Pub Date : 2009-06-24DOI: 10.1109/MED.2009.5164613
T. Estrada, P. Antsaklis
In this paper we study performance-related aspects for plants in a networked control setting, employing an approach known as Model-Based Networked Control Systems (MB-NCS) with Intermittent Feedback. Model-Based Networked Control Systems use an explicit model of the plant in order to reduce the network traffic while attempting to prevent excessive performance degradation. Intermittent Feedback consists of the loop remaining closed for some time interval, then open for another interval. We begin by investigating the behavior of the system while tracking a reference input. We provide the full response of the system and a condition for stability. We then shift our attention to controller design for MB-NCS. We use dynamic programming techniques to design an optimal controller to optimize an LQ-like performance index.
{"title":"Performance of Model-Based Networked Control Systems with discrete-time plants","authors":"T. Estrada, P. Antsaklis","doi":"10.1109/MED.2009.5164613","DOIUrl":"https://doi.org/10.1109/MED.2009.5164613","url":null,"abstract":"In this paper we study performance-related aspects for plants in a networked control setting, employing an approach known as Model-Based Networked Control Systems (MB-NCS) with Intermittent Feedback. Model-Based Networked Control Systems use an explicit model of the plant in order to reduce the network traffic while attempting to prevent excessive performance degradation. Intermittent Feedback consists of the loop remaining closed for some time interval, then open for another interval. We begin by investigating the behavior of the system while tracking a reference input. We provide the full response of the system and a condition for stability. We then shift our attention to controller design for MB-NCS. We use dynamic programming techniques to design an optimal controller to optimize an LQ-like performance index.","PeriodicalId":422386,"journal":{"name":"2009 17th Mediterranean Conference on Control and Automation","volume":"123 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116576529","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 : 2009-06-24DOI: 10.1109/MED.2009.5164656
A. Topalov, N. Shakev, Severina Nikolova, D. Seyzinski, O. Kaynak
A neuro-adaptive trajectory control approach for unmanned aerial vehicles is proposed. The aerial robot's altitude and latitude-longitude is controlled by three neuro-adaptive controllers that are used to track the desired altitude, airspeed and roll angle of the vehicle. Each intelligent control module consists of a conventional and a neural network feedback controller. The former is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by a stable on-line learning algorithm to update the parameters of the neurocontroller. In this way the latter is able to eliminate gradually the conventional controller from the control of the system. The proposed learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in term of the neurocontroller parameters, leading the learning error toward zero. The performance of the proposed trajectory control scheme is evaluated with time based diagrams under MATLAB's standard configuration and the Aeronautical Simulation Block Set.
{"title":"Trajectory control of unmanned aerial vehicle using neural nets with a stable learning algorithm","authors":"A. Topalov, N. Shakev, Severina Nikolova, D. Seyzinski, O. Kaynak","doi":"10.1109/MED.2009.5164656","DOIUrl":"https://doi.org/10.1109/MED.2009.5164656","url":null,"abstract":"A neuro-adaptive trajectory control approach for unmanned aerial vehicles is proposed. The aerial robot's altitude and latitude-longitude is controlled by three neuro-adaptive controllers that are used to track the desired altitude, airspeed and roll angle of the vehicle. Each intelligent control module consists of a conventional and a neural network feedback controller. The former is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by a stable on-line learning algorithm to update the parameters of the neurocontroller. In this way the latter is able to eliminate gradually the conventional controller from the control of the system. The proposed learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in term of the neurocontroller parameters, leading the learning error toward zero. The performance of the proposed trajectory control scheme is evaluated with time based diagrams under MATLAB's standard configuration and the Aeronautical Simulation Block Set.","PeriodicalId":422386,"journal":{"name":"2009 17th Mediterranean Conference on Control and Automation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131394845","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 : 2009-06-24DOI: 10.1109/MED.2009.5164543
P. Rosa, J. Shamma, C. Silvestre, M. Athans
The Stability Overlay (SO) is a “safety net” that can be integrated with virtually any multiple-model adaptive control (MMAC) architecture, guaranteeing the stability of the closed-loop system. However, the arbitrary interconnection of the SO with a MMAC architecture can lead to severe performance deterioration. Thus, this paper proposes a systematic integration of the SO with the Robust Multiple-Model Adaptive Control (RMMAC), which provides stability guarantees, while maintaining the high levels of performance of the standard RMMAC observed in numerous simulations when the design assumptions are not violated.
