2017 American Control Conference (ACC)最新文献_第8页

Model-Predictive Control of a flexible spine robot 柔性脊柱机器人的模型预测控制

2017 American Control Conference (ACC) Pub Date : 2017-05-24 DOI: 10.23919/ACC.2017.7963738

Andrew P. Sabelhaus, Abishek K. Akella, Z. A. Ahmad, Vytas SunSpiral

引用次数: 22

Active noise control and secondary path modeling algorithms for earphones 耳机的主动噪声控制和二次路径建模算法

2017 American Control Conference (ACC) Pub Date : 2017-05-24 DOI: 10.23919/ACC.2017.7962961

Yi-Rou Chen, Cheng-Yuan Chang, S. Kuo

引用次数: 4

Life prediction of large lithium-ion battery packs with active and passive balancing 具有主动和被动平衡的大型锂离子电池组寿命预测

2017 American Control Conference (ACC) Pub Date : 2017-05-24 DOI: 10.23919/ACC.2017.7963682

Ying Shi, K. Smith, R. Zane, Dyche Anderson

{"title":"Life prediction of large lithium-ion battery packs with active and passive balancing","authors":"Ying Shi, K. Smith, R. Zane, Dyche Anderson","doi":"10.23919/ACC.2017.7963682","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963682","url":null,"abstract":"Lithium-ion battery packs take a major part of large-scale stationary energy storage systems. One challenge in reducing battery pack cost is to reduce pack size without compromising pack service performance and lifespan. Prognostic life model can be a powerful tool to handle the state of health (SOH) estimate and enable active life balancing strategy to reduce cell imbalance and extend pack life. This work proposed a life model using both empirical and physical-based approaches. The life model described the compounding effect of different degradations on the entire cell with an empirical model. Then its lower-level submodels considered the complex physical links between testing statistics (state of charge level, C-rate level, duty cycles, etc.) and the degradation reaction rates with respect to specific aging mechanisms. The hybrid approach made the life model generic, robust and stable regardless of battery chemistry and application usage. The model was validated with a custom pack with both passive and active balancing systems implemented, which created four different aging paths in the pack. The life model successfully captured the aging trajectories of all four paths. The life model prediction errors on capacity fade and resistance growth were within ±3% and ±5% of the experiment measurements.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127444046","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: 11

Data Predictive Control for building energy management 建筑能源管理的数据预测控制

2017 American Control Conference (ACC) Pub Date : 2017-05-24 DOI: 10.23919/ACC.2017.7962928

Achin Jain, Madhur Behl, R. Mangharam

{"title":"Data Predictive Control for building energy management","authors":"Achin Jain, Madhur Behl, R. Mangharam","doi":"10.23919/ACC.2017.7962928","DOIUrl":"https://doi.org/10.23919/ACC.2017.7962928","url":null,"abstract":"Decisions on how to best optimize energy systems operations are becoming ever so complex and conflicting, that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC in buildings, is the cost, time, and effort associated with learning first-principles based dynamical models of the underlying physical system. This paper introduces an alternative approach for implementing finite-time receding horizon control using control-oriented data-driven models. We call this approach Data Predictive Control (DPC). Specifically, by utilizing separation of variables, two novel algorithms for implementing DPC using a single regression tree and with regression trees ensembles (random forest) are presented. The data predictive controller enables the building operator to trade off energy consumption against thermal comfort without having to learn white/grey box models of the systems dynamics. We present a comprehensive numerical study which compares the performance of DPC with an MPC based energy management strategy, using a single zone building model. Our results demonstrate that performance of DPC is comparable to an MPC controller, with only 3.8% additional cost in terms of optimal objective function and within 95% in terms of R2 score, thereby making it an alluring alternative to MPC, whenever the associated cost of learning the model is high.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126241490","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: 37

