Hasil untuk "Control engineering systems. Automatic machinery (General)"

Yushun Tan, Jiajing Liu, Xiang-peng Xie et al.

This paper presents an investigation of the sliding-mode secure control for nonlinear semi-Markov jump systems in the presence of non-periodic denial-of-service attacks and false data injection attacks. First of all, we employ Takagi-Sugeno fuzzy model to describe the nonlinear semi-Markov jump control systems. In order to optimize transmission efficiency and control performance, a novel dynamic-memory event-triggered mechanism is developed by incorporating auxiliary dynamic variable and historical transmitted data. Then, a memory-based fuzzy sliding surface is put forward to attenuate the influences of stochastic cyber attacks with the aid of event-triggered state information. Moreover, by utilizing Lyapunov stability theory, sufficient conditions are derived to guarantee the exponentially mean-square stability of the system with an $H_{\infty }$ performance index, even in the cases of generally uncertain and unknown transition rates. Furthermore, a memory-based sliding mode secure controller is designed to ensure the reachability of the predefined switching surface and desirable sliding motion within finite time. Finally, the efficacy of the proposed control scheme is demonstrated through a tunnel diode circuit model. Note to Practitioners—This study focuses on the issue of secure control for nonlinear semi-Markov jump systems, which holds practical significance across various domains, including applications in robotic manipulators, circuit models, and DC motors. We broaden the scope by considering more general jump parameter matrices to align more closely with the real-world system environment. Moreover, networked environments pose two primary challenges: network bandwidth constraints and the external network attacks. To tackle these issues, this paper introduces an innovative dynamic memory event triggering mechanism to enhance network transmission efficiency and optimize communication resource utilization. Meanwhile, to address cyber attacks resulting from the inherent openness of networks, the current study adopts a defensive sliding mode control strategy to provide robust protection against network attacks and disturbances. The effectiveness of the suggested approach is confirmed through a circuit system model. It is worth mentioning that the proposed method has the potential for broader application within real-world engineering scenarios, particularly those involving network-based nonlinear systems with various practical constraints.

Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar et al.

This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.

Junchang Zhai, Yuping Qin, Hongxia Cui

This paper formulates fault-tolerant tracking control for high-order nonlinear systems with actuator faults, and each state signal is measured by a single sensor which possibly suffers partial failure. The unknown fault parameters of sensors and actuator are assumed to be time-varying and their upper and lower bounds are unknown. A parameter separating tactic is employed to tackle the coupling issues in view of real state signals and unknown fault parameters. The actuator fault with high-order power $u^{\kappa _{n}}(t)$ is transformed into a linear function of $v^{\kappa _{n} }(t)$ plus a bounded term $dv(t)$ to overcome the restriction of high-order power and separate the fault parameters, such that the control signal can be constructed directly. Neural networks (NNS) are utilized to identify the unknown coexisting nonlinear uncertainties. By formulating adaptive bound estimation scheme to construct the NNS-based fault-tolerant tracking controller, which can eliminate the effects of multiple sensor faults, actuator faults, external disturbances and identify errors. Finally, the simulations verify the feasibility of the proposed tactic. Note to Practitioners—HNSs has a more general system structure. In practice, many systems can be described or transformed as HNSs, such as space vehicles and mechanical systems. Owing to the influence of environment and the physical components of the practical engineering systems, sensor faults and actuator faults are inevitable in engineering systems. Due to the restrictions of high-order power, the actuator faults cannot be converted into a friendly form such that the controller cannot be designed directly. Thus, the conventional results often assume the powers equals to 1 or the fault parameters are known.

Xiaojie Peng, Yong He, Jianhua Shen

In this paper, the time-varying formation tracking (TVFT) problem of multi-leader multi-agent systems (MASs) under sampled-data control is studied. First, an improved TVFT criterion is derived by employing a general looped-functional and a zero integral functional. Then a new TVFT controller is designed based on our proposed method. Unlike the existing works, the TVFT controller can allow for bigger sampling intervals of multi-leader MASs. The results demonstrate that the states of the followers can form the designated formation and rotate around the convex combination formed by the multiple leaders. In addition, we generalize the results of TVFT to the sampled-data consensus for MASs, a sampled-data consensus controller which can reduce communication consumption is designed. Meanwhile, the values of the allowable maximum sampling intervals (AMSIs) are calculated. Finally, the superiority and availability of the theoretical results are verified by two simulation experiments. Note to Practitioners— The motivation of this paper is to address the problem of TVFT for MAS with multiple leaders. It has been applied to several practical engineering systems, e.g., satellite formation control and unmanned air vehicles (UAVs) formation control. Note that information exchange is a prerequisite to ensure safe and stable control of multi-UAV formation. However, continuous-time communication is difficult to be realized in the actual environment, and it will inevitably consume a lot of energy. In addition, sampled-data controller for MAS with a small sampling interval may cause network congestion. Therefore, a sampled-data controller which allows for a bigger sampling interval for MAS is designed to save network resources in this paper. Through simulation experiments, it can be proved that the followers not only track the leaders, but also form the designated formation.

