2022 American Control Conference (ACC)最新文献

A Baseline Repositioning Controller for a Floating Offshore Wind Farm 浮式海上风电场的基线重新定位控制器

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867574

Yuan Gao, A. Padmanabhan, O. Chen, Ali C. Kheirabadi, R. Nagamune

{"title":"A Baseline Repositioning Controller for a Floating Offshore Wind Farm","authors":"Yuan Gao, A. Padmanabhan, O. Chen, Ali C. Kheirabadi, R. Nagamune","doi":"10.23919/ACC53348.2022.9867574","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867574","url":null,"abstract":"This paper presents a baseline repositioning controller for floating offshore wind farms. It is assumed that each turbine in the farm receives a lateral position reference command by a high-level wind farm controller. A low-level controller for each wind turbine in the farm manipulates the aerodynamic force direction and magnitude to achieve the reference command, while either power maximization or power regulation at the rated power is sought depending on the generator speed feedback signal. The low-level repositioning controller is a combination of the existing controller of blade pitch angle and generator torque, and the gain-scheduled proportional-integral nacelle yaw controller. To validate the repositioning controller, a previously-developed low-fidelity dynamic wind farm simulator, called FOWFSimDyn, will be utilized. The simulation results demonstrate that repositioning wind turbines in a wind farm has a great potential in increasing the total energy capture of the wind farm with sufficiently long mooring lines. The result in this paper can be a baseline to be compared with other advanced wind farm repositioning control in future research.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114983518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Transmissibility-based Fault Detection in Systems with Unknown Time-Varying Parameters 未知时变参数系统中基于传输率的故障检测

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867203

A. Khalil, Khaled F. Aljanaideh, M. A. Janaideh

{"title":"Transmissibility-based Fault Detection in Systems with Unknown Time-Varying Parameters","authors":"A. Khalil, Khaled F. Aljanaideh, M. A. Janaideh","doi":"10.23919/ACC53348.2022.9867203","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867203","url":null,"abstract":"This paper investigates transmissibility operators for time-variant systems with bounded nonlinearities. Transmissibility operators are mathematical relations between a set of system responses to another set of responses within the same system. Both parameter variation and system nonlinearities are considered to be unknown. Transmissibility operators are shown in the literature to be independent of the system inputs. The bounded nonlinearities are considered as independent excitations on the system, which renders transmissibilities independent of these nonlinearities. To overcome the unknown parameter variation, we propose identifying transmissibilities using recursive least-squares in the form of noncausal FIR models. The recursive least squares algorithm is used to optimize what dynamics to include in the transmissibility operator, and what dynamics to exclude with the system nonlinearities. The identified transmissibilities then become robust against parameter variation and unknown nonlinearities. Next, transmissibilities are proposed for fault detection an autonomous multi-robotic system formulated to emulate connected autonomous vehicle platoons. The variant parameters are the robot mass and the ground friction coefficient.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116650948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Data-Driven Passivity-Based Control of Underactuated Systems via Neural Approximators and Bayesian Inference 基于神经逼近器和贝叶斯推理的欠驱动系统鲁棒无源控制

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867143

Wankun Sirichotiyakul, N. Ashenafi, A. Satici

引用次数: 1

An Optimal Control Strategy to Distribute Element Wear for Adaptive High-Rise Structures 自适应高层结构单元磨损分布的最优控制策略

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867396

Spasena Dakova, Julia L. Heidingsfeld, M. Böhm, O. Sawodny

{"title":"An Optimal Control Strategy to Distribute Element Wear for Adaptive High-Rise Structures","authors":"Spasena Dakova, Julia L. Heidingsfeld, M. Böhm, O. Sawodny","doi":"10.23919/ACC53348.2022.9867396","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867396","url":null,"abstract":"Adaptive civil engineering structures can actively counteract external disturbances by using actuators integrated in their elements, which reduces the structural weight roughly by half. However, the mass reduction leads to a higher susceptibility to vibrations and therefore increased wear of the structural elements. This publication presents an optimal control strategy that is able to redistribute external loads over the redundant actuators of the system by adaptation of the cost function. With this, the individual wear level of each element can explicitly be controlled, e.g. to realize a uniform wear of all elements. The proposed model predictive control (MPC) law increases the damping of vibrations and minimizes the elongation of worn elements. The proposed algorithm is evaluated in simulations by means of an exemplary wind disturbance. As a result, a significant decline of the elongation of worn elements compared to the application of the reference controller is achieved. Furthermore, the stress reduction of multiple elements is possible and the loads are distributed over elements with higher predicted life expectancy.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124701171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Reliable Diagnosability for Decentralized Diagnosis of Discrete Event Systems with Single-Level Inference 具有单级推理的离散事件系统分散诊断的可靠诊断性

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867608

Takumi Hamada, S. Takai

引用次数: 1

Finite-Time Model-Based Event-Triggered Implementation of Hybrid Controllers 基于有限时间模型的事件触发混合控制器实现

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867583

Xuan-Zhi ZHU, Pedro Casau, C. Silvestre

引用次数: 0

Recent Progress on State and Parameter Estimation for Lithium-Sulfur Batteries 锂硫电池状态及参数估计研究进展

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867392

H. Fathy

引用次数: 0

PrivOpt: an intrinsically private distributed optimization algorithm PrivOpt:本质上私有的分布式优化算法

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867222

Amir-Salar Esteki, Solmaz S. Kia

引用次数: 1

Reduced SARX modeling and control via Regression Trees 通过回归树减少SARX建模和控制

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867228

Luis Felipe Florenzan Reyes, Francesco Smarra, A. D’innocenzo

引用次数: 0

Modeling of Li-ion batteries for real-time analysis and control: A data-driven approach* 锂离子电池的实时分析和控制建模:一种数据驱动的方法*

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867616

Omidreza Ahmadzadeh, Renato Rodriguez, D. Soudbakhsh

{"title":"Modeling of Li-ion batteries for real-time analysis and control: A data-driven approach*","authors":"Omidreza Ahmadzadeh, Renato Rodriguez, D. Soudbakhsh","doi":"10.23919/ACC53348.2022.9867616","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867616","url":null,"abstract":"This paper presents a data-driven model (DDM) of Li-ion batteries (LIBs). Accurate real-time modeling of LIBs allows for faster and more aggressive inputs and operations, improving their performance and safety. The DDM was developed using the enhanced single-particle model with electrolyte as the plant dynamics. A sparse model of the system was developed based on a library of potential terms. A sparsity-promoting optimization algorithm was used to balance the trade-off between the model accuracy and complexity. We compared the Sequentially Thresholded Ridge regression (STRidge) and a LASSO optimization and showed the advantages of using the STRidge. First, we developed a model using only voltage and current signals (Model I). Model I’s performance and robustness were assessed via validation and generalization tests, where the model achieved normalized root mean square error (NRMSE) values of less than 1.6%. Additionally, we evaluated Model I’s robustness to noise, achieving NRSME< 2%. We showed the trend of Model I parameters with the charge/discharge curves. However, this model required switching parameters as state-of-charge (SOC) changes. Therefore, we augmented the model by including terms related to the SOC in the library (Model II) to avoid switching. We showed the performance of Model II using the US06 highway driving cycle as the cell’s charge varied from 40%SOC to 20%SOC with small errors (NRMSE=4.78%). The results showed that the models accurately predict the dynamic response of the cells in the simulated scenarios. The models are interpretable as they have explicit input and output terms and allow for efficient control design.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"28 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123579370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5