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Harmonic Source State Identification Using Random Forest

Harmonic Source State Identification Using Random Forest

引用

data driven control and learning systems (ddcls)

作者： Yundi Chu Haixia Li Yu Liu Ruihai Sun Ronghao Wang College of Artificial Intelligence and Automation Hohai University Changzhou China College of Defense Engineering Army Engineering University of PLA Nanjing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Identification of harmonic source types in the power grid is a prerequisite for comprehensive system rectification solutions to address grid harmonics and is of great significance. this article investigates the problem of harmonic pollution in the power system and proposes a machine learning-based method using a random forest model to identify five types of harmonic sources, including single-phase rectification, three-phase rectification, inverter rectification, wind power generation, and photovoltaic power generation. By measuring the current and voltage characteristics of harmonic sources and combining the accuracy and interpretability of the random forest algorithm, classification of harmonic source types is achieved. Experimental results show that the classification accuracy of wind power generation and photovoltaic power generation harmonic sources is 100%, while the classification effects of single-phase rectification, three-phase rectification, and inverter rectification harmonic sources are 90.5%, 98.2%, and 95.8%, respectively. Overall, this method has achieved good classification effects for harmonic sources.

关键词： Training Photovoltaic systems learning systems Voltage measurement Power system harmonics Wind power generation Harmonic analysis

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Fixed- Time Adaptive Neural Pitch Angle control for Variable-Speed Wind Turbines

Fixed- Time Adaptive Neural Pitch Angle Control for Variable...

引用

data driven control and learning systems (ddcls)

作者： Shuzong Xie Qinmin Yang Qingyi Li Shaodong Wang Jun Yang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Zhejiang Provincial Energy Group Company Ltd. Jia Xing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper mainly focuses on the challenging problem of fixed-time adaptive neural pitch angle control for variable-speed wind turbines. Firstly, a switching continuous fixed-time sliding mode variable is adopted to avoid the singularity problem. then, an adaptive sigmoid function neural network is introduced to approximate the unknown nonlinear function of wind tur-bines. On this basis, a fixed-time adaptive neural pitch angle controller is systematically developed, such that the tracking error can converge to a small neighborhood of the origin in fixed-time, thereby improving the power generation efficiency of wind turbines. Finally, the fixed-time stability of the whole closed-loop system is analyzed with rigorous theoretical proof, and the effectiveness of the proposed scheme is verified through a 1.5-MW three-blade wind turbine in Matlab/Simulink.

关键词： Adaptive systems Wind speed Simulation Neural networks Switches Wind power generation Stability analysis

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Design and Application of Integrated Condition Monitoring and Diagnostic Architecture for Airborne systems

Design and Application of Integrated Condition Monitoring an...

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data driven control and learning systems (ddcls)

作者： Chen Li Jinjin Huang Jie Pei Air Force Engineering University Aviation Maintenance NCO School Xinyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

According to the requirements of high security, high reliability and easy maintenance of the airborne system, the comprehensive condition monitoring and diagnosis architecture based on the “health management domain” is proposed to solve the problem of comprehensive integration of the state monitoring and diagnosis of the airborne system under multi-platform and multi-scenario conditions. this architecture proposes the comprehensive state monitoring method of general modules, functional threads and bus network according to the high integration design characteristics of the airborne system, so as to improve the online health perception capability of the onboard system without increasing hardware. It designs the health assessment and diagnosis model, proposes the multi-model integration algorithm framework of data fusion, and solves the application problem of multi-platform diagnostic model integration in the airborne system. Additionally, it also develops the graphical health management display and control interface and object-oriented health management database, and improves the interface friendliness and functional scalability of the health management system. this provides a reference for the development of the condition monitoring and diagnosis subsystem of the airborne system.

关键词： Condition monitoring Degradation Analytical models databases Object oriented modeling Atmospheric modeling Instruction sets

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Adaptive Optimal control of Completely Unknown systems with Relaxed PE Conditions 11

Adaptive Optimal Control of Completely Unknown Systems with ...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Luo, Rui Peng, Zhinan Hu, Jiangping Ghosh, Bijoy Kumar School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China Texas Tech University Department of Mathematics and Statistics LubbockTX79409-1042 United States

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper proposes a novel identifier-critic (IC) learning control strategy for completely unknown nonlinear system. Different from the existing results, the proposed IC control is capable of obtaining the optimal control under relaxed persistence of excitation (PE). A neural network (NN) based identifier is established to approximate the unknown system dynamics. After that, an only-critic NN framework is proposed to solve the Hamiltonian-Jacobi-Bellman (HJB) equation such that the control policy is obtained. To estimate the unknown weights of both identifier NN and critic NN simultaneously without strictly PE limitation, the dynamic regressor extension and mixing (DREM) technique is introduced to design the NN weight update laws. Meanwhile, new easy-to-check online convergence conditions for the proposed adaptive laws are given to ensure the unknown weights converge to their ideal values. In addition, theoretical analysis is also given to prove the significant relaxation of the proposed convergence conditions compared with the standard PE assumption. © 2022 ieee.

