Pub Date : 2020-07-01DOI: 10.23919/ACC45564.2020.9147975
P. Rivera-Ortiz, Y. Diaz-Mercado, Marin Kobilarov
The concept of reach-avoid (RA) games via coverage control is generalized to players with nonlinear dynamics and in arbitrary dimensions. Pursuer coordination on defense surfaces is formally shown sufficient as a cooperative strategy for RA games in any dimensions. Nonlinear control synthesis strategies with convergence guarantees are provided to enforce coverage on said surfaces. The effectiveness of two coverage control formulations is verified through simulation.
{"title":"Multi-Player Pursuer Coordination for Nonlinear Reach-Avoid Games in Arbitrary Dimensions via Coverage Control","authors":"P. Rivera-Ortiz, Y. Diaz-Mercado, Marin Kobilarov","doi":"10.23919/ACC45564.2020.9147975","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147975","url":null,"abstract":"The concept of reach-avoid (RA) games via coverage control is generalized to players with nonlinear dynamics and in arbitrary dimensions. Pursuer coordination on defense surfaces is formally shown sufficient as a cooperative strategy for RA games in any dimensions. Nonlinear control synthesis strategies with convergence guarantees are provided to enforce coverage on said surfaces. The effectiveness of two coverage control formulations is verified through simulation.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128326157","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147582
J. Katz, Iosif Pappas, Styliani Avraamidou, E. Pistikopoulos
For highly nonlinear systems, using deep learning models to capture complex dynamics is a promising feature for advanced control applications. Recently it has been shown that a particular class of deep learning models can be exactly recast in a mixed-integer linear programming formulation. Recasting a deep learning model as a set of piecewise linear functions enables the incorporation of advanced predictive models in model-based control strategies such as model predictive control. To alleviate the computational burden of solving the piecewise linear optimization problem online, multiparametric programming is utilized to obtain the full, offline, explicit solution of the optimal control problem. In this work, a strategy is presented for the integration of deep learning models, specifically neural networks with rectified linear units, and explicit model predictive control. The proposed strategy is demonstrated on the advanced control of a benchmark chemical process involving multiple reactors, a flash separator, and a recycle stream. The positive results showcase the relevance and strength of the proposed methodology.
{"title":"Integrating Deep Learning and Explicit MPC for Advanced Process Control","authors":"J. Katz, Iosif Pappas, Styliani Avraamidou, E. Pistikopoulos","doi":"10.23919/ACC45564.2020.9147582","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147582","url":null,"abstract":"For highly nonlinear systems, using deep learning models to capture complex dynamics is a promising feature for advanced control applications. Recently it has been shown that a particular class of deep learning models can be exactly recast in a mixed-integer linear programming formulation. Recasting a deep learning model as a set of piecewise linear functions enables the incorporation of advanced predictive models in model-based control strategies such as model predictive control. To alleviate the computational burden of solving the piecewise linear optimization problem online, multiparametric programming is utilized to obtain the full, offline, explicit solution of the optimal control problem. In this work, a strategy is presented for the integration of deep learning models, specifically neural networks with rectified linear units, and explicit model predictive control. The proposed strategy is demonstrated on the advanced control of a benchmark chemical process involving multiple reactors, a flash separator, and a recycle stream. The positive results showcase the relevance and strength of the proposed methodology.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128385729","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147776
Alexander Von Moll, Zachariah E. Fuchs, M. Pachter
The Pure Pursuit strategy is ubiquitous both in the control literature but also in real-world implementation. In this paper, we pose and solve a variant of Isaacs’ Two Cutters and Fugitive Ship problem wherein the Pursuers’ strategy is fixed to Pure Pursuit, thus making it an optimal control problem. The Pursuers are faster than the Evader and are endowed with a finite capture radius. All agents move with constant velocity and can change heading instantaneously. Although capture is inevitable, the Evader wishes to delay capture as long as possible. The optimal trajectories cover the entire state space. Regions corresponding to either solo capture or isochronous (dual) capture are computed and both types of maximal time-to-capture optimal trajectories are characterized.
