Intelligent Control Research Articles

Empowering Fuel Cell Electric Vehicles Towards Sustainable Transportation: An Analytical Assessment, Emerging Energy Management, Key Issues, and Future Research Opportunities

Fuel cell electric vehicles (FCEVs) have received significant attention in recent times due to various advantageous features, such as high energy efficiency, zero emissions, and extended driving range. However, FCEVs have some drawbacks, including high production costs; limited hydrogen refueling infrastructure; and the complexity of converters, controllers, and method execution. To address these challenges, smart energy management involving appropriate converters, controllers, intelligent algorithms, and optimizations is essential for enhancing the effectiveness of FCEVs towards sustainable transportation. Therefore, this paper presents emerging energy management strategies for FCEVs to improve energy efficiency, system reliability, and overall performance. In this context, a comprehensive analytical assessment is conducted to examine several factors, including research trends, types of publications, citation analysis, keyword occurrences, collaborations, influential authors, and the countries conducting research in this area. Moreover, emerging energy management schemes are investigated, with a focus on intelligent algorithms, optimization techniques, and control strategies, highlighting contributions, key findings, issues, and research gaps. Furthermore, the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains, relevant outcomes, and existing challenges. Additionally, this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs, emphasizing the importance of suitable algorithms, controllers, and optimization techniques to enhance their performance. The outcomes and key findings of this review will be helpful for researchers and automotive engineers in developing advanced methods, control schemes, and optimization strategies for FCEVs towards greener transportation.

Robot intelligent perception and control

Abstract. In recent years, with the rapid development of robotics, intelligent perception and control has become the core research direction in the field of robotics. This technology plays a key role in many fields such as industrial automation, medical assistance, and autonomous driving, significantly improving production efficiency and service quality, and also promoting the intelligent processes of various industries. On the basis of an in-depth analysis of robot intelligent perception and control technology, this paper systematically reviews the development of visual and non-visual perception technology and discusses the application of multi-modal perception in complex environments. The latest development of motion control and force control technology is analyzed, and its importance in fine operation is expounded. The future development direction of robot intelligent perception and control technology is proposed, and the necessity of enhancing real-time robustness and system integration is emphasized, which provides theoretical support and technical reference for promoting the continuous innovation of robot intelligent perception and control technology.

Adaptive neuro-fuzzy inference system based active force control with iterative learning for trajectory tracking of a biped robot

ABSTRACT We investigate the integration of the active force control (AFC) scheme and the adaptive neuro-fuzzy inference system (ANFIS) as an intelligent controller algorithm to address trajectory-tracking problems in robotic systems, The AFC-ANFIS model exploits iterative learning (IL) to improve the tracking performance based on its trained model. The ANFIS parameters are tuned using both particle swarm optimisation (PSO) and beetle antennae search (BAS) algorithms. The simulation results of two different robots, i.e. a five-link biped robot and a PUMA 560 robot arm, indicate that the proposed AFC-ANFIS controller performs well for trajectory tracking and disturbances rejection. The AFC-ANFIS performance is evaluated and compared with those from other controllers using the average tracking error (ATE) metric. The comparative results reveal that AFC-ANFIS offers a viable approach with a rapid training process to undertaking trajectory tracking and disturbance rejection tasks.

Research on the role of multi-sensor system information fusion in improving hardware control accuracy of intelligent system

Abstract Multi-sensor management and control technology generally constructs a reasonable objective function to solve the optimal control command set to control a limited number of sensors to obtain higher quality measurement information, thus obtaining better target tracking performance. In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. A sensor fusion algorithm combined with global optimization algorithm is innovatively proposed. According to the key frames saved in the previous steps, feature points in local maps, sensor information, and loop information, a global optimization algorithm based on graph optimization model is constructed to optimize the position and pose of intelligent hardware system and the position of spatial feature points. Moreover, this work studies and experiments on multi-sensor fusion simultaneous localization and mapping (SLAM) comprehensively and systematically, and the experimental results show that the algorithm proposed in this work is superior to common open-source SLAM algorithm in positioning accuracy and mapping effect under special circumstances. Therefore, the method proposed in this work can be applied to intelligent driving of vehicles, vision-assisted movement of robots and intelligent control of unmanned aerial vehicles, thus effectively improving the hardware control accuracy of intelligent systems.

