2022 26th International Conference on System Theory, Control and Computing (ICSTCC)最新文献_第6页

Combined MPC and reinforcement learning for traffic signal control in urban traffic networks 结合MPC和强化学习的城市交通网络交通信号控制

2022 26th International Conference on System Theory, Control and Computing (ICSTCC) Pub Date : 2022-10-19 DOI: 10.1109/ICSTCC55426.2022.9931771

Willemijn Remmerswaal, D. Sun, A. Jamshidnejad, B. Schutter

{"title":"Combined MPC and reinforcement learning for traffic signal control in urban traffic networks","authors":"Willemijn Remmerswaal, D. Sun, A. Jamshidnejad, B. Schutter","doi":"10.1109/ICSTCC55426.2022.9931771","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931771","url":null,"abstract":"In general, the performance of model-based controllers cannot be guaranteed under model uncertainties or disturbances, while learning-based controllers require an extensively sufficient training process to perform well. These issues especially hold for large-scale nonlinear systems such as urban traffic networks. In this paper, a new framework is proposed by combining model predictive control (MPC) and reinforcement learning (RL) to provide desired performance for urban traffic networks even during the learning process, despite model uncertainties and disturbances. MPC and RL complement each other very well, since MPC provides a sub-optimal and constraint-satisfying control input while RL provides adaptive control laws and can handle uncertainties and disturbances. The resulting combined framework is applied for traffic signal control (TSC) of an urban traffic network. A case study is carried out to compare the performance of the proposed framework and other baseline controllers. Results show that the proposed combined framework outperforms conventional control methods under system uncertainties, in terms of reducing traffic congestion.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121414280","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: 2

System Identification and Process Modelling of Dynamic Systems Using Machine Learning 使用机器学习的动态系统识别和过程建模

2022 26th International Conference on System Theory, Control and Computing (ICSTCC) Pub Date : 2022-10-19 DOI: 10.1109/ICSTCC55426.2022.9931831

Ravi kiran Inapakurthi, K. Mitra

{"title":"System Identification and Process Modelling of Dynamic Systems Using Machine Learning","authors":"Ravi kiran Inapakurthi, K. Mitra","doi":"10.1109/ICSTCC55426.2022.9931831","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931831","url":null,"abstract":"Nonlinear system identification of complex and nonlinear unit operations and unit processes requires accurate modelling approaches. For this, first-principles based models were initially explored as they enable the causal explanation available among variables. However, the numerical integration issues along with the availability of voluminous data for developing data-based models has resulted in the shift from the conventional modelling approach to Machine Learning (ML) based modelling. In this study, Support Vector Regression (SVR) is used to model complex Industrial Grinding Circuit (IGC). To aid the accurate model requirement in process systems engineering domain, the tunable parameters of SVR are optimized using a novel multi-objective optimization formulation, which helps in minimizing the chances of over-fitting while simultaneously ensuring accurate models for IGC. The formulation is optimized using evolutionary algorithm to track and retain the most accurate models. The Pareto optimal SVR models have a minimum accuracy of 99. 786% and the prediction performance of the best model selected using knee point from the Pareto optimal set is compared with a model selected using arbitrary approach to show the competitiveness of the proposed technique.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123062034","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 network transmission to minimize state-estimation error and channel usage 优化网络传输以最小化状态估计误差和信道使用

2022 26th International Conference on System Theory, Control and Computing (ICSTCC) Pub Date : 2022-10-19 DOI: 10.1109/ICSTCC55426.2022.9931801

Sayeh Rezaee, César Nieto, Abhyudai Singh

{"title":"Optimal network transmission to minimize state-estimation error and channel usage","authors":"Sayeh Rezaee, César Nieto, Abhyudai Singh","doi":"10.1109/ICSTCC55426.2022.9931801","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931801","url":null,"abstract":"We consider the problem of transmitting the state value of a dynamical system through a communication network. The dynamics of the error in state estimation is modeled using a stochastic hybrid system formalism, where the error grows exponentially over time. Transmission occurs over the network at specific times to acquire the system's state, and whenever a transmission is triggered, the error is reset to a zero-mean random variable. Our goal is to uncover transmission strategies that minimize a combination of the steady-state error variance and the average number of transmissions per unit of time. We found that a constant Poisson rate of transmission results in a heavy-tailed distribution for the estimation error. Next, we consider a random non-threshold transmission rate that varies as a power law of the error. Finally, we explore a threshold-based rate in which transmission occurs exactly when the error reaches a threshold. Our results show that if the error's variance after transmission is small enough, a threshold-based strategy is the optimal paradigm. On the other hand, if this variance is large, and the error does not grow fast enough, the random non-threshold transmission strategy emerges as optimal. These analytical results are verified by simulations of the stochastic hybrid system.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115837328","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

