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At DLR neural networks, as potential future controller for rocket engines, are studied. A neural network-based chamber pressure controller for a simplified cold gas thruster is presented and analyzed in simulation and experiment. The goal of the controller is twofold: It can track a trajectory with different changes of setpoints and it allows to set and control a wide variety of steady state chamber pressures. The neural network gets feeding line pressure measurement data as input and calculates valve positions as output values. The training phase of the controller is done with a reinforcement learning algorithm in an EcosimPro/ESPSS simulation, that is validated with data from the corresponding experimental set up. To increase the robustness and to allow a transfer from the simulation directly to the test facility domain randomization is applied. The controller is evaluated in simulations and experiment. It was found that – in the range of physically possible operation points – the controller achieves a constantly high reward which corresponds to a low error and a good control performance. In the simulation the controller was able to adjust all required set points with a steady state error of less than 0.1 bar while retaining a small overshoot and an optimal settling time. It is found that the controller is also able to regulate all desired set points in the real experiment. A reference trajectory with different steps, linear and sinus changes in target pressure is tested in simulation and experiment. The controller was in both cases able to successfully follow the given trajectory. • Simulation trained, reinforcement learning based thrust chamber pressure controller. • Experimental and simulative evaluation of control policy. • Steady state error less than 0.1 bar. • Controller follows trajectories successfully. [ABSTRACT FROM AUTHOR]

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

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

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

In this study, artificial neural networks (NN) are applied to the design of a transtibial prosthesis to adapt triplanar and bidirectional movements of human locomotion for rehabilitation. NN-based control is used because the prosthesis system is highly nonlinear and has variables with too many uncertainties caused by variations in ankle movements, weight damping, dorsiflexion, and flexion in the amputation area due to biological stimuli. To identify and detect these movements in the transtibial prosthesis, myoelectric signals are used that determine its position and adapt its trajectory through linear and rotary actuators. The input and desired parameters for the NN controller and the backpropagation algorithm are obtained based on the movements of the human ankle and foot based on their trajectory. The prototype is manufactured from different types of plastic using a 3D grapher, which can perform the main stages of human locomotion due to the learning carried out by the NN, reducing the risk of falls, and having a more comfortable and natural gait cycle in the rehabilitation of people. From the output response obtained from the NN controller, only a time delay is obtained without overshoot terms, and the trajectory tracking is adjusted. Simulation and experimental results show that the proposed NN-based control system can ensure the stability of the system and maintain good tracking of human locomotion. [ABSTRACT FROM AUTHOR]

This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system's adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT. [ABSTRACT FROM AUTHOR]

Intelligent control systems can effectively assist in the construction and management of laboratory animal facilities, improving operational efficiency, ensuring the reliability of animal experimental results, and significantly saving human resources. The intelligent control system for laboratory animal facilities at Shenzhen Institute for Drug Control was completed in April 2021. It includes an intelligent management platform and an information management system for animal laboratories. The intelligent management platform regulates room environment parameters such as temperature, humidity, and pressure through building equipment management system, controlling devices such as the Venturi valve, electric air valve, electric water valve, and steam humidification valve. At the same time, various environmental parameters are monitored online through the environmental monitoring system. The laboratory’s intelligence is further enhanced by systems such as automatic lighting control, full HD video monitoring, automatic access control and door system, independent ventilation and feeding, automatic cleaning, automatic exhaust gas treatment, centralized gas supply, and real-time instrument parameter monitoring. The information management system for animal laboratories integrates inspection, instrument and equipment, personnel, documents, standard substances, reagents, inspection standards, books, records, scientific research management, relevant applications, quality management, and query statistics. For animal experimentation, a management module has been developed to achieve a comprehensive digitization of animal management. Furthermore, real-time collection and recording of data such as balance calibration, sample quality, and animal weight are facilitated through electronic experimental recording. In summary, the Animal Laboratory of Shenzhen Institute for Drug Control has extensively utilized intelligent systems to achieve real-time online control and monitoring, improve efficiency, ensure high-quality facility operation, and meet standard requirements. Smooth execution of all inspection and research activities has been achieved over the past three years. This paper provides insights into the construction, management, and operation of laboratory animal facilities at Shenzhen Institute for Drug Control, offering guidance for the implementation of intelligent control in similar facilities across China. [ABSTRACT FROM AUTHOR]

In this study, the correspondence between froth state and flotation reagent dosage was investigated by quantitatively analyzing the froth state in the flotation operation process using online detection equipment such as froth image analyzer and level gauge. The relationship between froth state and reagent control was established. Based on the quantified froth characteristic variables, guidance on reagent dosages was provided, laying a foundation for intelligent control of flotation reagents in the future. Through extensive on-site observations and data analysis, the rationality of the theoretical research was verified, which is of significant importance for improving the efficiency and stability of the flotation operation process. [ABSTRACT FROM AUTHOR]

In view of the characteristics of a domestic concentrator s nickel flotation process, including unstable ore quality, large fluctuation of concentrate grade and unsatisfactory recovery rate. Combined with the current situation of incomplete testing equipment) long testing cycles, and high costs in the flotation production site; an intelligent control system for flotation process based on grade prediction model was designed. After the system was put into operation the stability of foam flow rate was significantly improved and the fluctuation of concentrate grade was significantly reduced, which proved the practicability of the system. [ABSTRACT FROM AUTHOR]

Using electroencephalography (EEG) signals to drive a vehicle could help disabled people expand their range of motion and improve their independence. A brain-controlled vehicle (BCV) is a vehicle that is commanded by analyzing EEG signals. However, the analysis and transmission effect of EEG signals is not ideal, the driving performance of the BCV solely relying on EEG signals is relatively poor. In this paper, to solve this problem, we propose a dynamic shared control method based on adaptive network-based fuzzy inference system (ANFIS). First, an ANFIS intelligent controller is designed to automatically make decisions according to the state of the vehicle. Then, safety coefficient and intention coefficient are proposed to evaluate the safety and driving intention of the brain-controlled driver. Finally, a fuzzy controller with safety and intention coefficients as inputs and brain-controlled driver weights as outputs is designed. The controller is the embodiment of a human–machine interaction, which allows the driver to maintain maximum control authority over the BCV under safe conditions by dynamically balancing the control authority of the brain-controlled driver and the ANFIS controller on the BCV. To verify the effectiveness of the proposed method, a joint simulation platform of Carsim and Matlab is established, and several groups of comparative simulation experiments are carried out, through which, it is demonstrated that the proposed method can effectively avoid road deviation while well maintaining the control authority of the brain-controlled driver. [ABSTRACT FROM AUTHOR]

