of wax deposition. The contact angles of oil and water on the PE surface were both larger than those on the SS surface. Experimental investigation was conducted to determine the phase inversion point of oil and water emulsions, which was found to be around 50 vol.%. The results indicated that when water was the bulk phase, greater water adherence to the steel pipe wall resulted in reduced wax deposition on the SS test section. As a result, distinct deposition behaviors were observed both before and after the phase inversion point. [ABSTRACT FROM AUTHOR]

The control of Proton Exchange Membrane Fuel Cell (PEMFC) is exceptionally challenging due to its complex nonlinearity, slow dynamics and load disturbance. In this paper, an Adaptive Model Free Control (AMFC) approach is introduced for regulating the PEMFC output voltage under load perturbation and measurement noise conditions. The proposed method consists of four sub-elements. Firstly, an Extended State Observer (ESO) based on a Single-Input Interval Type-2 Fuzzy intelligent PI (SI-IT-2-FLC-iPI) scheme is designed to enhance the performance of PEMFC. Secondly, a Nonlinear Sliding Mode Control-based Super Twisting Algorithm (NSMC-STA) is used as an auxiliary control approach to reduce the estimation error and ameliorate the system robustness. Thirdly, a Forgotten Recursive Least Square Error (FRLSE) is applied for adjusting the physical parameter of MFC and further ameliorating the control response. Finally, all these elements are merged together within what it is called Ultra Local Model (ULM). Through extensive simulation, the efficacy of the proposed regulator in accurately tracking the output signal is showcased, outperforming conventional controllers with remarkable improvements. The system achieves 0.001 steady-state error, 0% overshoot and settling time of approximately 8.9 s, which is good enough in the context of standalone fuel cell applications. • An intelligent Model Free Control structure is developed for tracking the output voltage of PEMFC. • Integrating FLC type-2 PI, NSMC-STA, ESO and online tuner based FRLSE within ULM. • The finite stability is fully demonstrated using Lyapunov theorems. • The proposed control enhances the performance of PEMFC under various operating conditions. [ABSTRACT FROM AUTHOR]

With the continuous expansion of urban scale in China, the increase of passenger flow has brought great pressure to the urban public transport system. An accurate and timely prediction of the short-term passenger flow at each metro station is extremely important for the metro intelligent control system to make a timely decision. In this paper, based on the measured passenger flow data of Zijingshan station of Zhengzhou Metro Line 1, an improved whale optimization algorithm is proposed to predict the passenger flow on different time scales. The results show that the method has higher accuracy than the traditional least squares support vector machines algorithm. The paper opens a new window for nowcasting warning in the rush hour and long-period optimization of the public transport. [ABSTRACT FROM AUTHOR]

To combat global environmental deterioration and energy scarcities, it is crucial to implement energy-saving upgrades for urban road lighting. Comparatively, LEDs have emerged as an advanced and eco-friendly lighting option due to their low energy consumption, excellent performance, high color rendering index, and prolonged lifespan. By incorporating solar cell technology, a smart LED street light controller based on small-scale integrated circuits was developed to enable intelligent control for various lighting needs such as dimming, timing, automatic detection, and sound and light control. Through circuit simulations and experimental outcomes, it has been validated that the controller's structure and performance parameters align with the design specifications. This design encompasses knowledge from diverse fields, including fundamentals of circuit and electronic technology, photovoltaic cell technology, power electronics, and sensor technology, showcasing robust engineering and practicality. Its utilization in the experimental course for second-year college students majoring in electrical engineering contributes to the grooming of professionals and expands the perspectives of future talents, enriching their application of knowledge and practical innovation capabilities. [ABSTRACT FROM AUTHOR]

