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This paper aims to propose a more efficient control algorithm for improving the control performance of uncertain nonlinear systems. A novel improved Takagi–Sugeno–Kang (TSK) wavelet type-2 fuzzy brain emotional learning controller (ITSKWT2FBC) is proposed. The proposed ITSKWT2FBC contains a brain emotional learning controller (BELC) combined with an improved TSK wavelet type-2 fuzzy system. By taking the advantages of BELC and the improved wavelet type-2 TSK fuzzy system, the control performance of this controller can be improved. The adaptive laws of controller parameters are derived from the defined Lyapunov function and gradient descent method. Moreover, the system stability can be guaranteed based on the Lyapunov stability theory. Finally, three nonlinear systems, a double inverted pendulum system, a Mackey–Glass time series prediction (TSP) and a four dimensional (4D) chaotic system synchronization are simulated to illustrate the effectiveness of the proposed method. The simulation results have demonstrated the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

The paper proposes a WADC approach using CART technique to dampen inter-area oscillations (IAOs) in bulk power systems. In this case, PMU data are filtered to estimate inter-area dynamics in which using pade approximation, a pole-zero IAO compensation block is designed. An online random decrement technique is also developed to identify the coherent groups and damping ratios to activate the WADC for oscillation damping. An offline process is provided to identify 200 critical IAO contingencies and tunes WADC gains using PSO for training CARTs via a set of 200 input inter-area signals and assigning output controlling gains pre-trained data and evaluating the CART estimations through online operation. The WADC approach is validated for oscillation damping on a 39-bus system and a realistic 561-generator Iranian grid. Simulations show 98 % accuracy in achieving sufficient damping ratios (>0.6) across various operating conditions. • A new approach based on WADC is proposed for damping inter-area oscillations. • A new scheme based on individual WADC for each generator is proposed. • CART technique is used for estimating optimal gains of individual WADC controller. • A combination index is proposed to find the most effective individual WADCs. [ABSTRACT FROM AUTHOR]

To address the problem of underactuated surface vessel (USV) formation control in static obstacle environments with model uncertainties and time-varying external disturbances, a model-free formation control strategy is proposed in this paper. First, based on the guiding vector field (GVF), a composite GVF is developed to guide USV formation to the desired position and to avoid multiple static obstacles. Second, a flexible constraint strategy is introduced, and the constraint boundary conditions are appropriately relaxed to avoid singularities in the obstacle environment. Then, based on the Mexican hat wavelet function, the self-structuring fuzzy Mexican hat wavelet cerebellar model articulation controller (SCMAC), and a self-structuring fuzzy Mexican hat wavelet brain emotional learning controller (SBELC), are proposed to achieve model-free control. In addition, the self-structuring algorithm is embedded into SCMAC and SBELC to achieve autonomous optimization of the controller structure and to reduce the computational effort of the control system. The salient features in the proposed control strategy are as follows. First, the proposed model-free formation control strategy does not have to rely on accurate model information. Second, collisions are effectively avoided, and good control performance is guaranteed even under the influence of disturbances and static obstacles. Third, the proposed self-structuring algorithm achieves automatic construction of the controller structure. Finally, the signals in the control system are proven to be bounded, and the simulation results verify the feasibility and superiority of the proposed model-free control strategy. • An adaptive intelligent formation control method is proposed based on SCMAC and SBELC. • A Mexican hat wavelet function is developed to enhance the ability of the formation control system to process signals. • The GVF and flexible constraints strategy are introduced to avoid static obstacles and ensure control performance. • A self-structuring algorithm is embedded to optimize the structure of SCMAC and SBELC. [ABSTRACT FROM AUTHOR]

In this research, a new hybrid backstepping control strategy based on a neural network is proposed for tractor-trailer mobile manipulators in the presence of unknown wheel slippage and disturbances. To minimize the negative impacts of wheel slippage, the desired velocities of the tractor's wheels are computed with a proposed kinematic control model with an adaptive term. As the system's dynamical model contains unavoidable uncertainties, model-based backstepping control technique is unable to effectively manage these systems. Hence, the proposed controller blends a radial basis function neural network with the merits of a dynamical model-based backstepping approach. The neural networks are employed to approximate the non-linear unknown smooth function. To minimize the impact of external disturbances, and network reconstruction error an adaptive term is added to the control law. The Lyapunov theorem and Barbalat's lemma are employed to guarantee the stability of the control method. The tracking error is shown to be bounded and to rapidly converge to zero with the proposed method. To demonstrate the efficacy and validity of the control mechanism, comparison simulation results are presented. • A hybrid controller is proposed for tractor-trailer systems with wheel slippage. • By putting forward a kinematic control law, the desired velocities are calculated. • At the dynamical level, RBFNN is used to approximate the uncertainties of the system. • The stability of the system is analyzed with Lyapunov theory and Barbalat's Lemma. • The proposed control scheme is verified through simulation studies. [ABSTRACT FROM AUTHOR]

Hypothermic preservation (HP) is highly desired for the maintenance of the viability of living cell specimens, e.g. rare cells in whole-blood samples or therapeutic cells, in an unfrozen state. However, the extension of the viable preservation time is a challenge because of the multiple injuries suffered by hypothermically preserved cells. Here, based on a dynamic bond crosslinked zwitterionic hydrogel, we established a sensing preservation system that could monitor the levels of reactive oxygen species (ROS) via real-time electronic signals and intelligent control of antioxidant addition, to completely prevent an excess of ROS in the whole-cell specimen. Furthermore, the hydrogel-based system can counter the extracellular-matrix-loss-induced anoikis of living cells. Based on the design aimed at affording protection against two primary HP injuries (i.e. ROS overproduction and anoikis) to cells, this system extended the preservation time of cell specimens under refrigerated conditions to 24 days. After preservation, the use of a mild cell retrieval process guaranteed the activity of the preserved living cells. This work not only possesses the potential to facilitate intelligent cell-based clinical applications, but also paves the way for the preparation of living materials that can host programmed cells with long-term survival. An intelligent system based on a zwitterionic sensing hydrogel is established, which can afford ultra-long hypothermic cell-preservation times of up to 24 days. The system enables the real-time monitoring of ROS overproduction and intelligent antioxidant addition, because of the merging of the smart hydrogel with a computer intelligent detection and control system. Furthermore, the automatic addition of an antioxidant according to the ROS-signal changes produced by the ZBA hydrogel effectively prevented HP lesions, including ROS over-production and ECM loss, in the preserved living cells. Subsequently, the system could also be gently dissociated, to retrieve the preserved cells. This work provides a solution for the real-time monitoring and long-term HP of living specimens, which holds the promise of benefiting cell-based medicine and the development of genetically programmed cell-based living materials. [Display omitted] [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) technology is developing and requires effective and adaptive controllers to enhance performance and be the best sustainable solution to the population concerns caused by oil from other vehicles. This paper proposes a nonlinear Proportional Derivative (PD) controller to control motor speed in EV. The controller's optimal gains are tuned using Gorilla Troops Optimization (GTO). Two classical controllers (PID, PD) are compared with nonlinear PD controller, all tuned controller variables are determined using the GTO algorithm, and the Integral Time Absolute Error (ITAE) fitness function is adopted to maintain the system performance. To simulate the system response, MATLAB (Platforms for m-file and Simulink) is used. The achieved results are compared with the classical controllers (PD &PID) to demonstrate the efficacy of the suggested nonlinear PD controller. The benefits of using the nonlinear hyperbolic function effect on system behavior can be seen in its quick settling time, which is 9.6% faster than the traditional PID controller and 47% faster than the traditional PD controller. It also became apparent in its stable response, which was free of noise and overshoot while the PID controller overshoots with a value of (8.8). While in rise time, the proposed controller with 0.0083s outperforms the PID controller 0.4905s, and the PD controller 0.1315s. According to the simulation results, the nonlinear PD controller settling time is faster and more smoothly (0.0187s) than the PID (0.18s) and PD (0.88s) controllers, which are also optimized with the same GTO tuning algorithm. The suggested controller and the intelligent tuning algorithm guided the system's response to produce the best outputs. The simulation results demonstrated a steady behavior and an effective response in tracking the intended speed value; transient analysis is utilized to explain the performance of the suggested controller. [ABSTRACT FROM AUTHOR]

