Pub Date : 2019-07-01DOI: 10.23919/acc.2019.8814343
Vijay Kumar
Provides an abstract for each of the plenary presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.
提供每个全体会议报告的摘要和每个演讲者的简短专业简介。完整的发言没有作为会议记录的一部分提供出版。
{"title":"Plenary and semi-plenary sessions","authors":"Vijay Kumar","doi":"10.23919/acc.2019.8814343","DOIUrl":"https://doi.org/10.23919/acc.2019.8814343","url":null,"abstract":"Provides an abstract for each of the plenary presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114407212","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}
Pub Date : 2017-07-03DOI: 10.23919/ACC.2017.7962945
Fredrik Bagge Carlson, A. Robertsson, Rolf Johansson
We develop a linear parameter-varying (LPV) spectral decomposition method, based on least-squares estimation and kernel expansions. Statistical properties of the estimator are analyzed and verified in simulations. The method is linear in the parameters, applicable to both the analysis and modeling problems and is demonstrated on both simulated signals as well as measurements of the torque in an electrical motor.
{"title":"Linear parameter-varying spectral decomposition","authors":"Fredrik Bagge Carlson, A. Robertsson, Rolf Johansson","doi":"10.23919/ACC.2017.7962945","DOIUrl":"https://doi.org/10.23919/ACC.2017.7962945","url":null,"abstract":"We develop a linear parameter-varying (LPV) spectral decomposition method, based on least-squares estimation and kernel expansions. Statistical properties of the estimator are analyzed and verified in simulations. The method is linear in the parameters, applicable to both the analysis and modeling problems and is demonstrated on both simulated signals as well as measurements of the torque in an electrical motor.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129296167","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}
Pub Date : 2017-07-03DOI: 10.23919/ACC.2017.7963749
Merid Ljesnjanin, Y. Tan, D. Oetomo, C. Freeman
This paper proposes a novel Iterative Learning Control (ILC) framework for spatial tracking. Spatial tracking means that the temporal component is not fixed which violates the standing assumption on time intervals in traditional ILC. The proposed framework allows for the length of the time interval to change with each iteration. To relate the spatial information from the past to the present iteration, the concept of spatial projection is proposed. A class of nonlinear uncertain systems with input saturation is chosen for demonstration. An a appropriate ILC control law, exploiting the spatial projection idea, is proposed and the corresponding convergence analysis, based on the Composite Energy Function, is carried out. It is shown that spatial tracking is achieved under appropriate assumptions related to spatial projection and provided that the desired trajectory is realizable within the saturation bound. Finally, simulation results illustrate the predicted convergence.
{"title":"Spatial Iterative Learning Control: Systems with input saturation","authors":"Merid Ljesnjanin, Y. Tan, D. Oetomo, C. Freeman","doi":"10.23919/ACC.2017.7963749","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963749","url":null,"abstract":"This paper proposes a novel Iterative Learning Control (ILC) framework for spatial tracking. Spatial tracking means that the temporal component is not fixed which violates the standing assumption on time intervals in traditional ILC. The proposed framework allows for the length of the time interval to change with each iteration. To relate the spatial information from the past to the present iteration, the concept of spatial projection is proposed. A class of nonlinear uncertain systems with input saturation is chosen for demonstration. An a appropriate ILC control law, exploiting the spatial projection idea, is proposed and the corresponding convergence analysis, based on the Composite Energy Function, is carried out. It is shown that spatial tracking is achieved under appropriate assumptions related to spatial projection and provided that the desired trajectory is realizable within the saturation bound. Finally, simulation results illustrate the predicted convergence.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116109069","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}
Pub Date : 2017-07-03DOI: 10.23919/ACC.2017.7963454
J. Na, G. Herrmann, K. Vamvoudakis
This paper presents an optimal observer design framework using a recently emerging method, approximate dynamic programming (ADP), to minimize a predefined cost function. We first exploit the duality between the linear optimal observer and the linear quadratic tracking (LQT) control. We show that the optimal observer design can be formulated as an optimal control problem subject to a specific cost function, and thus the solution can be obtained by solving an algebraic Riccati equation (ARE). For nonlinear systems, we further introduce an optimal observer design formulation and suggest a modified policy iteration method. Finally, to solve the problem online we propose a framework based on ADP and specifically on an approximator structure. Namely, a critic approximator is used to estimate the optimal value function, and a newly developed tuning law is proposed to find the parameters online. The stability and the performance are guaranteed with rigorous proofs. Numerical simulations are given to validate the theoretical studies.