{"title":"Optimal Evasion Against Dual Pure Pursuit *","authors":"Alexander Von Moll, Zachariah E. Fuchs, M. Pachter","doi":"10.23919/ACC45564.2020.9147776","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147776","url":null,"abstract":"The Pure Pursuit strategy is ubiquitous both in the control literature but also in real-world implementation. In this paper, we pose and solve a variant of Isaacs’ Two Cutters and Fugitive Ship problem wherein the Pursuers’ strategy is fixed to Pure Pursuit, thus making it an optimal control problem. The Pursuers are faster than the Evader and are endowed with a finite capture radius. All agents move with constant velocity and can change heading instantaneously. Although capture is inevitable, the Evader wishes to delay capture as long as possible. The optimal trajectories cover the entire state space. Regions corresponding to either solo capture or isochronous (dual) capture are computed and both types of maximal time-to-capture optimal trajectories are characterized.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128173777","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147475
A. Komaee
Optimal control of state-affine systems with finite or infinite dimensions is considered. The control performance is measured by a cost functional with state-affine Lagrangian and terminal cost. Relying upon such affine structure, a simple proof of Pontryagin’s maximum principle as a necessary condition for optimality is presented. This principle requires any optimal control to resolve a certain two-point boundary value problem. As the main contribution of this paper, an iterative algorithm is proposed that converges to the solution of this boundary value problem. This solution is regarded then as a candidate optimal control. Several applications are outlined for the optimal control problem of this paper, including: optimal control of unobserved stochastic systems (continuous-time Markov chain and diffusion process), convection-diffusion partial differential equations, and Lyapunov matrix differential equations.
{"title":"Optimal Control of State-Affine Dynamical Systems","authors":"A. Komaee","doi":"10.23919/ACC45564.2020.9147475","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147475","url":null,"abstract":"Optimal control of state-affine systems with finite or infinite dimensions is considered. The control performance is measured by a cost functional with state-affine Lagrangian and terminal cost. Relying upon such affine structure, a simple proof of Pontryagin’s maximum principle as a necessary condition for optimality is presented. This principle requires any optimal control to resolve a certain two-point boundary value problem. As the main contribution of this paper, an iterative algorithm is proposed that converges to the solution of this boundary value problem. This solution is regarded then as a candidate optimal control. Several applications are outlined for the optimal control problem of this paper, including: optimal control of unobserved stochastic systems (continuous-time Markov chain and diffusion process), convection-diffusion partial differential equations, and Lyapunov matrix differential equations.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128220318","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147868
Henry Pigot, C. Sancho, A. Paskevicius, S. Steen, K. Soltesz
First responders to cardiac arrest depend on cardiopulmonary resuscitation to keep patients alive. A new ventilation method, phase-controlled intermittent insufflation of oxygen, was previously shown to improve heart perfusion during cardiopulmonary resuscitation in a large-animal study, outperforming the best currently known ventilation method. This paper investigates whether the advantage of the new method can be explained using standard linear lumped-parameter models of respiratory mechanics. The simple models were able to qualitatively capture the improvement.
{"title":"Advantage of new ventilation method for cardiopulmonary resuscitation qualitatively captured by simple respiratory mechanics models","authors":"Henry Pigot, C. Sancho, A. Paskevicius, S. Steen, K. Soltesz","doi":"10.23919/ACC45564.2020.9147868","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147868","url":null,"abstract":"First responders to cardiac arrest depend on cardiopulmonary resuscitation to keep patients alive. A new ventilation method, phase-controlled intermittent insufflation of oxygen, was previously shown to improve heart perfusion during cardiopulmonary resuscitation in a large-animal study, outperforming the best currently known ventilation method. This paper investigates whether the advantage of the new method can be explained using standard linear lumped-parameter models of respiratory mechanics. The simple models were able to qualitatively capture the improvement.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128667175","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147644
Aleksandr Shkoruta, Sandipan Mishra, S. Rock
This paper addresses process modeling for the selective laser melting (SLM) process. We experimentally investigate the response of the SLM process output (measured by a coaxial near-infrared camera) to changing input laser power. We determined that first and second order models can be used to capture this input-output behavior. Next, we studied the dependency of this transfer function on laser scan speed and other process variables that evolve over a typical part build, such as thermal properties of surrounding medium (bulk powder, build plate, or solidified part) or layer number. The transfer function was found to strongly depend on the material environment (solidified material or bulk powder). Further, transfer function also depended on the layer number, exhibiting transient behavior. We report identified 1st order transfer functions for different scan speeds, locations on the build plate, and different layer numbers. Identified models and quantification of their variability will serve as foundational work for the future implementation of advanced real-time process control algorithms.
