Pub Date : 2024-07-08DOI: 10.1016/j.isatra.2024.07.012
The application of optimal control theory in practical engineering is often limited by the modeling cost and complexity of the mathematical model of the controlled plant, and various constraints. To bridge the gap between the theory and practice, this paper proposes a model-free direct method based on the sequential sampling and updating of surrogate model, and extends the ability of direct method to solve model-free optimal control problems with general constraints. The algorithm selects sample points from the current actual trajectory data to update the surrogate model of controlled plant, and solve the optimal control problem of the constantly refined surrogate model until the result converges. The presented initial and subsequent sampling strategies eliminate the dependence on the model. Furthermore, the new stopping criteria ensure the overlap of final actual and planned trajectories. The several examples illustrate that the presented algorithm can obtain constrained solutions with greater accuracy and require fewer sample data.
{"title":"An optimal control algorithm toward unknown constrained nonlinear systems based on the sequential sampling and updating of surrogate model","authors":"","doi":"10.1016/j.isatra.2024.07.012","DOIUrl":"10.1016/j.isatra.2024.07.012","url":null,"abstract":"<div><p><span>The application of optimal control theory in practical engineering is often limited by the modeling cost and complexity of the mathematical model of the controlled plant, and various constraints. To bridge the gap between the theory and practice, this paper proposes a model-free direct method based on the sequential sampling and updating of surrogate model, and extends the ability of direct method to solve model-free </span>optimal control problems with general constraints. The algorithm selects sample points from the current actual trajectory data to update the surrogate model of controlled plant, and solve the optimal control problem of the constantly refined surrogate model until the result converges. The presented initial and subsequent sampling strategies eliminate the dependence on the model. Furthermore, the new stopping criteria ensure the overlap of final actual and planned trajectories. The several examples illustrate that the presented algorithm can obtain constrained solutions with greater accuracy and require fewer sample data.</p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141693067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-08DOI: 10.1016/j.isatra.2024.07.003
In this paper, for macro–micro composite motion platform with piezoelectric hysteresis, an finite-time adaptive robust control method based on extended hysteresis observer is proposed. The dynamic model of macro–micro system is constructed at first. An extended hysteresis observer was designed to estimate the actual displacement and speed of motion system. Then, an adaptive robust control law is designed to eliminate the uncertain hysteresis model parameters. After this, exponential convergence result of the proposed control method is given and proved. By setting the expected bandwidth of macro–micro system, the gain adjustment process of the control method can be reduced in computation. The effectiveness of the proposed control method is demonstrated by comparison with other control methods in simulation, and the proposed control method has more stable tracking effect and smaller tracking error.
{"title":"An extended hysteresis observer-based adaptive robust control method for nonlinear macro–micro motion system","authors":"","doi":"10.1016/j.isatra.2024.07.003","DOIUrl":"10.1016/j.isatra.2024.07.003","url":null,"abstract":"<div><p>In this paper, for macro–micro composite motion platform with piezoelectric hysteresis, an finite-time adaptive robust control method based on extended hysteresis observer is proposed. The dynamic model of macro–micro system is constructed at first. An extended hysteresis observer was designed to estimate the actual displacement and speed of motion system. Then, an adaptive robust control law is designed to eliminate the uncertain hysteresis model parameters. After this, exponential convergence result of the proposed control method is given and proved. By setting the expected bandwidth of macro–micro system, the gain adjustment process of the control method can be reduced in computation. The effectiveness of the proposed control method is demonstrated by comparison with other control methods in simulation, and the proposed control method has more stable tracking effect and smaller tracking error.</p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-08DOI: 10.1016/j.isatra.2024.07.007
This study discusses a finite-time compensation tracking control method for a rehabilitative training walker. The dynamic model with input dead zone was constructed to describe the walker, and a finite-time disturbance forces observation method was proposed based on the impact mechanism on tracking performance. This approach is novel in that the disturbance forces were observed in reverse through their effects on tracking performance, thus successfully obtaining the disturbance forces of the walker. To ensure the practical finite-time stability of the system, the nonlinear finite-time compensation tracking controller with stochastic configuration networks (SCN) dead-zone estimation was built for the rehabilitative walker. Simulation results and comparative analyses confirmed that the proposed compensation control method effectively restrains dead zone and internal disturbance forces.