{"title":"Adaptive optimal observer design via approximate dynamic programming","authors":"J. Na, G. Herrmann, K. Vamvoudakis","doi":"10.23919/ACC.2017.7963454","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963454","url":null,"abstract":"This paper presents an optimal observer design framework using a recently emerging method, approximate dynamic programming (ADP), to minimize a predefined cost function. We first exploit the duality between the linear optimal observer and the linear quadratic tracking (LQT) control. We show that the optimal observer design can be formulated as an optimal control problem subject to a specific cost function, and thus the solution can be obtained by solving an algebraic Riccati equation (ARE). For nonlinear systems, we further introduce an optimal observer design formulation and suggest a modified policy iteration method. Finally, to solve the problem online we propose a framework based on ADP and specifically on an approximator structure. Namely, a critic approximator is used to estimate the optimal value function, and a newly developed tuning law is proposed to find the parameters online. The stability and the performance are guaranteed with rigorous proofs. Numerical simulations are given to validate the theoretical studies.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117040863","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}
Pub Date : 2017-07-03DOI: 10.23919/ACC.2017.7963480
M. Cucuzzella, Sebastian Trip, C. D. Persis, A. Ferrara
This paper proposes a Distributed Second Order Sliding Mode (D-SOSM) control strategy for Optimal Load Frequency Control (OLFC) in power networks, where besides frequency regulation also minimization of generation costs is achieved. Because of unknown load dynamics and possible network parameters uncertainties, the sliding mode control methodology is particularly appropriate for the considered control problem. This paper considers a power network partitioned into control areas, where each area is modelled by an equivalent generator including second-order turbine-governor dynamics. On a suitable designed sliding manifold, the controlled system exhibits an incremental passivity property that allows us to infer convergence to a zero steady state frequency deviation minimizing the generation costs.
{"title":"Distributed Second Order Sliding Modes for Optimal Load Frequency Control","authors":"M. Cucuzzella, Sebastian Trip, C. D. Persis, A. Ferrara","doi":"10.23919/ACC.2017.7963480","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963480","url":null,"abstract":"This paper proposes a Distributed Second Order Sliding Mode (D-SOSM) control strategy for Optimal Load Frequency Control (OLFC) in power networks, where besides frequency regulation also minimization of generation costs is achieved. Because of unknown load dynamics and possible network parameters uncertainties, the sliding mode control methodology is particularly appropriate for the considered control problem. This paper considers a power network partitioned into control areas, where each area is modelled by an equivalent generator including second-order turbine-governor dynamics. On a suitable designed sliding manifold, the controlled system exhibits an incremental passivity property that allows us to infer convergence to a zero steady state frequency deviation minimizing the generation costs.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116912921","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}
Pub Date : 2017-07-03DOI: 10.23919/ACC.2017.7963319
H. Jardón-Kojakhmetov, J. Scherpen, D. D. Puerto-Flores
Non-hyperbolic points of slow-fast systems (also known as singularly perturbed ordinary differential equations) are responsible for many interesting behavior such as relaxation oscillations, canards, mixed-mode oscillations, etc. Recently, the authors have proposed a control strategy to stabilize non-hyperbolic points of planar slow-fast systems. Such strategy is based on geometric desingularization, which is a well suited technique to analyze the dynamics of slow-fast systems near non-hyperbolic points. This technique transforms the singular perturbation problem to an equivalent regular perturbation problem. This papers treats the nonlinear adaptive stabilization problem of slow-fast systems. The novelty is that the point to be stabilized is non-hyperbolic. The controller is designed by combining geometric desingularization and Lyapunov based techniques. Through the action of the controller, we basically inject a normally hyperbolic behavior to the fast variable. Our results are exemplified on the van der Pol oscillator.
