2022 Eighth Indian Control Conference (ICC)最新文献

Discrete Time Predictive Control with Dual State System Around Unstable Origin Case Study 不稳定原点周围双状态系统的离散时间预测控制

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093430

Chinmay Rajhans, Surender Kannaiyan, Sowmya Gupta

引用次数: 0

Stability and bifurcation analysis of the predator-prey model with Michaelis-Menten type harvesting and immigration Michaelis-Menten型捕食-食饵模型的稳定性和分岔分析

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093658

M. Priyanka, P. Muthukumar

引用次数: 0

Wheel Slip Balance Based Anti-Slip Regulation on Dissymmetric Road Grip 基于车轮滑移平衡的非对称路面抓地力防滑调节

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093618

Gaël G. Atheupe, R. Sreekanth, S. Arrvind, K. Bordignon, B. Monsuez, A. Tapus

{"title":"Wheel Slip Balance Based Anti-Slip Regulation on Dissymmetric Road Grip","authors":"Gaël G. Atheupe, R. Sreekanth, S. Arrvind, K. Bordignon, B. Monsuez, A. Tapus","doi":"10.1109/ICC56513.2022.10093618","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093618","url":null,"abstract":"Anti-Slip regulation system (ASR) as the name suggests, prevents excessive wheel spin by limiting wheel slip on the spinning wheel using brake torque or engine torque intervention when starting off and accelerating, particularly on a slippery road surface. These kinds of circumstances can overtax the driver not only causing them to react inappropriately but also causing the vehicle to become unstable. (ASR) tackles these problems, providing the vehicle remains within the physical limits. This paper presents an inter-axle anti-slip regulation logic for vehicle stability in the midst of dissymmetric road grip conditions. It employs the inter-axle wheel slip balance control strategy, to increase driving stability and improve traction with respect to the driver's torque request. The proposed method is assessed and validated by simulation in AMESim. Results demonstrate the effectiveness of the proposed method in terms of vehicle stability and steer ability through the regulation of drive torque at each driven wheel based on the inter- axle slip balance target. In addition to this safety relevant-task of ensuring stability and steering ability of the vehicle in the acceleration phase, ASR improves traction by helping find levels of wheel slip not easily accessible.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131367983","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

Finite Time Control on Unit Sphere with an Application to Trajectory Tracking Control of UAVs 单位球面有限时间控制及其在无人机轨迹跟踪控制中的应用

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093301

Gaurav Ghosh, Rakesh R. Warier

引用次数: 0

Sliding Mode Observer-Based Sliding Mode Control for Path Following of a Differential Drive Automated Mobile Robot 基于滑模观测器的差动驱动自动移动机器人路径跟踪滑模控制

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093578

Rudra Prakash, J. Samantaray, S. Chakrabarty

引用次数: 0

Self-Feedback-Based Resilient Consensus Network 基于自反馈的弹性共识网络

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093390

Sujeet Kumar, I. Kar

引用次数: 0

Statistical Inference and Analysis for Efficient Modeling of Environmental Pollution using Deep Neural Networks 基于深度神经网络的环境污染有效建模的统计推断与分析

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093411

Chilukuri Lakshmi Sravani, S. Miriyala, K. Mitra

{"title":"Statistical Inference and Analysis for Efficient Modeling of Environmental Pollution using Deep Neural Networks","authors":"Chilukuri Lakshmi Sravani, S. Miriyala, K. Mitra","doi":"10.1109/ICC56513.2022.10093411","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093411","url":null,"abstract":"Rapid development, due to industrialization and urbanization happening worldwide, has become a prominent cause of air pollution. In such a situation, it is important to create an air quality prediction model development methodology, which not only models the data but also provides inferences understandable to the policymakers. Therefore, in this research, a new methodology has been proposed, where the prediction model is created by combining the concepts of Statistical Inferencing and Deep Learning [Gated Recurring Units (GRU)]. Hourly air pollutants concentration and meteorological data with 14 features measured over one year from 25 different monitoring stations in Northern Taiwan are considered as the dataset. Using methodologies such as Analysis of Variance, Tukey Honestly Significant Difference, Graph theory, and Chi-Square analysis, the voluminous dataset is first clustered based on geographical correlations, and for each cluster, the most significant features responsible for modulating Particulate Matter (PM10) concentrations are identified. Subsequently, the new datasets obtained through the statistical study are used to train the GRU model for final predictions. The proposed model has exhibited an overall accuracy between 90.4% to 99.2% for all clusters. The generic nature of the proposed methodology allows for its extension to predict the transient behaviour of other pollutants across different geographical locations.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"57 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123296911","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