{"title":"Integration of the Stability Overlay (SO) with the Robust Multiple-Model Adaptive Control (RMMAC)","authors":"P. Rosa, J. Shamma, C. Silvestre, M. Athans","doi":"10.1109/MED.2009.5164543","DOIUrl":"https://doi.org/10.1109/MED.2009.5164543","url":null,"abstract":"The Stability Overlay (SO) is a “safety net” that can be integrated with virtually any multiple-model adaptive control (MMAC) architecture, guaranteeing the stability of the closed-loop system. However, the arbitrary interconnection of the SO with a MMAC architecture can lead to severe performance deterioration. Thus, this paper proposes a systematic integration of the SO with the Robust Multiple-Model Adaptive Control (RMMAC), which provides stability guarantees, while maintaining the high levels of performance of the standard RMMAC observed in numerous simulations when the design assumptions are not violated.","PeriodicalId":422386,"journal":{"name":"2009 17th Mediterranean Conference on Control and Automation","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132789069","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 : 2009-06-24DOI: 10.1109/MED.2009.5164600
R. García-Hernández, E. Sánchez, M. Saad, E. Bayro-Corrochano
This paper deals with adaptive trajectory tracking for a five DOF robot manipulator, A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The HONN learning is performed online by an Extended Kalman Filter (EKF) algorithm. The applicability of the proposed scheme is illustrated via simulations.
{"title":"Discrete-time decentralized neural backstepping controller for a five DOF robot manipulator","authors":"R. García-Hernández, E. Sánchez, M. Saad, E. Bayro-Corrochano","doi":"10.1109/MED.2009.5164600","DOIUrl":"https://doi.org/10.1109/MED.2009.5164600","url":null,"abstract":"This paper deals with adaptive trajectory tracking for a five DOF robot manipulator, A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The HONN learning is performed online by an Extended Kalman Filter (EKF) algorithm. The applicability of the proposed scheme is illustrated via simulations.","PeriodicalId":422386,"journal":{"name":"2009 17th Mediterranean Conference on Control and Automation","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133271043","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 : 2009-06-24DOI: 10.1109/MED.2009.5164511
V. Hassani, M. Athans, A. Pascoal
In this paper we extend and generalize previous work on Robust Adaptive Control of uncertain plants using Multiple Models (the RMMAC methodology) [1]. We formulate and study the problem of robust adaptive control of open-loop unstable plants with structured and unstructured uncertainty in the presence of external disturbances, an issue that poses considerable theoretical and practical challenges. In particular, we show how a slight modification of the technique introduced in previous work on the RMMAC for stable plants yields a methodology that can deal with unstable plants. A design example and computer simulations are presented and discussed.
{"title":"An application of the RMMAC methodology to an unstable plant","authors":"V. Hassani, M. Athans, A. Pascoal","doi":"10.1109/MED.2009.5164511","DOIUrl":"https://doi.org/10.1109/MED.2009.5164511","url":null,"abstract":"In this paper we extend and generalize previous work on Robust Adaptive Control of uncertain plants using Multiple Models (the RMMAC methodology) [1]. We formulate and study the problem of robust adaptive control of open-loop unstable plants with structured and unstructured uncertainty in the presence of external disturbances, an issue that poses considerable theoretical and practical challenges. In particular, we show how a slight modification of the technique introduced in previous work on the RMMAC for stable plants yields a methodology that can deal with unstable plants. A design example and computer simulations are presented and discussed.","PeriodicalId":422386,"journal":{"name":"2009 17th Mediterranean Conference on Control and Automation","volume":"493 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113997656","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 : 2009-06-24DOI: 10.1109/MED.2009.5164568
G. Fourlas
Many diagnosis approaches are based in the assumption of single faults. This assumption may result to erroneous diagnosis statement in case where multiple faults occurs. Thereby multiple fault diagnosis is a challenging task especially in the control of large scale complex systems that can be viewed as hybrid systems. This owed to the fact that multiple faults are hard to detect because there consequences can mask or compensate to each other. The goal is to detect multiple faults as early as possible and provide a timely warning. A key issue is to prevent local faults to be developed into system failures that may cause safety hazards, stop temporarily the production and possible detrimental environment impact. In this work we introduce the notion of multiple faults diagnosability of Hybrid Systems in the framework of Hybrid Input Output Automata (HIOA). We present a methodology for detection of multiple faults imposing the condition for a Hybrid System to be diagnosable. This approach is applicable to a wide rage of systems since Hybrid Systems involve both continuous and discrete dynamics. The proposed method is tested via a simple application to a two tank system.