The drying and preheating process plays a crucial role in the induration of iron ore pellets. It is conducted on a closed, moving bed in both the straight grate and grate-kiln processes. The internal state of the pellet bed cannot be measured and the manipulated parameters are set according to the experience. An integrated optimal control model based on the numerical simulation and intelligent algorithm is proposed. In this model, the moisture content and temperature of pellets in each stage are predicted via the mathematical simulation, and the inlet gas temperature is optimally set and real-time controlled through the multi-objective optimisation and Takagi–Sugeno fuzzy control methods. The proposed model has been successfully applied to a large-scale pelletising plant. The closed-loop running results illustrate that the thermal state of the pellet bed becomes stable, and the pellet production is improved while the mixed gas consumption is reduced. [ABSTRACT FROM AUTHOR]

This paper proposes a non‐singular fixed‐time fault‐tolerant adaptive tracking control method for a kind of multi‐input and multi‐output nonlinear system, which is in form of non‐strict feedback and is subject to actuator faults. The major contribution, compared to other practical fixed‐time fault‐tolerant control methods, is that the fixed time can be calculated precisely. By applying fuzzy logic systems, the identification of unknown nonlinearities is achieved, and by the boundness of fuzzy basis functions, the algebraic loop problem is avoided. Based on the fixed‐time stability theory, the practical fixed‐time fault‐tolerant adaptive tracking control method is proposed with adaptive back‐stepping control technology, in which the control singular problem is overcome by applying the adding power integration method. By the Lyapunov function theory, the practical fixed‐time stability of the closed‐loop system is theoretically proved. And even in the case of unknown actuator faults, the tracking error of the controlled system for the given command signal can still converge to a small neighbourhood of zero within a fixed time, regardless of the system initial state. Finally, the proposed control method is successfully implemented on a multi‐motor control platform, proving its effectiveness, and multiple comparative simulations are carried out, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

With the rapid growth of logistics transportation in the framework of Industry 4.0, automated guided vehicle (AGV) technologies have developed speedily. These systems present two coupled control problems: the control of the longitudinal velocity, essential to ensure the application requirements such as throughput and tag time, and the trajectory tracking control, necessary to ensure the proper accuracy in loading and unloading manoeuvres. When the paths are very short or have abrupt changes, the kinematic constraints play a restrictive role, and the tracking control becomes more challenging. In this case, advanced control strategies such as those based on intelligent techniques, including machine learning (ML) can be useful. Hence, in this work, we present an intelligent hybrid control scheme that combines reinforcement learning‐based control (RLC) with conventional PI regulators to face both control problems simultaneously. On the one hand, PIs are used to control the speed of each wheel. On the other hand, the input reference of these regulators is calculated by the RLC in order to reduce the guiding error of the path tracking and to maintain the longitudinal speed. The latter is compared with a PID path following controller. The PID regulators have been tuned by genetic algorithms. The RLC allows the vehicle to learn how to improve the trajectory tracking in an adaptive way and thus, the AGV can face disturbances or unknown physical system parameters that may change due to friction and degradation of AGV mechanical components. Extensive simulation experiments of the proposed intelligent control strategy on a hybrid tricycle and differential AGV model, that considers the kinematics and the dynamics of the vehicle, prove the efficiency of the approach when following different demanding trajectories. The performance of the RL tracking controller in comparison with the optimized PID gives errors around 70% smaller, and the average maximum error is also 48% lower. [ABSTRACT FROM AUTHOR]

Servo motors play an important role in automation equipment and have been used in several manufacturing fields. However, the commonly used control methods need their parameters to be set manually, which is rather difficult, and this means that these methods generally cannot adapt to changes in operation conditions. Therefore, in this study, we propose an intelligent control method for a servo motor based on reinforcement learning and that can train an agent to produce a duty cycle according to the servo error between the current state and the target speed or torque. The proposed method can adjust its control strategy online to reduce the servo error caused by a change in operation conditions. We verify its performance on three different servo motors and control tasks. The experimental results show that the proposed method can achieve smaller servo errors than others in most cases. [ABSTRACT FROM AUTHOR]

In this paper, we formulate high-type intelligent control as a multi-objective problem and apply evolutionary algorithms to search for optimal solutions. Specifically, we consider the metrics of the system in both the frequency domain and the time domain. Integrated time and absolute error is used as a performance metric in the time domain, while bandwidth is used as a measure in the frequency domain. Simultaneously, the amplitude margin and phase margin are used as constraints to ensure the stability of the high-type control system. Then, we adopt evolutionary algorithms to solve the formulated multi-objective problem. Unlike most of the existing approaches, we formulate intelligent high type control as a multi-objective optimization problem based on our knowledge about the control system. Furthermore, evolutionary algorithms are adopted to search for optimal solutions to real-world controlling systems. Extensive experiments are conducted to evaluate the effectiveness of our proposed approach. Compared to the Z-N method and the extending symmetrical optimum criterion, our proposed method achieves an improvement in bandwidth of more than 126.6%, while reducing the overshoot by more than 56.8% and the settling time by more than 48.4% for all controlled objects used in the experiments. At the same time, the tracking errors of the ramp and parabolic signals are significantly reduced, which means this method effectively improves the system performance. [ABSTRACT FROM AUTHOR]

This study is to determine how a lightweight active soft waist exoskeleton (ASWE) reduces the oxygen consumption and activity of lower back muscles of the wearer performing the repetitive lifting tasks. The heavy and frequent manual lifting operations are usually associated with an increased risk of injury in the industry. An ASWE is designed to assist workers' spine for lifting weights. The structural composition and operation principle were described for the ASWE. Twelve men were recruited in the experiments as the test subjects. Oxygen consumption and electromyography of the thoracic erector spinae (TES) at the T9 level and lumbar erector spinae (LES) at the L3 level were recorded during 90 lifts in 15 min. Subjects' discomfort and effectiveness evaluation were collected after lifting trials. The average value of oxygen consumption was decreased from form 15.9 ml/kg/min (Without-ASWE condition) to 13.7 ml/kg/min (With-ASWE condition). The increase in electromyography root mean square amplitude from the start until the end of the lifting trial was significantly lower when the ASWE was in use for the TES (162.79 vs. 82.08%) and the LES (122.48 vs. 83.87%). The use of the ASWE showed less oxygen consumption and back muscle contraction compared to the nonuse, which might reduce metabolic consumption or slow down the muscle fatigue level of the wearer's back across the lifting trial. Therefore, wearing the ASWE can reduce the discomfort of body parts, lumbar regions that exercise for a long time. [ABSTRACT FROM AUTHOR]

