2023 American Control Conference (ACC)最新文献_第3页

Discrete-Time Transverse Feedback Linearization⋆ 离散时间横向反馈线性化

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156159

Rollen S. D’Souza

引用次数: 0

A Tutorial on Real-Time Computing Issues for Control Systems 控制系统实时计算问题教程

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156102

D. Abramovitch, S. Andersson, K. Leang, W. Nagel, S. Ruben

{"title":"A Tutorial on Real-Time Computing Issues for Control Systems","authors":"D. Abramovitch, S. Andersson, K. Leang, W. Nagel, S. Ruben","doi":"10.23919/ACC55779.2023.10156102","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156102","url":null,"abstract":"This paper presents a tutorial on the elements of computation in a real-time control system. Unlike conventional computation or even computation in digital signal processing systems, computation in a feedback loop must be sensitive to issues of latency and noise around the loop. This presents some fundamental requirements, limitations, and design constraints not seen in other computational applications. The logic of presenting such a tutorial is that while the computer technology changes at a rapid pace, the principles of how we match that technology to the constraints of a feedback loop remain consistent over the years. We will discuss the different computational chains in a feedback system, ways to conceptualize the effects of time delay and jitter on the system, and present a three-layer-model for programming real-time computations. The tutorial also presents some filter and state-space structures that are useful for real-time computation. It concludes with an overview of the different sample rate ranges currently used in some typical control problems and a short discussion of how business models affect our choices in real-time computation.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"24 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":"114511117","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

Noniterative Model Predictive Control with Soft Input Constraints for Real-Time Trajectory Tracking 基于软输入约束的非迭代模型预测实时轨迹跟踪控制

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156191

John W. Handler, M. Harker, G. Rath, Mathias Rollett

引用次数: 1

An introduction to learning in finite games 在有限游戏中学习的介绍

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156273

J. Shamma

{"title":"An introduction to learning in finite games","authors":"J. Shamma","doi":"10.23919/ACC55779.2023.10156273","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156273","url":null,"abstract":"In the setting of learning in games, player strategies evolve in an effort to maximize utility in response to the evolving strategies of other players. In contrast to the single agent case, learning in the presence of other learners induces a non-stationary environment from the perspective of any individual player. Depending on the specifics of the game and the learning dynamics, the evolving strategies may exhibit a variety of behaviors ranging from convergence to Nash equilibrium to oscillations to even chaos. This talk presents a basic introduction to learning in games through the presentation of selected results for finite normal form games, i.e., games with a finite number of players having a finite number of actions. The talk starts with a representative sample of learning dynamics that converge to Nash equilibrium for special classes of games. Specific learning dynamics include better reply dynamics, joint strategy fictitious play, and log-linear learning, with results for potential games and weakly acyclic games. These results apply to specifically pure Nash equilibrium. The talk also presents dynamics that address mixed/randomized strategy Nash equilibria, specifically smooth fictitious play and gradient play. The talk concludes with limitations in learning that stem from the notion of uncoupled dynamics, where a player’s learning dynamics cannot depend explicitly on the utility functions of other players.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"94 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":"127911612","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

Optimal Control via Linearizable Deep Learning 基于线性深度学习的最优控制

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10155810

Vinícius Lima, D. Phan, Lam M. Nguyen, J. Kalagnanam

引用次数: 0

Dynamics and Control of AUVs using Buoyancy-Based Soft Actuation 基于浮力软驱动的auv动力学与控制

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156334

C. Hoppe, F. Ghorbel, Zheng Chen

引用次数: 0

LSTM-based control of cellulose degree of polymerization in a batch pulp digester 基于lstm的间歇式纸浆蒸煮器中纤维素聚合度控制

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156480

Parth Shah, Hyun-Kyu Choi, J. Kwon

{"title":"LSTM-based control of cellulose degree of polymerization in a batch pulp digester","authors":"Parth Shah, Hyun-Kyu Choi, J. Kwon","doi":"10.23919/ACC55779.2023.10156480","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156480","url":null,"abstract":"Due to ever increasing demand for different types of paper, it is crucial to optimize the Kraft pulping process to achieve the required paper properties. This work proposes a framework to regulate these paper properties by building a novel closed-loop long short-term memory (LSTM)-based model predictive control system. First, a multiscale model was developed by combining the mass and thermal energy balance equations adopted from Purdue model with a layered kinetic Monte Carlo (kMC) algorithm that describes the time-evolution of microscopic events such as fiber morphology, kappa number, and cellulose Degree of Polymerization (DP). Then, this model was run over different operating conditions by varying the temperature, concentration, and cooking time to generate data. An LSTM-ANN network was trained using these datasets with a prediction accuracy of over 98% capturing the behavior of cellulose DP and considering the effects of time-varying and time-invariant operating conditions together. Finally, a closed-loop LSTM-based optimal controller was designed, which was demonstrated to achieve the target set-point values and obtain optimal constant value inputs along with time-series inputs while considering process constraints. The results showed excellent accuracy and the controller was computationally less expensive due to the use of a well-trained LSTM network in the proposed framework.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"221 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131653268","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

Trade-offs in the Design of Multi-Loop Controllers for Floating Wind Turbines 漂浮式风力发电机多回路控制器设计中的权衡

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156143

David Stockhouse, L. Pao

引用次数: 1

Distributed formation trajectory planning for multi-vehicle systems 多车辆系统的分布式编队轨迹规划

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156635

Binh Nguyen, Truong X. Nghiem, Linh Nguyen, Tung Nguyen, Hung M. La, Mehdi Sookhak, Thang Nguyen

{"title":"Distributed formation trajectory planning for multi-vehicle systems","authors":"Binh Nguyen, Truong X. Nghiem, Linh Nguyen, Tung Nguyen, Hung M. La, Mehdi Sookhak, Thang Nguyen","doi":"10.23919/ACC55779.2023.10156635","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156635","url":null,"abstract":"This paper addresses the problem of distributed formation trajectory planning for multi-vehicle systems with collision avoidance among vehicles. Unlike some previous distributed formation trajectory planning methods, our proposed approach offers great flexibility in handling computational tasks for each vehicle when the global formation of all the vehicles changes. It affords the system the ability to adapt to the computational capabilities of the vehicles. Furthermore, global formation constraints can be handled at any selected vehicles. Thus, any formation change can be effectively updated without recomputing all local formations at all the vehicles. To guarantee the above features, we first formulate a dynamic consensus-based optimization problem to achieve desired formations while guaranteeing collision avoidance among vehicles. Then, the optimization problem is effectively solved by ADMM-based or alternating projection-based algorithms, which are also presented. Theoretical analysis is provided not only to ensure the convergence of our method but also to show that the proposed algorithm can surely be implemented in a fully distributed manner. The effectiveness of the proposed method is illustrated by a numerical example of a 9-vehicle system.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"355 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":"127582524","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

Sensor Safety and Multi-Objective Satellite Control under Nonlinear Dynamics 非线性动力学下传感器安全与多目标卫星控制

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156074

Kristina Miller, J. Brewer, Alexander A. Soderlund, S. Phillips

引用次数: 0