{"title":"Multiple faults diagnosability of Hybrid Systems","authors":"G. Fourlas","doi":"10.1109/MED.2009.5164568","DOIUrl":"https://doi.org/10.1109/MED.2009.5164568","url":null,"abstract":"Many diagnosis approaches are based in the assumption of single faults. This assumption may result to erroneous diagnosis statement in case where multiple faults occurs. Thereby multiple fault diagnosis is a challenging task especially in the control of large scale complex systems that can be viewed as hybrid systems. This owed to the fact that multiple faults are hard to detect because there consequences can mask or compensate to each other. The goal is to detect multiple faults as early as possible and provide a timely warning. A key issue is to prevent local faults to be developed into system failures that may cause safety hazards, stop temporarily the production and possible detrimental environment impact. In this work we introduce the notion of multiple faults diagnosability of Hybrid Systems in the framework of Hybrid Input Output Automata (HIOA). We present a methodology for detection of multiple faults imposing the condition for a Hybrid System to be diagnosable. This approach is applicable to a wide rage of systems since Hybrid Systems involve both continuous and discrete dynamics. The proposed method is tested via a simple application to a two tank system.","PeriodicalId":422386,"journal":{"name":"2009 17th Mediterranean Conference on Control and Automation","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116749460","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 : 2009-06-24DOI: 10.1109/MED.2009.5164591
Sivakumar Pitchaiah, A. Armaou
The problem of robust feedback control of spatially distributed processes described by highly dissipative partial differential equations (PDEs) is considered. Typically, this problem is addressed through model reduction where finite dimensional approximations to the original PDE system are derived. A common approach to this task is the Karhunen-Loève expansion combined with the method of snapshots. To circumvent the issue of a priori availability of a sufficiently large ensemble of PDE solution data, we focus on the recursive computation of eigenfunctions as additional data from the process become available. Initially, an ensemble of eigenfunctions is constructed with a relatively small number of snapshots. The dominant eigenspace of this ensemble is then identified to compute the empirical eigenfunctions required for model reduction. This dominant eigenspace is reevaluated with the addition of new snapshots the dominant eigenspace is reevaluated and its dimensionality may increase or decrease. Because this dimensionality is typically small the computational burden is also small. This approach is applied to a representative example of dissipative PDEs, to demonstrate the effectiveness of the approach to design robust controllers.
{"title":"Robust control of dissipative PDE systems in the presence of uncertainty using adaptive model reduction","authors":"Sivakumar Pitchaiah, A. Armaou","doi":"10.1109/MED.2009.5164591","DOIUrl":"https://doi.org/10.1109/MED.2009.5164591","url":null,"abstract":"The problem of robust feedback control of spatially distributed processes described by highly dissipative partial differential equations (PDEs) is considered. Typically, this problem is addressed through model reduction where finite dimensional approximations to the original PDE system are derived. A common approach to this task is the Karhunen-Loève expansion combined with the method of snapshots. To circumvent the issue of a priori availability of a sufficiently large ensemble of PDE solution data, we focus on the recursive computation of eigenfunctions as additional data from the process become available. Initially, an ensemble of eigenfunctions is constructed with a relatively small number of snapshots. The dominant eigenspace of this ensemble is then identified to compute the empirical eigenfunctions required for model reduction. This dominant eigenspace is reevaluated with the addition of new snapshots the dominant eigenspace is reevaluated and its dimensionality may increase or decrease. Because this dimensionality is typically small the computational burden is also small. This approach is applied to a representative example of dissipative PDEs, to demonstrate the effectiveness of the approach to design robust controllers.","PeriodicalId":422386,"journal":{"name":"2009 17th Mediterranean Conference on Control and Automation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121693971","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 : 2009-06-24DOI: 10.1109/MED.2009.5164709
S. Zanoli, L. Barboni
In this paper, a supervisory control system for oxygen consumption optimization on a Syngas Manufacturing Process Plant is proposed. A grey-box multivariable parametric identification of the oxygen compressor system is first performed. Consequently, by means of dynamic simulations the structure of an optimal control system has been determined, also reflecting the implementation constraints linked with the use of a DCS. Finally, operating results of the system implemented on the real process are shown which confirmed the expected results obtained by simulations.