In recent years, significant progress has been made in the research and development of conventional hydrogel sensors. However, the development of hydrogels with special structures, achieved through specific designs or fabrication strategies, remains relatively scarce. Inspired by aloe, a specially structured hydrogel (named Skin‐Polyvinyl alcohol‐Polyaniline‐AgNWs, S‐PPA) is successfully prepared with skin. Innovatively utilizing the hydrogen bonding interaction between ions and water molecules, the surface of the hydrogel is treated to create a protective skin. The S‐PPA hydrogel with a protective skin demonstrates strong resistance to damage (with a tensile strength of 5 MPa, >11 times higher compared to hydrogel without skin) and exhibits dual conductivity (0.8 S m−1 for the inner skin and 0.33 S m−1 for the outer skin). In addition, the S‐PPA hydrogel also possesses water retention capabilities, antibacterial properties (89.4% inhibition rate against Staphylococcus aureus (S. aureus)), and minimal corrosion to metal electrodes. Based on the S‐PPA hydrogel and combined with wireless Bluetooth technology and Python programming, intelligent applications are developed such as multi‐gradient intelligent control and finger muscle condition evaluation, achieving real‐time human‐ computer interaction (HCI). The intelligent pressure‐sensitive hydrogel proposed by this study shows great potential in the fields of medical rehabilitation, artificial intelligence, and the Internet of Things. [ABSTRACT FROM AUTHOR]

An important action-planning problem is considered for participants of the pursuit–evasion game with multiple pursuers and a high-speed evader. The objects of study are mobile robotic systems and specifically small unmanned aerial vehicles (UAVs). The problem is complicated by the presence of significant wind loads that affect the trajectory and motion strategies of the players. It is assumed that UAVs have limited computing resources, which involves the use of computationally fast and real-time heuristic approaches. A novel and rapidly developing intelligent–geometric theory is applied to address the discussed problem. To accurately calculate the points of the participant's rapprochement, we use a geometric approach based on the construction of circles or spheres of Apollonius. Intelligent control methods are applied to synthesize complex motion strategies of participants. A method for quickly predicting the evader's trajectory is proposed based on a two-layer neural network containing a new activation function of the "s-parabola" type. We consider a special backpropagation training scheme for the model under study. A simulation scheme has been developed and tested, which includes mathematical models of dynamic objects and wind loads. The conducted simulations on pursuit–evasion games in close to real conditions showed the prospects and expediency of the presented approach. [ABSTRACT FROM AUTHOR]

The ageing of coal-fired power stations in South Africa has led to regular power outages. Therefore, the country will need to urgently increase its electricity capacity to meet further energy demand from growing urbanization and population growth. This challenge has increased interest in alternative energy sources, such as renewable energy (RE). South Africa is gradually implementing appropriate renewable energy practices, reducing carbon emissions, cleansing the air, and assuring a more sustainable future. This paper summarizes the availability, current state, and future potential of renewable energy choices in South Africa. This paper also evaluates specific policy measures and government actions aimed at eliminating barriers and increasing renewable energy deployment in the future. It also considers the South African network's specialized techno-economic analysis. The findings show that South Africa is still heavily reliant on coal, with 78% of the current installed capacity coming from coal power in 2022, compared to 9.3% for solar and wind energy. [ABSTRACT FROM AUTHOR]

Sustainable freight transportation is one of the essential concepts in the smart city. Under this concept, many people connected with mobile devices produce location data and potential opportunities for transporting small objects in a more environmentally friendly and sustainable way. Crowdshipping, which utilises public people as transportation, is one of the terminal solutions in the last mile delivery scenario. Nevertheless, precisely assigning the delivery to the right crowd willing to accept the job is challenging because the solution space is too large to perform a full search. This article proposes a trajectory feature extraction algorithm and a task‐to‐crowd matching (T2CM) algorithm for coping with the job‐to‐crowd assignment problem. A simulation based on the real‐world dataset is conducted on three different scenarios to justify the outcome from our proposed method to the job assignment results. [ABSTRACT FROM AUTHOR]

The rapid adoption of electric vehicles (EVs) has driven the industry to develop advanced traction systems for improved performance and dependability. Such criteria are determined mainly by the magnetic materials used in brushless DC or AC motor commonly used in EV traction systems. More complex motor designs, such as those with two rotors or two stators, have been influenced by improvements in traction systems for transportation tools. The electric motor with multiple rotors or stators has a complex system and lies on parameters that change over time with complex dynamics. Consequently, this motor type needs an intelligent controller to enhance its efficiency and robustness under various drive conditions. Therefore, this article aims to resolve some control complexity for one of these recently used motors. The goal of this is to maximize the efficiency while also increasing motor torque. This motor design is based on a double-stator single-rotor (DSSR) form. The global traction chain is described in this paper by showing the specifications of each part as the interior motor architecture and a suitable AC/DC converter. Examining the best control tool for managing this machine is based on the control topology by using on the field-oriented control (FOC) strategy. A comparison of the effects of three distinct speed controllers, including fuzzy logic, neural networks, and traditional PI controllers, was used to support the FOC architecture. The global traction system architecture and its corresponding control scheme were established on the MATLAB/Simulink platform, and all results were depicted to compare the diverse architectures. At the end of this study, the given results and their corresponding discussions show each control solution's benefits and drawbacks and prove the importance of choosing the appropriate control system for the DSSR motor on the battery state of charge and then on the vehicle autonomy. [ABSTRACT FROM AUTHOR]