Nowadays, intelligent control methods play an important role in the advancement of technology and the human movement towards further evolution. The development of new frameworks for power generation and distribution systems by designing a microgrid structure with economic capabilities is one of these areas of progress. Therefore, this paper introduces a new practical method for controlling the frequency of provisional microgrid and is able to cover the following issues at the same time including (1) It considers the nonlinear model of provisional microgrid which has a hybrid structure (AC and DC) in addition to renewable energy sources; (2) Introduces a method for microgrid frequency control under different operational conditions that performs based on the brain emotional learning; (3) Ensures the operation and applicability of the control method for the provisional microgrid through implementation of FPGA for the first time; (4) Confirms the robustness of the proposed method under severe load changes and generation from renewable sources. So, the effects of wind turbines and solar energy are considered in the simulation scenario and under the influence of various changes in load and system uncertainties, the robustness and efficiency of the proposed method are well demonstrated. [ABSTRACT FROM AUTHOR]

Parallel-connected digital valve arrays are commonly utilized in the pilot stage of the proportional directional valve to enhance dynamic performance and reliability. However, when the digital valve array is driven by a digital signal, it is difficult to optimally assign the signal pulses to each valve. If the assignment is not well executed, it can significantly reduce the switching uniformity of the digital valves or lead to performance degradation of the system. In this paper, a model-based sliding mode control strategy based on the intelligent distribution of control law is proposed and successfully applied to a proportional valve driven by digital valve arrays. The intelligent distribution strategy encompasses a logic distribution algorithm and a circular sliding distribution algorithm that automatically assigns control laws to different valves based on the rolling of the PWM signal cycle. Experimental results confirm that the proposed strategy not only simultaneously reduces the total number of valve switches and enhances the switching uniformity among the valves, but also adapts to the variation in the number of valves. The proposed strategy is not limited to the application of digital valve arrays, it is also applicable in other fields of multi-actuators driven by digital signals, and can simultaneously improve the control accuracy, lifetime, and maintenance friendliness. • A digital proportional valve is proposed, where the pilot stage consists of fixed orifices and digital valve arrays. • The intelligent distribution strategy is designed based on a logic distribution and a circular sliding distribution. • The proposed MSMC with intelligent distribution can be adapted to DVAs with any number of valves. [ABSTRACT FROM AUTHOR]

In order to understand the energy intelligent control energy-saving system in computer rooms, the author proposes a research on an artificial intelligence based energy intelligent control energy-saving system in computer rooms. The author first analyzed the energy monitoring and analysis system of artificial intelligence in computer rooms. Based on the existing energy consumption data resources of computer rooms, through monitoring and scientific analysis of various aspects of energy consumption data in computer rooms, it provides decision-making support for managers to better formulate energy-saving measures. The system functions are divided into four sub modules: energy consumption environment monitoring, energy consumption behavior analysis, energy-saving renovation suggestions, and energy-saving effect evaluation; We designed an energy consumption anomaly detection method using the GESD algorithm and implemented a B/S architecture using the SSH framework. Finally, in terms of data display, we utilized two technologies: Extjs and BIRT reports, and used POI to export energy consumption data files in Excel format. Practical applications have shown that the system provides assistance for managers to develop energy-saving and emission reduction measures. [ABSTRACT FROM AUTHOR]

The intelligent warehousing control system is a system based on PLC (Programmable Logic Controller) and human-computer interaction (HCI), aiming to improve the efficiency and accuracy of warehousing operations. The system controls the operation and monitoring of storage equipment through PLC, and interacts with operators through human-computer. The system design includes equipment selection, PLC control system design, and human-computer design. In equipment selection, it is necessary to select equipment suitable for automation control to ensure that the system can meet the needs of warehousing operations. This paper introduces a programmable controller design method, that is, using soft and hard to achieve automatic control of memory. The hardware part is mainly composed of PLC, sensor, actuator and so on, which controls the operation of memory and monitors the relevant parameters. In the aspect of software, the related control program is designed by PLC programming language. The test results show that in the traditional warehouse system, the processing time of each commodity is more than 169 seconds; the utilization rate of warehouse is less than 70%, and the logistics operation time of the enterprise using PLC and HCI for intelligent storage control can be reduced to 57 seconds; on this basis, the data processing time of the system is reduced to 43 seconds, and the utilization rate of the warehouse is 93%. Therefore, this paper presents an intelligent warehouse control system based on PLC and man-machine interaction. The system can not only effectively improve the efficiency and accuracy of warehouse operation, but also enhance the safety and reliability of warehouse operation. It provides a reliable and flexible solution for automatic control and monitoring of warehouse. [ABSTRACT FROM AUTHOR]

In the contemporary energy landscape, characterized by a global commitment to sustainability, the effective management and forecasting of energy consumption play pivotal roles in achieving environmental and economic goals. As nations strive to meet sustainable development targets, optimizing energy use becomes imperative. This paper addresses these challenges by focusing on load forecasting and energy management within the context of a Savona campus microgrid. In this thesis, the NNR algorithm based load profile prediction model was proposed. The development process involved a detailed exploration of the correlation between weather information and electricity consumption. Furthermore, the outputs of the load forecasting model, namely the predicted load profiles, were subsequently utilized in the Energy Management System (EMS) to optimally manage power flows in the campus microgrid using an Internet of things as a service (IoTaaS) with day-ahead forecasting model. The overall results of the model evaluation across all periods reveal a Mean Absolute Error (MAE) of 9.63 kW, a Coefficient of Determination (R2) of 0.79, and a Mean Absolute Percentage Error (MAPE) of 9.02%. These metrics provide a comprehensive assessment of the model's performance across various temperature conditions. The proposed load profile forecasting model was integrated into the Energy Management System (EMS) developed for Savona campus microgrid in Italy. The findings provide a valuable framework for optimizing microgrid operations, aligning with global sustainability objectives. [ABSTRACT FROM AUTHOR]