{"title":"Finite-time compensation control with dead-zone estimation for a rehabilitative walker considering internal disturbance forces","authors":"","doi":"10.1016/j.isatra.2024.07.007","DOIUrl":"10.1016/j.isatra.2024.07.007","url":null,"abstract":"<div><p>This study discusses a finite-time compensation tracking control method for a rehabilitative training walker. The dynamic model with input dead zone was constructed to describe the walker, and a finite-time disturbance forces observation method was proposed based on the impact mechanism on tracking performance. This approach is novel in that the disturbance forces were observed in reverse through their effects on tracking performance, thus successfully obtaining the disturbance forces of the walker. To ensure the practical finite-time stability of the system, the nonlinear finite-time compensation tracking controller with stochastic configuration networks (SCN) dead-zone estimation was built for the rehabilitative walker. Simulation results and comparative analyses confirmed that the proposed compensation control method effectively restrains dead zone and internal disturbance forces.</p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-07DOI: 10.1016/j.isatra.2024.06.034
Spatially resolved capacitance-based stress self-sensing in unmodified concrete has been demonstrated. The spatial resolution is 45 mm in one dimension, which is in the direction of the capacitance measurement. Parallel coplanar component electrodes (aluminum, 5-mm wide), attached to the concrete using double-sided adhesive tape) separated by 45 mm are used to measure the in-plane capacitance in the direction perpendicular to the length of the electrodes. Combinations of component electrodes are electrically connected to form an electrode. The capacitance ranges from ∼200 pF to ∼750 pF. The greater is the number of component electrodes in an electrode, the higher is the capacitance. The compressive loading is applied at selected areas located between adjacent component electrodes. The stress (defined as load divided by the 300 ×300-mm2 concrete area) is up to 3000 Pa. The load decreases the capacitance monotonically and reversibly. The fractional decrease in capacitance ranges from ∼0.1 % to ∼0.5 %. More spatially concentrated loading, as for loading near the edges of the specimen, gives greater fractional decrease in capacitance. The capacitance decreases with increasing inter-electrode distance. Embedded steel rebars with a 20.0-mm concrete cover do not affect the capacitance or capacitance-based sensing.
{"title":"Spatially resolved capacitance-based stress self-sensing in concrete","authors":"","doi":"10.1016/j.isatra.2024.06.034","DOIUrl":"10.1016/j.isatra.2024.06.034","url":null,"abstract":"<div><p><span><span>Spatially resolved capacitance-based stress self-sensing in unmodified concrete has been demonstrated. The spatial resolution is 45 mm in one dimension, which is in the direction of the capacitance measurement. Parallel coplanar component electrodes (aluminum, 5-mm wide), attached to the concrete using double-sided adhesive tape) separated by 45 mm are used to measure the in-plane capacitance in the </span>direction perpendicular<span> to the length of the electrodes. Combinations of component electrodes are electrically connected to form an electrode. The capacitance ranges from ∼200 pF to ∼750 pF. The greater is the number of component electrodes in an electrode, the higher is the capacitance. The compressive loading is applied at selected areas located between adjacent component electrodes. The stress (defined as load divided by the 300 ×300-mm</span></span><sup>2</sup><span> concrete area) is up to 3000 Pa. The load decreases the capacitance monotonically and reversibly. The fractional decrease in capacitance ranges from ∼0.1 % to ∼0.5 %. More spatially concentrated loading, as for loading near the edges of the specimen, gives greater fractional decrease in capacitance. The capacitance decreases with increasing inter-electrode distance. Embedded steel rebars with a 20.0-mm concrete cover do not affect the capacitance or capacitance-based sensing.</span></p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141565423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-06DOI: 10.1016/j.isatra.2024.06.022
Prediction of Remaining Useful Life (RUL) for Rolling Element Bearings (REB) has attracted widespread attention from academia and industry. However, there are still several bottlenecks, including the effective utilization of multi-sensor data, the interpretability of prediction models, and the prediction across the entire life cycle, which limit prediction accuracy. In view of that, we propose a knowledge-based explainable life-cycle RUL prediction framework. First, considering the feature fusion of fast-changing signals, the Pearson correlation coefficient matrix and feature transformation objective function are incorporated to an Improved Graph Convolutional Autoencoder. Furthermore, to integrate the multi-source signals, a Cascaded Multi-head Self-attention Autoencoder with Characteristic Guidance is proposed to construct health indicators. Then, the whole life cycle of REB is divided into different stages based on the Continuous Gradient Recognition with Outlier Detection. With the development of Measurement-based Correction Life Formula and Bidirectional Recursive Gated Dual Attention Unit, accurate life-cycle RUL prediction is achieved. Data from self-designed test rig and PHM 2012 Prognostic challenge datasets are analyzed with the proposed framework and five existing prediction models. Compared with the strongest prediction model among the five, the proposed framework demonstrates significant improvements. For the data from self-designed test rig, there is a 1.66 % enhancement in Corrected Cumulative Relative Accuracy (CCRA) and a 49.00 % improvement in Coefficient of Determination (R2). For the PHM 2012 datasets, there is a 4.04 % increase in CCRA and a 120.72 % boost in R2.