Asadullah Shaikh, Wahidur Rahman, Kaniz Roksana et al.

Bangladesh has plentiful water, which is essential to its freshwater fish traditions. Environmental concerns and other causes have reduced the country's water resources, threatening many native freshwater fish species. Thus, the younger generation deficiencies recognition of local freshwater fish and struggles to recognize them. Traditional methods are very insufficient to overcome these issues. To address these research gaps, the research proposes an automatic system for categorizing Bangladesh's freshwater fish. The proposed methodology involves several key steps, including building a comprehensive dataset, extracting features from fish images using pre-trained Convolutional Neural Network (CNN) models, and employing typical ML approaches. Initially comprising eight classes, the dataset undergoes feature extraction using CNN algorithms, followed by the utilization of various feature selection methods such as Support Vector Classifier, Principal Component Analysis, Linear Discriminant Analysis, and optimization models like Particle Swarm Optimization, Bacterial Foraging Optimization, and Cat Swarm Optimization. In the final phase, seven conventional ML techniques are applied to classify the images of local fishes. Empirical measurements are gathered and analyzed to assess the proposed framework's performance. Particularly, the present approach achieves the highest accuracy of 98.71% through the utilization of the ML mechanism Logistic Regression with Resnet50, SVC, and CSO models.

Satoshi Otsuki, Miki Taya, Kenichi Nakashima et al.

This paper presents a novel hierarchical control architecture designed for navigating multiple tugboats to perform complex maneuvers, including approaching, enclosing, and capturing a large target vessel, while taking into account external disturbances and operational constraints. The proposed hierarchical control architecture includes a high-level nominal controller for trajectory generation, tracking, and coordination, and a low-level model-following controller for multitask execution. The low-level controller integrates constraint-driven control to refine nominal control inputs generated by the high-level controller, and local control to mitigate disturbances. To achieve multitask execution across all phases, the constraint-driven controller activates only essential constraints, enforcing seamless maneuver transitions without relying on ad-hoc controller switching. Specifically, we design constraints to facilitate effective enclosing behaviour while guiding the tugboat fleet toward the target in formation. We also adopt a so-called Prescribed-Time Safety Filter to enforce mild contact with the surface of the target vessel within a specified finite-time interval. Simulation studies validate the proposed control architecture across all maneuvers throughout the operation and demonstrate its capability to achieve complex multi-tugboat coordination.

Panagiotis S. Trakas, Charalampos P. Bechlioulis

In this work, we propose an approximation-free adaptive performance control scheme for unknown high-relative degree, multi-input-multioutput (MIMO) nonlinear systems with saturation on the control input signal. We introduce a novel reconciling adaptive modification of the predefined performance specifications based on the input constraints of the controlled plant, providing the best-feasible output performance. The automatic gain tuning in combination with the simplicity of the proposed controller enhance its robustness and enable its easy deployment in practical scenarios. Notably, the introduced control methodology ensures the necessary compromise between input-output constraints on a semi-global sense for ISS systems. However, the stability attributes for general nonlinear systems are inevitably limited to compact domains due to the inherent conflict between performance demand and actuation capability. In this context, we provide a sufficient closed-loop stability criterion through Lyapunov analysis. Finally, illustrative simulation studies and experimental results clarify and verify the efficacy of the proposed controller.

Shuai Sui, Zhuo Liu, Wenshan Bi et al.

This paper investigates adaptive neural network filtering control for uncertain nonlinear systems with general model state and input quantization. The plants under consideration contain quantized states, quantized input, and unknown nonlinear system functions. A universal quantizer is established for both system states and control input. In the control design process, neural networks and the command filter are used to approximate the unknown nonlinear system functions and overcome the discontinuities of virtual control signals, respectively. A new command filtering-based control strategy is proposed using the backstepping design technique. It is testified that the proposed control approach can guarantee that the closed-loop signals are semi-global uniform ultimate boundedness. A simulation example is presented to further demonstrate our proposed scheme’s effectiveness. Note to Practitioners—This work is motivated by the quantized control problem for a class of nonlinear systems with state and input quantization. In modern control engineering applications, quantization plays a crucial role due to the prevalent use of digital processors that operate with finite precision arithmetic. It is valuable and inevitable to minimize information flow, reduce communication burden, and improve system security. However, quantization will introduce significant discontinuous characteristics and strong nonlinearity, which may decrease the system’s performance and even drive the closed-loop system to instability. This paper demonstrates how to use backstepping and adaptive control methods with command filter to complete controller design and deal with the quantization effects. Therefore, it provides a feasible approach for engineering applications.