With the increasing functionality of intelligent programmable controllers and the continuous improvement in power and component density, the problem of rapidly rising temperatures during operation has become increasingly prominent, directly affecting the controller's lifespan and stability[1]. Therefore, improving the heat dissipation efficiency of intelligent programmable controller modules has become a key focus of research. This paper introduces a method of installing aluminum alloy heat sinks on the core chip of the controller and carefully designing the fin shape and size of the heat sink to enhance heat conduction and heat convection effects, improving heat dissipation performance. This paper also constructs a thermal equivalent model of the controller and simulates the impact of different fin parameters on heat dissipation performance. Experimental results show that when the fin height is 5 millimeters, thickness is 2 millimeters, and spacing is 4 millimeters, the heat sink exhibits the highest thermal conductivity and the lowest thermal resistance, achieving optimal heat dissipation effectiveness. [ABSTRACT FROM AUTHOR]

Complexity science is a new stage in the development of systems science that is the frontier areas of contemporary scientific development. Complexity science takes complex systems as the research object, which has attracted widespread attention from researchers in the fields of economy, control, management, and society. In recent years, with the rapid development of science and technology and people's deepening understanding for the theory of complex systems, the systems are no longer an object with a single function, but the systems are composed of multiple individuals with autonomous capabilities through cooperative and cooperation, namely multi-agent system(MAS). Currently, MAS is one of the main models for studying such complex systems. The intelligent control is to break the traditional multiagent fault-tolerant control (FTC) concept and produce a new type of compensation mechanism. In this paper, the applications of fault-tolerant control methods for MASs are presented, and a discussion is given about development and challenges in this field. [ABSTRACT FROM AUTHOR]

This study presents a novel control framework integrating feedforward (FF) and feedback (FB) strategies for the management of nonlinear systems. The intelligent control component employs a wavelet neural network (WNN) to construct the FF controller. Additionally, the FB loop incorporates H-infinity control, renowned for its robustness and resilience. The controller parameters are optimized using Particle Swarm Optimization (PSO). The proposed control framework's efficacy is evaluated through its application to knee joint motion, assessing both control accuracy and robustness against external disturbances. [ABSTRACT FROM AUTHOR]

In the contemporary energy landscape, characterized by a global commitment to sustainability, the effective management and forecasting of energy consumption play pivotal roles in achieving environmental and economic goals. As nations strive to meet sustainable development targets, optimizing energy use becomes imperative. This paper addresses these challenges by focusing on load forecasting and energy management within the context of a Savona campus microgrid. In this thesis, the NNR algorithm based load profile prediction model was proposed. The development process involved a detailed exploration of the correlation between weather information and electricity consumption. Furthermore, the outputs of the load forecasting model, namely the predicted load profiles, were subsequently utilized in the Energy Management System (EMS) to optimally manage power flows in the campus microgrid using an Internet of things as a service (IoTaaS) with day-ahead forecasting model. The overall results of the model evaluation across all periods reveal a Mean Absolute Error (MAE) of 9.63 kW, a Coefficient of Determination (R2) of 0.79, and a Mean Absolute Percentage Error (MAPE) of 9.02%. These metrics provide a comprehensive assessment of the model's performance across various temperature conditions. The proposed load profile forecasting model was integrated into the Energy Management System (EMS) developed for Savona campus microgrid in Italy. The findings provide a valuable framework for optimizing microgrid operations, aligning with global sustainability objectives. [ABSTRACT FROM AUTHOR]

With the increasing functionality of intelligent programmable controllers and the continuous improvement in power and component density, the problem of rapidly rising temperatures during operation has become increasingly prominent, directly affecting the controller's lifespan and stability[1]. Therefore, improving the heat dissipation efficiency of intelligent programmable controller modules has become a key focus of research. This paper introduces a method of installing aluminum alloy heat sinks on the core chip of the controller and carefully designing the fin shape and size of the heat sink to enhance heat conduction and heat convection effects, improving heat dissipation performance. This paper also constructs a thermal equivalent model of the controller and simulates the impact of different fin parameters on heat dissipation performance. Experimental results show that when the fin height is 5 millimeters, thickness is 2 millimeters, and spacing is 4 millimeters, the heat sink exhibits the highest thermal conductivity and the lowest thermal resistance, achieving optimal heat dissipation effectiveness. [ABSTRACT FROM AUTHOR]

This article explores the research and development undertaken as part of a Master's degree in Computer Engineering, with a primary focus on enhancing control mechanisms for natural wood drying. While this method is known for its cost-effectiveness in terms of labor and energy, it suffers from slower and unstable drying cycles. The project's objective is to implement an intelligent control system that significantly improves monitoring and recording of humidity levels in each wooden stack. Additionally, the system incorporates the capability to predict humidity based on data sourced from a weather forecasting API. The proposed solution entails a three-layer system: data collection, relay, and analysis. In the data collection layer, low-computing devices, utilizing a Raspberry Pi, measure humidity levels in individual wood stacks. These devices then transmit the data via Low Power Bluetooth to the subsequent layer. The data relay layer incorporates an Android application designed to aggregate, normalize, and transmit collected data. Furthermore, it provides users with visualization tools for comprehensive data understanding. The data storage and analysis layer, developed with Django, serves as the back-end, offering management functionalities for stacks, sensors, overall data, and analysis capabilities. This layer can generate humidity forecasts based on real-time weather information. The implementation of this intelligent control system enables accurate insights into humidity levels, triggering alerts for any anomalies during the drying process. This reduces the necessity for constant on-site supervision, optimizes work efficiency, lowers costs, and eliminates repetitive tasks. [ABSTRACT FROM AUTHOR]

An overview of intelligent control techniques for the speed control of a direct current (DC) motor has been described in this study. Using the MATLAB SIMULINK platform, the individually excited DC motor speed control system implemented as a physical model. A mathematical model for both the sliding mode control (SMC) method and PID control, a traditional control methodology that ensures the speed controller has been developed. For comparison's sake, fuzzy logic is constructed using the Mamdani Technique with two inputs to obtain the necessary speed control. In addition, to select the optimal gain, the output signal of The PID controller and SMC were contrasted with fuzzy logic in terms of overshoot peak and stability time period. The outcomes prove the SMC's superiority over the PI controller and fuzzy logic approach. Based on time domain characteristics, this article presents a comparative study between Proportional-Integral-Derivative (PID), a sliding mode control (SMC), and fuzzy logic controller (FLC) controllers. The study concludes the less overshoot peak and fast response through fixed Properties for the DC motor and its mechanical variations due to operating conditions. Based on transient response study, the results show that SMC is superior to fuzzy logic and classical controllers PID. [ABSTRACT FROM AUTHOR]