{"title":"An experimental study on process modeling for selective laser melting*","authors":"Aleksandr Shkoruta, Sandipan Mishra, S. Rock","doi":"10.23919/ACC45564.2020.9147644","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147644","url":null,"abstract":"This paper addresses process modeling for the selective laser melting (SLM) process. We experimentally investigate the response of the SLM process output (measured by a coaxial near-infrared camera) to changing input laser power. We determined that first and second order models can be used to capture this input-output behavior. Next, we studied the dependency of this transfer function on laser scan speed and other process variables that evolve over a typical part build, such as thermal properties of surrounding medium (bulk powder, build plate, or solidified part) or layer number. The transfer function was found to strongly depend on the material environment (solidified material or bulk powder). Further, transfer function also depended on the layer number, exhibiting transient behavior. We report identified 1st order transfer functions for different scan speeds, locations on the build plate, and different layer numbers. Identified models and quantification of their variability will serve as foundational work for the future implementation of advanced real-time process control algorithms.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129651930","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147693
Shangcheng Chen, C. Freeman
Collaborative tracking control and formation control are common approaches in which multiple agents work together to perform a global objective. They are increasingly used in a diverse range of applications, however few controllers simultaneously address both tasks. To improve performance of repeated tasks, iterative learning control (ILC) has been independently applied to both methodologies. However, focus has been on centralized structures, and existing solutions typically have limited convergence rates and robustness properties.This paper addresses these limitations by developing a powerful decentralised ILC framework that unites both collaborative tracking and formation control objectives. It enables broad classes of ILC algorithm to be derived with well-defined convergence rates, optimal tracking solutions, and transparent robustness properties. The framework is illustrated through derivation of three new ILC updates: inverse, gradient and norm optimal ILC. Convergence analysis for the proposed framework is also given.
{"title":"Decentralised Collaborative and Formation Iterative Learning Control for Multi-Agent Systems","authors":"Shangcheng Chen, C. Freeman","doi":"10.23919/ACC45564.2020.9147693","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147693","url":null,"abstract":"Collaborative tracking control and formation control are common approaches in which multiple agents work together to perform a global objective. They are increasingly used in a diverse range of applications, however few controllers simultaneously address both tasks. To improve performance of repeated tasks, iterative learning control (ILC) has been independently applied to both methodologies. However, focus has been on centralized structures, and existing solutions typically have limited convergence rates and robustness properties.This paper addresses these limitations by developing a powerful decentralised ILC framework that unites both collaborative tracking and formation control objectives. It enables broad classes of ILC algorithm to be derived with well-defined convergence rates, optimal tracking solutions, and transparent robustness properties. The framework is illustrated through derivation of three new ILC updates: inverse, gradient and norm optimal ILC. Convergence analysis for the proposed framework is also given.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127452489","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147496
M. Maghenem, R. Sanfelice
The minimal-time function with respect to a closed set for a constrained continuous-time system provides the first time that a solution starting from a given initial condition reaches that set. In this paper, we propose infinitesimal necessary and sufficient conditions for the minimal-time function to be locally Lipschitz. As an application of our results, we show that, in constrained continuous-time systems, the Lipschitz continuity of the minimal-time function with respect to the boundary of the set where the solutions are defined plays a crucial role on the Lipschitz continuity of the reachable set.