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China's coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China's and even the world's coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as "humans, machines, the environment, and management" in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation has transformed into the most reassuring strategy for power generation among the renewable sources in view of its intrinsic favorable circumstances. The significant snag that photovoltaic system poses like other inexhaustible sources is the power intermittency in regard to atmospheric conditions like irradiation and temperature. These photovoltaic systems are interfaced to the grid through filters via inverters to prevent harmonics getting in to the grid. Generally, inductor–capacitor–inductor filters are employed in grid-connected system, as they provide better harmonic attenuation than inductor and inductor-capacitor filters. However, they bring resonance as well as oscillations into the grid-connected system. So, in order to suppress the resonance that brings instability problems in the system, active damping control strategies are adopted. But these conventional active damping methods increase the complexity of the system as they need extra sensors. This article proposes a fuzzy logic control-based active damping method to ease the control activity. The proposed controller is contrasted with the injected grid current feedback controller to investigate the resonance damping and the stability of grid interfaced multilevel inverter with inductor–capacitor–inductor filter. When compared with the injected grid current feedback controller, the fuzzy logic control approach promises satisfactory performance based on harmonic content, resonance peak, and stability analysis. The compliance according to the industrial standard in terms of harmonics is met in this article and is duly documented within the manuscript. Simulation results verify the design strategies and the effectiveness of the proposed fuzzy logic control strategy. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid approach for optimizing the maximum power point tracking of photovoltaic (PV) systems in electric vehicles. The hybrid technique involves the simultaneous utilization of the Gannet Optimization Algorithm (GOA) and Quantum Neural Network (QNN), collectively referred to as the GOA-QNN technique. The primary aim is to enhance the efficiency and maximize the power output of PV systems. The proposed hybrid methodology boosts the performance of the photovoltaic system by managing the power interface. A high step-up DC/DC converter is employed to adjust the photovoltaic source power and load, ensuring optimal power transfer under various operating conditions. The proposed method optimally determines the duty cycle of the converter. Subsequently, the model is implemented in the MATLAB/Simulink platform, and its execution is evaluated using established procedures. The results clearly demonstrate the superiority of the proposed method over existing approaches in terms of power quality, settling time, and controller stability. The proposed technique achieves an impressive efficiency level of 95%, exceeding the efficiency of other existing techniques. [ABSTRACT FROM AUTHOR]

The unmanned farm control platform is of great significance in promoting the supervision of farm production with less manpower or autonomous operation of farm machinery and the construction of farm informatization. Addressing the existing control platform for farm location information acquisition is time-consuming, labor-intensive, and lacks the whole process control of multiple types of farm machinery. In this paper, we propose an Internet of Things (IoT) control scheme for intelligent farm machinery operation of unmanned farms and design the access standards for multiple types of farm machinery, as well as realize the remote control of intelligent farm machinery operation by constructing a remote control model. A high-precision map construction method is designed to improve the DeepLabV3+ algorithm to identify fields and roads. The control models of path planning, remote task, remote control, and safety system are built to achieve the remote control of intelligent agricultural machinery operation. The proposed technology is implemented in the platform integration and application tests are carried out. The error of the constructed high-precision map is less than 3 cm, the completeness rate of the automatic boundary extraction rate is 96.71%, and the correctness rate is 95.63%, which can be used to obtain the boundary instead of manual labeling or on-site point picking. The use of the platform for the simultaneous control of three farm machinery operations reduces the number of people in operation and production and reduces the professional requirements of the personnel, which will promote the management of the entire farm by one person or even by no one in the future. [ABSTRACT FROM AUTHOR]