The work furnished in this paper projects the application of fuzzy-aided tilt-integral-derivative (TID) with filter (TIDF) for regulating the dynamical behavior of the electrical interconnected power system (IPS) during uncertain conditions. The optimal design of the fuzzy TIDF is performed using the efficient soft computing technique of the water cycle algorithm (WCA). The suggested fuzzy TIDF controller performance is tested on the multi-area diverse source realistic (MADSR) IPS (MADSRIPS) subjected to control area 1 with step load disturbance (SLD) of 10%. Moreover, the analysis carried out on MADSRIPS is considered with the communication time delays (CTDs) for the investigation near to realistic environment. The efficacy of fuzzy-aided TIDF is contrasted with reported controllers available in literature, and the simulation analysis is acquired from the framework of MATLAB, which revealed the sovereignty of the suggested controller. Later, the MADSRIPS is operated with the integration of a HVDC line in conjunction with the tie-line to improve the performance. [ABSTRACT FROM AUTHOR]

Efficient Unmanned Aerial Vehicle (UAV) trajectory generation is crucial for successful area coverage missions, aiming to maximize coverage while minimizing resource consumption. In this research, we present a comprehensive study on optimizing UAV trajectory generation using Deep Neural Networks (DNNs) with the Adam optimization algorithm. The DNNs are trained on historical data to produce smooth and continuous trajectories, thereby reducing abrupt changes in direction and enhancing overall efficiency during the mission. To evaluate the performance of the proposed approach, we conducted experiments comparing different activation functions, namely tanh, sigmoid, and ReLU, with the Adam-optimized DNN model. The trajectories generated by each activation function were analyzed using key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) scores for both X and Y coordinates. The results of the comparative analysis revealed that the DNN model with the Adam optimizer exhibited superior performance over the other activation functions. It achieved lower MSE, MAE, and RMSE values, indicating better trajectory accuracy and smoother paths. Additionally, the R2 scores demonstrated a higher correlation between the generated trajectories and the actual trajectories, highlighting the model's ability to capture underlying patterns effectively. The findings underscore the significance of leveraging the Adam-optimized DNN approach for UAV trajectory planning, offering promising opportunities for resource optimization, increased mission success, and further advancements in autonomous aerial systems. This research contributes to the ongoing efforts in UAV path planning, optimization, and intelligent control strategies, paving the way for enhanced autonomous systems in various real-world applications. [ABSTRACT FROM AUTHOR]

This paper portrays the design, operation, and application of an artificial neural network-based intelligent controller, the structure of which is constructed by integrating a function-link neural network with a fuzzy brain emotional learning controller (FBELC). The resulting model is called the function-link fuzzy brain emotional learning controller (FLFBELC). The FBELC consists of two sets of neural networks inspired by the two sets of systems of the brain, the orbitofrontal cortex and amygdala, which are responsible for decision-making and emotional response, respectively. These two networks are the sensory network and the emotional network, which affect each other's learning capabilities and are jointly responsible for the control output of the controller. The function-link neural network (FLNN) is a method shown to facilitate faster convergence and reduce computational load through function approximation. The FLNN structure is integrated with the FBELC, with the resulting controller's intent to acquire these characteristics. The weights of the proposed FLFBELC network can be approximated using function expansions of the control system's input. Finally, the FLFBELC is applied for the tracking control of nonlinear systems to illustrate the effectiveness of the proposed control method. The results are compared to other recent controllers to contrast the favorable performance of the FLFBELC. [ABSTRACT FROM AUTHOR]

The work furnished in this paper projects the application of fuzzy-aided tilt-integral-derivative (TID) with filter (TIDF) for regulating the dynamical behavior of the electrical interconnected power system (IPS) during uncertain conditions. The optimal design of the fuzzy TIDF is performed using the efficient soft computing technique of the water cycle algorithm (WCA). The suggested fuzzy TIDF controller performance is tested on the multi-area diverse source realistic (MADSR) IPS (MADSRIPS) subjected to control area 1 with step load disturbance (SLD) of 10%. Moreover, the analysis carried out on MADSRIPS is considered with the communication time delays (CTDs) for the investigation near to realistic environment. The efficacy of fuzzy-aided TIDF is contrasted with reported controllers available in literature, and the simulation analysis is acquired from the framework of MATLAB, which revealed the sovereignty of the suggested controller. Later, the MADSRIPS is operated with the integration of a HVDC line in conjunction with the tie-line to improve the performance. [ABSTRACT FROM AUTHOR]

Closed-circuit television (CCTV) videos provide important data for intelligent control systems; however, sensitive information such as human faces and vehicle license plates must be de-identified by a de-identification framework before such videos are used. Previous research has mainly focused on enhancing the performance of de-identification methods, and only a few are devoted to detecting sensitive information. To address this issue, this study proposes an artificial intelligence-based sensitive information de-identification framework. The framework detects objects and utilizes their structural information to estimate the relative position of sensitive data, followed by the de-identification process. To enhance the recall of the detection module, this study analyzes difficult-to-detect faces and license plates in CCTV videos and proposes relative position estimation methods. These methods minimize the possibility of sensitive information leakage while reducing the number of locations of sensitive information that need to be annotated. When evaluated using actual urban CCTV videos, the recall of the framework was 84.63%, which is 33.01% higher than that of the conventional algorithm. [ABSTRACT FROM AUTHOR]

Traffic intersections act as pivotal junctions where various streams of traffic intersect and intersecting movements occur. When traffic volumes are high, intersections that are efficiently designed and equipped with intelligent traffic control systems can optimize the movement of vehicles. Accordingly, the time spent by vehicles at intersections increases, and these vehicles cause the vehicles behind them to slow down. This study aims to enhance the efficacy of reservation-based intersection control for platoons comprising vehicles arriving at intersections and turning in various directions. To assess the impact of different platooning algorithms on both the average speed of vehicles within the platoons and the total time spent at intersections, simulations were conducted using the SUMO environment. These simulations observed variations in the average speed values of vehicles and the duration spent by vehicles at intersections. The simulation results showed that the proposed reservation-based platoon ordering algorithm significantly contributed to increasing the average speed and reducing waiting time. [ABSTRACT FROM AUTHOR]