{"title":"A knowledge-data integration framework for rolling element bearing RUL prediction across its life cycle","authors":"","doi":"10.1016/j.isatra.2024.06.022","DOIUrl":"10.1016/j.isatra.2024.06.022","url":null,"abstract":"<div><p><span><span>Prediction of Remaining Useful Life (RUL) for Rolling Element Bearings<span><span> (REB) has attracted widespread attention from academia and industry. However, there are still several bottlenecks, including the effective utilization of multi-sensor data, the interpretability of prediction models, and the prediction across the entire life cycle, which limit prediction accuracy. In view of that, we propose a knowledge-based explainable life-cycle RUL prediction framework. First, considering the feature fusion of fast-changing signals, the </span>Pearson correlation </span></span>coefficient matrix<span> and feature transformation objective function are incorporated to an Improved Graph Convolutional Autoencoder<span><span>. Furthermore, to integrate the multi-source signals, a Cascaded Multi-head Self-attention Autoencoder with Characteristic Guidance is proposed to construct health indicators. Then, the whole life cycle of REB is divided into different stages based on the Continuous Gradient Recognition with Outlier Detection. With the development of Measurement-based Correction Life Formula and Bidirectional Recursive Gated Dual Attention Unit, accurate life-cycle RUL prediction is achieved. Data from self-designed </span>test rig and PHM 2012 Prognostic challenge datasets are analyzed with the proposed framework and five existing prediction models. Compared with the strongest prediction model among the five, the proposed framework demonstrates significant improvements. For the data from self-designed test rig, there is a 1.66 % enhancement in Corrected Cumulative Relative Accuracy (CCRA) and a 49.00 % improvement in Coefficient of Determination (R</span></span></span><sup>2</sup>). For the PHM 2012 datasets, there is a 4.04 % increase in CCRA and a 120.72 % boost in R<sup>2</sup>.</p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-06DOI: 10.1016/j.isatra.2024.06.025
To guarantee the safety and reliability of equipment operation, such as liquid rocket engine (LRE), carrying out system-level anomaly detection (AD) is crucial. However, current methods ignore the prior knowledge of mechanical system itself, and seldom unite the observations with the inherent relation in data tightly. Meanwhile, they neglect the weakness and nonindependence of system-level anomaly which is different from component fault. To overcome above limitations, we propose a separate reconstruction framework using worsened tendency for system-level AD. To prevent anomalous feature being attenuated, we first propose to divide single sample into two equal-length parts along the temporal dimension. And we maximize the mean maximum discrepancy (MMD) between feature segments to force encoders to learn normal features with different distributions. Then, to fully explore the multivariate time series, we model temporal-spatial dependence by temporal convolution and graph attention. Besides, a joint graph learning strategy is proposed to handle prior knowledge and data characteristics simultaneously. Finally, the proposed method is evaluated on two real multi-sensor datasets from LRE and the results demonstrate the effectiveness and potential of the proposed method on system-level AD.
