2022 Eighth Indian Control Conference (ICC)最新文献_第3页

A Hybrid Model-Based and Rule-Based Algorithm for an Antilock Brake System Using a Discrete-Time Switching Mode Controller 基于离散时间切换模式控制器的防抱死制动系统的混合模型与规则算法

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093346

Pavel Vijay Gaurkar, Harshal Patil, Gunasekaran Vivekanandan, S. Sivaram, S. Subramanian

引用次数: 0

Nonlinear Opinion Dynamics using Disagreement Laplacian Flows in Antagonistic Networks 对立网络中基于分歧拉普拉斯流的非线性意见动态

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093460

Aashi Shrinate, Twinkle Tripathy, L. Behera

引用次数: 0

Emulation Alternating Direction Method of Multipliers 乘法器的交替方向仿真方法

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093531

Chinmay Routray, S. R. Sahoo

引用次数: 0

Safely increasing capacity of traffic intersections with mixed autonomous vehicles using delayed self reinforcement 使用延迟自我强化的混合自动驾驶汽车安全增加交通交叉口的通行能力

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093255

Yudong Lin, Anuj Tiwari, B. Fabien, S. Devasia

{"title":"Safely increasing capacity of traffic intersections with mixed autonomous vehicles using delayed self reinforcement","authors":"Yudong Lin, Anuj Tiwari, B. Fabien, S. Devasia","doi":"10.1109/ICC56513.2022.10093255","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093255","url":null,"abstract":"Capacity at traffic intersections can be increased if all autonomous vehicles (AVs) have traffic-light-to-vehicle-communication (TLVC), leading to better inter-vehicle spacing control and reduction of time needed to start and move through an intersection. However, with a mix of autonomous vehicles, some vehicles with TLVC and some without TLVC, the capacity improvements of TLVC are lost and safety can be compromised with potential collisions. Such scenarios can be expected during the transition to fully connected autonomous vehicles (CAVs). The main contribution of this work is to show that the increased capacity of CAVs with TLVC at traffic lights can be recovered safely for mixed AVs network by using delayed self reinforcement (DSR). The advantage of using DSR, where each autonomous vehicles augment its action using delayed versions of past actions, is that it can keep the spacing errors small (i.e., maintain cohesion) even if centralized communication is not available. Therefore, the use of DSR maintains the capacity of traffic intersections even for mixed AVs network, where some vehicles do not have access to the TLVC. Simulation results show the improvement of capacity and safety with the use of DSR when compared to the case without DSR. In particular, DSR improves the traffic capacity by increasing the traffic flow speed by 38% when TLVC is not available. Furthermore, DSR improves safety by reducing the variation in maximum spacing error by 96% under different mixed-connectivity scenarios at the intersection, and thereby, removes the potential for collision seen in the case without DSR.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"33 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":"131849217","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

Restless Bandits for Sensor Scheduling in Energy Constrained Networks 能量约束网络中传感器调度的不宁强盗

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093670

R. Meshram, Kesav Kaza, Varun Mehta, S. Merchant

{"title":"Restless Bandits for Sensor Scheduling in Energy Constrained Networks","authors":"R. Meshram, Kesav Kaza, Varun Mehta, S. Merchant","doi":"10.1109/ICC56513.2022.10093670","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093670","url":null,"abstract":"We consider the problem of sensor scheduling in energy constrained network. It is modeled using restless multi-armed bandits with dynamic availability of arms. An arm represents the sensor and due to the energy constrained its availability is dynamic. The data transmission rate depends on the channel quality. Sensor scheduling problem is a sequential decision problem which needs to account both for the evolution of the channel quality and fluctuation in energy levels of sensor nodes. When sensor with available energy is scheduled, it yields data rate based on channel quality, this is referred to as immediate reward. The channel quality is modeled using two state Markov model. The higher channel state corresponds to higher quality, and hence higher immediate reward. When sensors are not scheduled, it yields no reward. Sensors with non-availability of energy are not scheduled. Further, channel quality of sensors is not observable to the decision maker but signals after data transmissions are observable. It is called as partially observable restless bandits. The objective of decision maker is to maximize infinite horizon discounted cumulative reward by sequentially scheduling sensors. We study Whittle's index policy, and describe algorithm to compute index formula. We also study online rollout policy and analyze the computation complexity. The simulation examples compare the performances of different policies-index policy, rollout policy, and myopic policy.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"63 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":"132697954","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: 1

Multi-objective Bayesian Optimization for Computationally Expensive Reaction Network Models 计算代价昂贵的反应网络模型的多目标贝叶斯优化