Owing to the deep integration of intelligent control, big data and communication, networked Takagi–Sugeno fuzzy control systems is widely studied and applied to the real-world scenarios. This paper studies the issue of resilient event-triggered dynamic output feedback controller design of networked Takagi–Sugeno fuzzy systems under denial-of-service (DoS) attacks launched by adversaries. Considering the types of the attack limited by frequency and duration, a resilient event-triggered mechanism is established to resist the impact of DoS attacks and improve resource utilization efficiency, and two new switched fuzzy system models are constructed by integrating the underlying system and the event-based static and dynamic output feedback intelligent controllers. Then sufficient conditions are obtained to guarantee that the switched systems are exponential stable and then the control gains are designed by using a novel iterative algorithm. Finally, two examples are employed to verify the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

Customer demand and profit potential have pushed companies to offer diverse customization options in their products and move towards mass customization. To move towards mass customization reconfigurable manufacturing systems (RMS) have been prosed as a solution. However, it comes with challenges, including labor, costs, time, and complexity. Introducing intelligence, smartness and state-of-the-art industry 4.0 technologies into reconfigurable manufacturing systems (RMS) can be a solution to these challenges. Combining these concepts can be used to develop an intelligent RMS that can be automatically reconfigured without any human intervention. In this paper, a novel architecture has been proposed that explains all the details from the idealization phase to design and physical implementation. The architecture covers individual elements of the RMS, introduces the technologies that enable intelligence and follows through the steps that add up to the complete functionality of the system. In addition, we propose an intelligent process that automates the reconfiguration phase of our proposal. Based on this architecture and intelligent recon-figuration, a physical demonstration is also presented that shows how the system can be implemented. The constructed system is demonstrated as an intelligent RMS capable of mass customization without human effort. [ABSTRACT FROM AUTHOR]

Online ship response prediction is one of the emerging interests in seakeeping due to the extensive range of applications for autonomous control of marine vehicles. In particular, the short-term prediction and online updates of ship response have received special attention. Despite a body of studies on different predictors, the asymptotic properties of estimators given the time series sample size have not been addressed specifically, so the predictors only have been analyzed for a fixed observation window regardless of the intrinsic statistical characteristics in the time series. To this end, the current research has explored the performance of two nonlinear and linear based regressors: support vector regression (SVR) and the adaptive Seasonal Auto-Regressive and Integrated Moving Average (SARIMA) model on the time series data obtained from a simulated semisubmersible platform in different sea states. The experiment demonstrated that the prediction accuracy depends significantly on the observation window length statistical properties with respect to the ship responses in various wave conditions. Therefore, an innovative filter using the weighted average of a proposed function has been introduced to adaptively adjust the buffer window based on the signal statistical characteristics. The encouraging outcomes underscore that the introduced filter not only holds the potential to enhance various predictors employed in autonomous onboard decision-making, guidance, and stabilization systems for both existing and forthcoming intelligent onboard control systems but also offers an avenue to bolster the autonomy of dynamic systems. More precisely, the proposed mechanism could extend its applicability to enhance cognitive systems and enable deterministic decision-making in diverse fields beyond the scope of present contribution. [ABSTRACT FROM AUTHOR]

Wireless sensor-based smart home systems are increasingly gaining prevalence, offering new opportunities for automation and an improved quality of life. However, the seamless integration of multi-layer wireless sensor networks remains a significant challenge. This article delves into a comprehensive approach to integrate wireless networks that employ distinct protocols, such as Zigbee, Z-Wave, Wi-Fi, Bluetooth, and LoRa, within the context of Home Assistant-based smart homes. The article explores architectural solutions and data transformation methods, and presents the outcomes of performance experiments, as well as the stability of multi-layered wireless sensor networks. These findings culminate in the identification of an optimal configuration. The formulation of this research problem sets the stage for novel perspectives in the development of efficient and adaptive smart homes. [ABSTRACT FROM AUTHOR]

This article proposes innovative solutions in the field of traffic management – smart traffic lights with computer vision, aimed at improving traffic flow in the large cities of Kazakhstan. With a growing number of vehicles and increasing traffic volume, the issues of congestion and delays at intersections have become increasingly relevant. The article reviews the operational principle of such smart traffic lights, which is based on analyzing computer vision data using sensors and cameras. The benefits of applying this technology, including responsiveness, efficiency, and environmental friendliness, are considered. Furthermore, the potential of smart traffic lights in major megacities of Kazakhstan, such as Astana and Almaty, is analyzed in detail. The conclusion supports the assessment of traffic flow, travel time, and an overall evaluation of the traffic situation. Ultimately, this article highlights the improved performance of smart traffic lights with computer vision coverage in modern cities of Kazakhstan, aiming to ensure higher traffic safety and efficiency. [ABSTRACT FROM AUTHOR]

A new multifunctional proportional control triaxial stress loading apparatus is presented in this study, which can apply an output force on coal rock to simulate the conditions of triaxial stress loading, aiming at solving the problems of excessive consideration of static indices in the design and the incompleteness of the simulation and test verification system for the test parts at the performance analysis stage. This apparatus mainly combines the configuration of a triaxial stress loading system which can meet the stress loading requirements under different operating conditions, with the effective integration of multiple pressure loading operations based on the electrohydraulic proportional control method. In this context, a pressure and position combined control strategy based on the sliding mode is proposed to control the vertical and longitudinal loading hydraulic cylinders. Then, a co-simulation mode including the triaxial stress loading hydraulic system is established to verify the control strategy, system response characteristics and selection of the controller parameters. Furthermore, a multifunctional stress loading experimental platform is developed, and the stress loading characteristics with the proposed strategy are tested and analyzed. The results show that the triaxial stress loading hydraulic system can meet the response characteristics, the fluctuating deviation of the constant loading test can be restricted to 8.5%, the tracking error of the variable loading test is small, the minimum response time of instantaneous loading can reach 2.8 s, and the stress loading effect is noticeable. The experimental platform fully indicates that the stress loading system with the state perception and processing method as the core can meet a variety of verification indices of constant, variable and instantaneous loading tests. This research provides technical support for the smooth, synchronous control and intelligent operation of various types of hydraulic actuator machines. • A new multifunction proportional control triaxial stress loading apparatus is presented. • A state perception and processing method is illustrated to realize the effective integration of multipleloading operations. • A viable control strategy and controller are designed to the force control that rejecting the fluctuate deviation. • The coupled position and pressure loading controller are designed utilizing a sliding mode control method. • Experiment results are executed to verify the control effectiveness of the controller. [ABSTRACT FROM AUTHOR]

The design of autopilots is often enabled by simulating the behavior of the aircraft when it is steered by these systems. The automatic pilot of an airplane consists of relieving the workload of the crew by allowing it to display an altitude, speed and/or heading instruction and letting the control system manage the trajectory to reach this instruction. The autopilot uses control laws to compare the pilot's flight parameters with the predetermined instructions, then adjusts the control system accordingly. These control laws are parametrized as a function of the stability and precision performances to be respected. The objective of our proposed approach is to demonstrate the usage of Model-Free Control (MFC) using an intelligent controller to check the autopilots altitude based on retention of the aircrafts attitude. The proposed MFC approach is applied in the aircraft system and numerical results have presented good performances in terms that the autopilot altitude regulation error using the MFC controller is more better than the error used by the PID controller. [ABSTRACT FROM AUTHOR]