PV power generation is developing fast in both centralized and distributed forms under the background of constructing a new power system with high penetration of renewable sources. However, the control performance and stability of the PV system is seriously affected by the interaction between PV internal control loops and the external power grid. The impact of the PV system on the reliability, stability, and power quality of power systems has restricted them to further participate in power supplies with a large capacity. Traditional control methods have become ineffective at dealing with these problems as the PV system becomes increasingly complex and nonlinear. Intelligent control as a more advanced technology has been integrated into the PV system to improve system control performance and stability. However, intelligent control for the PV system is still in the early stages due to the extensive calculation and intricate implementation of intelligent algorithms. Further investigations should be carried out to effectively combine intelligent control with the PV system to constitute an intelligent PV power system with multiple functions, high stability, and high-performance. This paper provides a systematic classification and detailed introduction of various intelligent optimization methods in a PV inverter system based on the traditional structure and typical control. The future trends and research topics are given to provide a reference for the intelligent optimization control in the PV system. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a distributed joint parameter and state variables estimation algorithm for large‐scale state‐space interconnected systems. In this distributed estimation scheme, each interconnected sub‐system is described by a linear discrete‐time state space mathematical model. Each sub‐system is supposed to be controlled by an intelligent controller that can communicate with its interconnected neighbors and exchange information, such as state variables. The proposed approach comprises two recursive estimation algorithms, a parameter estimation algorithm considering the state space model and a distributed Kalman filter for state variables estimation. It is a fully distributed cooperative approach that allows to reduce complexity and saves computational and communication resources. Theoretical analysis and numerical examples are provided to prove the feasibility and effectiveness of this joint estimation algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a model-free super-twisting terminal sliding mode controller (MF-STTSMC) for the n-DOF upper-limb rehabilitation exoskeleton in the presence of external disturbance and backlash hysteresis. This ultra-local model-based control scheme consists of a sliding mode disturbance observer (SMDO) and a super-twisting non-singular fast terminal sliding mode controller (ST-NFTSMC). The ultra-local model is used to approximate the origin complex exoskeleton system in a short sliding time window, which avoids the requirement for accurate modelling and decreases the difficulty of controller design. To compensate for the lumped uncertainties, the SMDO is constructed to achieve finite-time observation and zero estimation error. Besides, to realise finite-time convergence of sliding surface and tracking error with less chattering, the ST-NFTSMC is proposed by employing a super-twisting algorithm. Furthermore, the stability of a closed-loop system with MF-STTSMC is ensured by using the Lyapunov theorem. Finally, to validate the proposed strategy, co-simulations of 7-DOF iReHave upper-limb exoskeleton are realised by using SolidWorks, SimMechanics, and Matlab/Simulink. Compared to time-delay estimation-based intelligent proportional-derivative control (TDE-iPD), α-variable adaptive model-free controller (α-AMFC), and inclusive and enhanced time delay controller (IETDC), the higher performances of the proposed MF-STTSMC are demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, a new model predictive control termed as intelligent model predictive control (IMPC) combined with an improved new competitive swarm optimizer (CSO) is designed. The analytical predictive model is not necessarily established a priori in the proposed IMPC algorithm, and the control plant can be used directly as the predictive model to reduce the complexity of the algorithm. In addition, two new techniques, dynamic initialization and back steps methods, are proposed and utilized to improve the traditional CSO to realize constraints management during the optimization process. An application to aeroengine transient-state control is studied to verify the effectiveness of the presented IMPC algorithm. It is shown that, benefitting from the IMPC algorithm, the control task is well completed and all the constraints are satisfied. Practical Applications: This paper proposes a new optimization algorithm to solve some complex optimization problems with constraints. The designed new optimization algorithm is simple and easy to implement and it is more suitable for applications with black-box models or complex optimization objective functions compared with some existing optimization algorithms. This paper may provide some help to researchers who are interested in model predictive control or metaheuristic algorithms and to people whose work contains some complex optimization problems. This paper also provides a design method of aeroengine transient-state control plans. The simulation results showed that compared with some existing control methods or optimization algorithms, the aeroengine could accelerate to the desired steady-state point with shorter transient time and all constraints are satisfied when utilizing the proposed new optimization algorithm. [ABSTRACT FROM AUTHOR]

