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Directed energy deposition (DED) has progressively emerged as a highly promising technology for the rapid, cost-effective, and high-performance fabrication of hard-to-process metal components with shorter production cycles. Recognized as one of the most widely utilized metal additive manufacturing (AM) techniques, DED has found extensive applications in critical industrial sectors such as aerospace and aviation. Despite its potential, challenges such as inconsistent part quality and low process repeatability continue to restrict its broader adoption. The core issue underlying these challenges is the complex, dynamic nature of the DED process, which involves the coupling of multiple physical fields. Within this context, the molten pool plays a pivotal role, serving as a key carrier that encapsulates abundant process characteristic information. The dynamic characteristics of the molten pool are intrinsically linked to the final part quality and the repeatability of the process. Consequently, integrating machine learning (ML) methodologies into the monitoring framework can offer robust data-driven support for enhancing both product quality and process consistency. This paper provides a comprehensive review of the research advancements and prospective trends in the dynamic monitoring and control of molten pool characteristics within DED processes underpinned by machine learning techniques. The review is structured around five key areas: an overview and fundamental principles of DED technology, methods for process information sensing during part monitoring, approaches for dynamically monitoring molten pool characteristics, the primary challenges currently faced in intelligent monitoring systems, and the potential future directions for further research and development. Through this detailed examination, the paper aims to shed light on the pivotal role of intelligent monitoring systems in advancing DED technology, ultimately contributing to more reliable and repeatable additive manufacturing processes. [ABSTRACT FROM AUTHOR]

In the era of rapid development of synthetic biology, biomanufacturing, as a bridge between life sciences and engineering technologies, is gradually demonstrating its extraordinary potential to reshape industrial landscapes. However, challenges such as production efficiency, cost control, and process monitoring still hinder the smooth transition from laboratory innovations to industrial- scale implementation. Intelligent biomanufacturing has emerged as a new form of productive force, offering innovative solutions to these problems. This study reviews the latest advances in bioprocess engineering and intelligent biomanufacturing, focusing on three key technological systems: intelligent sensing, intelligent analysis, and intelligent control. Intelligent sensing technology acts as the "eyes" of biological processes, enabling real-time, high-precision environmental monitoring. Intelligent analysis, as the "brain", uncovers underlying patterns in data, providing decision-making insights. Intelligent control, the "commander", precisely regulates bioprocesses to ensure efficient and stable operation. The integration of these three technologies significantly enhances the intelligence of biomanufacturing, providing powerful momentum to overcome industrialization bottlenecks. Looking ahead, intelligent biomanufacturing will continue to evolve through ongoing technological innovation and interdisciplinary collaboration, expanding its applications toward greater efficiency, intelligence, and sustainability, contributing to the progress and prosperity of human society. [ABSTRACT FROM AUTHOR]

The aim of this paper regards the laboratory validation of an energy management system (EMS) for an industrial site on the Eigerøy island (Norway). It will be the demonstration district in the ROBINSON project for a consequent concept replication. This activity in cyber-physical mode is an innovative approach to finalize the EMS tool with real measurement data with prime movers available at laboratory level, considering the necessary EMS robustness and flexibility for replication on other industrial islands. This EMS was designed and developed to minimize variable costs, producing on/off and set-point signals that, through a model predictive control (MPC) software, establish the system status. This smart grid includes renewable sources (e.g., solar panels, a wind turbine, and syngas) and traditional prime movers, such as a steam boiler for the industry needs. Moreover, an energy storage device is installed composed of an electrolyzer with a hydrogen pressure vessel. The main results reported in this work regard 26-h tests performed in cyber-physical mode thanks to the real-time interaction of hardware and software. So, a real microturbine and real photovoltaic (PV) panels were managed by the EMS in conjunction with software models for components not physically present in the laboratory. Although the optimization target was cost minimization, significant improvement was also obtained in terms of efficiency increase and CO2 emission decrease. [ABSTRACT FROM AUTHOR]