Unlike two-wheeled balancing robots, which are mechanically stable in one direction, unicycle robots use only one bottom wheel to maintain pitching balance, leaving the robot unable to maintain rolling balance. This study proposes an intelligent balancing control (IBC) system to address the highly nonlinear and underactuated behavior of the unicycle robot through the fuzzy control approach. The IBC system comprises three controllers: the fuzzy movement control (FMC), the fuzzy rolling control (FRC) and the fuzzy steering control (FSC). The FMC enables the unicycle robot to move at a desired speed in the longitudinal direction while maintaining balance, the FRC causes the roll angle to approach zero in the lateral direction, and the FSC controls the robot to rotate in place in the yaw direction. In addition, because the movement of bicycle robot is relatively complex, most of them only tend to simple movement patterns or linear movements. Further, this study proposes a trajectory tracking control (TTC) system using the fuzzy control approach to enable the bicycle robot to track a desired trajectory with high accuracy. Finally, the proposed control strategy is implemented using a 32-bit STM32F446RE microcontroller. Six experimental scenarios are studied to help test the robot's stability and maneuverability. The experimental results not only proved the balance of the IBC system in challenging terrain, but also proved the accuracy of the TTC system in tracking the command trajectory. [ABSTRACT FROM AUTHOR]

The wind turbine generators (WTG's) incapability of primary frequency support during system contingencies due to its decoupled nature from the system frequency causes profound integration and stability issues. The present study focuses on resolving such issues by enabling the WTGs to participate in long-term frequency support under the derated operation of WTGs. The deloading operation of WTGs can provide a specific reserve power margin by reducing its rotor speed, which can be utilized during system contingencies. In literature, linear and quadratic deloading techniques have been proposed but these fail to replicate the nonlinear characteristics of the WTG accurately, thereby making deloading ineffective. To effectively implement the deloading, this work uses a more-accurate higher-order Newton's interpolation polynomial (HNIP), to cope with the highly nonlinear characteristics of WTG. The proposed deloading approach is also augmented with a fuzzy-based intelligent supplementary control structure to handle the inherent and incorporated nonlinearities in WTG. The microgrid system, consisting of a conventional energy source with WTG, has been considered as system under investigation. The integral time absolute error for step wind profile and variable speed wind profile was found to be improved by 97.65% and 97.29%, respectively, with the proposed novel deloading technique with fuzzy-PID compared to PID. Further, to ensure the implementation viability of the proposed control scheme, real-time validation of the same is carried out on OPAL-RT 4510, having a Xilinx Kintex-7 FPGA board. It was found that for all the scenarios considered for real-time digital simulation purposes, the results unerringly matched with MATLAB/Simulink. [Display omitted] • Design and implementation of novel nonlinear deloading strategy for WTG. • Implementation of nonlinear fuzzy control structure as supplementary controller. • Real-time validation of proposed novel scheme on OPAL-RT 4510 for investigated MGS. [ABSTRACT FROM AUTHOR]

Recently, the three-phase induction motor (IM) has been widely used in AC motor drives and in industrial applications. The IM suffers from the accuracy of controlling the speed when it operates at different loads; this problem attracts the attention of many researchers in this field. This paper presents an Intelligent Hybrid Control System using ANFIS (Adaptive Neuro-Fuzzy Inference System)-Optimization for Scalar Control (SC) of an Induction Motor. In order to obtain optimum performance of the motor and to decrease the Total Harmonics Distortion (THD) of the motor current a Voltage Source Inverter (VSI) based on the Pulse Width Modulation technique (PWM) is used to drive the motor. To improve the speed response, accuracy and the motor's performance, an improved hybrid control system involves an optimization control method in addition to an Adaptive Neuro-Fuzzy Inference System used to adjust the amplitude and the Modulation Index (MI) of the reference signal. The proposed hybrid system improves the transient stability of motor speed and reaches a steady state much faster than the traditional controller. The Matlab-Simulink results proved the remarkable effectiveness of the proposed controller when comparing the results with two other controllers, the usual PI controller and the optimization controller. [ABSTRACT FROM AUTHOR]

This research presents a solution that combines deep learning-based image processing, blockchain technology, and the Internet of Things (IoT) to achieve smarter control and traceability in greenhouse operations within the agricultural sector. By integrating these technologies, the aim is to overcome challenges posed by climate change, plant growth, limited agricultural land, and water scarcity, while enhancing crop yields and ensuring efficient and secure operations. The proposed system automates image capture, measurement, storage, and monitoring of environmental parameters in greenhouses, utilizing highly accurate image processing techniques with a 98% success rate. The integration of blockchain technology establishes an immutable and transparent record of transactions and data points, thereby improving traceability across the agricultural supply chain. This comprehensive approach fosters accountability, transparency, and trust, empowering consumers to make well-informed decisions regarding the products they purchase. Ultimately, this research contributes to advancing efficient and sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

This work studies the H ∞ performance of T-S fuzzy networked control systems under denial-of-service (DoS) attacks. First, the weight coefficient is introduced to measure the damage to the control signal caused by DoS attacks. Then, novel looped Lyapunov-Krasovskii functions are constructed based on the fuzzy membership function, and the nonlinear problem of the system is considered under the premise of reducing the initial condition constraints. Next, the issue of finding the suitable sampling period is transformed into an optimization problem of finding the optimal sampling period to reduce sampling times. An intelligent sampling controller is designed to ensure the asymptotic stability of the system. Finally, the effectiveness of the proposed method is verified with a truck trailer model. [ABSTRACT FROM AUTHOR]

In this paper, an iterative neural network adaptive robust control (INNARC) strategy is proposed for the maglev planar motor (MLPM) to achieve good tracking performance and uncertainty compensation. The INNARC scheme consists of adaptive robust control (ARC) term and iterative neural network (INN) compensator in a parallel structure. The ARC term founded on the system model realizes the parametric adaptation and promises the closed-loop stability. The INN compensator based on the radial basis function (RBF) neural network is employed to handle the uncertainties resulted from the unmodeled non-linear dynamics in the MLPM. Additionally, the iterative learning update laws are introduced to tune the network parameters and weights of the INN compensator simultaneously, so the approximation accuracy is improved along the system repetition. The stability of the INNARC method is proved via the Lyapunov theory, and the experiments are conducted on an home-made MLPM. The results consistently demonstrate that the INNARC strategy possesses the satisfactory tracking performance and uncertainty compensation, and the proposed INNARC is an effective and systematic intelligent control method for MLPM. • A iterative neural network scheme with network parameters and weights tuned via iterative learning is proposed to improve the uncertainty approximation capability. • The proposed scheme integrating of model-based and data-based method improves the tracking performance of the maglev planar motor. • The stability of maglev planar motor regulated by the proposed iterative neural network adaptive robust control method is proved via Lyapunov theory. [ABSTRACT FROM AUTHOR]