Blind subspace system identification with Riemannian optimization 黎曼优化盲子空间系统辨识

2017 American Control Conference (ACC) Pub Date : 2017-05-24 DOI: 10.23919/ACC.2017.7963161

Cassiano O. Becker, V. Preciado

{"title":"Blind subspace system identification with Riemannian optimization","authors":"Cassiano O. Becker, V. Preciado","doi":"10.23919/ACC.2017.7963161","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963161","url":null,"abstract":"Subspace identification methods provide a reliable set of methods to recover system parameters of linear dynamical systems based on the observation of their inputs and outputs. However, in the common case where one does not have access to the inputs, the identification problem becomes harder, and is referred to as blind system identification. On the other hand, if the inputs can be assumed to lie on a known subspace, identification techniques based on low-rank matrix recovery can be applied. In this case, blind subspace system identification has been formulated as the problem of simultaneously recovering structured low-rank matrices associated with both the system and inputs. Notwithstanding, the convex relaxation approach to this problem, where the objective function is defined as a sum of the nuclear norms of two matrices, has been shown to be significantly sub-optimal as it typically favors one of the objective terms. In this work, we propose a method for the joint identification of system and inputs using optimization over Riemann manifolds. Riemannian optimization defines operators that allow low-rank matrix constraints to be incorporated in the search space, producing feasible solutions by construction. Our approach takes advantage of this capability and formulates blind subsystem identification as a low-rank matrix approximation problem over the product manifold of fixed-rank matrices.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125161683","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

Static optimal decoupling control for linear over-actuated systems regarding time-varying references 考虑时变参考的线性过度驱动系统静态最优解耦控制

2017 American Control Conference (ACC) Pub Date : 2017-05-24 DOI: 10.23919/ACC.2017.7963091

Sebastian Bernhard, J. Adamy

{"title":"Static optimal decoupling control for linear over-actuated systems regarding time-varying references","authors":"Sebastian Bernhard, J. Adamy","doi":"10.23919/ACC.2017.7963091","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963091","url":null,"abstract":"We address static decoupling control for linear over-actuated systems and time-varying references given by exogenous systems with arbitrary eigenvalues. Based on mild assumptions, additional degrees of freedom in form of an input are provided. Then an optimal tracking problem for quadratic integral cost is formulated. Despite the time dependency of the cost and dynamics, we derive a static feedback and pre-filter satisfying necessary optimality conditions for infinite final time. These can be calculated by the solution of an algebraic Riccati equation and a Sylvester equation, respectively. In spite of its simplicity in derivation as well as implementation - offering great convenience for practical use - we prove optimal transient behavior to a unique optimal stationary trajectory of the system states. Or, more precisely, of the internal dynamics which are proven to exist. Moreover, the static control law is verified to be a close approximation of the computationally expensive finite time optimal solution if simple qualitative criteria are met. An application to a helicopter model reveals the high efficiency of our approach compared to others.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124333794","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: 4

Predefined-time stabilization of high order systems 高阶系统的预定义时间镇定

2017 American Control Conference (ACC) Pub Date : 2017-05-24 DOI: 10.23919/ACC.2017.7963865

Esteban Jiménez‐Rodríguez, J. Sánchez‐Torres, D. Gómez‐Gutiérrez, A. Loukianov

引用次数: 13

Super twisting control of linear induction motor considering end effects with unknown load torque 考虑未知负载转矩端部效应的直线感应电机超扭控制

2017 American Control Conference (ACC) Pub Date : 2017-05-24 DOI: 10.23919/ACC.2017.7963069

Lei Zhang, S. Laghrouche, M. Harmouche, M. Cirrincione

引用次数: 8

Adaptive position tracking control of high-speed trains with piecewise dynamics 高速列车分段动态自适应位置跟踪控制

2017 American Control Conference (ACC) Pub Date : 2017-05-24 DOI: 10.23919/ACC.2017.7963321

Zehui Mao, G. Tao, B. Jiang, Xing-gang Yan

引用次数: 4

Shrinking Horizon Model Predictive Control with chance-constrained signal temporal logic specifications 具有机会约束信号时序逻辑规范的收缩地平线模型预测控制

2017 American Control Conference (ACC) Pub Date : 2017-05-24 DOI: 10.23919/ACC.2017.7963204

S. Farahani, R. Majumdar, Vinayak S. Prabhu, S. Soudjani

引用次数: 21