Contactless printing of food micro-particles controlled by ultrasound

Traditional food 3D printing technology, which primarily relies on mechanical control, often faces challenges such as low resolution, stringent material requirements, limited flexibility, and cumbersome equipment. To overcome these limitations, we developed an innovative food 3D printing method utilizing ultrasonic phased arrays and subsequently designed a contactless printing device employing this advanced technology. The device effectively manipulates the phase and amplitude of the ultrasonic phased array to focus the sound field on a specific location. We have successfully demonstrated preliminary control over the suspension of small food particles, including popcorn, chocolate, honey, and marshmallows, using this apparatus. Following this, popcorn particles were selected for detailed practical printing and further analysis. Additionally, we established a software platform, Ultr_printing, based on Ultraino, for intelligent simulation and control of the printing process. Leveraging FPGA and Arduino controllers, this setup facilitates rapid signal transmission from personal computers to sensors, ensuring quick and adaptable movement of suspended particles. Experimental results confirm that the device consistently controls the food particles, irrespective of their motion state. Notably, it achieves a printing precision of 0.16 mm when handling popcorn particles measuring 1.5 mm in diameter. The process, entirely contactless and free from contamination, preserves the original flavor of the food particles. This technology is particularly effective for high-precision printing of micro-scale shapes and offers considerable benefits in assembly and precision manufacturing.

Feasibility and safety of flexible ureteroscopy with intelligent control of renal pelvis pressure without urinary catheter: a retrospective study

BackgroundTo explore the feasibility and safety of aflexible ureteroscopy with intelligent control of renal pelvic pressure(FUS-ICP) without a post-operative indwelling urinary catheter .MethodsIn this retrospective study, we assessed patients with upper urinary tract stones who were treated with FUS-ICP at the Ganzhou People’s Hospital from February 2022 to December 2023. Patients were divided into the non-urinary catheter (non-UC) and urinary catheter (UC) groups according to whether an indwelling catheter was used after surgery.ResultsIn total, 142 patients were included in the study. There was no significant difference in the preoperative general data between the two groups. Patients in the non-UC group performed better than those in the UC group in terms of catheter-related bladder irritation (P = 0.001), the Sedation-Agitation Scale score (P = 0.012), and the numerical rating scale (P = 0.003). The incidences of urinary retention (P = 0.620), urinary tract infection (P = 0.529), and replacement of urethral catheter s (P = 0.438) in the UC group were inferior to those in the non-UC group, but there was no statistical significance.ConclusionsIt is feasible and safe to perform FUS-ICP without a post-procedure indwelling urinary catheter.

Intelligent robust controllers for tribotronic conical fluid film bearings

The article presents the results of the development of means for intelligent robust control of the parameters of a tribotronic rotor-support system with a tapered bearing with a removable bush. The proposed controller is implemented on the basis of deep Q-network reinforcement learning (DQN) synthesized on the basis of a numerical model of a rotor support system. The control strategy involved simultaneous control of the shaft position and friction in the lubrication layer. Methods for control synthesis are presented for both a deterministic system and a system with stochastic parameters. In the latter case, a controller synthesis technique is proposed that takes into account uncertainties in the system at the training stage. Testing of the resulting controllers shows the better ability of a controller trained to take into account uncertainties to cope with variable loads, as well as predict possible changes in the system and proactively transfer the system to more advantageous states.

Research on the Optimization Path of Strategic Cost Management under the Background of the Digital Economy

Under the background of the digital economy, the research on the optimization path of strategic cost management for enterprises is particularly important. This paper discusses key paths such as data-driven cost management systems, intelligent cost control, and collaborative cost management, revealing the profound impact of the digital economy on corporate cost management and proposing a series of optimization strategies. The data-driven cost management system requires enterprises to establish comprehensive data collection and analysis mechanisms, using big data and advanced analytical tools to achieve real-time monitoring and dynamic analysis of costs. This not only improves the accuracy of cost management but also enhances the enterprise's ability to respond to market changes. Secondly, the implementation of intelligent cost control, through automated and predictive cost management, enables enterprises to more effectively control and predict cost changes, thereby improving the efficiency of resource allocation and the quality of cost management decisions. Furthermore, the development of collaborative cost management emphasizes the synergy between internal departments of enterprises and with external partners. By breaking down information silos and promoting resource sharing and business collaboration, enterprises can achieve cost optimization throughout the entire value chain.

