Pub Date : 2017-05-24DOI: 10.23919/ACC.2017.7962967
Wei Shi, Lacra Pavel
We introduce a linearized alternating direction method of multipliers (ADMM)-like Nash equilibrium seeking algorithm (LANA) for a class of non-cooperative games over generally connected networks. This model differs from conventional settings because the communication graph is not necessarily the same as the players' objective dependency network and thus players have to deal with incomplete information issues. To solve this game theoretic problem, the introduced algorithm involves every player performing gradient (projection) play to minimize his own objective selfishly while sharing, retrieving, and combining information locally among his network neighborhood. Convergence guarantees are provided for the algorithm. We further extend the introduced algorithm to asynchronous updates and find it works well. Numerical experiments verify the viability of the algorithms.
{"title":"LANA: An ADMM-like Nash equilibrium seeking algorithm in decentralized environment","authors":"Wei Shi, Lacra Pavel","doi":"10.23919/ACC.2017.7962967","DOIUrl":"https://doi.org/10.23919/ACC.2017.7962967","url":null,"abstract":"We introduce a linearized alternating direction method of multipliers (ADMM)-like Nash equilibrium seeking algorithm (LANA) for a class of non-cooperative games over generally connected networks. This model differs from conventional settings because the communication graph is not necessarily the same as the players' objective dependency network and thus players have to deal with incomplete information issues. To solve this game theoretic problem, the introduced algorithm involves every player performing gradient (projection) play to minimize his own objective selfishly while sharing, retrieving, and combining information locally among his network neighborhood. Convergence guarantees are provided for the algorithm. We further extend the introduced algorithm to asynchronous updates and find it works well. Numerical experiments verify the viability of the algorithms.","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":"123875105","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963401
Yuanzhe Wang, Danwei W. Wang, B. C. Ng
This paper presents a novel nonlinear control scheme for finite time tracking of a moving target using nonholonomic ground vehicles, where the distance and bearing angle of the target with respect to the vehicles are constrained. A generalized tan-type Barrier Lyapunov Function is proposed and incorporated with the control laws to deal with the non-symmetric distance and bearing angle constraints. For the target tracking implementation, relative position of the target is the only measured signal and no additional sensor information is required. Stability and performance analyses indicate that convergence of the tracking error to the neighbourhood of zero can be achieved in finite time, while the distance and bearing angle remain within the constraints. Further simulation has been performed using one target and two unmanned ground vehicles to verify the effectiveness of the proposed control method.
{"title":"Finite time moving target tracking using nonholonomic vehicles with distance and bearing angle constraints","authors":"Yuanzhe Wang, Danwei W. Wang, B. C. Ng","doi":"10.23919/ACC.2017.7963401","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963401","url":null,"abstract":"This paper presents a novel nonlinear control scheme for finite time tracking of a moving target using nonholonomic ground vehicles, where the distance and bearing angle of the target with respect to the vehicles are constrained. A generalized tan-type Barrier Lyapunov Function is proposed and incorporated with the control laws to deal with the non-symmetric distance and bearing angle constraints. For the target tracking implementation, relative position of the target is the only measured signal and no additional sensor information is required. Stability and performance analyses indicate that convergence of the tracking error to the neighbourhood of zero can be achieved in finite time, while the distance and bearing angle remain within the constraints. Further simulation has been performed using one target and two unmanned ground vehicles to verify the effectiveness of the proposed control method.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"47 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":"125058297","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 : 2017-05-24DOI: 10.23919/ACC.2017.7962940
Mohammad T. Freigoun, César A. Martín, Alicia B. Magann, D. Rivera, Sayali S. Phatak, E. Korinek, E. Hekler
There is significant evidence to show that physical activity reduces the risk of many chronic diseases. With the rise of mobile health (mHealth) technologies, one promising approach is to design interventions that are responsive to an individual's changing needs. This is the overarching goal of Just Walk, an intensively adaptive physical activity intervention that has been designed on the basis of system identification and control engineering principles. Features of this intervention include the use of multisine signals as pseudo-random inputs for providing daily step goals and reward targets for participants, and an unconventional ARX estimation-validation procedure applied to judiciously-selected data segments that seeks to balance predictive ability over validation data segments with overall goodness of fit. Analysis of the estimated models provides important clues to individual participant characteristics that influence physical activity. The insights gained from black-box modeling are critical to building semi-physical models based on a dynamic extension of Social Cognitive Theory.
