2021 Seventh Indian Control Conference (ICC)最新文献_第7页

Machine Learning based Surrogate Assisted Multi-Objective Optimization of Continuous Casting Process 基于机器学习的代理辅助连铸工艺多目标优化

2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703180

Ravi kiran Inapakurthi, K. Mitra

{"title":"Machine Learning based Surrogate Assisted Multi-Objective Optimization of Continuous Casting Process","authors":"Ravi kiran Inapakurthi, K. Mitra","doi":"10.1109/ICC54714.2021.9703180","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703180","url":null,"abstract":"Optimization of industrial continuous casting process requires faster models tuned across various operating regimes. Data based modelling techniques like Support Vector Regression (SVR) are proven to be efficient modelling techniques as they are based on structural risk minimization principle. However, the hyper-parameters of SVR are usually tuned on trial-and-error basis without any rationale leading to inappropriate model. To generate an efficient model for the continuous casting process, we propose an algorithm for estimating the hyper-parameters of SVR by considering Root Mean Square Error (RMSE) of the model and sample size required for modelling as the conflicting objectives. Differing importance to various inputs under different conditions leads us to use different kernel parameters for different inputs during model development. Additionally, many kernels are explored to decipher the unknown nature of the continuous casting process. Simulation results show that the proposed algorithm could develop temperature and bulging models, with which the optimization of the casting process has been shown to be effective.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121999544","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

Multivariable Causal Analysis of Nonlinear Dynamical Systems using Convergent Cross Mapping 基于收敛交叉映射的非线性动力系统多变量因果分析

2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703137

S. Nithya, A. Tangirala

{"title":"Multivariable Causal Analysis of Nonlinear Dynamical Systems using Convergent Cross Mapping","authors":"S. Nithya, A. Tangirala","doi":"10.1109/ICC54714.2021.9703137","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703137","url":null,"abstract":"Convergent Cross Mapping (CCM) was introduced as a data-driven technique to identify causal links in weakly coupled deterministic non-linear systems where other causal definitions, like the celebrated Granger Causality, fail due to their limited applicability to stochastic systems only. CCM is based on the idea of quantifying the extent to which a potential causal signal $x[k]$ is recoverable from another effect signal $y[k]$ with increasing data length. A major drawback of the CCM is its inability to distinguish between direct and indirect causal links that is necessary for reconstructing the direct causal network from observed time series. In this work, we propose a multivariable approach to solve this issue. First, we perform the pair-wise CCM analysis and identify all the effects (both direct and indirect) linked to a cause. Next, we perform a multivariable state-space reconstruction using the identified effect variables and use it to recover the cause variable. We then evaluate the incremental improvement in the recovery as compared to the univariable case. A significant improvement indicates that the effect is an indirect one, while the converse indicates a direct effect. We also address a second shortcoming of CCM by proposing an improved metric for quantifying cross mapping of variables. Case studies on simulated and real data sets are presented to demonstrate the success of proposed developments.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122929466","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

Gain Scheduled Centralized PI Control Design for a Boiler-Turbine Unit 锅炉汽轮机组增益调度集中PI控制设计

2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703139

Falguni Gopmandal, V. Jain, A. Ghosh

引用次数: 0

Discrete-Time Integral Sliding Mode Observer Design for Linear Systems 线性系统离散时间积分滑模观测器设计

2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703162

K. Shah, N. Satyanarayana

引用次数: 0

Ultrafast Learning-Based Nonlinear Model Predictive Control and its Embedded Realization 基于超快速学习的非线性模型预测控制及其嵌入式实现

2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703165

Shaunak Ghatpande, Neeraj Garole, Manali Durgule, N. Mohanty, Deepak D. Ingole, D. Sonawane