{"title":"A DCS supervisory control of a centrifugal compessor for oxygen consumption optimization","authors":"S. Zanoli, L. Barboni","doi":"10.1109/MED.2009.5164709","DOIUrl":"https://doi.org/10.1109/MED.2009.5164709","url":null,"abstract":"In this paper, a supervisory control system for oxygen consumption optimization on a Syngas Manufacturing Process Plant is proposed. A grey-box multivariable parametric identification of the oxygen compressor system is first performed. Consequently, by means of dynamic simulations the structure of an optimal control system has been determined, also reflecting the implementation constraints linked with the use of a DCS. Finally, operating results of the system implemented on the real process are shown which confirmed the expected results obtained by simulations.","PeriodicalId":422386,"journal":{"name":"2009 17th Mediterranean Conference on Control and Automation","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122487688","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 : 2009-06-24DOI: 10.1109/MED.2009.5164742
J. Ding, S. Balakrishnan, M. Mantrala
Advertising departments in companies face hard choices when have to decide how much and when to spend on advertising over the year in order to maximize the customer goodwill and minimize the advertising expenditure. To address these issues, this paper proposes an optimal impulsive control method, in which the control is discrete and only applied when the goodwill is below some pre-specified level. Optimality conditions for impulse driven systems are derived first. Simulation results based on optimal impulsive control method and an existing technique are presented. Analysis of the results shows that integrated goodwill is greater with the optimal impulsive control method. Furthermore, this paper describes how to find a feasible suboptimal solution when an optimal solution does not exist.
{"title":"Application of optimal impulsive control method to advertising","authors":"J. Ding, S. Balakrishnan, M. Mantrala","doi":"10.1109/MED.2009.5164742","DOIUrl":"https://doi.org/10.1109/MED.2009.5164742","url":null,"abstract":"Advertising departments in companies face hard choices when have to decide how much and when to spend on advertising over the year in order to maximize the customer goodwill and minimize the advertising expenditure. To address these issues, this paper proposes an optimal impulsive control method, in which the control is discrete and only applied when the goodwill is below some pre-specified level. Optimality conditions for impulse driven systems are derived first. Simulation results based on optimal impulsive control method and an existing technique are presented. Analysis of the results shows that integrated goodwill is greater with the optimal impulsive control method. Furthermore, this paper describes how to find a feasible suboptimal solution when an optimal solution does not exist.","PeriodicalId":422386,"journal":{"name":"2009 17th Mediterranean Conference on Control and Automation","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122910452","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 : 2009-06-24DOI: 10.1109/MED.2009.5164746
D. Ipsakis, S. Voutetakis, P. Seferlis, S. Papadopoulou, M. Stoukides
The integrated power system under consideration, consists of the fuel processor (reformer and preferential oxidation reactors), the fuel cell and the heat management system. In the reformer reactor, methanol, air and water are co-fed to produce hydrogen under autothermal conditions. The produced hydrogen due to the high content of CO (≫5000ppm), is treated in the preferential oxidation reactor (PROX) for the CO minimization at acceptable levels (≪50ppm). After the oxidation clean-up step, the anode of the polymer electrolyte membrane (PEM) fuel cell is fed with the reformate gas (∼60–65% H2, ∼15–25% CO2, ∼15–20% N2, ∼1–3%CH3OH and traces of CO). The present paper is focused on the mathematical analysis of the main subsystems of the integrated power unit. The two reactors are modeled via a system of partial differential equations (PDE's) and the species flowrates and reactor temperature are analyzed along the length of each reactor. Moreover, the PEM fuel cell voltage-current characteristic is modeled via a non-linear equation that depends on the mass & energy balances (ordinary differential equations) of the concerned species. Finally, the heat management system is analyzed in order to provide insights for future control studies that will depend on the developed mathematical model (model-based control).
{"title":"Modeling and analysis of an integrated power system based on methanol autothermal reforming","authors":"D. Ipsakis, S. Voutetakis, P. Seferlis, S. Papadopoulou, M. Stoukides","doi":"10.1109/MED.2009.5164746","DOIUrl":"https://doi.org/10.1109/MED.2009.5164746","url":null,"abstract":"The integrated power system under consideration, consists of the fuel processor (reformer and preferential oxidation reactors), the fuel cell and the heat management system. In the reformer reactor, methanol, air and water are co-fed to produce hydrogen under autothermal conditions. The produced hydrogen due to the high content of CO (≫5000ppm), is treated in the preferential oxidation reactor (PROX) for the CO minimization at acceptable levels (≪50ppm). After the oxidation clean-up step, the anode of the polymer electrolyte membrane (PEM) fuel cell is fed with the reformate gas (∼60–65% H2, ∼15–25% CO2, ∼15–20% N2, ∼1–3%CH3OH and traces of CO). The present paper is focused on the mathematical analysis of the main subsystems of the integrated power unit. The two reactors are modeled via a system of partial differential equations (PDE's) and the species flowrates and reactor temperature are analyzed along the length of each reactor. Moreover, the PEM fuel cell voltage-current characteristic is modeled via a non-linear equation that depends on the mass & energy balances (ordinary differential equations) of the concerned species. Finally, the heat management system is analyzed in order to provide insights for future control studies that will depend on the developed mathematical model (model-based control).","PeriodicalId":422386,"journal":{"name":"2009 17th Mediterranean Conference on Control and Automation","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126513164","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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