During the construction of smart grids, due to technological limitations, traditional sequential control systems have been unable to meet the needs of grid operation, exposing problems such as single control methods and functions, low operational efficiency, inability to independently complete complex tasks, and poor anti-interference ability, which in turn affect the power supply quality and operational stability of the grid, and fail to truly achieve the reliability, safety, and reliability of smart grids Economic and efficient goals. For this purpose, the author describes an adaptive intelligent control method for an integrated biomass energy heat conversion system consisting of more than one suction biomass gasifier, biomass gas burner, and steam boiler. The system uses PLC as a tool and takes the boiler outlet steam pressure as the control parameter. Based on the changes in the boiler outlet steam pressure, it automatically adjusts the feed and gas production of the gasifier, and subsequently adjusts the air supply of the burner. Ultimately, it achieves the goal of automatically adjusting the boiler heating volume with changes in steam pressure. The integrated heating and adaptive intelligent control system based on biomass gasification has been reliably verified in industrial applications. [ABSTRACT FROM AUTHOR]

In order to understand the application of artificial intelligence in the control system of cold and hot energy station, the author proposes a research based on artificial intelligence control in the control system of cooling and heating energy station. The author first introduces artificial intelligence technology on the basis of heating and precision heating, relies on the "Internet plus" and IoT platform, applies indoor temperature measurement technology, intelligent control and information docking technology, and establishes a heating system based on artificial intelligence technology. This system has improved the level of enterprise management, promoted the further development of heating and intelligent energy conservation, reduced energy consumption, reduced pollution, and achieved the need for precise heating of the pipe-line network. Secondly, combined with the application case of a district, this district is selected as the application pilot to describe the role and effect of this system in the Hydronics. The experimental results indicate that the heat consumption of the residential area is 27.07 W/m2, while the heat consumption of the comparison residential area is 28 W/m2. Through indoor temperature measurement, it was found that the average indoor temperature of the comparison residential area is 22 °C. Therefore, the application of artificial intelligence heating systems has improved the thermal comfort of residents, bringing users a high quality heating experience while relatively reducing energy consumption. The intelligent application of the Hydronics can more accurately grasp the heating situation, timely and efficiently adjust the heating scheme, achieve heating balance, and improve the utilization efficiency of heat sources. At the same time, it can also effectively improve the troubleshooting ability of the hydronics, prevent safety risks, improve management efficiency, and will certainly provide more powerful support for the energy-saving society and environment-friendly society. [ABSTRACT FROM AUTHOR]

Thermoelectric cooling technology has important applications for processes such as precise temperature control in intelligent electronics. The bismuth telluride (Bi2Te3)-based coolers currently in use are limited by the scarcity of Te and less-than-ideal cooling capability. We demonstrate how removing lattice vacancies through a grid-design strategy switched PbSe from being useful as a medium-temperature power generator to a thermoelectric cooler. At room temperature, the seven-pair device based on n-type PbSe and p-type SnSe produced a maximum cooling temperature difference of ~73 kelvin, with a single-leg power generation efficiency approaching 11.2%. We attribute our results to a power factor of ‘52 microwatts per centimeter per square kelvin, which was achieved by boosting carrier mobility. Our demonstration suggests a path for commercial applications of thermoelectric cooling based on Earth-abundant Te-free selenide-based compounds. [ABSTRACT FROM AUTHOR]