With the increase in the proportion of new energy power generation in grids, there is an urgent need for the flexible operation of coal‐fired power plants. However, as the most efficient commercial coal‐fired power generation technology, the flexibility of double‐reheat ultra‐supercritical units is limited due to the steam temperature fluctuation in transient processes. To inhibit the steam temperature fluctuations, a data‐driven method, called long short‐term memory (LSTM) was introduced to model the dynamics of the steam temperatures of a 1000‐MW double‐reheat coal‐fired boiler. Moreover, an advanced controller was proposed by combining the LSTM‐based dynamic model and the model predictive control architecture. The results demonstrate that the LSTM model can be used to capture the time delay, multivariable coupling and non‐linear dynamics of the steam temperatures and the proposed controller exhibits excellent performance in steam temperature regulation. The superheated steam temperature fluctuation decreases from 15.4 to 9.8°C, the primary reheat temperature fluctuation decreases from 34.6 to 8.3°C, and the secondary reheat temperature fluctuation decreases from 41.4 to 8.8°C. The proposed method has good potential to stabilize steam temperatures during transient processes of wide‐range load cycling, and finally improves the flexibility of the double‐reheat ultra‐supercritical unit. [ABSTRACT FROM AUTHOR]

Light is one of the most important influencing factors on plant growth. Light environments have also a significant impact on plant growth and development. In the absence of natural light, crops in many areas can suffer from long harvest cycles, low yields and poor quality. Fortunately, light-supplementation devices have been invented to supplement the light for the crops. Among them, the light-emitting diodes (LEDs) have been the main light source for the plant-filling light. But the conventional LEDs can not fully meet large-scale production in recent years, due to the limited lighting range, low efficiency of electricity-light exchange, high cost of line installation, and limited usage environment of light filler for plants. It is particularly important to develop a new light source for plant filling. Compared with the commonly used light sources, laser can be expected to the strong directionality, excellent monochromaticity, strong coherence, and high brightness. Moreover, lasers have been widely used in various scientific and technological fields in the past few decades, such as medicine, material processing, and communications. The lasers can also be used to improve the germination rate and the quality of seeds for the better growth and development of plants, even to delay the decay of plant fruits. In this study, the laser diode (LD) was selected as the light source of the fill light device, in order to design a plant light filler with the intelligent laser. The light source module was used as 35 LDs with wavelengths of 450 nm (blue light) and 660 nm (red light). A heat sink was also designed with the aluminum alloy, in order to ensure the output power and wavelength. The reason was that the LDs generated high heat when working for a long time. The laser driver circuit achieved the adjustable LD drive current, that is, the output power of LD was adjustable. The light intensities of red and blue lights were adjusted to set the light-matching ratio. The laser plant light filler was tested using an adjustable direct current (DC) regulator with a voltage of 24 V, a current of 1.6 A, and a power of 38 W. The photosynthetic photon flux density (PPFD) of the laser plant light filler was measured using a sensor for a total of 16 tests, with an average value of about 55 μmol/(m²·s). An experimental and the control group were set with the "Red Face strawberry" as the subject of the supplemental light test. The control group received the natural light, while the experimental group received the laser supplemental light of 80 cm above the strawberries for 4 hours from 06:00 pm to 10:00 pm every day until the strawberry fruits ripened. Twelve strawberries were randomly selected from the experimental and control groups and then examined for the leaf thickness, chlorophyll content, fruit firmness and sugar content. The results show that the leaf thickness and chlorophyll content of the plant increased by 6.6% and 10.5%, respectively; and the hardness and sugar content were significantly improved by 20.2% and 15.5%, respectively, compared with the natural light. The light-supplementation device can be expected to promote the growth, development and quality of fruits, particularly on the precise adjustment and uniform light output. The compact structure and fine waterproof properties can greatly contribute to the field of plant light in agricultural modernization. [ABSTRACT FROM AUTHOR]

Intelligent greenhouse can promote the development of modern agriculture, realize the high quality and high yield of crops, and also bring greater economic benefits. In accordance with the climate conditions in northwest China, a set of intelligent control system for diversified environment of solar greenhouse was designed. The system divides the annual greenhouse control into six stages according to the optimal energy saving. It uses modern detection technology to collect the greenhouse environmental temperature, environmental humidity, soil humidity, C02 concentration and illumination parameters under different working modes. It uses programmable logic control technology to realize the data processing of various parameters and the action control of rolling film, wet curtain fan and other actuators. It uses KingView monitoring software to realize the monitoring and manual control of greenhouse environment parameters. The operation results indicate that the control system runs stably and basically meets the control requirements. [ABSTRACT FROM AUTHOR]

In aerospace load simulation loading systems, due to the coupling between the loading system and the system under testing, surplus forces and poor loading accuracies remain to be key issues. To solve this, this paper introduces a new intelligent dynamic force loading algorithm. First, based on the mechanical geometry and fluid theory of the system, a nonlinear mathematical model that can describe the system more accurately is established. By combining the mechanical structure and mathematical model of the system, it is found that the surplus force caused by external disturbances has the greatest impact on the force loading accuracy. The causes of the surplus force are then analyzed and the solutions provided. To improve the force loading performance and eliminate excess force on the controller, an intelligent dynamic force loading algorithm is proposed. This new control algorithm ensures that the surplus force caused by an external disturbance can be suppressed to within the required range in a short time as well as improve the controller performance and dynamic force loading accuracy of the system. Finally, the algorithm is applied to an aerospace air rudder load simulator and its performance is compared with the three other classical control algorithms. The results show that the proposed intelligent control algorithm can attain better accuracy in dynamic force loadings and can attain a control accuracy that meets technical requirements. The load simulator and digital controller involved in this paper are applied in a Chinese Aerospace Research Institute. The load simulator is currently used for testing and product acceptance of the institute's servo products. Because the control algorithm proposed in this paper meets the requirements of technical indicators and product performance testing requirements, it has been recognized by the institute and has been put into practical use. The technical experts of the institute have verified the experimental results and believe that its control accuracy and loading performance are better than those of the original old load simulator. [ABSTRACT FROM AUTHOR]