为保证液体火箭发动机(LRE)等设备运行的安全性和可靠性,进行系统级异常检测(AD)至关重要。然而,目前的方法忽视了机械系统本身的先验知识,很少将观测结果与数据的内在关系紧密结合起来。同时,它们还忽视了系统级异常不同于部件故障的弱点和非独立性。为了克服上述局限性,我们提出了一个利用系统级 AD 的恶化趋势进行重建的独立框架。为了防止异常特征被衰减,我们首先建议将单个样本沿时间维度分成两个等长的部分。我们将特征段之间的平均最大差异(MMD)最大化,以迫使编码器学习不同分布的正常特征。然后,为了充分探索多元时间序列,我们通过时间卷积和图注意来建立时空依赖模型。此外,我们还提出了一种联合图学习策略,以同时处理先验知识和数据特征。最后,我们在两个真实的 LRE 多传感器数据集上对所提出的方法进行了评估,结果证明了所提出的方法在系统级 AD 方面的有效性和潜力。
{"title":"Complex system anomaly detection via learnable temporal-spatial graph with degradation tendency segmentation","authors":"","doi":"10.1016/j.isatra.2024.06.025","DOIUrl":"10.1016/j.isatra.2024.06.025","url":null,"abstract":"<div><p><span><span><span><span>To guarantee the safety and reliability of equipment operation, such as liquid rocket engine (LRE), carrying out system-level </span>anomaly detection<span> (AD) is crucial. However, current methods ignore the prior knowledge of mechanical system itself, and seldom unite the observations with the inherent relation in data tightly. Meanwhile, they neglect the weakness and nonindependence of system-level anomaly which is different from component fault. To overcome above limitations, we propose a separate reconstruction framework using worsened tendency for system-level AD. To prevent anomalous feature being attenuated, we first propose to divide single sample into two equal-length parts along the temporal dimension. And we maximize the mean maximum discrepancy (MMD) between feature segments to force encoders to learn normal features with different distributions. Then, to fully explore the </span></span>multivariate time series, we model temporal-spatial dependence by temporal convolution and graph attention. Besides, a </span>joint graph learning strategy is proposed to handle prior knowledge and </span>data characteristics simultaneously. Finally, the proposed method is evaluated on two real multi-sensor datasets from LRE and the results demonstrate the effectiveness and potential of the proposed method on system-level AD.</p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-06DOI: 10.1016/j.isatra.2024.07.005
Disturbance observer (DOB) and extended state observer (ESO) are extensively utilized to handle external disturbances and model uncertainties in the control system. Nevertheless, the integration of these two methods to improve disturbance suppression remains an open question. In this research, the disturbance compensation mechanism of DOB is employed to compensate the disturbance estimation error of ESO, thereby achieving an effective integration of DOB and ESO. Additionally, a generalized ESO (GESO) is proposed to replace ESO. A robust internal mode control (RIMC) scheme is then developed by incorporating GESO into a two-degree-of-freedom internal mode control (TDF-IMC) framework. Moreover, the equivalence of RIMC and classical TDF-IMC is given by a rigorous derivation under the frequency domain description. Finally, the RIMC is applied to the control of a two-inertia system to verify its superiority in terms of robustness, disturbance rejection, and resonance suppression.