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In this study, an intelligent PID (i-PID) controller is designed for position control of a nonlinear electro-hydraulic system with uncertain valve characteristics and supply pressure variations. The proposed controller uses estimation of ultra-local model of the system. To exhibit the capability of the proposed method, the controller parameters are optimized via the particle swarm optimization method through a nominal nonlinear model of the system. Then, the performance of the i-PID controller, parameters of which are optimized by using the nominal model, is examined under uncertainties caused by valve characteristics and supply pressure variations. Moreover, the friction between the piston and the hydraulic cylinder is also considered in experiments. A PID controller whose parameters are also optimized based on the same performance criteria, is used for comparison purposes with i-PID control both in simulations and experiments. Performance metrics of the controllers are examined and presented by employing two separate reference signals: Sine wave and ramp. The results show that the i-PID controller shows significantly better results than the classical PID controller in tracking the test signals under various supply pressures and valve modes. • Intelligent PID (i-PID) controller excels in industrial hydraulics. • It is easy to design and implementation. • i-PID control is implemented experimentally in an electro-hydraulic system. • Parameters of i-PID controller are optimized via particle swarm optimization. • The controller tested against uncertainties (supply pressure and valve variations). • i-PID outperforms optimized PID for sine and ramp tracking in diverse conditions. [ABSTRACT FROM AUTHOR]

Research background: With the development of science and technology and the intelligence of the construction industry, the traditional extensive construction method can no longer meet the social demand for safety, which requires the construction enterprises and construction units to constantly change the safety management mode. With the advancement of urbanization, intelligent management and control has become the future development direction of the construction industry, and BIM software has also become an important synchronization platform, which will continuously improve the promotion of the overall project and the management of construction safety. Research methods: This paper mainly uses literature survey, quantitative and qualitative analysis, empirical analysis and other methods, which can study the construction safety management and intelligent management and control as a whole. Through literature survey, this paper has obtained a large amount of literature, which can enrich theoretical analysis and comprehensively understand the current construction safety management mode and intelligent management and control mode. Through relevant theories, this paper can fully demonstrate the follow-up research and empirical analysis. Through quantitative and qualitative analysis method, this paper can use statistical analysis method to analyze construction safety accident data, which will analyze the internal relationship through relevant software. Therefore, the quantitative and qualitative analysis method provides important data support for the subsequent research, which can provide statistical methods for the analysis of building safety hazard sources. Through the empirical simulation method, this paper can analyze specific projects with real cases, which will truly simulate the intelligent monitoring system. Through the empirical simulation method, this paper can verify the scientific nature of the algorithm, and can also demonstrate the management and control mode of the intelligent monitoring system. Research conclusion: Through BIM and RFID, this paper builds an intelligent monitoring system, which also builds the information collection, processing and application stages of the overall framework, and realizes the intelligent management and control of the future construction safety. [ABSTRACT FROM AUTHOR]

The popularization of the Internet of Things has made our daily life more and more convenient. By using the Internet of Things, we can create an intelligent home control system that makes our rooms safer, more comfortable, and can meet our growing demand for items. This article discusses how to use the Internet of Things to improve the efficiency of smart homes, and focuses on how to use wireless sensor networks, embedded systems, and various technologies such as network communication, audio and video to achieve these goals. This can enable these intelligent home control systems to provide efficient services. This article also introduces how to use Internet of Things technology in the design process of home control systems to better handle wiring and other related issues. We will also explore the differences between traditional and smart homes. The results showed that four buyers rated the smart home system, with an average score of 4 before use indicating a lower score, and an average score of 9 after use indicating a higher score. [ABSTRACT FROM AUTHOR]

The introduction of several small and large-scale industries, malls, shopping complexes, and domestic applications has significantly increased energy consumption. The aim of the work is to simulate a technically viable and economically optimum hybrid power system for residential buildings. The proposed micro-grid model includes four power generators: solar power, wind power, Electricity Board (EB) source, and a Diesel Generator (DG) set, with solar and wind power performing as major sources and the EB supply and DG set serving as backup sources. The core issue in direct current to alternate current conversion is harmonics distortion, a five-stage multilevel inverter is employed with the assistance of an intelligent control system is simulated and the optimum system configuration is estimated to reduce harmonics and improve the power quality. The monthly demand for residential buildings is 13-15 Megawatts. So, almost 433 Kilo-Watts (KW) of electricity is required every day, and if it is used for 8 h per day, 50-60 KW of electricity is needed per hour. The overall micro-grid model's operation and performance are established using MATLAB/SIMULINK software, and simulation results are provided. The simulation results show that the developed system is both cost-effective and environment friendly resulting in yearly cost reductions. [ABSTRACT FROM AUTHOR]

In this paper, an electric vehicle (EV) charging station powered by a photovoltaic (PV) system has been created to charge EVs. Furthermore, the maximum available power from the PV system was extracted using a new algorithm called the simplified universal intelligent PID (SUIPID) controller, which has the advantages of simplicity in design and intelligence. The SUIPID controller was compared to the artificial neural networks (ANNs), the fuzzy logic controller (FLC) based on linear membership functions, and the FLC based on particle swarm optimization (PSO) to optimize its parameters under various conditions. The system simulation was first performed using MATLAB software, then an experimental set up was conducted to confirm the theoretical work. The proposed SUIPID responds 28.5%, 44.4%, and 61.5% faster than the PSO-FLC, ANN, and FLC, respectively, whereas the ITAE error was reduced by 27.3%, 52.9%, and 65.2%, respectively. Also, the charging procedure of the EV battery is precisely steady under different atmospheric conditions. The SUIPID controller has several advantages over other intelligent algorithms, most notably its ease of design and implementation. • A new controller (SUIPID) was proposed to extract the MPPT from a PV system. • The dynamic response of the system was improved by 28.5%, 44.4%, and 61.5% compared to the PSO-FLC, ANN, and FLC. • A better tracking of the system maximum power point was recorded. • The proposed controller is much easier in design and implementation compared to other intelligent algorithms. • Experimental results confirm the superiorities of proposed algorithm. [ABSTRACT FROM AUTHOR]

