Proceedings of the ... American Control Conference. American Control Conference最新文献_第2页

A Heat Exchanger Simulator for Testing and Demonstrating Automation Algorithms 用于测试和演示自动化算法的热交换器模拟器

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2022-01-01 DOI: 10.23919/ACC53348.2022.9867196

R. Rhinehart

引用次数: 0

Spacecraft Attitude Control using Derivative-free Purely Adaptive Controller 无导数纯自适应航天器姿态控制

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2022-01-01 DOI: 10.23919/ACC53348.2022.9867258

Irene Grace Karot Polson, D. Giri

引用次数: 0

PDE-Based Modeling and Non-collocated Feedback Control of Electrosurgical-Probe/Tissue Interaction. 基于pde的电外科-探针/组织相互作用建模与非配置反馈控制。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2021-05-01 Epub Date: 2021-07-28 DOI: 10.23919/acc50511.2021.9483240

Hamza El-Kebir, Joseph Bentsman

引用次数: 5

Tracking Multiple Diffusing Particles Using Information Optimal Control. 基于信息最优控制的多扩散粒子跟踪。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2021-05-01 Epub Date: 2021-07-28 DOI: 10.23919/acc50511.2021.9482619

Samuel C Pinto, Nicholas A Vickers, Fatemeh Sharifi, Sean B Andersson

引用次数: 6

EM-based algorithms for single particle tracking of Ornstein-Uhlenbeck motion from sCMOS camera data. 基于em的基于sCMOS相机数据的Ornstein-Uhlenbeck运动单粒子跟踪算法。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2021-05-01 Epub Date: 2021-07-28 DOI: 10.23919/acc50511.2021.9483034

Ye Lin, Sean B Andersson

引用次数: 0

Modeling zebrafish geotaxis as a feedback control process. 作为反馈控制过程的斑马鱼地向性建模。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2021-05-01 Epub Date: 2021-07-28 DOI: 10.23919/acc50511.2021.9483149

Daniel A Burbano-L, Maurizio Porfiri

{"title":"Modeling zebrafish geotaxis as a feedback control process.","authors":"Daniel A Burbano-L, Maurizio Porfiri","doi":"10.23919/acc50511.2021.9483149","DOIUrl":"https://doi.org/10.23919/acc50511.2021.9483149","url":null,"abstract":"Developing mathematical models of the feedback control process underlying animal behavior is of critical importance to understand their interactions with the environment and emotional responses. For instance, fish geotaxis (the tendency to swim at the bottom of the tank) is known to be a highly sensitive measure of anxiety, but how and why animals tend to display such a complex response is yet to be fully clarified. Leveraging the theory of stochastic differential equations, we develop a data-driven model of geotaxis in the form of a feedback control loop where fish use information about the hydrostatic pressure to dive towards the bottom of the tank. The proposed framework extends open-loop models by incorporating a simple, yet effective, control mechanism to explain geotaxis. We focus on the zebrafish animal model, which is a species of choice in the study of anxiety disorders. We calibrate the model using available experimental data on acute ethanol treatment of adult zebrafish, and demonstrate its effectiveness across a wide range of comparisons between theoretical predictions and empirical observations.","PeriodicalId":74510,"journal":{"name":"Proceedings of the ... American Control Conference. American Control Conference","volume":"2021 ","pages":"660-665"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522050/pdf/nihms-1746541.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39560687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Learning from Having Learned: An Environment-adaptive Parking Space Detection Method 从已有的经验中学习:一种适应环境的停车位检测方法

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2020-07-01 DOI: 10.23919/ACC45564.2020.9147934

Yi Yang, Sitan Jiang, Lu Zhang, Jianhang Wang

引用次数: 1

Safety-Guaranteed, Accelerated Learning in MDPs with Local Side Information. 安全保证，加速学习在mdp与本地侧信息。

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2020-07-01 Epub Date: 2020-07-27 DOI: 10.23919/acc45564.2020.9147372

Pranay Thangeda, Melkior Ornik

{"title":"Safety-Guaranteed, Accelerated Learning in MDPs with Local Side Information.","authors":"Pranay Thangeda, Melkior Ornik","doi":"10.23919/acc45564.2020.9147372","DOIUrl":"https://doi.org/10.23919/acc45564.2020.9147372","url":null,"abstract":"In environments with uncertain dynamics, synthesis of optimal control policies mandates exploration. The applicability of classical learning algorithms to real-world problems is often limited by the number of time steps required for learning the environment model. Given some local side information about the differences in transition probabilities of the states, potentially obtained from the agent's onboard sensors, we generalize the idea of indirect sampling for accelerated learning to propose an algorithm that balances between exploration and exploitation. We formalize this idea by introducing the notion of the value of information in the context of a Markov decision process with unknown transition probabilities, as a measure of the expected improvement in the agent's current estimate of transition probabilities by taking a particular action. By exploiting available local side information and maximizing the estimated value of learned information at each time step, we accelerate the learning process and subsequent synthesis of the optimal control policy. Further, we define the notion of agent safety, a vital consideration for physical systems, in the context of our problem. Under certain assumptions, we provide guarantees on the safety of an agent exploring with our algorithm that exploits local side information. We illustrate agent safety and the improvement in learning speed using numerical experiments in the setting of a Mars rover, with data from onboard sensors acting as the local side information.","PeriodicalId":74510,"journal":{"name":"Proceedings of the ... American Control Conference. American Control Conference","volume":"2020 ","pages":"1099-1104"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/acc45564.2020.9147372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38640130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Constrained and Sparse Switching Times Optimization via Augmented Lagrangian Proximal Methods 基于增广拉格朗日近端方法的约束稀疏切换时间优化

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2020-07-01 DOI: 10.23919/ACC45564.2020.9147892

A. Marchi

引用次数: 3

A time-varying approach to single particle tracking with a nonlinear observation model. 采用非线性观测模型的单粒子时变跟踪法

Proceedings of the ... American Control Conference. American Control Conference Pub Date : 2020-07-01 Epub Date: 2020-07-27 DOI: 10.23919/acc45564.2020.9147877

Boris I Godoy, Ye Lin, Sean B Andersson

{"title":"A time-varying approach to single particle tracking with a nonlinear observation model.","authors":"Boris I Godoy, Ye Lin, Sean B Andersson","doi":"10.23919/acc45564.2020.9147877","DOIUrl":"10.23919/acc45564.2020.9147877","url":null,"abstract":"Single Particle Tracking (SPT) is a powerful class of tools for analyzing the dynamics of individual biological macromolecules moving inside living cells. The acquired data is typically in the form of a sequence of camera images that are then post-processed to reveal details about the motion. In this work, we develop a local time-varying estimation algorithm for estimating motion model parameters from the data considering nonlinear observations. Our approach uses several well-known existing tools, namely the Expectation Maximization (EM) algorithm combined with an Unscented Kalman filter (UKF) and an Unscented Rauch-Tung-Striebel smoother (URTSS), and applies them to the time-varying case through a sliding window methodology. Due to the shot noise characteristics of the photon generation process, this model uses a Poisson distribution to capture the measurement noise inherent in imaging. In order to apply our time-varying approach to the UKF, we first need to transform the measurements into a model with additive Gaussian noise. This is carried out using a variance stabilizing transform. Results from simulations show that our approach is successful in tracing time-varying diffusion constants at a range of physically relevant signal levels. We also discuss the initialization for the EM algorithm based on the available data.","PeriodicalId":74510,"journal":{"name":"Proceedings of the ... American Control Conference. American Control Conference","volume":"2020 ","pages":"5151-5156"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8411988/pdf/nihms-1611707.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39385379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0