In order to optimize the economic operation level of the active distribution network and improve the energy utilization rate, a layered coordinated intelligent control method of source network load–storage for the active distribution network is studied. In this method, a layered coordinated intelligent control model of source network load and storage is established. The upper model takes the minimum annual comprehensive cost as the coordination control goal and optimizes the installation capacity of distributed generation and energy storage systems in the candidate installation locations; the lower level model proposes a peak-shaving and valley-filling operation strategy for the energy storage system, which aims at minimizing the network loss to control the stable operation of the power system; using a dual-objective Pareto solution method based on a modified ε-constraint method, solve the upper and lower control models, obtain the Pareto optimal solution set of available schemes, and then use the fuzzy decision method to extract the optimal location and capacity scheme of distributed generation and energy storage systems that can ensure the stable operation of power systems. After testing, the annual investment cost and maintenance cost of DG and ESS decreased by 5,139,500 yuan and 0.024 yuan, respectively, after using this method; The fluctuation of load power in energy storage systems is significantly reduced. At the same time, the consumption of wind/photovoltaic power has increased from around 1800 MW h to around 3200 MW h, indicating a significant improvement in the consumption of new energy. It has higher economic efficiency and good peak-shaving and valley-filling effects. [ABSTRACT FROM AUTHOR]

The sedimentation process of coal slurry water is influenced by numerous factors and has complex mechanisms. Its nonlinear and large hysteresis characteristics pose great challenges to process optimization control, making it a current research hotspot. This paper takes the typical slime water treatment process of a coal preparation plant as the object, and, on the basis of selecting raw coal quantity, flocculation dosage, coagulation dosage, overflow turbidity, raw coal ash content, underflow concentration, and slime quantity as the key variables, establishes a quality control method for process detection data consisting of data acquisition → data anomaly detection → data filling and noise reduction; subsequently, different machine-learning algorithms are used to predict the performance of coal-slurry-settling agents. It was found that Long Short-Term Memory shows the highest prediction accuracy for coagulants, with corresponding root mean square errors of 2.72% and 6.23%. Finally, using iFix software (version 5.5), an intelligent control system for the settling process of coal slurry water was constructed, which reduced the usage of coagulants by 31.56% and 37.21%. [ABSTRACT FROM AUTHOR]

The operational environments of engineering systems are becoming increasingly complex and require automatic control systems to be more intelligent. Cognitive control extends the domain of intelligent control, whereby cognitive science theories are applied to guide the design of automatic control systems to make them conform to the human cognition paradigm and behave like a real person, hence improving physical systems performance. Cognitive control has been investigated in several fields, but a comprehensive review covering all these fields has yet to be provided in any paper. This paper first presents a review of cognitive control development and related works. Then, the relationship between cognitive control and cognitive science is analyzed, based on which the definition and framework of cognitive control are summarized from the perspective of automation and control. Cognitive control is then compared with similar concepts, such as cognitive radio and cognitive radar, and similar control methods, such as intelligent control, robust control, and adaptive control. Finally, the main issues, research directions, and development prospects are discussed. We expect that this paper will contribute to the development of cognitive control. [ABSTRACT FROM AUTHOR]

Abstract Active Disturbance Rejection Control (ADRC) possesses robust disturbance rejection capabilities, making it well‐suited for longitudinal velocity control. However, the conventional Extended State Observer (ESO) in ADRC fails to fully exploit feedback from first‐order and higher‐order estimation errors and tracking error simultaneously, thereby diminishing the control performance of ADRC. To address this limitation, an enhanced car‐following algorithm utilising ADRC is proposed, which integrates the improved ESO with a feedback controller. In comparison to the conventional ESO, the enhanced version effectively utilises multi‐order estimation and tracking errors. Specifically, it enhances convergence rates by incorporating feedback from higher‐order estimation errors and ensures the estimated value converges to the reference value by utilising tracking error feedback. The improved ESO significantly enhances the disturbance rejection performance of ADRC. Finally, the effectiveness of the proposed algorithm is validated through the Lyapunov approach and experiments.