The design of Reconfigurable Control Systems (RCS) is a tremendous task since that their increasing need for reconfiguration imposes the anticipation of all potential runtime changes at design time, which is in most cases infeasible. To deal with the high dynamicity of RCS at design-time we propose a novel design approach, RLReC (R einforcement L earning-based Re configuration C ontrol) allowing for the improvement of reconfiguration knowledge through online (runtime) exploration. This approach combines the benefits of meta-modelling with Reinforcement Learning (RL) principals for reconfiguration knowledge modelling in order to handle early and effective exploration of various design choices. In this work, the reconfiguration controller is an autonomous software agent designed as a RL agent (RLRA) that handles both offline (exploitation) and online (exploration) learning through interactions with the controlled system at runtime. In particular, the RLRA has to select reconfiguration rules that were not selected before, which is known as exploration. The main aim of this proposal is to cope with design-time unplanned changes by giving the RLRA the ability of improving its knowledge through online learning feedback. We illustrate the use of our contributions with a running example from the manufacturing domain. [ABSTRACT FROM AUTHOR]

As smart grids advance rapidly, they are evolving along two primary trajectories: (1) digitalization through the incorporation of Internet of Things (IoT) technology and intelligent control, and (2) decentralization by leveraging small-scale distributed energy sources for control. However, these developments also introduce complexities in the functioning, management, and control of smart grids. Consequently, there is an urgent need for a transparent, secure, robust, transactive, and scalable framework for all stakeholders and operators. Blockchain technology emerges as a promising solution for this new smart grid paradigm, providing a range of features, including a distributed ledger, immutability, consensus, security, and automation. This article highlights the challenges in smart grid management, control, and operations, and discusses the potential of blockchain-based solutions to address these issues. It demonstrates that the adoption of blockchain can significantly enhance the overall functioning of the infrastructure by establishing a decentralized architecture without central governance. [ABSTRACT FROM AUTHOR]

Digital twin technology has gained significant attention in recent years as a promising approach for improving the performance and efficiency of various systems including vehicular networks. Vehicular networks are critical for intelligent transportation systems, providing communication and coordination among vehicles, roadside infrastructure, and other entities to enable efficient traffic management, enhanced safety, and improved driving experiences. SDN is a potential networking architecture that isolates the control plane from the data plane, enabling centralized administration and control of network resources. SDN provides programmability, flexibility, and scalability, making it well-suited for managing the complex and dynamic nature of vehicular networks. Combining digital twin technology with SDN can enable intelligent management and control of vehicular networks, leading to improved performance, enhanced reliability, and efficient resource utilization. In this paper, a novel framework is proposed that leverages digital twin technology in vehicular networks using SDN. The architecture presented integrates digital twin models with SDN controllers, enabling real-time monitoring, analysis, and control of vehicular network components. The digital twin models are used to represent virtual replicas of physical vehicular components, such as vehicles, roadside units, and traffic signals, providing a holistic view of the vehicular network's behavior and performance. The digital twin models are discussed and can be used for various vehicular network management tasks, including traffic flow optimization, congestion detection and mitigation, predictive maintenance, and incident management. [ABSTRACT FROM AUTHOR]

Intelligent control using interval type-2 fuzzy systems and cerebellar model articulation networks (CMAN) have been extensively studied. Although they perform well with nonlinear problems, they often have two significant issues: the choice of the learning rate and the design of adaptive laws to update the network parameters. To address these drawbacks, we proposed an improved gray wolf optimizer (IGWO) that optimizes the learning rate of type-2 fuzzy CMAN. We also designed an adaptation law to adjust the proposed network parameters online. Additionally, a three-dimensional Gaussian membership function (3DGMF) was developed to handle external disturbances and system uncertainties. Finally, numerical simulation results on micro-electro-mechanical system (MEMS) motion control were provided to validate the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent control scheme for a two-stage integrated onboard electric vehicle (EV) battery charger connected to a single-phase household outlet which offers a close to ideal battery charging profile with power factor correction feature. Generally, the front-end AC–DC​ conversion stage is controlled by dual loop proportional–integral (PI) controllers, and tuning their gain constants is a difficult task. Furthermore, to achieve a close to ideal charging profile for an EV battery, the DC–DC conversion stage switches from constant current (CC) and constant voltage (CV) mode after a certain state of charge (SOC) which may lead to discontinuity in the charging current and voltage. This paper attempts to solve these issues by proposing an intelligent control scheme that includes the dynamic estimation of PI controller gain constants as well as provides a seamless mode transfer feature for battery charging. It is achieved by using fuzzy-PI-based control in the AC–DC conversion stage and Bayesian Regularization (BR) algorithm trained artificial neural network (ANN)-based control in the DC–DC conversion stage. The performance of the proposed control scheme is assessed both in steady-state and transient conditions in MATLAB® Simulink environment by comparing it against similar control schemes. The proposed intelligent control approach improves the dynamic response of DC link voltage, offers unity power factor operation and maintains the line current harmonics within IEEE 519 standards even during the switchover from CC to CV charging mode. Also, there is a decrease of 85% in the third harmonic component of the source current, 23.2% improvement in DC link voltage undershoot and 6.5% reduction in DC link voltage overshoot with reduced settling times using the proposed unified control scheme. [Display omitted] • An intelligent control scheme is proposed for a single-phase two stage integrated on-board charger. • Adaptive fuzzy PI-based control improved line power quality. • Artificial neural networks are trained for seamless transition from CC to CV mode. • Battery charging current and voltage profile match with ideal CC/ CV charging. • The proposed control approach maintains setpoint DC link voltage and the line current complies international standards during mode transition. [ABSTRACT FROM AUTHOR]

This study aims to address the challenge of low-cost hardware implementation of a combined backstepping with fractional order PID (FOPID) controller for mobile robots in real-time applications. Moreover, this work proposes a self-designed mobile robot prototype that is easy to realize, low in cost, spares time, and reduces human effort. This robot platform was equipped with two DC motors with quadratic encoders and two passive wheels, controlled by an Arduino mega, where the software code was developed in the Matlab-Simulink environment, using Simulink support package for Arduino. Four case studies were conducted to demonstrate the effectiveness of the suggested methodology. Experimental results demonstrate improved trajectory tracking performance with less tracking error and smooth control efforts, and is capable of handling trajectories with continuous and non-continuous gradients. [ABSTRACT FROM AUTHOR]

A comparative study of two mixes of data-driven algorithms is proposed in this paper. The discrete-time second-order model-free control known as an intelligent Proportional (iP) controller is tuned considering Virtual Reference Feedback Tuning (VRFT) and the overall performance of the resulting algorithm iP-VRFT is compared with that of iP controller that is tuned considering Fictitious Reference Feedback Tuning (FRIT), an algorithm that is further referred as iP-FRIT. The comparison is carried out as far as the control systems with these two control algorithms are concerned. The purpose of the comparison study is to determine if VRFT and FRIT optimally determine the iP parameters such that both control system structures perform in the same manner. Both algorithm mixes are experimentally validated on the Tower Crane System equipment. [ABSTRACT FROM AUTHOR]