{"title":"Nonlinear adaptive stabilization of a class of planar slow-fast systems at a non-hyperbolic point","authors":"H. Jardón-Kojakhmetov, J. Scherpen, D. D. Puerto-Flores","doi":"10.23919/ACC.2017.7963319","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963319","url":null,"abstract":"Non-hyperbolic points of slow-fast systems (also known as singularly perturbed ordinary differential equations) are responsible for many interesting behavior such as relaxation oscillations, canards, mixed-mode oscillations, etc. Recently, the authors have proposed a control strategy to stabilize non-hyperbolic points of planar slow-fast systems. Such strategy is based on geometric desingularization, which is a well suited technique to analyze the dynamics of slow-fast systems near non-hyperbolic points. This technique transforms the singular perturbation problem to an equivalent regular perturbation problem. This papers treats the nonlinear adaptive stabilization problem of slow-fast systems. The novelty is that the point to be stabilized is non-hyperbolic. The controller is designed by combining geometric desingularization and Lyapunov based techniques. Through the action of the controller, we basically inject a normally hyperbolic behavior to the fast variable. Our results are exemplified on the van der Pol oscillator.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123219685","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}
Pub Date : 2017-06-29DOI: 10.23919/ACC.2017.7963032
Ji Liu, D. Fullmer, A. Nedić, T. Başar, A. Morse
This paper studies a distributed algorithm for finding a common fixed point of a family of m > 1 nonlinear maps Mi: ℝn → ℝn assuming that each map is strongly quasi-nonexpansive, and that at least one such common fixed point exists. A common fixed point is simultaneously and recursively computed by m agents assuming that each agent i knows only Mi, the current estimates of the fixed point generated by its neighbors, and nothing more. Neighbor relationships are described by a time-varying directed graph ℕ(t) whose vertices correspond to agents and whose arcs depict neighbor relationships. It is shown that for any sequence of repeatedly jointly strongly connected neighbor graphs ℕ(t), t ∈ {1, 2, …}, the algorithm causes all agents' estimates to converge to a common fixed point of Mi, i ∈ {1, 2, …, m}.
{"title":"A distributed algorithm for computing a common fixed point of a family of strongly quasi-nonexpansive maps","authors":"Ji Liu, D. Fullmer, A. Nedić, T. Başar, A. Morse","doi":"10.23919/ACC.2017.7963032","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963032","url":null,"abstract":"This paper studies a distributed algorithm for finding a common fixed point of a family of m > 1 nonlinear maps M<inf>i</inf>: ℝ<sup>n</sup> → ℝ<sup>n</sup> assuming that each map is strongly quasi-nonexpansive, and that at least one such common fixed point exists. A common fixed point is simultaneously and recursively computed by m agents assuming that each agent i knows only M<inf>i</inf>, the current estimates of the fixed point generated by its neighbors, and nothing more. Neighbor relationships are described by a time-varying directed graph ℕ(t) whose vertices correspond to agents and whose arcs depict neighbor relationships. It is shown that for any sequence of repeatedly jointly strongly connected neighbor graphs ℕ(t), t ∈ {1, 2, …}, the algorithm causes all agents' estimates to converge to a common fixed point of M<inf>i</inf>, i ∈ {1, 2, …, m}.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115226370","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}
Pub Date : 2017-06-29DOI: 10.23919/ACC.2017.7963638
G. Thomas, L. Sentis
We investigate the use of frequency domain identification and convex optimization for obtaining robust models of series elastic actuators. This early work focuses on identifying a lower bound on the ℋ∞ uncertainty, based on the non-linear behavior of the plant when identified under different conditions. An antagonistic testing apparatus allows the identification of the full two input, two output system. The aim of this work is to find a model which explains all the observed test results, despite physical non-linearity. The approach guarantees that a robust model includes all previously measured behaviors, and thus predicts the stability of never-before-tested controllers. We statistically validate the hypothesis that a single linear model cannot adequately explain the tightly clustered experimental results. And we also develop an optimization problem which finds a lower bound on the ℋ∞ uncertainty component of the robust models which we use to represent the plant in all the tested conditions.