关键词： Adaptive control systems

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Cooperative Awareness Message Generation Interval Prediction Model Based on Bayesian Optimized Long Short-Term Memory Neural Network

Cooperative Awareness Message Generation Interval Prediction...

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data driven control and learning systems (ddcls)

作者： Xiaoping Zhang Hui Feng Li Wang School of Electrical and Control Engineering North China University of Technology Beijing China Department of Engineering University of Cambridge Cambridge UK

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the prediction accuracy of the generation interval of Cooperative Awareness Message (CAM) is known to directly affect the accuracy of reserved communication resources for CAM in cellular vehicle-to-everything (C-V2X) systems. In order to improve the prediction accuracy of CAM generation interval, a prediction model based on the Bayesian optimized long short-term memory neural network (BAO-LSTM) is proposed. the model utilizes LSTM to predict the generation interval of CAM while employing the Bayesian optimization algorithm iteratively to optimize the hyperparameters of LSTM until they reach their optimal values. the pre-processed Next Generation Simulation (NGSIM) dataset is used to verify the performance of the BAO-LSTM model. Experimental results demonstrate that the BAO-LSTM model outperforms both of the BP model and the LSTM model without the Bayesian optimization process in terms of capturing, and constructing patterns and rules for the generation time interval of CAM.

关键词： Wireless communication Accuracy Neural networks Predictive models Prediction algorithms data models Bayes methods

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Application of Bilateral control Based on Additive Decomposition to Image Rotation Compensation

Application of Bilateral Control Based on Additive Decomposi...

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data driven control and learning systems (ddcls)

作者： Xinchao Sun Yan Ran Jiachen Wang Liyun Zhao Wei Qin Xinwei Li School of Automation and Electrical Engineering Inner Mongolia University of Science and Technology Key Laboratory of Integrated automation for process industries at Universities of Inner Mongolia Autonomous Region Baotou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper focuses on the image rotation compensation servo mechanism in aerial imaging systems and the influence of disturbances on its tracking accuracy. Meanwhile, a bilateral control structure based on servo motor drive is utilized to realize the image rotation compensation, and the additive decomposition theory is employed to decompose the system into two subsystems, where the main system is responsible for accurate tracking, and the auxiliary system is designed with a generalized nonlinear extended state observer (G-NESO) to observe and compensate for equivalent perturbations, which is combined with a novel higher-order sliding mode compensator to refine further and compensate the system perturbations. the stable tracking of a desired trajectory and the consistent output of the two systems are achieved, and the image rotation problem caused by the motion asynchrony between the scanning mirror and the imaging plane is solved. the stability of the control system is demonstrated using Lyapunov theory. Finally, MATLAB simulation is utilized to verify the effectiveness and feasibility of the method.

关键词： Additives Accuracy Perturbation methods Imaging Observers control systems Stability analysis

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the Pareto Optimal control of Bilinear Stochastic systems

The Pareto Optimal Control of Bilinear Stochastic Systems

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data driven control and learning systems (ddcls)

作者： Yanshuang Wang Xiushan Jiang Dongya Zhao Weihai Zhang College of New Energy China University of Petroleum (East China) Qingdao P. R. China College of Information and Electrical Engineering Shandong University of Science and Technology Qingdao P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In complex industrial big-data systems, bilinear stochastic systems have the ability to provide accurate industrial process modeling, precise control, optimization and decision support. Bilinear stochastic systems can provide effective solutions for the intelligence and optimization of industrial processes, thus achieving precise control and stable operation. In this paper, a bilinear stochastic system based on multi-player in finite horizon is investigated. the Pareto cooperative game problem can be solved by applying a weighted sum approach for the objective function of each player. Firstly, we obtain the Pareto-optimal control strategy for the system under the consideration of the indefinite objective function. Secondly, because of the difficulty of solving the Hamilton-Jacobi equation (HJE), the obtained control strategy is represented according to the method of tensor power series expansion. thirdly, we obtain all Pareto solutions and the Pareto frontier. Finally, a simulation example is provided to illustrate the accuracy of the theory.