Model Predictive Control of Simulated Moving Bed Chromatographic Processes Using Conservation Element/Solution Element Method 守恒元/溶液元法在模拟移动床色谱过程中的模型预测控制

2022 26th International Conference on System Theory, Control and Computing (ICSTCC) Pub Date : 2022-10-19 DOI: 10.1109/ICSTCC55426.2022.9931774

Valentin Plamenov Chernev, Lino O. Santos, A. Wouwer, A. Kienle

{"title":"Model Predictive Control of Simulated Moving Bed Chromatographic Processes Using Conservation Element/Solution Element Method","authors":"Valentin Plamenov Chernev, Lino O. Santos, A. Wouwer, A. Kienle","doi":"10.1109/ICSTCC55426.2022.9931774","DOIUrl":"https://doi.org/10.1109/ICSTCC55426.2022.9931774","url":null,"abstract":"Simulated moving bed chromatographic (SMB) processes are used for difficult separations in pharmaceutical, biotechnological and petrochemical industries. Due to high sensitivity to disturbances these processes are usually operated in open-loop mode under suboptimal conditions. In the present work, operation of such processes based on the online optimizing model predictive control (MPC) using the full blown chromatographic model is proposed. For the fast and accurate solution of the underlying model described by a system of partial differential algebraic equations, the so-called space-time conservation element/solution method (CE/SE) is used. As an application example, the separation of racemic mixture of bicalutamides, one of which is a valuable active pharmaceutical component, is considered. To evaluate the performance of the controller, reference tracking (change of the purity requirements) and disturbance rejection (change of the composition of the feed mixture) scenarios are simulated. Since there is no plant-model mismatch, the controller is able to follow the change of the reference from complete to reduced purity separation closely. However, the results of the disturbance rejection simulation shows that the controller requires an adaption mechanism in order to efficiently reject the disturbance.","PeriodicalId":220845,"journal":{"name":"2022 26th International Conference on System Theory, Control and Computing (ICSTCC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132480174","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

Vehicle Trajectory Planning for Collision Avoidance with Mobile Obstacles 移动障碍物下车辆避碰轨迹规划

2022 26th International Conference on System Theory, Control and Computing (ICSTCC) Pub Date : 2022-10-19 DOI: 10.1109/ICSTCC55426.2022.9931808

Ovidiu Pauca, A. Maxim, C. Caruntu

引用次数: 0

Explainable artificial intelligence for deep learning-based model predictive controllers 基于深度学习的模型预测控制器的可解释人工智能

2022 26th International Conference on System Theory, Control and Computing (ICSTCC) Pub Date : 2022-10-19 DOI: 10.1109/ICSTCC55426.2022.9931794

Christian Utama, B. Karg, Christian Meske, S. Lucia

引用次数: 0

Chen-Fliess Series for Linear Distributed Systems with One Spatial Dimension 一维线性分布系统的Chen-Fliess级数

2022 26th International Conference on System Theory, Control and Computing (ICSTCC) Pub Date : 2022-10-19 DOI: 10.1109/ICSTCC55426.2022.9931853

Natalie Pham, W. Gray

引用次数: 0

Distributing Co-safe LTL Specifications to Mobile Robots 向移动机器人分发共同安全LTL规范

2022 26th International Conference on System Theory, Control and Computing (ICSTCC) Pub Date : 2022-10-19 DOI: 10.1109/ICSTCC55426.2022.9931878

Ioana Hustiu, C. Mahulea, M. Kloetzer

引用次数: 2

Parking facility management solution based on dynamic traffic distribution 基于动态交通分布的停车设施管理解决方案

2022 26th International Conference on System Theory, Control and Computing (ICSTCC) Pub Date : 2022-10-19 DOI: 10.1109/ICSTCC55426.2022.9931862

Florin Sandru, Vlad-Ilie Ungureanu, I. Silea

引用次数: 0

Least squares moment matching-based model reduction using convex optimization 基于最小二乘矩匹配的凸优化模型约简

2022 26th International Conference on System Theory, Control and Computing (ICSTCC) Pub Date : 2022-10-19 DOI: 10.1109/ICSTCC55426.2022.9931837

T. Ionescu, Lahcen El Bourkhissi, I. Necoara

引用次数: 0