Closed-loop electricalstimulation of brain structures is one of the most promising techniques to suppress epileptic seizures in drug-resistant refractory patients who are also ineligible to ablative neurosurgery. In this work, an intelligent controller is presented to block the aberrant activity of a network of Izhikevich neurons of three different types, used here to model the electrical activity of the basolateral amygdala during ictogenesis, i.e. its transition from asynchronous to hypersynchronous state. A Lyapunov-based nonlinear scheme is used as the main framework for the proposed controller. To avoid the issue of accessing each neuron individually, local field potentials are used to gain insight into the overall state of the Izhikevich network. Artificial neural networks are integrated into the control scheme to manage unknown dynamics and disturbances caused by brain electrical activity that are not accounted for in the model. Four different cases of ictogenesis induction were tested. The results show the efficacy of the proposed control strategy to suppress epileptic seizures and suggest its capability to address both patient-specific and patient-to-patient variability.

Achieving precise and robust control in dynamic environments remains a challenge, particularly in nonlinear and uncertain systems. This paper proposes a novel two-layer intelligent learning control framework to address these issues, integrating an output recurrent fuzzy neural long short-term memory broad learning system (ORFNLSTM-BLS) with a root mean squared propagation (RMSprop) optimization algorithm. The first layer focuses on real-time system identification, leveraging ORFNLSTM-BLS to learn complex nonlinear system dynamics and effectively capture long-term dependencies in sequential data. The second layer implements an intelligent adaptive controller based on the ORFNLSTM-BLS, which dynamically adjusts control actions for time-varying setpoint tracking and disturbance rejection. The RMSprop optimization algorithm is applied across both layers to adaptively regulate learning rates, ensuring faster convergence and mitigating gradient issues. The convergence of the proposed method is provided through five theorems, ensuring gradient boundedness and learning rate conditions. Simulation and experimental results validate that, compared to conventional PID, the proposed method achieves superior tracking accuracy, with the best case showing an 82% reduction in RMSE, and enhanced control efficiency, with an 85% reduction in ITAE. Additionally, computational analysis shows a 10% increase in processing time for SISO tasks and 84% for MIMO cases. Despite the higher complexity, the method remains feasible for real-time applications. This work advances intelligent control systems, offering a robust solution for environments with sequential data and uncertainties.

Among the renewable energy sources, wind energy plays a crucial role in meeting the increasing global demand for clean energy. The variability in energy production makes wind turbines produce intermittent and varying power resources, variable mechanical loads, and a non-linear dynamic. The primary parameters to be controlled in wind turbines are the blade pitch angle and the generator torque. The challenges in designing the pitch controller include various non-linearities of wind turbines, constraints on pitch angle and its rate, and the stochastic nature of wind speed and unstructured model dynamics. Therefore, effectively addressing these complexities is important to ensure the stability and safety of a wind turbine. Existing literature extensively covered various control technologies applied to wind turbine systems, highlighting the diverse control methodologies and their applicability. However, there is limited evidence of the existing review papers focusing on applying intelligent control approaches to the collective pitch regulation of wind turbines. A comprehensive survey of the intelligent control approaches in collective pitch regulation was presented in this paper. It highlights the advantages of intelligent control strategies over traditional control methods in mitigating the challenges posed by system non-linearities. These approaches significantly enhance the performance of the wind energy generation system. Moreover, this paper presents the critical assessment areas for effective wind farm development and corresponding grid integration. This, in turn, will facilitate the growth of wind power utilization and assist researchers and engineers in integrating wind energy into the grid.

In recent years, agriculture has significantly evolved with the integration of technology, enabling the development of new cultivation techniques that respond to the growing demand for food and the need to conserve natural resources. In this context, we conducted a comprehensive review of models of intelligent control for managing nutrients in hydroponic systems by analyzing studies from the last five years. The selection of articles was based on the guidelines of PRISMA and research questions, focusing on control techniques based on fuzzy Logic, Artificial Intelligence and artificial Vision. These models are essential to automatically adjust the concentrations of nutrients, adapting to the needs of the plants at each stage of their growth. The review results highlight essential advances but also identify significant challenges, such as the need for precise sensors, the management of large volumes of data, and adapting the models to different crops and conditions. Despite these challenges, the benefits include a more efficient use of nutrients, a reduction in the consumption of water, and increased crop yields. Continuous research in this field is essential to improve the sustainability and productivity of hydroponic systems, offering new opportunities for agriculture in the future. The findings of this review provide a solid basis for evaluating the effectiveness of the control models and their application in real agricultural scenarios.