Green water electrolysis holds promise for clean energy production. One low-cost variant, Dynamic Magnetic Field (DMF) assisted water electrolysis, shows high hydrogen evolution rates (HER). However, its efficiency diminishes at high magnet rotational speeds, posing a challenge to its widespread adoption. This study aims to enhance the efficiency of DMF-assisted water electrolysis by providing adaptive control for DMF, particularly at high magnet rotational speeds. We employ a Double Deep Q Learning (DDQN) based artificial intelligence (AI) system to design and implement this adaptive control. We utilized a Double Deep Q Learning (DDQN) based artificial intelligence (AI) system to design an adaptive control mechanism for DMF. The DDQN agent learned parameter tuning to adjust the rotational speed of the motor, with a magnet bar attached, for optimal performance. The optimum control behavior was achieved after 52 training episodes, and minor adjustments were made to ensure practical applicability. Our study reveals that the DDQN-based AI control is effective in continuously tuning the rotational speed of the motor with a magnet bar attached, triggering a bifurcation in the electrolyte. This adaptive control mechanism significantly improves and maintains the efficiency of DMF-assisted water electrolysis. The DDQN based AI control is effective to alter and maintain efficiency of DMF assisted water electrolysis. [Display omitted] • The hydrogen production of DDQN controlled DMF is superior to fixed RPM DMF. • DDQN AI provide realtime optimization of DMF electrolysis efficiency. • DDQN ensures correct and applicable control behavior after 52 episodes. • DDQN AI control optimizes DMF electrolysis to sustain the efficiency. • RPM tuning enhances performance by triggers electrolyte bifurcation. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution has broadly transformed the manufacturing system. However, this transformation is somewhat lacking in traditional or manual production systems due to the absence of IT infrastructure. Such traditional industries need to have the advantage of real-time control and monitoring. This study has developed economic assembly planning, scheduling, and control for a traditional assembly system. We used the concept of the configurable virtual workstation as the digitalization framework. Then, we employed the decentralized scheduling concept to reduce the computational effort in scheduling the complex product. The implementation result showed that scheduling and planning have transformed the traditional assembly process into intelligent scheduling and control with low digitalization effort. [ABSTRACT FROM AUTHOR]

The current motion interaction model has the problems of insufficient motion fidelity and lack of self-adaptation to complex environments. To address this problem, this study proposed to construct a human motion control model based on the muscle force model and stage particle swarm, and based on this, this study utilized the deep deterministic gradient strategy algorithm to construct a motion interaction control model based on the muscle force model and the deep reinforcement strategy. Empirical analysis of the human motion control model proposed in this study revealed that the joint trajectory correlation and muscle activity correlation of the model were higher than those of other comparative models, and its joint trajectory correlation was up to 0.90, and its muscle activity correlation was up to 0.84. In addition, this study validated the effectiveness of the motion interaction control model using the depth reinforcement strategy and found that in the mixed-obstacle environment, the model's desired results were obtained by training 1.1 × 103 times, and the walking distance was 423 m, which was better than other models. In summary, the proposed motor interaction control model using the muscle force model and deep reinforcement strategy has higher motion fidelity and can realize autonomous decision making and adaptive control in the face of complex environments. It can provide a theoretical reference for improving the effect of motion control and realizing intelligent motion interaction. [ABSTRACT FROM AUTHOR]

Heavy lifting operations frequently lead to upper limb muscle fatigue and injury. In order to reduce muscle fatigue, auxiliary force for upper limbs can be provided. This paper presents the development and evaluation of a wearable upper limb exoskeleton (ULE) robot system. A flexible cable transmits auxiliary torque and is connected to the upper limb by bypassing the shoulder. Based on the K-nearest neighbors (KNN) algorithm and integrated fuzzy PID control strategy, the ULE identifies the handling posture and provides accurate active auxiliary force automatically. Overall, it has the quality of being light and easy to wear. In unassisted mode, the wearer's upper limbs minimally affect the range of movement. The KNN algorithm uses multi-dimensional motion information collected by the sensor, and the test accuracy is 94.59%. Brachioradialis muscle (BM), triceps brachii (TB), and biceps brachii (BB) electromyogram (EMG) signals were evaluated by 5 kg, 10 kg, and 15 kg weight conditions for five subjects, respectively, during lifting, holding, and squatting. Compared with the ULE without assistance and with assistance, the average peak values of EMG signals of BM, TB, and BB were reduced by 19–30% during the whole handling process, which verified that the developed ULE could provide practical assistance under different load conditions. [ABSTRACT FROM AUTHOR]