{"title":"System identification of Just Walk: A behavioral mHealth intervention for promoting physical activity","authors":"Mohammad T. Freigoun, César A. Martín, Alicia B. Magann, D. Rivera, Sayali S. Phatak, E. Korinek, E. Hekler","doi":"10.23919/ACC.2017.7962940","DOIUrl":"https://doi.org/10.23919/ACC.2017.7962940","url":null,"abstract":"There is significant evidence to show that physical activity reduces the risk of many chronic diseases. With the rise of mobile health (mHealth) technologies, one promising approach is to design interventions that are responsive to an individual's changing needs. This is the overarching goal of Just Walk, an intensively adaptive physical activity intervention that has been designed on the basis of system identification and control engineering principles. Features of this intervention include the use of multisine signals as pseudo-random inputs for providing daily step goals and reward targets for participants, and an unconventional ARX estimation-validation procedure applied to judiciously-selected data segments that seeks to balance predictive ability over validation data segments with overall goodness of fit. Analysis of the estimated models provides important clues to individual participant characteristics that influence physical activity. The insights gained from black-box modeling are critical to building semi-physical models based on a dynamic extension of Social Cognitive Theory.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"64 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":"121921335","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963456
M. Ghanes, J. Barbot, L. Fridman, A. Levant
In this article, a new second order sliding mode differentiator with a variable exponent is proposed. Inspired by the classical super twisting differentiator, the dedicated differentiator allows to give a solution for reducing the effect of variable noise of sensor output measurement. To achieve this objective, the parameter α that is fixed in a super twisting differentiator is made variable in the proposed differentiator. First of all, the proposed differentiator is presented in free noise case, after that the extension to the case with output noise is given in details. In both cases the practical convergences of the observation error are guaranteed. In the first the radius of the practical stability is depending on the considered unknown input while in the second case this radius depends also on the noise. Finally some simulation results are given in order to show the performances and the effectiveness of the proposed differentiator compared to existing one.
{"title":"A second order sliding mode differentiator with a variable exponent","authors":"M. Ghanes, J. Barbot, L. Fridman, A. Levant","doi":"10.23919/ACC.2017.7963456","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963456","url":null,"abstract":"In this article, a new second order sliding mode differentiator with a variable exponent is proposed. Inspired by the classical super twisting differentiator, the dedicated differentiator allows to give a solution for reducing the effect of variable noise of sensor output measurement. To achieve this objective, the parameter α that is fixed in a super twisting differentiator is made variable in the proposed differentiator. First of all, the proposed differentiator is presented in free noise case, after that the extension to the case with output noise is given in details. In both cases the practical convergences of the observation error are guaranteed. In the first the radius of the practical stability is depending on the considered unknown input while in the second case this radius depends also on the noise. Finally some simulation results are given in order to show the performances and the effectiveness of the proposed differentiator compared to existing one.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"6 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":"115906470","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963783
T. Barto, D. Simon
A voltage source converter (VSC) is incorporated in an active prosthetic leg design. The VSC supplies power to the prosthesis motor and regenerates energy from the prosthesis motor for storage in a supercapacitor bank. An artificial neural network controls the VSC switching so that the prosthesis motor generates a knee torque that matches the torque that is output from a passivity-based controller (PBC). The neural network, PBC, and prosthesis motor parameters are optimized with an evolutionary algorithm to achieve knee angle tracking. Several reference trajectories from able-bodied walking were tracked with an RMS tracking error of less than 0.5° while regenerating up to 67 Joules of energy during four gait cycles.