Research on intelligent control of fresh air introduction based on electric vehicle air conditioning system

The high power consumption of air conditioning systems is an important reason for the significant reduction in EVs (electric vehicles) range. Reducing its energy consumption has become an urgent industry problem to be solved. In order to reduce the energy consumption of the air conditioning system, this paper proposes an intelligent control strategy for the proportion of fresh air in the cabin of EVs, taking into account the impact of factors such as the number of passengers in the cabin, ambient temperature, and humidity on demand for fresh air, and analyzes the energy-saving effect of the intelligent control strategy through simulation. The analysis found that the minimum fresh air demand in the passenger compartment is greatly affected by the number of passengers in the cabin. The developed intelligent control strategy can significantly reduce the energy consumption of air conditioning systems. The energy-saving effect in northern China is significantly better than in southern China. The actual driving range of refrigeration and heating modes can be improved by 50 and 126 km, respectively.

Bridging to Commercialization: Record-Breaking of Ultra-Large and Superior Cyclic Stability Tungsten Oxide Electrochromic Smart Window.

Electrochromic smart windows (ESWs) can significantly reduce energy consumption in buildings, but their cost-effective, large-scale production remains a challenge. In this study, the instability of black phosphorus is leveraged to induce the growth of the tungsten oxide film through its decomposition process, inspired by the 2D material-assisted in situ growth (TAIG) method. This approach results in the preparation of large-scale, high-performance WO3-x·nH2O (n<2) films. Characterization techniques and DFT calculations confirm efficient regulation ofstructural water and oxygen vacancies during TAIG preparation. The WO3-x·nH2O films exhibit excellent electrochromic (EC) properties, including high transmittance modulation (74.2%@1100nm), fast switching time (tc=5.5s, tb=3.8s), high coloration efficiency (124.7cm2C-1), and superior cyclic stability (transmittance modulation retained 94.7% after 20000 cycles). Ultra-largeWO3-x·nH2O film are prepared via a simple immersion process, and fabricated into a large-area ESW under facile laboratory conditions, demonstrating the economic and practical feasibility of this approach in industrial-scale production. Operated by the intelligent control circuit, the ESWexhibits remarkable EC properties and cyclic stability This research represents a milestone in improving the performance and industrial-scale production of ESWs, bridging the gap to the commercialization of EC technology.

Optimizing flotation froth image segmentation via parallel branch network and hybrid loss supervision

Flotation is a crucial technology for fine coal separation, and accurately acquiring bubble size information during the flotation process is essential for monitoring flotation conditions and achieving intelligent control. However, existing semantic segmentation models encountered issues with boundary disconnection when segmenting flotation bubbles, resulting in deviations between the extracted bubble sizes and their true values. To address the aforementioned challenges, a semantic segmentation model was proposed to maintain high-resolution feature maps throughout the network by designing a parallel branch network structure. Additionally, a ConvTranspose module was proposed to preserve the detailed feature information of images while gradually enhancing the resolution of feature maps. In the model training phase, a hybrid loss function combining pixel classification loss with shape similarity loss was proposed to alleviate the sample imbalance problem caused by the substantial difference in the number of pixels between bubble boundaries and the interior of bubbles. Moreover, since traditional semantic segmentation evaluation metrics, such as MIoU, lack a mechanism for measuring bubble boundary continuity and cannot effectively penalize the problem of boundary disconnection, this paper proposed a new evaluation method for assessing the segmentation performance of flotation froth images. To comprehensively evaluate the effectiveness of the proposed method, this paper conducted tests using flotation froth images collected from actual production processes. Compared with existing methods, the segmentation model proposed in this paper exhibited clear superiority in mitigating the problem of bubble boundary disconnection. The prediction error for the number of bubbles was 6.38 %, which is significantly better than other methods.

Intelligent Robust Controllers Applied to an Auxiliary Energy System for Electric Vehicles

This paper presents two intelligent robust control strategies applied to manage the dynamics of a DC-DC bidirectional buck–boost converter, which is used in conjunction with a supercapacitor as an auxiliary energy system (AES) for regenerative braking in electric vehicles. The Neural Inverse Optimal Controller (NIOC) and the Neural Sliding Mode Controller (NSMC) utilize identifiers based on Recurrent High-Order Neural Networks (RHONNs) trained with the Extended Kalman Filter (EKF) to track voltage and current references from the converter circuit. Additionally, a driving cycle test tailored specifically for typical urban driving in electric vehicles (EVs) is implemented to validate the efficacy of the proposed controller and energy improvement strategy. The proposed NSMC and NIOC are compared with a PI controller; furthermore, an induction motor and its corresponding three-phase inverter are incorporated into the EV control scheme which is implemented in Matlab/Simulink using the “Simscape Electrical” toolbox. The Mean Squared Error (MSE) is computed to validate the performance of the neural controllers. Additionally, the improvement in the State of Charge (SOC) for an electric vehicle battery through the control of buck–boost converter dynamics is addressed. Finally, several robustness tests against parameter changes in the converter are conducted, along with their corresponding performance indices.