M. Keblawi, Tomer Toledo

Efficient traffic signal control design is essential to provide a high level of service at intersections. Current signal control plans are increasingly complex due to advances in sensing and communication capabilities, and the introduction of advanced control functionalities, such as transit signal priority or pedestrian actuation. Most optimization programs for actuated signal plans focus on setting the values of the main design parameters, such as cycle length and maximum and minimum green times. Other design components, such as phase compositions, detector placements and priority strategies, are selected by traffic engineers based on their experience or according to general engineering guidelines. This paper presents a system and its various components that fully automates the design process and generates optimal actuated signal plans with transit signal priority functions. The system consists of two main components, a traffic simulation model that evaluates candidate signal plans and a grammatical evolution algorithm that generates these plans within the optimization process. The control design system was evaluated with a case study on an intersection in Haifa, Israel controlled by an actuated plan with transit priority to Bus Rapid Transit (BRT) vehicles. An automatically designed signal plan improved average person delay by 25% compared to the existing plan. It also outperformed, by 3%, a signal plan that kept the existing structure, and optimized its timing parameters (i.e., cycle length, minimum and maximum green time). Thus, the system can generate plans that are at least as good as human-designed ones, with lower effort, cost, and design time.

Journal of Electrical and Computer Engineering

Lei Zhang, Yiru Huang, Leiming Yan et al.

Making computers automatically extract latent scientific knowledge from literature is highly desired for future materials and chemical research in the artificial intelligence era. Herein, the natural language processing (NLP)‐based machine learning technique to build language models and automatically extract hidden information regarding perovskite solar cell (PSC) materials from 29 060 publications is employed. The concept that there are light‐absorbing materials, electron‐transporting materials, and hole‐transporting materials in PSCs is successfully learned by the NLP‐based machine learning model without a time‐consuming human expert training process. The NLP model highlights a hole‐transporting material that receives insufficient attention in the literature, which is then elaborated via density functional theory calculations to provide an atomistic view of the perovskite/hole‐transporting layer heterostructures and their optoelectronic properties. Finally, the above results are confirmed by device experiments. The present study demonstrates the viability of NLP as a universal machine learning tool to extract useful information from existing publications.

Journal of Healthcare Engineering

Takeshi Yoshida, Yuki Onishi, Takuya Kawahara et al.

Abstract In this study, we propose a method to automate fruit harvesting with a fruit harvesting robot equipped with robotic arms. Given the future growth of the world population, food shortages are expected to accelerate. Since much of Japan’s agriculture is dependent on imports, it is expected to be greatly affected by this upcoming food shortage. In recent years, the number of agricultural workers in Japan has been decreasing and the population is aging. As a result, there is a need to automate and reduce labor in agricultural work using agricultural machinery. In particular, fruit cultivation requires a lot of manual labor due to the variety of orchard conditions and tree shapes, causing mechanization and automation to lag behind. In this study, a dual-armed fruit harvesting robot was designed and fabricated to reach most of the fruits on joint V-shaped trellis that was cultivated and adjusted for the robot. To harvest the fruit, the fruit harvesting robot uses sensors and computer vision to detect and estimate the position of the fruit and then inserts end-effectors into the lower part of the fruit. During this process, there is a possibility of collision within the robot itself or with other fruits depending on the position of the fruit to be harvested. In this study, inverse kinematics and a fast path planning method using random sampling is used to harvest fruits with robot arms. This method makes it possible to control the robot arms without interfering with the fruit or the other robot arm by considering them as obstacles. Through experiments, this study showed that these methods can be used to detect pears and apples outdoors and automatically harvest them using the robot arms.

Nirmal Choudhary, Henning Clever, Robert Ludwigs et al.

The increasing global demand for high‐quality and low‐cost battery electrodes poses major challenges for battery cell production. As mechanical defects on the electrode sheets have an impact on the cell performance and their lifetime, inline quality control during electrode production is of high importance. Correlation of detected defects with process parameters provides the basis for optimization of the production process and thus enables long‐term reduction of reject rates, shortening of the production ramp‐up phase, and maximization of equipment availability. To enable automatic detection of visually detectable defects on electrode sheets passing through the process steps at a speed of 9 m s−1, a You‐Only‐Look‐Once architecture (YOLO architecture) for the identification of visual detectable defects on coated electrode sheets is demonstrated within this work. The ability of the quality assurance (QA) system developed herein to detect mechanical defects in real time is validated by an exemplary integration of the architecture into the electrode manufacturing process chain at the Battery Lab Factory Braunschweig.