• The current study presents a systematic approach to identify and suggest the most effective charging policies for building a smart EV charging station. • The study employed a three-step process: first, a critical analysis of smart charging policies from the literature is performed and corresponding control parameters are identified. • The final step recommends a set of parameters for a smart EV charging station controller, covering the basic design level, real-time operational level, and enhancement level. Installation of refueling facilities for electric vehicles (EVs) in the range of conventional fuel filling stations demands huge initial investments. It also necessitates the expansion of power grid infrastructure. Furthermore, the problem is critical in remote places and resource-limited environments. One alternative solution for this evaluative problem is to incorporate EV smart charging. Smart charging circumscribes load fluctuations on the grid while charging EVs and paves the way for renewable energy use in recharging EVs. The current study presents a systematic approach to identify and suggest the most effective charging policies for building a smart EV charging station. The study employed a three-step process. First, a critical analysis of smart charging policies from the literature is performed and corresponding control parameters are identified. Secondly, the identified parameters are classified into categories of direct influence, indirect influence, and enhancement parameters. The final step recommends a set of parameters for a smart EV charging station controller, covering the basic design level, real-time operational level, and enhancement level. While numerous studies have explored various EV charging policies, control parameters, and optimization techniques, there is still a lack of procedural approaches for identifying the optimal control parameters specific to a given charging policy. This study aims to address this gap by taking a focused approach that centers on the adoption of a particular charging policy for the development of a smart charging station. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel controller architecture and design for the automatic control of agricultural mobile robots to be used in farms and greenhouses. There are two novelties of this study. The first novelty is a completely new type of controller architecture proposed in which reference inputs and measured outputs are fed separately independent from each other to the controller. The controller architecture currently used in the literature uses only the difference between reference and measurement which is the error signal. The proposed architecture in this study is completely novel in the sense that not only the error information is used in the controller but also the information in reference inputs and information in measured outputs are used separately. This means a completely new type of look to control system by utilizing the information maximally in order to achieve superior performance. This performance boost is shown in the paper where the proposed architecture achieves up to 2000% better performance compared with state-of-the-art controllers. Second, controller architecture is grown to a complex structure from an initially simple PID structure. Using the maximal information comes with the cost of computational complexity to design the controller. The second novelty of growing the controller from initially simple PID equivalent controller tackles this difficulty by making the problem tractable and efficient to compute. This way the proposed novel controller can be designed within minutes in a commercially available laptop computer. The proposed controller is tested on a simulated agricultural mobile robot and results are compared with a previous state-of-the-art optimal controller. It is believed that the proposed architecture will be dominant in future automatic controllers and make current state-of-the-art controllers obsolete. This is because of the full utilization of information in controller design which results in robust disturbance rejection performance. [ABSTRACT FROM AUTHOR]

Controlling the position of the final load and the anti-swing control of the loads during the operation of the tower crane are challenging tasks. These are the most important control issues for safe operation, which are difficult to achieve easily with conventional control systems. Hence, the need to integrate the concepts of soft-computing into the tower crane control system. The aim of this research work is to design an adaptive-network-based fuzzy inference system (ANFIS) controller to move the payload to the final position with the lowest possible swing angle. To evaluate the ability of the proposed controller to meet the control requirements, its performance was compared to three other controllers: a conventional proportional derivative (PD) controller, a fuzzy-tuned PD controller and a fuzzy controller. MATLAB-based computer simulations of the crane and controllers were carried out to verify and compare the performance of the proposed controllers. The obtained results show the effectiveness of the ANFIS-based controller in adjusting the load position while keeping the load fluctuations small at the final position. The load oscillation angle is about ±2.28° with the ANFIS controller while it is about ±10° when using the PD controller. In addition, only one ANFIS controller is used for both load position and swing angle control. [ABSTRACT FROM AUTHOR]

The control of unmanned aerial vehicle quadrotor is challenging because of non‐linearities, coupling and disturbance. Here, a novel control method which includes reinforcement learning (RL) component and robust component is proposed. In this method, the RL component only relies on collected data instead of modelling to handle coupling and disturbance from aerodynamics and model. To ensure safety during training and improve training speed, the robust component is used to reduce the disturbance.The stability of the system with our controller is proven by Lyapunov method. The results of the simulation exhibit advanced performance of our controller. [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptive dynamic programming (ADP) method to solve the optimal consensus problem for a class of discrete‐time multi‐agent systems with completely unknown dynamics. Different from the classical RL‐based optimal control algorithms based on one‐step temporal difference method, a multi‐step‐based (also call n‐step) policy gradient ADP (MS‐PGADP) algorithm, which have been proved to be more efficient owing to its faster propagation of the reward, is proposed to obtain the iterative control policies. Moreover, a novel Q‐function is defined, which estimates the performance of performing an action in the current state. Then, through the Lyapunov stability theorem and functional analysis, the proof of optimality of the performance index function is given and the stability of the error system is also proved. Furthermore, the actor‐critic neural networks are used to implement the proposed method. Inspired by deep Q network, the target network is also introduced to guarantee the stability of NNs in the process of training. Finally, two simulations are conducted to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

There are great challenges in the driving of human–machine shared control vehicles (HSCVs) in uncertain environments. Aiming at solving the collision avoidance problem of HSCV considering the uncertainty of surrounding vehicles (SVs), a human–machine cooperative control method for collision avoidance based on stochastic non‐cooperative game (SNCG) is proposed. This method realizes the safe driving of HSCV combined with the prediction of surrounding environment and constructs the interaction model between a driver and an automation system based on the game theory. Firstly, a vehicle–driver coupling model with driver's lateral and longitudinal operation characteristics is established. Next, utilizing the prediction equation, the prediction of the SV motion considering uncertainty is realized. Then, the safety constraints of HSCV are established by considering the chance constraints and the saturation characteristics of the actuators. Finally, based on the distributed stochastic model predictive control (DSMPC) method, the coupled optimization problem with chance constraints is constructed. To solve the coupled optimization problem with chance constraints, the idea of Nash equilibrium solution and the method of chance constraints tightening is adopted. The simulation results illustrate that the proposed method ensures the safe driving of HSCV under uncertain environments and realize the cooperative control between the automation system and the driver. [ABSTRACT FROM AUTHOR]