{"title":"Neural network control of an optimized regenerative motor drive for a lower-limb prosthesis","authors":"T. Barto, D. Simon","doi":"10.23919/ACC.2017.7963783","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963783","url":null,"abstract":"A voltage source converter (VSC) is incorporated in an active prosthetic leg design. The VSC supplies power to the prosthesis motor and regenerates energy from the prosthesis motor for storage in a supercapacitor bank. An artificial neural network controls the VSC switching so that the prosthesis motor generates a knee torque that matches the torque that is output from a passivity-based controller (PBC). The neural network, PBC, and prosthesis motor parameters are optimized with an evolutionary algorithm to achieve knee angle tracking. Several reference trajectories from able-bodied walking were tracked with an RMS tracking error of less than 0.5° while regenerating up to 67 Joules of energy during four gait cycles.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"14 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":"127895128","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963564
Abdulelah H. Habib, V. Disfani, J. Kleissl, R. A. Callafon
Despite the increase of modern residential rooftop solar PhotoVoltaic (PV) installation with smart inverters, islanded operation during grid blackouts is limited for most PV owners. This paper presents an optimization method to construe an resource sharing algorithm for islanded operation during blackouts by using shared PV energy. The optimization methods determine if rooftop PV power is either used directly or distributed to neighbors within a residential subsystem. Residential customers, each with a fixed size rooftop PV system are assumed to be connected by a single point of common coupling to a distribution network. The algorithm derives the optimal power distribution to improve the reliability of electricity supply to each residential customer and the results are benchmarked against the isolated self-consumption only mode. In addition, an energy storage system (ESS) is added to quantify the improvement in reliability, whereas a comparison is made between a distributed and centralized ESS deployment strategy.
{"title":"Optimal energy storage sizing and residential load scheduling to improve reliability in islanded operation of distribution grids","authors":"Abdulelah H. Habib, V. Disfani, J. Kleissl, R. A. Callafon","doi":"10.23919/ACC.2017.7963564","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963564","url":null,"abstract":"Despite the increase of modern residential rooftop solar PhotoVoltaic (PV) installation with smart inverters, islanded operation during grid blackouts is limited for most PV owners. This paper presents an optimization method to construe an resource sharing algorithm for islanded operation during blackouts by using shared PV energy. The optimization methods determine if rooftop PV power is either used directly or distributed to neighbors within a residential subsystem. Residential customers, each with a fixed size rooftop PV system are assumed to be connected by a single point of common coupling to a distribution network. The algorithm derives the optimal power distribution to improve the reliability of electricity supply to each residential customer and the results are benchmarked against the isolated self-consumption only mode. In addition, an energy storage system (ESS) is added to quantify the improvement in reliability, whereas a comparison is made between a distributed and centralized ESS deployment strategy.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"34 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":"128929823","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963055
Kwang-Bok Seo, M. Yamakita
Gaussian process model provides a flexible, probabilistic, non-parametric model. Many examples for system identification using Gaussian process have been reported and verified. However, for real plants that have secular change or whose properties change upon time, it is difficult to apply standard Gaussian process and it is important to keep the uncertainties of the modeling properly. In this paper, we consider a system identification for nonlinear time-varying systems using recursive Gaussian process (RGP). We propose two methods for this problem. One is RGP for long-term prediction, and the another is robust RGP for outliers. The effectiveness of the proposed methods will be shown by numerical simulations.
{"title":"Nonlinear time-varying system identification with recursive Gaussian process","authors":"Kwang-Bok Seo, M. Yamakita","doi":"10.23919/ACC.2017.7963055","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963055","url":null,"abstract":"Gaussian process model provides a flexible, probabilistic, non-parametric model. Many examples for system identification using Gaussian process have been reported and verified. However, for real plants that have secular change or whose properties change upon time, it is difficult to apply standard Gaussian process and it is important to keep the uncertainties of the modeling properly. In this paper, we consider a system identification for nonlinear time-varying systems using recursive Gaussian process (RGP). We propose two methods for this problem. One is RGP for long-term prediction, and the another is robust RGP for outliers. The effectiveness of the proposed methods will be shown by numerical simulations.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"32 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":"131052386","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963669
John N. Maidens, A. Barrau, S. Bonnabel, M. Arcak
We present a method of exploiting symmetries of discrete-time optimal control problems to reduce the dimensionality of dynamic programming iterations. The results are derived for systems with continuous state variables, and can be applied to systems with continuous or discrete symmetry groups. We prove that symmetries of the state update equation and stage costs induce corresponding symmetries of the optimal cost function and the optimal policies. Thus symmetries can be exploited to allow dynamic programming iterations to be performed in a reduced state space. The application of these results is illustrated using a model of spin dynamics for magnetic resonance imaging (MRI). For this application problem, the symmetry reduction introduced leads to a significant speedup, reducing computation time by a factor of 75×.