Lidia Gałda, Dariusz Pająk

The paper presents results obtained by SiC ceramic implementation for slide burnishing. Analyzes of the effect of the burnishing force and feed on selected surface texture parameters were conducted. The influence of technological parameters was assessed according to the analysis of variance. The burnishing force and feed affected most of the examined surface texture parameters in the analyzed ranges. Optimal values were found for the technological parameters. In most cases, the minimum of height parameters were achieved for 80 N of burnishing force and 0,063 mm/rev of feed. Limitations in the increase in the burnishing force were also found.

Shichao Li, Hongzhen Xu, Yuhan Ji et al.

Abstract To achieve large-scale automatic operation of agricultural machinery, we designed and developed a following agricultural machinery automatic navigation system. The system consists of a master and a slave agricultural machinery. The master is auto-pilot or manual driving, and the slave follows the master to perform an automatic navigation task. The automatic navigation system adopts a steering wheel control method and consists of a positioning unit, steering control device, wireless communication unit, and vehicle terminal device. Among which, the positioning unit is a global navigation satellite system receiver. The steering control device includes a steering wheel rotation control unit and a front-wheel rotation angle detection feedback unit. The wireless communication unit adopts a data transmission radio to transfer the information between the master and the slaves. The vehicle terminal equipment uses an industrial computer to make navigation decisions and generate instructions. The vehicle terminal software of the master–slave navigation system, including agricultural machinery positioning, data communication, navigation, and tracking control modules, was designed. This system can realize data communication, processing, storage, and display. To verify the performance of the designed system, a pilot battery car was used as the master, and the Euro-leopard M904-D tractor (LOVOL Inc., China) was used as the slave. Experimental results showed that the performance of the system is stable, and the slave can autonomously follow the master at a vehicle speed of 0.8 m/s. Furthermore, the root-mean-square error is 6.76 cm, which can satisfy the field operation requirements.

H. Patel, V. Shah

Fuzzy Inference Systems (FIS) are now widely employed to regulate a wide range of safety-critical engineering systems. The main reason for this is that FIS may be created using expert human knowledge. Furthermore, with the introduction of Type-2 Fuzzy Logic, the ability to handle uncertainty offers an alluring improvement for fault-tolerant abilities in fault-tolerant control methods, and, in fact, recent studies have shown that the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) provides better results than Type-1 Fuzzy Inference Systems (T1 FIS).The current research intends to suggest a novel strategy employing Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for significant features in fault-tolerant capability in the Passive Fault-Tolerant Control (PFTC) method based on the performance increase shown by IT2 FIS. The ST2 FIS is based on the ideas of Shadowed Fuzzy Sets and is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS). The fundamental rationale for utilising ST2 FIS instead of GT2 FIS is that the computational cost of GT2 FIS is too high for this application. The simulation results for the FTC using IT2 FIS and FTC using ST2 FIS for Coupled Conical Tank Level Control (CCTLC) system with 30 % and 40% loss of effectiveness in the main actuator are presented in this study. In addition, the article compares the proposed FTC approach using ST2 FIS to FTC using IT2 FIS in terms of fault-recovery time.

Sai Zhang, Zicheng Li, Hou‐Neng Wang et al.

Abstract A fractional order sliding mode control (FOSMC) based on single parameter adaptive law for nano‐positioning of Piezoelectric Actuators (PEAs) is proposed. First, the Bouc–Wen (B–W) model is used to describe the hysteresis of the nano‐position platform based on PEAs, which provides a mathematical model for the subsequent controller design. Then, theoretical support is provided to design the FOSMC based on adaptive law of different parameters, which are proposed for the displacement tracking problem of PEAs, and the position error convergence is also proved. Moreover, the core parameters of FOSMC based on single parameter adaptive law are identified by hybrid differential evolution (HDE) and adaptive differential evolution (ADE), which require considering the relationship between the scaling factor and the cross‐probability factor. Finally, experiments have been conducted with the displacement signals mixed with multiple frequencies and multiple amplitudes and the results obtained from them show that the proposed control scheme can produce a faster response and smaller tracking errors in PEAs system as compared to traditional control algorithms.

LI Tingting, SUN Weixin, LIU Chuan et al.

Due to the mismatch of main circuit parameters and the negative impedance characteristics of inverter-motor system under specific operating conditions, urban rail transit traction system is prone to DC-link voltage oscillation. In order to solve this problem, this paper establishes a characteristic equation model of traction system, and analyzes the influence of DC-link filter parameters on system stability. For inverter-motor drive system, it proposes an oscillation suppression method based on impedance reconstruction. By compensating d axis and q axis reference voltage signal of controller, negative impedance characteristic of the inverter-motor system is improved so that the system is stable. Simulation and hardware-in-the-loop experiment results show that the suppression strategy can effectively suppress the oscillation and improve the stability of system.