2023 American Control Conference (ACC)最新文献_第4页

Learning Residual Dynamics via Physics-Augmented Neural Networks: Application to Vapor Compression Cycles 通过物理增强神经网络学习残余动力学:应用于蒸汽压缩循环

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10155954

Raphael Chinchilla, Vedang M. Deshpande, A. Chakrabarty, C. Laughman

{"title":"Learning Residual Dynamics via Physics-Augmented Neural Networks: Application to Vapor Compression Cycles","authors":"Raphael Chinchilla, Vedang M. Deshpande, A. Chakrabarty, C. Laughman","doi":"10.23919/ACC55779.2023.10155954","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10155954","url":null,"abstract":"In order to improve the control performance of vapor compression cycles (VCCs), it is often necessary to construct accurate dynamical models of the underlying thermo-fluid dynamics. These dynamics are represented by complex mathematical models that are composed of large systems of nonlinear and numerically stiff differential algebraic equations (DAEs). The effects of nonlinearity and stiffness may be ameliorated by using physics-based models to describe characteristic system behaviors, and approximating the residual (unmodeled) dynamics using neural networks. In these so-called ‘physics-augmented’ or ‘physics-informed’ machine learning approaches, the learning problem is often solved by jointly estimating parameters of the physics component model and weights of the network. Furthermore, such approaches also often assume the availability of full-state information, which typically are not available in practice for energy systems such as VCCs after deployment. Rather than concurrently performing state/parameter estimation and network training, which often leads to numerical instabilities, we propose a framework for decoupling the network training from the joint state/parameter estimation problem by employing state-constrained Kalman smoothers customized for VCC applications. We show the effectiveness of our proposed framework on a Julia-based, high-fidelity simulation environment calibrated to a model of a commercially-available VCC and achieve an accuracy of 98% calculated over 24 states and multiple initial conditions under realistic operating conditions.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131059138","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

Infrastructure-Based Hierarchical Control Design for Congestion Management in Heterogeneous Traffic Networks 基于基础设施的异构交通网络拥塞管理层次控制设计

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156385

Pouria Karimi Shahri, B. Homchaudhuri, A. Ghaffari, A. Ghasemi

引用次数: 0

An efficient data-driven distributionally robust MPC leveraging linear programming 利用线性规划的高效数据驱动分布鲁棒MPC

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156224

Zhengang Zhong, E. A. Rio-Chanona, Panagiotis Petsagkourakis

引用次数: 2

Reducing Plant-Model Mismatch for Economic Model Predictive Control of Wind Turbine Fatigue by a Data-Driven Approach 基于数据驱动的风电机组疲劳经济模型预测控制中减少厂-模型失配

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156501

Abhinav Anand, C. Bottasso

{"title":"Reducing Plant-Model Mismatch for Economic Model Predictive Control of Wind Turbine Fatigue by a Data-Driven Approach","authors":"Abhinav Anand, C. Bottasso","doi":"10.23919/ACC55779.2023.10156501","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156501","url":null,"abstract":"This paper considers the inclusion of an adaptive element in the model-predictive control of a wind turbine. In fact, an adaptive internal model can reduce the plantmodel mismatch, in turn potentially leading to an improved performance. A Reduced Order Model (ROM) is augmented by training a Neural Network (NN) offline. The improvement in state predictions due to model augmentation is assessed and compared with the non-augmented ROM. The augmented ROM is then used as the internal model in an Economic Nonlinear Model Predictive Controller (ENMPC), which maximizes profit by optimally balancing tower fatigue damage costs with revenue due to power generation. The tower cyclic fatigue costs are formulated directly within the controller using the Parametric Online Rainflow Counting (PORFC) approach. The designed ENMPC is implemented using the state-of-the-art ACADOS framework. The performance of the controller and the impact of a reduced plant model mismatch is assessed in closed loop with the NREL 5MW onshore wind turbine, simulated using OpenFAST. Results show that the ENMPC utilizing the augmented ROM yields higher economic profit, slightly higher torque travel, and significantly lower pitch travel, compared to the ENMPC utilizing only the baseline ROM.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132901422","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