{"title":"Lipschitzness of Minimal-Time Functions in Constrained Continuous-Time Systems with Applications to Reachability Analysis","authors":"M. Maghenem, R. Sanfelice","doi":"10.23919/ACC45564.2020.9147496","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147496","url":null,"abstract":"The minimal-time function with respect to a closed set for a constrained continuous-time system provides the first time that a solution starting from a given initial condition reaches that set. In this paper, we propose infinitesimal necessary and sufficient conditions for the minimal-time function to be locally Lipschitz. As an application of our results, we show that, in constrained continuous-time systems, the Lipschitz continuity of the minimal-time function with respect to the boundary of the set where the solutions are defined plays a crucial role on the Lipschitz continuity of the reachable set.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129946560","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147472
T. Hegedüs, Dániel Fényes, B. Németh, P. Gáspár
Tire pressure has a high impact on the tire-road contact because it influences the characteristics of the tire forces. During the maneuvering of the vehicle the pressures of the tires may decrease over time, which results in performance degradation or the loss of controllability. This paper proposes a novel integration of tire pressure estimation and path-following control design based on machine learning and Linear Parameter-Varying (LPV) methods. In the estimation process the vehicle dynamic signals, which are available from the conventional on-board sensors, are fused. The values of the estimated tire pressures are incorporated in the LPV control as scheduling variables. The results of the control system are the steering and the differential drive interventions on the vehicle. The effectiveness of the method is illustrated through comprehensive simulation scenarios through the CarMaker simulation enviroment.
{"title":"Handling of tire pressure variation in autonomous vehicles: an integrated estimation and control design approach","authors":"T. Hegedüs, Dániel Fényes, B. Németh, P. Gáspár","doi":"10.23919/ACC45564.2020.9147472","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147472","url":null,"abstract":"Tire pressure has a high impact on the tire-road contact because it influences the characteristics of the tire forces. During the maneuvering of the vehicle the pressures of the tires may decrease over time, which results in performance degradation or the loss of controllability. This paper proposes a novel integration of tire pressure estimation and path-following control design based on machine learning and Linear Parameter-Varying (LPV) methods. In the estimation process the vehicle dynamic signals, which are available from the conventional on-board sensors, are fused. The values of the estimated tire pressures are incorporated in the LPV control as scheduling variables. The results of the control system are the steering and the differential drive interventions on the vehicle. The effectiveness of the method is illustrated through comprehensive simulation scenarios through the CarMaker simulation enviroment.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128981449","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 : 2020-07-01DOI: 10.23919/acc45564.2020.9147494
R. Mohajerpoor, C. Cai
Short links at signalized intersections, even in undersaturated traffic conditions are often subject to overflow. The spillback phenomena in those links can significantly degrade the performance of the optimal signal control algorithm by blocking other movements at the intersection or an adjacent one. In this paper, we analytically derive the necessary and sufficient conditions for the spillover phenomena in an undersaturated intersection. We further propose a real-time data-driven Spill-back Avoidance Signal Control (SASC) framework to treat the problem for a two-phase intersection. A microsimulation study is conducted to emphasize the effectiveness of the proposed SASC algorithm. Over 28 percent reduction in total vehicle delay of the system is achieved with respect to the optimal signal timing that ignores the spillback effects.
{"title":"A Traffic Signal Control Strategy to Avoid Spillback on Short Links","authors":"R. Mohajerpoor, C. Cai","doi":"10.23919/acc45564.2020.9147494","DOIUrl":"https://doi.org/10.23919/acc45564.2020.9147494","url":null,"abstract":"Short links at signalized intersections, even in undersaturated traffic conditions are often subject to overflow. The spillback phenomena in those links can significantly degrade the performance of the optimal signal control algorithm by blocking other movements at the intersection or an adjacent one. In this paper, we analytically derive the necessary and sufficient conditions for the spillover phenomena in an undersaturated intersection. We further propose a real-time data-driven Spill-back Avoidance Signal Control (SASC) framework to treat the problem for a two-phase intersection. A microsimulation study is conducted to emphasize the effectiveness of the proposed SASC algorithm. Over 28 percent reduction in total vehicle delay of the system is achieved with respect to the optimal signal timing that ignores the spillback effects.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129063938","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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