{"title":"MIMO identification of frequency-domain unreliability in SEAs","authors":"G. Thomas, L. Sentis","doi":"10.23919/ACC.2017.7963638","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963638","url":null,"abstract":"We investigate the use of frequency domain identification and convex optimization for obtaining robust models of series elastic actuators. This early work focuses on identifying a lower bound on the ℋ∞ uncertainty, based on the non-linear behavior of the plant when identified under different conditions. An antagonistic testing apparatus allows the identification of the full two input, two output system. The aim of this work is to find a model which explains all the observed test results, despite physical non-linearity. The approach guarantees that a robust model includes all previously measured behaviors, and thus predicts the stability of never-before-tested controllers. We statistically validate the hypothesis that a single linear model cannot adequately explain the tightly clustered experimental results. And we also develop an optimization problem which finds a lower bound on the ℋ∞ uncertainty component of the robust models which we use to represent the plant in all the tested conditions.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115380378","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}
Pub Date : 2017-06-29DOI: 10.23919/ACC.2017.7963031
Yang Liu, Ji Liu, T. Başar, M. Liu
This paper considers a distributed belief averaging problem with sequential observations in which a group of n > 1 agents in a network, each having sequentially arriving samples of its belief in an online manner, aim to reach a consensus at the average of their beliefs, by exchanging information only with their neighbors. The neighbor relationships among the n agents are described by a time-varying undirected graph whose vertices correspond to agents and whose edges depict neighbor relationships. A distributed algorithm is proposed to solve this problem over sequential observations with O(1/t) convergence rate. Extensions to the case of directed graphs are also detailed.
{"title":"Distributed belief averaging using sequential observations","authors":"Yang Liu, Ji Liu, T. Başar, M. Liu","doi":"10.23919/ACC.2017.7963031","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963031","url":null,"abstract":"This paper considers a distributed belief averaging problem with sequential observations in which a group of n > 1 agents in a network, each having sequentially arriving samples of its belief in an online manner, aim to reach a consensus at the average of their beliefs, by exchanging information only with their neighbors. The neighbor relationships among the n agents are described by a time-varying undirected graph whose vertices correspond to agents and whose edges depict neighbor relationships. A distributed algorithm is proposed to solve this problem over sequential observations with O(1/t) convergence rate. Extensions to the case of directed graphs are also detailed.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"33 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114104705","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}
Pub Date : 2017-06-29DOI: 10.23919/ACC.2017.7963264
T. Irita, T. Namerikawa
Ensuring cyber security of smart grid based on supervisory control and data acquisition using information and communications technology has been becoming a major challenge. Especially, a replay attack is a dangerous cyber attack affecting integrity and authorization. The goal of this paper is to detect replay attack that is one of cyber attacks on the sensors of a control system. We propose a detecting method adding intentional noise to not only sensors as code signal but also input. Replay attack can immediately be reflected by using fault diagnosis matrices that are composed of the estimator and observed values even if code signal is decrypted. Finally, we show simulation results to analyze effectiveness of the proposed method.
{"title":"Detection of replay attack on smart grid with code signal and bargaining game","authors":"T. Irita, T. Namerikawa","doi":"10.23919/ACC.2017.7963264","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963264","url":null,"abstract":"Ensuring cyber security of smart grid based on supervisory control and data acquisition using information and communications technology has been becoming a major challenge. Especially, a replay attack is a dangerous cyber attack affecting integrity and authorization. The goal of this paper is to detect replay attack that is one of cyber attacks on the sensors of a control system. We propose a detecting method adding intentional noise to not only sensors as code signal but also input. Replay attack can immediately be reflected by using fault diagnosis matrices that are composed of the estimator and observed values even if code signal is decrypted. Finally, we show simulation results to analyze effectiveness of the proposed method.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130814563","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}
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