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093513

Arjun Manoj, S. Miriyala, K. Mitra

{"title":"Multi-objective Bayesian Optimization for Computationally Expensive Reaction Network Models","authors":"Arjun Manoj, S. Miriyala, K. Mitra","doi":"10.1109/ICC56513.2022.10093513","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093513","url":null,"abstract":"Multi-objective optimization of complex reaction network models is often demanding due to the large computational expense for such calculations. We consider one such model for long-chain branched polyvinyl acetate (PVAc-LCB) production consisting of a large set of stiff differential equations. In such a case, where the evaluation of the solution becomes expensive, optimization of the problem using a relatively inexpensive surrogate is a feasibility to explore. We propose a Bayesian optimization framework that balances exploitation (maximizing information from the current best solution from a set of candidates) and exploration (minimizing the uncertainty in unexplored landscape) to solve this extremely complex problem. The Gaussian Process-based surrogate model provides the landscape that is evaluated using a suitable acquisition function for sampling the next best location towards the global optima. We also present a comparison study between the results of the proposed Bayesian approach and that obtained using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) without the use of a surrogate. The proposed strategy builds a Pareto front that is comparable to the high-fidelity Pareto front with a computational gain of almost 100-fold.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"33 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":"134146015","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

Dynamics based Observer for Differential-Drive Vehicles 基于动力学的差速驱动车辆观测器

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093310

Mohit Ludhiyani, A. Sadhu, Titas Bera, R. Dasgupta

{"title":"Dynamics based Observer for Differential-Drive Vehicles","authors":"Mohit Ludhiyani, A. Sadhu, Titas Bera, R. Dasgupta","doi":"10.1109/ICC56513.2022.10093310","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093310","url":null,"abstract":"Localization, i.e., pose estimation of a ground vehicle with respect to some inertial frame, is among key issues in mobile robotics. Majority of approaches employed to solve this problem depend primarily on fusion of information from multiple sensors. Frequently, these approaches rely on kinematics model of vehicle for predicting forward in time during fusion. The kinematic models are simple and fast, but do not account for dynamics. The use of dynamics based fusion framework can lead to relatively accurate prediction by exploiting information from the cause of the motion, which kinematic model based frameworks fail to do. Additionally, when depending mainly on exteroceptive sensors such as GPS, Vision, Lidar, etc, for localizing, there may be cases where the sensors may fail temporarily due to changes in environment which may cause localization accuracy to suffer degradation or even loss of localization. The information from dynamics can come in handy for localizing in such scenarios. In this work, we derive a continuous-time dynamic model, which also takes friction into account. Additionally, we propose a Continuous-Discrete Extended Kalman Filter(CD-EKF) based observer, which uses the derived dynamic model as process model for prediction, and measurements from position sensor as measurement updates. Moreover, we perform experiments to show that proposed observer can auto-calibrate the dynamic model in real time, which is more versatile than methods which calibrate dynamic models beforehand, as it enables adaptation to changing conditions like a terrain transition, payload or tyre air-pressure etc.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"26 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":"134290222","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

On Bilateral Teleoperation of Multi-agent Systems: Bearing-based Formation Tracking Control 多智能体系统的双向遥操作:基于方位的编队跟踪控制

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093378

Jeslin Jacob M, Ajul Dinesh, Ameer K. Mulla

引用次数: 0

Control of Temperature Profile in Counter Flow Heat Exchanger using Quantitative Feedback Theory 用定量反馈理论控制逆流式换热器温度分布

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093355

Aritra Sinha, S. P. Chakravarty, P. Roy

引用次数: 0

Evaluation of Optimal Strategy under False Data Injection Attack On Power System: A Reinforcement Learning Approach 电力系统虚假数据注入攻击下的最优策略评估:一种强化学习方法

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093663

G. Revati, Syed Shadab, K. Sonam, S. Wagh, N. Singh

{"title":"Evaluation of Optimal Strategy under False Data Injection Attack On Power System: A Reinforcement Learning Approach","authors":"G. Revati, Syed Shadab, K. Sonam, S. Wagh, N. Singh","doi":"10.1109/ICC56513.2022.10093663","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093663","url":null,"abstract":"Conventional power grids are transforming into more automated smart power grids, with a significant em-phasis on improved performance and enhanced reliability. However, due to the substantial incorporation of communication networks, this cyber-physical system (CPS) is vulnerable to frequent cyber threats. During the information interchange, the invader might penetrate the power system and introduce false data, causing intentional system instability, power outages, and financial loss. The paper focuses on safeguarding the system against the FDI (false data injection) attack on power system state measurement and estimation, which hinders the operator's insight of the system's actual operational status and prevents the operator from taking necessary countermeasures. Since FDI compromises the integrity of system state information, a Markov decision process (MDP) framework is proposed to model the strategy of FDI attack on state estimation in the power system, in order to assess the vulnerability of the power system to cyber-attack. Furthermore, a reinforcement learning (RL) paradigm is exploited to identify the optimal attack strategy, and the operator will solve the problem from the point of view of the intruder. The proposed framework is validated through numerical experiments conducted to investigate the optimal attack strategy with various test case scenarios.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"18 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":"131565162","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