{"title":"Ultrafast Learning-Based Nonlinear Model Predictive Control and its Embedded Realization","authors":"Shaunak Ghatpande, Neeraj Garole, Manali Durgule, N. Mohanty, Deepak D. Ingole, D. Sonawane","doi":"10.1109/ICC54714.2021.9703165","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703165","url":null,"abstract":"The core idea of nonlinear model predictive control (NMPC) is to solve an online optimization problem at each sample instant, considering the initial conditions and constraints into account. The online optimization process requires huge computations and is therefore hard to realize on resource-limited hardware. In this paper, a versatile, data-driven, deep learning-based NMPC is proposed for the embedded control applications, which eliminates the burden of solving online optimization problems. The learned controller is intended to reduce the numerical intricacy involved in classical NMPC while keeping its advantages intact. Our idea is to develop a deep neural network (DNN) NMPC based on the data generated by solving an open-loop optimization problem. Then the learned-based NMPC is implemented on resource-limited embedded hardware (Raspberry Pi v4), and its performance is analyzed on a continuous stirred tank reactor (CSTR) system. Furthermore, the performance of developed DNN-NMPC is compared with the classical NMPC implemented on Raspberry Pi v4. The hardware-in-the-line co-simulation results show that the DNN-NMPC imitates the behavior of NMPC while reducing the time complexity. Thus, eliminating the main bottleneck of classical NMPC, this paper elucidates an alternative algorithm to increase the use of NMPC for large-scale industrial applications for which classical NMPC is often limited due to its computational complexity.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125646975","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

Continuous Control of a Robot Manipulator Using Deep Deterministic Policy Gradient 基于深度确定性策略梯度的机器人机械臂连续控制

2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703155

M. Shetty, Brunda Vishishta, Shrinidhi Choragi, Karpagavalli Subramanian, Koshy George

引用次数: 0

Modeling, Control and Variational Integration for an inverted pendulum on $S^{1}$ $S^{1}$上倒立摆的建模、控制与变分积分

2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703183

Manmohan Sharma, I. Kar

引用次数: 0

Guidance for Spacecraft Docking incorporating Model Uncertainties: A Linear Quadratic Tracking Approach 考虑模型不确定性的航天器对接制导:一种线性二次跟踪方法

2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703145

Annie Jose, Divina D V, Dona Thomas, Malavika S, V. J, R. U P, I. T P

引用次数: 0

Optimizing controllability metrics for target controllability 优化目标可控性的可控性指标

2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703184

Anand Gokhale, Srighakollapu M Valli, R. Kalaimani, R. Pasumarthy

{"title":"Optimizing controllability metrics for target controllability","authors":"Anand Gokhale, Srighakollapu M Valli, R. Kalaimani, R. Pasumarthy","doi":"10.1109/ICC54714.2021.9703184","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703184","url":null,"abstract":"While dealing with the problem of control of complex networks, in addition to verifying qualitative properties of whether the system is controllable or not, one needs to quantify the effort needed to control the system. This is because the required control effort becomes significantly large, especially when there are constraints on the number of control inputs, rendering the system practically uncontrollable. In some cases, it may not be required to control all the nodes of the network but rather a subset of states called target nodes, in which case the energy requirements reduce substantially with dropping off few nodes for control. Building upon this finding, we attempt to solve three problems in this paper. First, using the average controllability as a metric, we identify the best set of $p$ target nodes that maximize the average controllability. In practical situations, one needs to know an upper bound on the input energy. Our second problem identifies the largest set of target nodes given worst case energy bound, using the minimum eigenvalue of the gramian as the metric. Lastly, given the size of a target set, we aim to identify the set of nodes that minimize the upper bound of worst case energy needed for control. We validate our findings on some tractable examples and randomly generated Erdos-Renyi Networks.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122216850","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

Stability of nonlinear filters - numerical explorations of particle and ensemble Kalman filters 非线性滤波器的稳定性-粒子和集合卡尔曼滤波器的数值探索

2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703185

Pinak Mandal, Shashank Kumar Roy, A. Apte

引用次数: 1