{"title":"Symmetry reduction for dynamic programming and application to MRI","authors":"John N. Maidens, A. Barrau, S. Bonnabel, M. Arcak","doi":"10.23919/ACC.2017.7963669","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963669","url":null,"abstract":"We present a method of exploiting symmetries of discrete-time optimal control problems to reduce the dimensionality of dynamic programming iterations. The results are derived for systems with continuous state variables, and can be applied to systems with continuous or discrete symmetry groups. We prove that symmetries of the state update equation and stage costs induce corresponding symmetries of the optimal cost function and the optimal policies. Thus symmetries can be exploited to allow dynamic programming iterations to be performed in a reduced state space. The application of these results is illustrated using a model of spin dynamics for magnetic resonance imaging (MRI). For this application problem, the symmetry reduction introduced leads to a significant speedup, reducing computation time by a factor of 75×.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"10 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":"131623796","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963223
Kevin J. Leahy, Derya Aksaray, C. Belta
In this work we consider an agent trying to maximize a submodular reward function while moving in a graph environment. Such reward functions can be used to capture a variety of crucial sensing objectives in robotics including, but not limited to, mutual information and entropy. Furthermore, the agent must satisfy a mission specified by temporal logic constraints, which can encode many rich and complex missions such as “visit regions A or B, then visit C, infinitely often. Never visit D before visiting C.” We present an algorithm to maximize a submodular reward function under these constraints and provide an approximation for the performance of the proposed algorithm. The results are validated via simulation.
{"title":"Informative path planning under temporal logic constraints with performance guarantees","authors":"Kevin J. Leahy, Derya Aksaray, C. Belta","doi":"10.23919/ACC.2017.7963223","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963223","url":null,"abstract":"In this work we consider an agent trying to maximize a submodular reward function while moving in a graph environment. Such reward functions can be used to capture a variety of crucial sensing objectives in robotics including, but not limited to, mutual information and entropy. Furthermore, the agent must satisfy a mission specified by temporal logic constraints, which can encode many rich and complex missions such as “visit regions A or B, then visit C, infinitely often. Never visit D before visiting C.” We present an algorithm to maximize a submodular reward function under these constraints and provide an approximation for the performance of the proposed algorithm. The results are validated via simulation.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"33 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114032165","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963473
A. Bušić, Md Umar Hashmi, Sean P. Meyn
Battery storage is increasingly important for grid-level services such as frequency regulation, load following, and peak-shaving. The management of a large number of batteries presents a control challenge: How can we solve the apparently combinatorial problem of coordinating a large number of batteries with discrete, and possibly slow rates of charge/discharge? The control solution must respect battery constraints, and ensure that the aggregate power output tracks the desired grid-level signal. A distributed stochastic control architecture is introduced as a potential solution. Extending prior research on distributed control of flexible loads, a randomized decision rule is defined for each battery of the same type. The power mode at each time-slot is a randomized function of the grid-signal and its internal state. The randomized decision rule is designed to maximize idle time of each battery, and keep the state-of-charge near its optimal level, while ensuring that the aggregate power output can be continuously controlled by a grid operator or aggregator. Numerical results show excellent tracking, and low stress to individual batteries.
{"title":"Distributed control of a fleet of batteries","authors":"A. Bušić, Md Umar Hashmi, Sean P. Meyn","doi":"10.23919/ACC.2017.7963473","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963473","url":null,"abstract":"Battery storage is increasingly important for grid-level services such as frequency regulation, load following, and peak-shaving. The management of a large number of batteries presents a control challenge: How can we solve the apparently combinatorial problem of coordinating a large number of batteries with discrete, and possibly slow rates of charge/discharge? The control solution must respect battery constraints, and ensure that the aggregate power output tracks the desired grid-level signal. A distributed stochastic control architecture is introduced as a potential solution. Extending prior research on distributed control of flexible loads, a randomized decision rule is defined for each battery of the same type. The power mode at each time-slot is a randomized function of the grid-signal and its internal state. The randomized decision rule is designed to maximize idle time of each battery, and keep the state-of-charge near its optimal level, while ensuring that the aggregate power output can be continuously controlled by a grid operator or aggregator. Numerical results show excellent tracking, and low stress to individual batteries.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"39 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":"128695526","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：