Energy efficiency is a global problem that has become a top priority for all developed countries in recent years. Limited energy resources and high pollution worldwide have led to an increase in research and development in terms of energy efficiency. In the present work, one of the energyefficient methods for heating by using infrared sources is focused. The advantages of infrared heaters compared to standard heating sources are underlined. A comparative study of the energy consumed during heating by the most frequently used by the heating systems has been made, and the results of the theoretically calculated power costs are presented. An intelligent system has been developed for the management of the infrared heaters to reduce the consumption of electricity better. The intelligent control system is based on a fuzzy-logic control and an internet of things. Results of simulation experiments of the intelligent system are presented as well as of the application of the system in a real environment. During the experiments, the system successes to control the heaters and to reduce power consumption. The achieved results prove that signal processing and control can improve the performance by using modern sensor systems, thereby additional efficiency can be achieved by using the infrared heating. It is possible to develop different optimization systems and algorithms for increasing the energy efficiency of different heating systems. [ABSTRACT FROM AUTHOR]

Intelligent greenhouse can promote the development of modern agriculture, realize the high quality and high yield of crops, and also bring greater economic benefits. In accordance with the climate conditions in northwest China, a set of intelligent control system for diversified environment of solar greenhouse was designed. The system divides the annual greenhouse control into six stages according to the optimal energy saving. It uses modern detection technology to collect the greenhouse environmental temperature, environmental humidity, soil humidity, C02 concentration and illumination parameters under different working modes. It uses programmable logic control technology to realize the data processing of various parameters and the action control of rolling film, wet curtain fan and other actuators. It uses KingView monitoring software to realize the monitoring and manual control of greenhouse environment parameters. The operation results indicate that the control system runs stably and basically meets the control requirements. [ABSTRACT FROM AUTHOR]

Interior permanent-magnet synchronous machines (IPMSMs) are widely used as traction motors in electric drive-trains because of their high torque-per-ampere characteristics and potential for wide field-weakening operations to expand the constant-power range. This paper offers a categorization and a comprehensive overview of the control techniques applied to IPMSM drives in addition to presenting the necessary theoretical background. The basic concept, features and limitations, as well as the latest developments of the strategies, are summarized in the paper. This overview helps to lay the theoretical basis as well as to clarify the opportunities, challenges and future trends for controlling IPMSM drives for traction applications. [ABSTRACT FROM AUTHOR]

In the fields of artificial intelligence and control engineering, generalized-Sylvester matrix equation is considered as an important mathematic problem, and its solving process is usually viewed as a challenge that deserves particular attention. In this paper, a creative discrete-form recurrent neural network (RNN) model is developed, analyzed and investigated for solving discrete-form time-variant generalized-Sylvester matrix equation (DF-TV-GSME), which is derived by a direct discretization numerical method. Specifically, first of all, DF-TV-GSME, which includes the well-known Lyapunov matrix equation and Sylvester matrix equation, is presented as the target problem of this research. Secondly, for solving such problem, different from the traditional discrete-form RNN design philosophy, second-order Taylor expansion is applied to derive the discrete-form RNN model. This creative process avoids involving the continuous time-variant environment and continuous-form model. Then, theoretical properties analyses are presented, which present the convergence and precision of the discrete-form RNN model. Abundant numerical experiments are further carried out with different perspectives of DF-TV-GSME, which further confirm the excellent properties of discrete-form RNN model. [ABSTRACT FROM AUTHOR]

Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope. In recent years, the application of reinforcement learning algorithms in the field of bionic underwater robots has gained considerable attention, and continues to grow. In this paper, we present a comprehensive survey of the accomplishments of reinforcement learning algorithms in the field of bionic underwater robots. Firstly, we classify existing reinforcement learning methods and introduce control tasks and decision making tasks based on the composition of bionic underwater robots. We further discuss the advantages and challenges of reinforcement learning for bionic robots in underwater environments. Secondly, we review the establishment of existing reinforcement learning algorithms for bionic underwater robots from different task perspectives. Thirdly, we explore the existing training and deployment solutions of reinforcement learning algorithms for bionic underwater robots, focusing on the challenges posed by complex underwater environments and underactuated bionic robots. Finally, the limitations and future development directions of reinforcement learning in the field of bionic underwater robots are discussed. This survey provides a foundation for exploring reinforcement learning control and decision making methods for bionic underwater robots, and provides insights for future research. [ABSTRACT FROM AUTHOR]

In this paper, the operation process of cleaning of intelligent rice–wheat combine harvester is divided into two key steps: initial setting of cleaning device operation parameters and dynamic control of cleaning device operation parameters. Combined with the operation experience of cleaning control of agricultural machinery operators, the dynamic control knowledge-based system of cleaning device operation parameters was built based on production rule reasoning. The cleaning device of the rice–wheat combine harvester is intelligently controlled based on the dynamic monitoring and control system of the cleaning device operation quality and operation parameters, so as to achieve the purpose of controlling the cleaning operation quality of the rice–wheat combine harvester in the normal range. Through the field experiment results and analysis, it is proved that the intelligent control system of the cleaning device operation parameters based on the dynamic control knowledge-based system of cleaning device operation parameters can effectively keep the cleaning impurity content and loss rate of intelligent rice–wheat combine harvester in the normal range, so as to verify the effectiveness of the intelligent control knowledge-based system of the cleaning device operation parameters. [ABSTRACT FROM AUTHOR]

The main task of the impurity removal process is to control the oxidation reduction potential (ORP) within the range of the optimized set value. The impurity removal process is essentially an oxidation-reduction process. Oxidation reduction potential (ORP) is an external reflection of reaction state inside the impurity removal reactor. However, actual industry is time-varying, nonlinear and multimode. It is difficult to determine the appropriate dosage of impurity remover in practice. This will lead to large fluctuations in the operation mode, affecting the safety and stability of the process and the final product quality. To solve these problems, an intelligent dynamic setting control framework (IDSCF) for the multimode impurity removal process is proposed in this paper. It includes a preset module of the dosage of impurity remover based on impurity remover utilization (IRU) estimation, an operation mode detection module based on autoencoder, a normal mode adjustment module based on fuzzy logic, and an unsteady mode adjustment module based on case-based reasoning (CBR). The framework can determine the reasonable preset dosage of impurity remover and adjust the dosage according to the current operation mode of the impurity removal process. Because the operation mode is related to the residual dosage of impurity remover added over a period of time, that is, the accumulative effect of the large-scale metallurgical reactor. When calculating the preset dosage of impurity remover, the reactant accumulation ratio (RAR) is calculated, which makes the calculation of the preset value more reasonable. In addition, it can detect the unsteady modes causing large fluctuations in the process and adjust them in time. Experiments are carried out in accordance with the data of an actual cobalt removal process. The results show that this method can effectively improve the stability of the impurity removal process, control the ORP within the set range and cope with complex mode changes. [ABSTRACT FROM AUTHOR]