Design of a robust intelligent controller based neural network for trajectory tracking of high-speed wheeled robots

The control design of wheeled mobile robots is often accomplished based on the robot’s kinematics which imposes critical challenges in the motion tracking control of such systems. In the related literature, most works designed dynamic controllers based on the terms of voltage or torque; however, the velocity is often utilized in industrial and commercial applications. To address this issue, this paper focuses on the design of a velocity-based control for the trajectory-tracking problem of mobile robots in practical applications including measurement acquisition. The control design of the mobile robot is realized in two separate parts using kinematic control and dynamic control. The design of kinematic control is carried out based on the robot’s kinematics where the motion is described without considering the forces and torques. A radial basis function (RBF) neural network (NN) based on the model-free controller is designed for the dynamic controller of mobile robots without relying on the system dynamics. In the proposed dynamic control, a time-delay estimation is adopted to estimate the disturbances and noises in the mobile robot and remove them from the feedback loop control. Several typical scenarios of mobile robots with high-speed movements are conducted to assess the feasibility of the proposed NN controller-based time-delay estimation under noises and disturbances. To show the supremacy of the suggested controller, the dynamic responses of the mobile robot test system are compared with the prevalent controllers. The extensive simulation and real-time examinations reveal the capability of the proposed NN controller-based time-delay estimation to control high-speed mobile robots under high-level noises and disturbances.

Research on Intelligent Agricultural Monitoring and Control System Based on Neural Networks in the Internet of Things Environment

In today's rapidly advancing information technology landscape, the Internet of Things (IoT) has profoundly impacted traditional agriculture. IoT technology, as an intelligent solution, enables real-time data collection, processing, and analysis, significantly enhancing the efficiency and intelligence of agricultural production. Among these advancements, the neural network-based intelligent agricultural monitoring system stands out. This system not only monitors the agricultural production environment in real-time but also employs deep learning algorithms to predict future trends, guiding agricultural practices to achieve precision farming. Therefore, exploring the intelligent agricultural monitoring and control system based on neural networks in the IoT environment holds significant importance.

Unification of probabilistic graph model and deep reinforcement learning (UPGMDRL) for multi-intersection traffic signal control

Traffic signals play a pivotal role in modern life by preventing collisions, regulating traffic flow, and ensuring a predictable and efficient transportation system. Adaptive traffic light signal control (ATSC) is a promising paradigm for mitigating traffic congestion in Intelligent Transportation Systems (ITS). Among various AI-based approaches, Deep Reinforcement Learning (DRL) has gained widespread application, demonstrating superior performance. This paper aims to develop a latent space reinforcement learning method for intelligent traffic control, with a focus on making explainable decisions. According to the latent model and hidden Markov mixed model, this method integrated both to develop an ATSC framework for traffic networks with multiple intersections. Given the challenges posed by high-dimensional data and a limited understanding of the task, traditional decision-making methods often struggle with understanding the environment. This paper aims to provide semantic information and an enhanced understanding of the environment by offering interpretable states. The latent model is employed to extract task-relevant information from underlying representations within a framework that unifies representation learning and DRL. The experimental results demonstrate how our approach effectively and efficiently balances traffic flow, leading to improved traffic management.

Operating itself safely: merging the concepts of ‘safe to operate’ and ‘operate safely’ for lethal autonomous weapons systems containing artificial intelligence

ABSTRACT The Ministry of Defence, specifically the Royal Navy, uses the ‘Duty Holder Structure’ to manage how it complies with deviations to maritime laws and health and safety regulations where military necessity requires it. The output statements ensuring compliance are ‘safe to operate’ certification for all platforms and equipment, and the ‘operate safely’ declaration for people who are suitably trained within the organisation. Together this forms the Safety Case. Consider a handgun; the weapon has calibration, design and maintenance certification to prove it is safe to operate, and the soldier is trained to be qualified competent to make predictable judgement calls on how and when to pull the trigger (operate safely). Picture those statements as separate circles drawn on a Venn diagram. As levels of autonomy and complexity are dialled up the two circles converge. Should autonomy increase to the point that the decision to fire be under the control of an Artificial Intelligence within the weapon’s software then the two circles will overlap. This paper details research conclusions within the overlap, and proposes a new methodology able to certify that an AI based autonomous weapons system is “safe to operate itself safely” when in an autonomous state.