关键词： Accuracy Tensors Process modeling Stochastic systems Stochastic processes Games Pareto optimization

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A Dynamic Robust Predictive Model for Key Variable Estimation of Hydrogen Production Process

A Dynamic Robust Predictive Model for Key Variable Estimatio...

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data driven control and learning systems (ddcls)

作者： Yating Yao Weiming Shao Department of Chemical Equipment and Control Engineering College of New Energy China University of Petroleum (East China) Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Hydrogen is an ideal energy storage medium obtained mainly by the natural gas steam reforming hydrogen production process. For safe and efficient production of hydrogen, the concentrations of the gases CH 4 , CO, CO 2 , and H 2 must be measured accurately in real-time. However, traditional methods of measuring these gas concentrations are time-consuming and expensive, and the existing predictive models have not taken the complicated characteristics of the hydrogen production process into consideration. To this end, we develop a predictive model for key gas concentration estimation named “dynamic robust variational Bayesian principal component analysis (DRVBPCA)”. the DRVBPCA considers complex features of the hydrogen production process, including process dynamics, transportation delays, and outliers. Concretely, the dynamics and the outliers are modeled by the finite impulse response paradigm and Student's-t distribution, respectively, and the transportation delays are automatically determined using the differential evolution algorithm. Finally, the performance of the DRVBPCA is evaluated by a real-life natural gas steam reforming hydrogen production process and compared with other state-of-the-art models.

关键词： Heuristic algorithms Hydrogen Transportation Estimation Production Predictive models Real-time systems

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Trajectory Tracking control for Autonomous Vehicles with Physicsinformed Neural Network Vehicle Model

Trajectory Tracking Control for Autonomous Vehicles with Phy...

引用

data driven control and learning systems (ddcls)

作者： Zhanbo Sun Shuaifei Zhang Yafei Liu School of Transportation and Logistics Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Vehicle dynamics modeling is a crucial task for the trajectory tracking of autonomous vehicles. Due to the variable road conditions and the high cost of data acquisition, both physics-based and data-driven models face significant challenges with sparse data. To address this issue, we propose a vehicle model that combines the single-track model with physics-informed neural network methods. Firstly, we encode the single-track model in a physics-based computational graph to identify the key parameters in the physical model. then, we use these parameters to calibrate the single-track model, which is again encoded in a physics-based computational graph to design a physics-informed neural network for vehicle dynamics modeling (PINN-VM). the PINN-VM leverages the advantages of model-driven and data-driven approaches to achieve accurate vehicle dynamics modeling. Compared to models without physics, PINN-VM performs better, especially in situations with sparse data. Finally, the PINN-VM is incorporated into a novel model predictive control framework to design a trajectory tracking controller. Simulation experiments using CarSim and Simulink demonstrate that the proposed controller enhances trajectory tracking performance and lateral stability under the condition of large curvature at medium-high velocity with different friction coefficients.

关键词： Accuracy Trajectory tracking Software packages Computational modeling Roads Neural networks data models

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Adaptive Predefined-Time SMC for Uncertain Robotic systems

Adaptive Predefined-Time SMC for Uncertain Robotic Systems

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data driven control and learning systems (ddcls)

作者： Chaoqian Qiao Guangdeng Zong Zhenyu Chang School of Electronics and Information Engineering Tiangong University Tianjin P. R. China School of Control Science and Engineering Tiangong University Tianjin P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper presents a novel adaptive predefined-time sliding-mode control (SMC) strategy for the tracking problem of uncertain robotic manipulators. Firstly, the unknown dynamic parameters and the bound vector of the disturbances are integrated into a compounded uncertainty. Considering that the boundary of the compounded uncertainty is not readily available in practical applications, the adaptive neural networks (NNs) are adopted to compensate for the compounded uncertainty of the robot. Moreover, to enhance the robustness and accelerate the system convergence, a novel nonsingular sliding surface approach is integrated into the controller design. then, an NNs-based predefined-time controller is proposed to guarantee that the actual trajectory tracks the reference trajectory, allowing the maximum convergence time to be pre-adjusted using explicit parameters without losing robustness property. At last, simulation results conducted with a two-joint manipulator confirm both the exceptional performance and effectiveness of the developed method.

关键词： Uncertain systems Uncertainty Adaptive systems Simulation Artificial neural networks Robustness Vectors

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