In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can effectively suppress various types of interference such as base jitter, signal interference, time delay, etc., during the movement of the manipulator. The fuzzy neural network structure and self-organization method are used to realize the online self-organization of fuzzy rules based on control data. The stability of the closed-loop control systems are proved by Lyapunov stability theory. Simulations show that the algorithm is superior to a self-organizing fuzzy error compensation network and conventional sliding mode variable structure control methods in control performance. [ABSTRACT FROM AUTHOR]

Due to the difficulty in adopting the conventional centralized control method for seismic response control of high-rise buildings with complex structures and large sizes, the decentralized control method is applied. The challenge is that the control model of the high-rise structure divided into multiple subsystems changes as large nonlinear deformation under strong earthquakes. Therefore, with an application of the long short-term memory neural network, the long short-term memory intelligent decentralized control method is proposed for seismic response control of high-rise buildings. On the basis of the decentralized control theory for high-rise buildings, a long short-term memory network deep-learning framework is established to construct different types of decentralized controllers, and to determine the sufficient conditions for the stability of the decentralized controllers using the Lyapunov stability theory. The long short-term memory intelligent decentralized control system of a 20-story benchmark building mode is simulated, and its fault tolerance is studied. The simulation results show that the decentralized control method can reduce the complexity of the structure model by dividing the high-rise building structure into multiple subsystems. Compared with the centralized control method, the long short-term memory intelligent decentralized control method can effectively avoid the overall failure of the control system. The long short-term memory intelligent decentralized control method can still have a satisfactory performance under sensor noise and control devices failures. This verifies that the long short-term memory intelligent decentralized control system has a better fault tolerance and can provide an innovative solution for the decentralized control of high-rise buildings. [ABSTRACT FROM AUTHOR]

In this paper is presented the development of a terminal sliding mode attitude-position quaternion based control of a quadrotor unmanned aerial vehicle UAV. First, the dynamics of this UAV is divided into an attitude and position loop in order to design suitable independent control laws. The type of control law implemented for each loop is of the kind of hybrid terminal sliding mode and quaternion controller. The advantages of these control strategies over other techniques found in the literature are that the implementation of a quaternion based control strategy provides a relatively simple approach considering that the dynamics for the attitude and position loops can be described in this way. The quaternion based control strategy is suitable considering the kinematic and dynamic model of a quadrotor UAV, meanwhile the sliding mode controller part provides a relativelly simple control strategy taking into consideration that is reliable against uncertainties and provide a fast and accurate attitude-position tracking considering a reference to be followed by each loop. It is important to take into consideration that when an attitude command is given, the closed loop dynamics of the attitude block is stabilized generating at the same time the reference for the position loop. The terminal sliding mode controller part is obtained by selecting an appropriate sliding surface in order that the control law for each loop would be drove to the reaching phase faster and accurately. It is worthy to mention that both control loops posses feedforward neural networks as compensators in order that the tracking error will be reduced by both control strategies. Finally, a numerical experiment is conducted in order that the theoretical results are validated offering the appropriate discussion and conclusions about this research study. [ABSTRACT FROM AUTHOR]

This paper studies the intelligent sliding mode control for satellite dynamics based on neural learning and disturbance observer. We consider the dynamics uncertainty due to uncertain inertia matrix and the disturbance torque caused by orbit transfer engine. A neural network is used to approximate the unknown dynamics, while an adaptive observer is utilized to handle the unknown disturbance. The prediction error is constructed by introducing the estimation model to understand the uncertainties better. Based on the finite-time design, an efficient controller is designed to achieve the attitude tracking. The system stability is guaranteed under the proposed control design while the sliding mode converges in finite time. Better learning and control performances are presented by conducting the simulation studies. [ABSTRACT FROM AUTHOR]

The aim of the study was to study the intelligent management of electrical automation through Internet platform. This article takes the example of smart home to analyze the current state of smart home, explores relevant technologies for smart home applications, and offers solutions. The results are as follows: the ZigBee gateway control model, consisting of the ARM9 S3C2440 and the wireless radio frequency chip CC2530, developed the ZigBee wireless star network structure and introduced the ZigBee regulator and terminal node control flow. A four-state switch that may be operated manually and automatically, that utilizes IoT, and that ultimately is in the off-mode state was devised and used in the proposed control system. The suggested model is an extremely dependable system that offers a practical and satisfying solution for electrical management in the agricultural, industrial, and home consumption sectors. To show how well the suggested control system works, the model is applied to a real-world case study. The design controls the sending and receiving of SMS with a global system for mobile communication module, has a remote control for opening and closing of smart home equipment and real-time alarm, and reduces the response time by about 60%. It has made a new attempt for the development of smart home market. [ABSTRACT FROM AUTHOR]

This paper presents a novel fully decentralized and intelligent energy management system (EMS) for a smart microgrid based on reinforcement learning (RL) strategy. The purpose of the proposed EMS is to maximize the benefit of all microgrid entities comprising customers and distributed energy resources (DERs). Due to unpredictable features of renewable energy sources and variability of consumers' demands, designing the microgrid EMS is a complicated task. To overcome this issue, the multi‐agent hour‐ahead energy management problem is modelled as a finite Markov decision process. The microgrid entities are considered as intelligent agents. The optimal policy of agents is obtained through a newly developed framework of the model‐free Q‐learning algorithm to maximize the benefit of all renewable and non‐renewable energy resources and battery energy storage system. The degradation model of the battery is considered to reduce the number of battery replacements. To ensure customers' comfort, customers' expenses are decreased without demand curtailment via introducing two types of load shifting techniques. The microgrid operation is analysed under four scenarios comprising no‐learning, generator‐learning, customer‐learning, and whole‐learning. the performance of the proposed algorithm is compared to the Monte Carlo method and simulation results on the real power‐grid dataset show the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