With the development of intelligent vehicle technology, the probability of road traffic accidents occurring has been effectively reduced to a certain extent. However, there is still insufficient research on head injuries in human vehicle collisions, making it impossible to effectively predict pedestrian head injuries in accidents. To study the efficacy of a combined active and passive safety system on pedestrian head protection through the combined effect of the exterior airbag and the braking control systems of an intelligent vehicle, a "vehicle–pedestrian" interaction system is constructed in this study and is verified by real collision cases. On this basis, a combined active and passive system database is developed to analyze the cross-influence of the engine hood airbag and the vehicle braking curve parameters on pedestrian HIC (head injury criterion). Meanwhile, a hierarchy design strategy for a combined active and passive system is proposed, and a rapid prediction of HIC is achieved via the establishment of a fitting equation for each grading. The results show that the exterior airbag can effectively protect the pedestrian's head, prevent the collision between the pedestrian's head and the vehicle front structure, and reduce the HIC. The braking parameter H2 is significantly correlated with head injury, and when H2 is less than 1.8, the HIC value is less than 1000 in nearly 90% of cases. The hierarchy design strategy and HIC prediction method of the combined active and passive system proposed in this paper can provide a theoretical basis for rapid selection and parameter design. [ABSTRACT FROM AUTHOR]

Surfaces with high aspect ratio microarray structures can implement sophisticated assignment in typical fields including microfluidics, sensor, biomedicine, et al. via regulating their deformation or the material properties. Inspired by natural materials and systems, for example sea cockroaches, water spiders, cacti, lotus leaves, rice leaves, and cedar leaves, many researchers have focused on microneedle functional surface studies. When the surface with high aspect ratio microarray structures is stimulated by the external fields, such as optical, electric, thermal, magnetic, the high aspect ratio microarray structures can undergo hydrophilic and hydrophobic switching or shape change, which may be gifted the surfaces with the ability to perform complex task, including directional liquid/air transport, targeted drug delivery, microfluidic chip sensing. In this review, the fabrication principles of various surfaces with high aspect ratio microarray structures are classified and summarized. Mechanisms of liquid manipulation on hydrophilic/hydrophobic surfaces with high aspect ratio microarray structures are clarified based on Wenzel model, Cassie model, Laplace pressure theories and so on. Then the intelligent control strategies have been demonstrated. The applications in microfluidic, drug delivery, patch sensors have been discussed. Finally, current challenges and new insights of future prospects for dynamic manipulation of liquid/air based on biomimetic surface with high aspect ratio microarray structures are also addressed. [Display omitted] [ABSTRACT FROM AUTHOR]

Microgrids are local energy production and distribution networks that can operate independently when disconnected from the main power grid thanks to the integration of power generation systems, energy storage units and intelligent control systems. However, despite their advantages, the optimal energy management of real microgrids remains a subject that requires further investigation. Specifically, an effective management of microgrids requires managing a large number of electrical variables related to the power generated by the microgrid's power supplies, the power consumed by the loads and the aspects of power quality. This study analyzes how we can monitor different variables, such as the active power, reactive power, power factor, total harmonic distortion and frequency in the loads of a microgrid, using high-precision power meters. Our empirical study, conducted using a functional microgrid comprising a hybrid wind–solar power system and several household appliances, demonstrates the feasibility of using low-cost and high-performance power meters with IoT functionality to collect valuable power quality and energy consumption data that can be used to control the microgrid operation. [ABSTRACT FROM AUTHOR]

Intelligent‐PID (i‐PID) control proposed by Fliess is a simple control algorithm. The controller is designed based on ultra‐local model, and consisted of PID type controller and derivatives of reference signal and controlled variables. Authors have considered discrete i‐PID controller and its properties, and one of the result was that PD controller was sufficient in discrete time domain. In discrete i‐PID controller, PD type controller is still required to ensure convergence of controlled error to zero, and PD type parameters affects convergence property, fast‐response property and stability. In this paper, predictive functional control (PFC) based on an ultra‐local model is proposed. By introducing of PFC, controlled variable converge to reference value without PD type controller. User‐specified parameters of the proposed controller is smaller than the conventional i‐PID controller. Characteristics of the proposed controller are compared with discrete i‐PID controller by numerical examples. [ABSTRACT FROM AUTHOR]