Fixed Time Stability of Discrete-Time Stochastic Dynamical Systems 离散随机动力系统的定时稳定性

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156569

Junsoo Lee, W. Haddad

引用次数: 0

Multiple Model Switched Repetitive Control with Application to Tremor Suppression 多模型切换重复控制在震颤抑制中的应用

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156534

Tingze Fang, C. Freeman

引用次数: 0

Imitating Swarm Behaviors by Learning Agent-Level Controllers 通过学习智能体级控制器模仿群体行为

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156561

Ibrahim Musaddequr Rahman, Stanford White, Katelyn Crockett, Yu Gu, D. A. Dutra, Guilherme A. S. Pereira

{"title":"Imitating Swarm Behaviors by Learning Agent-Level Controllers","authors":"Ibrahim Musaddequr Rahman, Stanford White, Katelyn Crockett, Yu Gu, D. A. Dutra, Guilherme A. S. Pereira","doi":"10.23919/ACC55779.2023.10156561","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156561","url":null,"abstract":"A main challenge in swarm robotics is the unknown mapping between simple agent-level behavior rules and emergent global behaviors. Currently, there is no known swarm control algorithm that maps global behaviors to local control policies. This paper proposes a novel method to circumvent this problem by learning the agent-level controllers of an observed swarm to imitate its emergent behavior. Agent-level controllers are treated as a set of policies that are combined to dictate the agent’s change in velocity. The trajectory data of known swarms is used with linear regression and nonlinear optimization methods to learn the relative weight of each policy. To show our approach’s ability for imitating swarm behavior, we apply this methodology to both simulated and physical swarms (i.e., a school of fish) exhibiting a multitude of distinct emergent behaviors. We found that our pipeline was effective at imitating the simulated behaviors using both accurate and inaccurate assumptions, being able to closely identify not only the policy gains, but also the agent’s radius of communication and their maximum velocity constraint.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128831550","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

Multiple Model Iterative Learning Control for FES-based Stroke Rehabilitation 基于fes的脑卒中康复多模型迭代学习控制

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10155894

Junlin Zhou, C. Freeman, W. Holderbaum

引用次数: 0

Robust D-Stability Analysis of Fractional-Order Controllers 分数阶控制器的鲁棒d稳定性分析

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156257

Majid Ghorbani, A. Tepljakov, E. Petlenkov

引用次数: 0

Time Optimal Data Harvesting in Two Dimensions through Reinforcement Learning Without Engineered Reward Functions 无工程奖励函数的强化学习二维时间最优数据采集

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156033

Shili Wu, Yancheng Zhu, A. Datta, S. Andersson

{"title":"Time Optimal Data Harvesting in Two Dimensions through Reinforcement Learning Without Engineered Reward Functions","authors":"Shili Wu, Yancheng Zhu, A. Datta, S. Andersson","doi":"10.23919/ACC55779.2023.10156033","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156033","url":null,"abstract":"We consider the problem of harvesting data from a set of targets distributed throughout a two dimensional environment. The targets broadcast their data to an agent flying above them, and the goal is for the agent to extract all the data and move to a desired final position in minimum time. While previous work developed optimal controllers for the one-dimensional version of the problem, such methods do not extend to the 2-D setting. Therefore, we first convert the problem into a Markov Decision Process in discrete time and then apply reinforcement learning to find high performing solutions using double deep Q learning. We use a simple binary cost function that directly captures the desired goal, and we overcome the challenge of the sparse nature of these rewards by incorporating hindsight experience replay. To improve learning efficiency, we also utilize prioritized sampling of the replay buffer. We demonstrate our approach through several simulations, which show a similar performance as an existing optimal controller in the 1-D setting, and explore the effect of both the replay buffer and the prioritized sampling in the 2-D setting.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115560927","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