Multi-objective Optimization and control under Uncertainty for performance improvement of a Baculovirus Expression Vector System 杆状病毒表达载体系统的多目标优化与不确定性控制

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093623

Surbhi Sharma, L. Giri, K. Mitra

{"title":"Multi-objective Optimization and control under Uncertainty for performance improvement of a Baculovirus Expression Vector System","authors":"Surbhi Sharma, L. Giri, K. Mitra","doi":"10.1109/ICC56513.2022.10093623","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093623","url":null,"abstract":"Bioprocess optimization and control for large scale production of vaccine/protein remain challenging due to the adaptation of experiment-based route which needs numerous expensive and time intensive experiments. The presence of model uncertainties in such a nonlinear system further makes the optimization and scale -up challenging. In this context, we propose a robust framework amalgamating the paradigms of systems biology and dynamic optimization under uncertainty for improving the performance of one of the most widely used vaccine/protein production platform, the Baculovirus expression system [BEVs]. Here, the multi-objective optimal control problem is formulated with an objective of maximizing the productivity and minimizing raw material consumption in a semi-batch baculovirus system considering parametric uncertainty. A comprehensive comparison shows that a multifold increase in the productivity can be obtained using this computational framework considering controlled addition of feed material. This study provides a generic methodology for improving the performance of a bioprocess and represents the first instance where robust optimal control has been applied for optimizing the productivity of a baculovirus-insect cell system.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126805605","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

Topology Reconstruction of a Resistive Network with Limited Boundary Measurements 有限边界电阻网络的拓扑重构

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093371

S. Biradar, D.U Patil

引用次数: 1

A Deep Unsupervised Learning Algorithm for Clustering of Wind Frequency Maps 风频图聚类的深度无监督学习算法

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093581

P. D. Pantula, S. Miriyala, K. Mitra

{"title":"A Deep Unsupervised Learning Algorithm for Clustering of Wind Frequency Maps","authors":"P. D. Pantula, S. Miriyala, K. Mitra","doi":"10.1109/ICC56513.2022.10093581","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093581","url":null,"abstract":"Wind energy is now the world's second-fastest-growing electricity source. The power output of the wind farm depends on wind characteristics like wind speed and direction and wind farm layout. Specifically, these wind characteristics are modeled using a probability density function built using local wind measurements over the farm, called the Wind Frequency Maps (WFMs). The conventional approach for modeling this dynamic data is to perform manual feature extraction followed by static data clustering since the data is unlabeled. Nonetheless, since the features to be extracted are based on heuristics and may lead to information loss, this technique is inefficient. Thus, in this study, the wind characteristics data is treated in the form of images that are essentially the surface plots corresponding to the joint probability mass functions built over 12 direction sectors and 16-speed sectors. Moreover, the WFMs are modeled using a novel unsupervised Deep Learning framework where the required features are extracted using convolutional auto-encoders, followed by applying a soft clustering algorithm that can identify optimal cluster number. Here, 1400 such WFMs, were generated, 11 latent vectors were extracted, and finally, the images were grouped into 4 clusters with varying wind characteristics. Two of these clusters are found to be relatively denser. Further, this study will help perform wind farm layout optimization under uncertainty and control studies.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132583713","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