干扰观测器(DOB)和扩展状态观测器(ESO)被广泛用于处理控制系统中的外部干扰和模型不确定性。然而,如何整合这两种方法以提高干扰抑制能力仍是一个未决问题。本研究采用 DOB 的扰动补偿机制来补偿 ESO 的扰动估计误差,从而实现 DOB 和 ESO 的有效集成。此外,还提出了一种广义 ESO(GESO)来替代 ESO。然后,通过将 GESO 纳入两自由度内模控制(TDF-IMC)框架,开发了鲁棒内模控制(RIMC)方案。此外,通过频域描述下的严格推导,给出了 RIMC 与经典 TDF-IMC 的等价性。最后,将 RIMC 应用于双惯性系统的控制,以验证其在鲁棒性、干扰抑制和共振抑制方面的优越性。
{"title":"Robust internal model control based on a novel generalized extended state observer and its application on a two-inertia system","authors":"","doi":"10.1016/j.isatra.2024.07.005","DOIUrl":"10.1016/j.isatra.2024.07.005","url":null,"abstract":"<div><p><span>Disturbance observer (DOB) and extended state observer (ESO) are extensively utilized to handle </span>external disturbances and model uncertainties in the control system. Nevertheless, the integration of these two methods to improve disturbance suppression remains an open question. In this research, the disturbance compensation mechanism of DOB is employed to compensate the disturbance estimation error of ESO, thereby achieving an effective integration of DOB and ESO. Additionally, a generalized ESO (GESO) is proposed to replace ESO. A robust internal mode control (RIMC) scheme is then developed by incorporating GESO into a two-degree-of-freedom internal mode control (TDF-IMC) framework. Moreover, the equivalence of RIMC and classical TDF-IMC is given by a rigorous derivation under the frequency domain description. Finally, the RIMC is applied to the control of a two-inertia system to verify its superiority in terms of robustness, disturbance rejection, and resonance suppression.</p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-06DOI: 10.1016/j.isatra.2024.06.029
This paper presents an altitude and attitude control system for a newly designed rocket-type unmanned aerial vehicle (UAV) propelled by a gimbal-based coaxial rotor system (GCRS) enabling thrust vector control (TVC). The GCRS is the only means of actuation available to control the UAV’s orientation, and the flight dynamics identify the primary control difficulty as the highly nonlinear and tightly coupled control distribution problem. To address this, the study presents detailed derivations of attitude flight dynamics and a control strategy to track the desired attitude trajectory. First, a Proportional-Integral-Derivative (PID) control algorithm is developed based on the formulation of linear matrix inequality (LMI) to ensure robust stability and performance. Second, an optimization algorithm using the Levenberg–Marquardt (LM) method is introduced to solve the nonlinear inverse mapping problem between the control law and the actual actuator outputs, addressing the nonlinear coupled control input distribution problem of the GCRS. In summary, the main contribution is the proposal of a new TVC UAV system based on GCRS. The PID control algorithm and LM algorithm were designed to solve the distribution problem of the actuation model and confirm altitude and attitude tracking missions. Finally, to validate the flight properties of the rocket-type UAV and the performance of the proposed control algorithm, several numerical simulations were conducted. The results indicate that the tightly coupled control input nonlinear inverse problem was successfully solved, and the proposed control algorithm achieved effective attitude stabilization even in the presence of disturbances.
{"title":"Dynamics modeling and nonlinear attitude controller design for a rocket-type unmanned aerial vehicle","authors":"","doi":"10.1016/j.isatra.2024.06.029","DOIUrl":"10.1016/j.isatra.2024.06.029","url":null,"abstract":"<div><p>This paper presents an altitude and attitude control system for a newly designed rocket-type unmanned aerial vehicle<span><span> (UAV) propelled by a gimbal-based coaxial rotor system<span> (GCRS) enabling thrust vector control (TVC). The GCRS is the only means of </span></span>actuation<span><span> available to control the UAV’s orientation, and the flight dynamics identify the primary control difficulty as the highly nonlinear and tightly coupled control distribution problem. To address this, the study presents detailed derivations of attitude flight dynamics and a control strategy to track the desired attitude trajectory. First, a Proportional-Integral-Derivative (PID) control algorithm is developed based on the formulation of linear matrix inequality (LMI) to ensure robust stability and performance. Second, an optimization algorithm using the Levenberg–Marquardt (LM) method is introduced to solve the nonlinear inverse mapping problem between the control law and the actual actuator<span> outputs, addressing the nonlinear coupled control input distribution problem of the GCRS. In summary, the main contribution is the proposal of a new TVC UAV system based on GCRS. The PID control algorithm and LM algorithm were designed to solve the distribution problem of the actuation model and confirm altitude and attitude tracking missions. Finally, to validate the flight properties of the rocket-type UAV and the performance of the proposed control algorithm, several numerical simulations were conducted. The results indicate that the tightly coupled control input nonlinear </span></span>inverse problem was successfully solved, and the proposed control algorithm achieved effective attitude stabilization even in the presence of disturbances.</span></span></p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-06DOI: 10.1016/j.isatra.2024.06.030
Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.