Abstract An attention‐based long short‐term memory (ALSTM)‐fast model predictive control (MPC) thermal regulation system for buildings is presented. The proposed system is developed to address the challenges associated with traditional heating, ventilation, and cooling (HVAC) control systems, often designed with fixed setpoints and static control strategies, leading to poor performance and suboptimal energy efficiency. The ALSTM‐Fast MPC system, on the other hand, performs the integration of deep learning and optimisation algorithms to predict the thermal behaviour of buildings and optimise the HVAC system control for thermal comfort and energy efficiency. The ALSTM‐Fast MPC system was implemented and evaluated on a real‐world data collected from a building automation system. Additionally, extensive experiments were conducted to analyse the system's performance. The results demonstrated the system's adaptability to changing thermal dynamics and occupancy patterns and its ability to achieve robust and efficient thermal regulation. As a result, a solution for optimising HVAC control in buildings is provided by the proposed ALSTM‐Fast MPC system.

Abstract With the development of connected automated vehicles (CAVs), preview and large‐scale road profile information detected by different vehicles become available for speed planning and active suspension control of CAVs to enhance ride comfort. Existing methods are not well adapted to rough pavements of different districts, where the distributions of road roughness are significantly different because of the traffic volume, maintenance, weather, etc. This study proposes a comfortable driving framework by coordinating speed planning and suspension control with knowledge transfer. Based on existing speed planning approaches, a deep reinforcement learning (DRL) algorithm is designed to learn comfortable suspension control strategies with preview road and speed information. Fine‐tuning and lateral connection are adopted to transfer the learned knowledge for adaptability in different districts. DRL‐based suspension control models are trained and transferred using real‐world rough pavement data in districts of Shanghai, China. The experimental results show that the proposed control method increases vertical comfort by 41.10% on rough pavements, compared to model predictive control. The proposed framework is proven to be applicable to stochastic rough pavements for CAVs.

With the increasing number of security devices in data centers, such as network firewalls, application firewalls, database firewalls, intrusion prevention and detection systems, it is worth researching and exploring how to better protect the security of data center data and assets without affecting device functionality and performance. Based on this, used intelligent service chain as the research carrier, by deploying secure resource pools, reconstructing data center security architecture, implementing flexible traffic orchestration, optimizing network traffic agile scheduling and intelligent control, practical support is provided for the intelligent, personalized, and differentiated deployment of network security devices.

Abstract The imbalance between generated power and load demand often causes unwanted fluctuations in the frequency and tie-line power changes within a power system. To address this issue, a control process known as load frequency control (LFC) is essential. This study aims to optimize the parameters of the LFC controller for a two-area power system that includes a reheat thermal generator and a photovoltaic (PV) power plant. An innovative multi-stage TDn(1 + PI) controller is introduced to reduce the oscillations in frequency and tie-line power changes. This controller combines a tilt-derivative with an N filter (TDn) with a proportional-integral (PI) controller, which improves the system’s response by correcting both steady-state errors and the rate of change. This design enhances the stability and speed of dynamic control systems. A new meta-heuristic optimization technique called bio-dynamic grasshopper optimization algorithm (BDGOA) is used for the first time to fine-tune the parameters of the proposed controller and improve its performance. The effectiveness of the controller is evaluated under various load demands, parameter variations, and nonlinearities. Comparisons with other controllers and optimization algorithms show that the BDGOA-TDn(1 + PI) controller significantly reduces overshoot in system frequency and tie-line power changes and achieves faster settling times for these oscillations. Simulation results demonstrate that the BDGOA-TDn(1 + PI) controller significantly outperforms conventional controllers, achieving a reduction in overshoot by 75%, faster settling times by 60%, and a lower integral of time-weighted absolute error by 50% under diverse operating conditions, including parameter variations and nonlinearities such as time delays and governor deadband effects.

This study reviews numerous control approaches utilized to address various issues encountered by unmanned aerial vehicles (UAVs). Specifically, focusing on the quadrotor system. Due to its versatility and compact size, quadrotors have gained popularity as UAVs in recent decades. Quadrotors face challenges such as ambient disturbances, impediments, non-parametric and parametric perturbations while performing tasks. Consequently, a robust and efficient control system is essential for such a system to ensure the stability and enhance their performance. It should be noted that, in this review, we have examined and analyzed the most recent highly cited papers selected from esteemed journals and magazines renowned for their exceptional quality and reputation.

