2023 American Control Conference (ACC)最新文献_第6页

On XYZ-Motion Planning for Autonomous Vehicles with Active Suspension Systems 基于主动悬架系统的自动驾驶汽车xyz运动规划研究

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156511

Yu Jiang, W. Graves, Marco Giovanardi, Zack Anderson

引用次数: 0

Integrated Task and Motion Planning for Process-aware Source Seeking 基于过程感知的寻源集成任务与运动规划

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156291

Yingke Li, Mengxue Hou, Enlu Zhou, Fumin Zhang

引用次数: 1

Index of Papers Published in the IEEE ACC 2023 发表于IEEE ACC 2023的论文索引

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/acc55779.2023.10155829

引用次数: 0

On the complexity of linear systems: an approach via rate distortion theory and emulating systems 线性系统的复杂性:一种基于率失真理论和仿真系统的方法

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10155927

Eric D. B. Wendel, J. Baillieul, Joseph Hollmann

引用次数: 0

Lyapunov-based Current-Profile Feedback Control in Tokamaks with Nonsymmetric Individual Actuator Saturation∗ 基于李雅普诺夫的非对称单致动器饱和托卡马克电流分布反馈控制

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156029

S. Paruchuri, A. Pajares, E. Schuster

{"title":"Lyapunov-based Current-Profile Feedback Control in Tokamaks with Nonsymmetric Individual Actuator Saturation∗","authors":"S. Paruchuri, A. Pajares, E. Schuster","doi":"10.23919/ACC55779.2023.10156029","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156029","url":null,"abstract":"Advanced tokamak scenarios can achieve optimal tokamak operation by shaping the plasma internal profiles through the use of noninductive heating and current sources. As a result of the dynamic complexities, active control of the power of each noninductive heating and current source, a non-negative value, may be necessary to achieve the desired tokamak performance. However, due to the inherent physical limitations, arbitrary power prescription by the controller may saturate the heating and current drives. Therefore, it is highly desirable to develop a class of active control algorithms that account for the saturation limits of these actuators. A Lyapunov-based nonlinear feedback control algorithm that intrinsically accounts for saturation limits is proposed in this work to regulate the spatial distribution of the toroidal current density in the tokamak. The controller does not rely on constrained optimization techniques, which can be computationally expensive for real-time implementation. Furthermore, the controller can handle nonsymmetric saturation limits, i.e., the absolute values of the upper and lower saturation limits do not have to be equal. The effectiveness of the control algorithm is demonstrated for a DIII-D tokamak scenario in nonlinear simulations.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"25 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":"124001374","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

Chance-Constrained Control with Imperfect Perception Modules 不完全感知模块的机会约束控制

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10155868

Beomjun Kim, Heejin Ahn

{"title":"Chance-Constrained Control with Imperfect Perception Modules","authors":"Beomjun Kim, Heejin Ahn","doi":"10.23919/ACC55779.2023.10155868","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10155868","url":null,"abstract":"Autonomous systems are required to operate in different environments, but recognizing the current environment is often challenging. For example, an autonomous vehicle should stop or obey a speed limit according to a traffic sign, but state-of-the-art perception modules (e.g., neural networks) do not guarantee the correctness of their reading of the traffic sign. Considering such uncertain outputs of a perception module, which in effect determines modes, we propose a chance-constrained control formulation that with high probability guarantees the satisfaction of a set of constraints associated with the possible modes. To do this, we present a method based on the Bayes rule and sampling to calculate the probability of each mode. We prove that our approach can ensure satisfying constraints of novel situations, which have not been used during training of the perception module. Also, to account for the error due to limited data, we present a robust formulation that guarantees constraint satisfaction with high confidence. In an autonomous vehicle example, we train a neural network that classifies traffic signs and show that given each output of the neural network, our motion planning approach guarantees the constraint satisfaction with high probability.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"95 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":"124047107","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

Thermal Comfort Control on Sustainable Building via Data-Driven Robust Model Predictive Control 基于数据驱动鲁棒模型预测控制的可持续建筑热舒适控制

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10155818

Wei-Han Chen, Shiyu Yang, F. You

{"title":"Thermal Comfort Control on Sustainable Building via Data-Driven Robust Model Predictive Control","authors":"Wei-Han Chen, Shiyu Yang, F. You","doi":"10.23919/ACC55779.2023.10155818","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10155818","url":null,"abstract":"While implementing renewable energy systems and model predictive control (MPC) could reduce non-renewable energy consumption, one challenge to building climate control using MPC is the weather forecast uncertainty. In this work, we propose a data-driven robust model predictive control (DDRMPC) framework to address climate control of a sustainable building with renewable hybrid energy systems under weather forecast uncertainty. The control and energy system configurations include heating, ventilation, and air conditioning, geothermal heat pump, photovoltaic panel, and electricity storage battery. Historical weather forecast and measurement data are gathered from the weather station to identify the forecast errors and for the use of uncertainty set construction. The data-driven uncertainty sets are constructed with multiple machine learning techniques, including principal component analysis with kernel density estimation, K-means clustering coupled with PCA and KDE, density-based spatial clustering of applications with noise, and the Dirichlet process mixture model. Lastly, a data-driven robust optimization problem is developed to obtain the optimal control inputs for a building with renewable energy systems. A case study on controlling a building with renewable energy systems located on the Cornell University campus is used to demonstrate the advantages of the proposed DDRMPC framework.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"5 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":"125778739","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

Belief State Actor-Critic Algorithm from Separation Principle for POMDP 基于分离原理的POMDP信念状态行为者-批评家算法

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10155792

Yujie Yang, Yuxuan Jiang, Jianyu Chen, S. Li, Ziqing Gu, Yuming Yin, Qian Zhang, Kai Yu

{"title":"Belief State Actor-Critic Algorithm from Separation Principle for POMDP","authors":"Yujie Yang, Yuxuan Jiang, Jianyu Chen, S. Li, Ziqing Gu, Yuming Yin, Qian Zhang, Kai Yu","doi":"10.23919/ACC55779.2023.10155792","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10155792","url":null,"abstract":"Partially observable Markov decision process (POMDP) is a general framework for decision making and control under uncertainty. A large class of POMDP algorithms follows a two-step approach, in which the first step is to estimate the belief state, and the second step is to solve for the optimal policy taking the belief state as input. The optimality guarantee of their combination relies on the so-called separation principle. In this paper, we propose a new path to prove the separation principle for infinite horizon general POMDP problems under both discounted cost and average cost. We use a nominal horizon to split a virtual objective function into two parts and prove that it converges to the optimal state-value function. Based on the separation principle, we design a two-step POMDP algorithm called Belief State Actor-Critic (BSAC), which first estimates the belief state and then takes it as input to solve for the optimal policy. The belief state is learned using variational inference, and the policy is learned through model-based reinforcement learning. We test our algorithm in a partially observable multi-lane autonomous driving task. Results show that our algorithm achieves lower costs than the baselines and learns safe, efficient, and smooth driving behaviors.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"35 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":"126004308","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 of a bioeconomic model applied to the recovery of household waste* 应用于生活垃圾回收的生物经济模型的最优控制*

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156431

Othman Cherkaoui Dekkaki, W. Djema

引用次数: 0

Piecewise Quantization-Dependent Approach to Quantized Stabilization of Piecewise-Affine Systems* 基于分段量化的分段仿射系统的量化稳定方法*

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156205

Zepeng Ning, Yiming Cheng, Zhaojian Li, Xunyuan Yin

引用次数: 0