采用光伏(PV)技术的发电系统因其生产效率高而越来越受欢迎。在生产过程中,部分遮光缺陷是该系统最常见的缺陷,会降低发电量和发电质量。本文提出了一种基于人工神经网络和金鹰优化技术的独立光伏系统故障预测和检测方法,以恢复光伏系统的最佳性能并对其进行诊断。所提出的技术结合了人工神经网络(ANN)和金鹰优化(GEO)算法。这项工作的主要贡献在于提高光伏系统的性能。其结果是使用了人工神经网络进行分类和识别。GEO 的使用为 ANN 的训练提供了有效的优化技术,缩短了训练时间,提高了模型的准确性。提议的技术在 MATLAB 网站上执行,并与遗传算法(GA)、大象放牧优化(EHO)和粒子群优化(PSO)等不同的现有技术进行对比。研究结果表明,在检测和诊断光伏系统缺陷方面,所提出的技术比现有方法更准确、更有效。
{"title":"Fault causes and its detection in standalone PV system using ANN and GEO technique","authors":"","doi":"10.1016/j.isatra.2024.06.030","DOIUrl":"10.1016/j.isatra.2024.06.030","url":null,"abstract":"<div><p><span>Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an </span>Artificial Neural Network<span><span><span> and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system<span> to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the </span></span>Artificial Neural Network<span> (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like </span></span>genetic algorithm<span> (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.</span></span></p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141695917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-04DOI: 10.1016/j.isatra.2024.07.008
Rolling bearing is the key component of rotating machinery, and its vibration signal usually exhibits nonlinear and nonstationary characteristics when failure occurs. Multiscale permutation entropy (MPE) is an effective nonlinear dynamics analysis tool, which has been successfully applied to rolling bearing fault diagnosis in recent years. However, MPE ignores the deep amplitude information when measuring the complexity of the time series and the original multiscale coarse-graining is insufficient, which requires further research and improvement. In order to protect the integrity of information structure, a novel nonlinear dynamic analysis method termed refined composite multiscale slope entropy (RCMSlE) is proposed in this paper, which introduced the concept of refined composite to further boost the performance of MPE in nonlinear dynamical complexity analysis. Furthermore, RCMSlE utilizes a novel symbolic representation that takes full account of mode and amplitude information, which overcomes the weaknesses in describing the complexity and regularity of bearing signals. Based on this, a GWO-SVM multi-classifier is introduced to fulfill mode recognition, and then a new intelligent fault diagnosis method for rolling bearing based on RCMSlE and GWO-SVM is proposed. The experimental results show that the proposed method can not only accurately identify different fault types and degrees of rolling bearing, but also has a short computation time and better performance than other comparative methods.
{"title":"Refined composite multiscale slope entropy and its application in rolling bearing fault diagnosis","authors":"","doi":"10.1016/j.isatra.2024.07.008","DOIUrl":"10.1016/j.isatra.2024.07.008","url":null,"abstract":"<div><p>Rolling bearing is the key component of rotating machinery, and its vibration signal usually exhibits nonlinear and nonstationary characteristics when failure occurs. Multiscale permutation entropy (MPE) is an effective nonlinear dynamics analysis tool, which has been successfully applied to rolling bearing fault diagnosis in recent years. However, MPE ignores the deep amplitude information when measuring the complexity of the time series and the original multiscale coarse-graining is insufficient, which requires further research and improvement. In order to protect the integrity of information structure, a novel nonlinear dynamic analysis method termed refined composite multiscale slope entropy (RCMSlE) is proposed in this paper, which introduced the concept of refined composite to further boost the performance of MPE in nonlinear dynamical complexity analysis. Furthermore, RCMSlE utilizes a novel symbolic representation that takes full account of mode and amplitude information, which overcomes the weaknesses in describing the complexity and regularity of bearing signals. Based on this, a GWO-SVM multi-classifier is introduced to fulfill mode recognition, and then a new intelligent fault diagnosis method for rolling bearing based on RCMSlE and GWO-SVM is proposed. The experimental results show that the proposed method can not only accurately identify different fault types and degrees of rolling bearing, but also has a short computation time and better performance than other comparative methods.</p></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141708968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}