Hydroponics refers to a modern set of agricultural techniques that do not require the use of natural soil for plant germination and development. These types of crops use artificial irrigation systems that, together with fuzzy control methods, allow plants to be provided with the exact amount of nutrients for optimal growth. The diffuse control begins with the sensorization of the agricultural variables that intervene in the hydroponic ecosystem, such as the environmental temperature, electrical conductivity of the nutrient solution and the temperature, humidity, and pH of the substrate. Based on this knowledge, these variables can be controlled to be within the ranges required for optimal plant growth, reducing the risk of a negative impact on the crop. This research takes, as a case study, the application of fuzzy control methods to hydroponic strawberry crops (Fragaria vesca). It is shown that, under this scheme, a greater foliage of the plants and a larger size of the fruits are obtained in comparison with natural cultivation systems in which irrigation and fertilization are carried out by default, without considering the alterations in the aforementioned variables. It is concluded that the combination of modern agricultural techniques such as hydroponics and diffuse control allow us to improve the quality of the crops and the optimization of the required resources. [ABSTRACT FROM AUTHOR]

The research was to further improve the automation level and packaging accuracy of feed crushing machinery. The study designed an intelligent control and online monitoring platform for feed crushing system based on LabVIEW and PLC. Regarding the structure and process flow of feed crushing machinery, the hardware and software design of control system were completed using PLC as core controller. LabVIEW was an upper computer that achieves centralized control and data monitoring. Propose a control mode that can adapt to system to improve packaging accuracy. The results showed that the intelligent control and online monitoring platform of feed crushing system could operate smoothly, improve the platform's data collection and on-site application capabilities, the control mode that improved packaging accuracy had a significant effect, with an accuracy controlled within 0.5%. The study indicates that the research can provide reference for the design of feed crushing machinery control system. [ABSTRACT FROM AUTHOR]

The motion synchronization of the hybrid actuation system (composed of a servo-hydraulic actuator and an electro-mechanical actuator) is very important for all applications, especially for civil aircraft. The current research presents a nested-loop control design technique to synchronize motion between two different actuators, such as a servo-hydraulic actuator (SHA) and an electro-mechanical actuator (EMA). The proposed strategy consists of a trajectory, an intelligent position controller (fuzzy logic-based controller), a feed-forward controller, and an intelligent force controller (fuzzy logic-based controller). Position, speed, and acceleration signals are produced by trajectory at a frequency that both SHA and EMA can follow. The SHA/EMA system's position tracking performance is enhanced by the feed-forward controller and intelligent position controller working together, while the intelligent force tracking controller lowers the issue of force fighting by focusing on the rigid coupling effect. To verify the effectiveness of the proposed strategy, simulations are performed in the Matlab/Simulink environment. The result shows that the proposed intelligent control strategy not only reduces initial force fighting, but also improves load-rejection performance and output-trajectory tracking performance. [ABSTRACT FROM AUTHOR]

As new advanced vehicles, electromagnetic suspension (EMS)-type maglev trains have received wide attention because of their advantages such as high speed, no mechanical friction, low noise, low cost and energy consumption, strong climbing ability, and green environmental protection. The open-loop instability is one of the key points and difficulties for the levitation control systems of maglev trains. The closed-loop feedback control method must be applied to realize stable levitation. However, there are currently many levitation control methods just in theory. Considering their advantages and disadvantages, it is a major demand for maglev trains to select efficient, stable, applicable, and cost-saving methods to improve their dynamic performance and safety, which motivated this review. First, the current status of research on maglev trains is introduced in this paper, including types, system components, and research modes in various countries, followed by an analysis of the levitation control methods for EMS-type maglev trains. Then, the technical characteristics of the levitation control systems are described according to the basic principles of levitation systems, model building, mathematical derivation, and control objectives. Next, three kinds of typical levitation control methods are reviewed, namely, linear state feedback methods, nonlinear control methods, and intelligent control methods, according to their improvements and applications. Lastly, we summarize and evaluate the advantages and disadvantages of the three methods, and future developments of levitation control are suggested. [ABSTRACT FROM AUTHOR]

Cutting-edge building energy management systems (BEMS) interact with heating, ventilation, air conditioning (HVAC) systems, which generally account for much of the energy consumption. Major attention is focused on the BEMS themselves, barring on-field equipment. In HVAC equipment, sub-optimal controller settings may lead to energy losses and user discomfort, for instance, due to oscillations of air temperature and fan speeds. The way to solve this problem could be to replace classical PID controllers with an alternative concept that does not require tuning and works optimally for a wide range of parameters. This paper compares a fuzzy logic controller (FLC) with a standard PID for a model-based simulation of an HVAC system in Simulink for different conditions using real building measurement data. The end result is the implementation of the developed methods in a newly designed universal control board for air handling units (AHU). The proposed FLC achieves better integral control quality indicators (IAE, ISE, ITAE, ITSE) by at least 27.4%, and smaller supply air temperature variation; the daily mean square error (MSE) was reduced by an average of 36%, which leads immediately to better occupant comfort and a presumed reduction in energy consumption. Compared to the untuned PID, energy consumption was 12.7% lower; this will ensure improved economy from the lowest level, and paves the way for interoperability with high-level energy management schemes. [ABSTRACT FROM AUTHOR]

Highlights: A fully self-powered bimodal sensor is designed for patterned-displaying the force trajectories. Outstanding mechanoluminescence is achieved with a stimulation force as low as 0.3 N and 2000 cycles reproducibility. The designed bimodal sensor exhibits good potential for handwriting input to achieve visual intelligent control. Self-powered flexible devices with skin-like multiple sensing ability have attracted great attentions due to their broad applications in the Internet of Things (IoT). Various methods have been proposed to enhance mechano-optic or electric performance of the flexible devices; however, it remains challenging to realize the display and accurate recognition of motion trajectories for intelligent control. Here, we present a fully self-powered mechanoluminescent-triboelectric bimodal sensor based on micro-nanostructured mechanoluminescent elastomer, which can patterned-display the force trajectories. The deformable liquid metals used as stretchable electrode make the stress transfer stable through overall device to achieve outstanding mechanoluminescence (with a gray value of 107 under a stimulus force as low as 0.3 N and more than 2000 cycles reproducibility). Moreover, a microstructured surface is constructed which endows the resulted composite with significantly improved triboelectric performances (voltage increases from 8 to 24 V). Based on the excellent bimodal sensing performances and durability of the obtained composite, a highly reliable intelligent control system by machine learning has been developed for controlling trolley, providing an approach for advanced visual interaction devices and smart wearable electronics in the future IoT era. [ABSTRACT FROM AUTHOR]