Abstract As an emerging multi‐energy consumption subject, data centres (DCs) are bound to become crucial energy users for integrated energy systems (IES). Therefore, how to fully tap the potential of the collaborative operation between DCs and IES to improve total energy efficiency and economic performance is becoming a pressing need. In this article, the authors research an optimization coordinated by the energy scheduling and information service provision within the scenario of an integrated energy system with a data centre (IES‐DC). The mathematical model of IES‐DC is first established to reveal the energy conversion process of the electricity‐heat‐gas IES and the DC's energy consumption affected by the scale of active IT equipment. For dynamical providing multi‐energy and computing service by coordinating scheduling energy and information equipment, the formulations of IES‐DC scheduling, which is described as a Markov decision process (MDP), are presented, and it is solved by introducing the twin‐delayed deep deterministic policy gradient (TD3), which is a model‐free deep reinforcement learning (DRL) algorithm. Finally, the numerical studies show that compared with benchmarks, the proposed method based on the TD3 algorithm can effectively control the operation of energy conversion equipment and the number of active servers in IES‐DC.

Abstract The Wireless Sensor Network needs to become a dynamic and adaptive network to conserve energy stored in the wireless sensor network node battery. This dynamic and adaptive network sometimes are called SON (Self Organizing Network). Several SON concepts have been developed such as routing, clustering, intrusion detection, and other. Although several SON concepts already exist, however, there is no concept for SON in dynamic radio configuration. Therefore, the authors’ contribution to this field would be proposing a dynamic and adaptive Wireless Sensor Network node radio configuration. The significance of their work lies in the modelling of the SON network that builds based on our measurement in the real‐world jungle environment. The authors propose input parameters such as SNR, the distance between the transmitter and receiver, and frequency as the static parameter. For adaptive parameters, we propose bandwidth, spreading factor, and its most important parameter such as power for data transmission. Using the Levenberg Marquardt Artificial Neural Network (LM‐ANN) self‐organise Network model, power reduction and optimisation from 20 dBm to 14.9 dBm for SNR 3, to 11.5 dBm for SNR 6, and to 12.9 dBm for SNR 9 all within a 100‐m range can be achieved. With this result, the authors conclude that we can use LM‐ANN for the wireless sensor network SON model in the jungle environment.

Abstract Aiming at the problems of traditional ship cold chain monitoring systems being easily affected by environmental factors, difficult to achieve real‐time monitoring in the open sea without network signals, and low efficiency in transmitting Beidou short message data, a multi‐link compressed transmission ship cold chain monitoring system was designed by combining 5G technology, Beidou short message transmission technology, and multi‐protocol transmission technology. The system can adaptively switch the strength of wireless signals to ensure that information transmission is not lost. At the same time, a Beidou short message compressed transmission method was proposed to improve transmission efficiency. Test results show that the system can accurately complete data collection and processing, with small system errors, effectively improving the reliability and efficiency of the monitoring system, and has high application value.

Intelligent control systems developed for production facilities significantly contribute to production efficiency and quality. Using intelligent control systems has now become a necessity in iron and steel sintering plants that produce millions of tonnes annually. Automatic control of the sinter machine speed, which directly affects production efficiency and quality, is one of the first issues to be addressed. The complexity of the sintering process, being affected by many variables, and the nonlinearity of these variables make it difficult to control the machine speed. This study demonstrates that we have overcome this challenge using a fuzzy logic controller (FLC), which is optimized with an adaptive neuro-fuzzy inference system (ANFIS). The FLC we have designed operates with the characteristic point of the thermal state, the mixture level, the vacuum average, and the current speed parameters. We achieved an average success rate of 95%. The developed system automatically controls the speed of the sinter machine with high accuracy, independent of the operator. The system we have developed is used continuously at the Iskenderun Iron & Steel Co. sinter plant. The results obtained from the production facility show that the developed system captures the thermal change in the sinter pallet and manages the machine accordingly, increases the sintering efficiency by at least 10%, and ensures process safety. These results revealed that the developed system can be used effectively in the iron and steel industry and the use of the system will increase efficiency. [ABSTRACT FROM AUTHOR]