Research and Analysis of a Smart System for Intelligent Control of PCM Water Storage Tank for Stock Farm Needs

Energy efficiency is a very important component in modern systems. Modern research examines innovative methods and techniques for increasing the efficiency of heat generation systems. A popular approach is to apply combined methods of using energy from different heat sources. This article describes а basic algorithm for developing an intelligent control for a system of hot-water storage tanks with PCM adapted for a stock farm's needs. The main components of the system are described along with the PCM advantages. The prerequisites for the system’s operation are analyzed and adapted for its management. The developed algorithm and a control system are tested in a simulated environment and are presented conclusions of the system’s energy efficiency improvement.

Airport Express Passenger Flow Prediction Based on “Time-Feature” Cooperative Attention

Accurate prediction of airport express passenger flow is the basis for realizing intelligent, refined and efficient management and control of the airport rail transit system, and is of great significance to improving airport service levels and operational efficiency. Due to the numerous influencing factors that overlap with each other, and the complex mechanism of factors affecting passenger flow timing, accurate prediction of airport express passenger flow is extremely challenging. This paper proposes an airport express rail passenger flow prediction model based on the "time-feature" collaborative attention mechanism, which achieves accurate capture of the impact of multi-dimensional factors on airport express rail passenger flow in different time series. Experiments were conducted based on actual passenger flow data of the Beijing Capital International Airport Express Rail Link, and the results showed the effectiveness of the proposed method.

Device-free crowd counting using multi-link WiFi CSI for occupant-driven energy management of HVAC systems

ABSTRACT Accurate estimation of the number of occupants in a building’s thermal zone is a crucial input for the dynamic estimation of the cooling load of a heating, ventilation, and air conditioning equipment. This data, in conjunction with an intelligent control algorithm, can be judiciously employed for enhancing occupant comfort and achieving substantial energy savings through demand-driven operation. In this work, we propose a WiFi channel state information (CSI) based passive people counting model that utilizes a wavelet-based signal denoising scheme and an artificial neural network (ANN) model for automatic feature extraction. The application of the proposed model on a public dataset shows more than 99% accuracy in detecting up to eight people in three rooms of varying sizes. The proposed model was also validated on CSI samples collected from a laboratory environment with five occupants, using ESP32, a low cost and low power IoT device. The results show that the proposed model can perform people counting with an accuracy of up to 98%, using single- and multi-link measurements.

Traffic signal phase control at urban isolated intersections: an adaptive strategy utilizing the improved D3QN algorithm

Abstract The intelligent control of traffic signals at urban single intersections has emerged as an effective approach to mitigating urban traffic congestion. However, the existing fixed phase control strategy of traffic signal lights lacks capability to dynamically adjust signal phase switching based on real-time traffic conditions leading to traffic congestion. In this paper, an adaptive real-time control method employed by the traffic signal phase at a single intersection is considered based on the improved Double Dueling Deep Q Network (D3QN) algorithm. Firstly, the traffic signal phase control problem is modeled as a Markov decision process, with its state, action, and reward defined. Subsequently, to enhance the convergence speed and learning performance of the D3QN algorithm, attenuation action selection strategy and priority experience playback technology based on tree summation structure are introduced. Then, traffic flow data from various traffic scenarios are utilized to train the traffic signal control model based on the improved D3QN to obtain the optimal signal phase switch strategy. Finally, the effectiveness and optimal performance of the improved D3QN-based traffic signal control strategy are validated across diverse traffic scenarios. The simulation results show that, compared with the control strategy based on AC, DQN, DDQN, D3QN, and C-D3QN algorithms, the cumulative reward of the proposed I-D3QN strategy is increased by at least 6.57%, and the average queue length and average waiting time are reduced by at least 9.64% and 7.61%, which can effectively reduce the congestion at isolated intersections and significantly improve traffic efficiency.&amp;#xD;