2022 American Control Conference (ACC)最新文献_第2页

Data-Driven Safe Predictive Control Using Spatial Temporal Filter-based Function Approximators 基于时空滤波器的函数逼近器的数据驱动安全预测控制

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867573

Amin Vahidi-Moghaddam, Kaian Chen, Zhaojian Li, Yan Wang, Kai Wu

{"title":"Data-Driven Safe Predictive Control Using Spatial Temporal Filter-based Function Approximators","authors":"Amin Vahidi-Moghaddam, Kaian Chen, Zhaojian Li, Yan Wang, Kai Wu","doi":"10.23919/ACC53348.2022.9867573","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867573","url":null,"abstract":"Model predictive control (MPC) is a state-of-the-art control method that can explicitly tackle system constraints. However, its high computational cost still remains an open challenge for embedded systems. To achieve satisfactory performance with manageable computational complexity, a spatial temporal filter (STF)-based data-driven predictive control framework is developed to systematically identify system dynamics and subsequently learn the MPC policy using STF-based function approximations. Specifically, an online nonlinear system identification method that satisfies persistence of excitation (PE) is developed by using a discrete-time concurrent learning technique. An STF-based function approximation is then employed to learn the nonlinear MPC (NMPC) policy based on the identified model. Furthermore, a discrete-time robust control barrier function (RCBF) is introduced to guarantee system safety in the presence of additive disturbances and system identification errors. Finally, simulations on the cart inverted pendulum are performed to demonstrate the efficacy of the proposed control synthesis.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"6 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":"125374367","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

Low-Fidelity Gradient Updates for High-Fidelity Reprogrammable Iterative Learning Control 高保真可编程迭代学习控制的低保真梯度更新

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867601

Kuan-Yu Tseng, J. Shamma, G. Dullerud

引用次数: 0

A Novel Reinforcement Learning-based Unsupervised Fault Detection for Industrial Manufacturing Systems 基于强化学习的工业制造系统无监督故障检测方法

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867763

A. Acernese, A. Yerudkar, C. D. Vecchio

引用次数: 1

Fast Assignment in Asset-Guarding Engagements using Function Approximation 基于函数逼近的资产保护契约快速分配

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867720

Neelay Junnarkar, Emmanuel Sin, P. Seiler, D. Philbrick, M. Arcak

引用次数: 0

Aerial Interception of Non-Cooperative Intruder using Model Predictive Control 基于模型预测控制的非合作入侵者空中拦截

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867190

Raunak Srivastava, Rolif Lima, K. Das

引用次数: 1

A Probabilistic Perspective on Risk-sensitive Reinforcement Learning 风险敏感强化学习的概率视角

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867288

Erfaun Noorani, J. Baras

{"title":"A Probabilistic Perspective on Risk-sensitive Reinforcement Learning","authors":"Erfaun Noorani, J. Baras","doi":"10.23919/ACC53348.2022.9867288","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867288","url":null,"abstract":"Robustness is a key enabler of real-world applications of Reinforcement Learning (RL). The robustness properties of risk-sensitive controllers have long been established. We investigate risk-sensitive Reinforcement Learning (as a generalization of risk-sensitive stochastic control), by theoretically analyzing the risk-sensitive exponential (exponential of the total reward) criteria, and the benefits and improvements the introduction of risk-sensitivity brings to conventional RL. We provide a probabilistic interpretation of (I) the risk-sensitive exponential, (II) the risk-neutral expected cumulative reward, and (III) the maximum entropy Reinforcement Learning objectives, and explore their connections from a probabilistic perspective. Using Probabilistic Graphical Models (PGM), we establish that in the RL setting, maximization of the risk-sensitive exponential criteria is equivalent to maximizing the probability of taking an optimal action at all time-steps during an episode. We show that the maximization of the standard risk-neutral expected cumulative return is equivalent to maximizing a lower bound, particularly the Evidence lower Bound, on the probability of taking an optimal action at all time-steps during an episode. Furthermore, we show that the maximization of the maximum-entropy Reinforcement Learning objective is equivalent to maximizing a lower bound on the probability of taking an optimal action at all time-steps during an episode, where the lower bound corresponding to the maximum entropy objective is tighter and smoother than the lower bound corresponding to the expected cumulative return objective. These equivalences establish the benefits of risk-sensitive exponential objective and shed lights on previously postulated regularized objectives, such as maximum entropy. The utilization of a PGM model, coupled with exponential criteria, offers a number of advantages (e.g. facilitate theoretical analysis and derivation of bounds).","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"34 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":"114450696","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

Approximating the Fractional-Order Element for the Robust Control Framework 鲁棒控制框架的分数阶元逼近

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867658

Vlad Mihaly, Mircea Şuşcă, E. Dulf, P. Dobra

引用次数: 5

Cooperative Systems in Presence of Cyber-Attacks: A Unified Framework for Resilient Control and Attack Identification 网络攻击下的协作系统:弹性控制和攻击识别的统一框架

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867408

Azwirman Gusrialdi, Z. Qu

引用次数: 3

Hemodynamic Monitoring via Model-Based Extended Kalman Filtering: Hemorrhage Resuscitation and Sedation Case Study 基于模型的扩展卡尔曼滤波血流动力学监测:出血复苏和镇静案例研究

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867562

Weidi Yin, A. Tivay, J. Hahn

引用次数: 2

Efficient identification for modeling high-dimensional brain dynamics 高维脑动力学建模的有效识别

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867232

Matthew F. Singh, Michael Wang, Michael W. Cole, ShiNung Ching

{"title":"Efficient identification for modeling high-dimensional brain dynamics","authors":"Matthew F. Singh, Michael Wang, Michael W. Cole, ShiNung Ching","doi":"10.23919/ACC53348.2022.9867232","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867232","url":null,"abstract":"System identification poses a significant bottleneck to characterizing and controlling complex systems. This challenge is greatest when both the system states and parameters are not directly accessible, leading to a dual-estimation problem. Current approaches to such problems are limited in their ability to scale with many-parameter systems, as often occurs in networks. In the current work, we present a new, computationally efficient approach to treat large dual-estimation problems. In this work, we derive analytic back-propagated gradients for the Prediction Error Method which enables efficient and accurate identification of large systems. The PEM approach consists of directly integrating state estimation into a dual-optimization objective, leaving a differentiable cost/error function only in terms of the unknown system parameters, which we solve using numerical gradient/Hessian methods. Intuitively, this approach consists of solving for the parameters that generate the most accurate state estimator (Extended/Cubature Kalman Filter). We demonstrate that this approach is at least as accurate in state and parameter estimation as joint Kalman Filters (Extended/Unscented/Cubature) and Expectation-Maximization, despite lower complexity. We demonstrate the utility of our approach by inverting anatomically-detailed individualized brain models from human magnetoencephalography (MEG) data.","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":"129700098","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