Pub Date : 2021-06-29DOI: 10.23919/ecc54610.2021.9654844
Sutrisno, Stephan Trenn
In this paper, we investigate the observability of singular linear switched systems in discrete time. As a preliminary study, we restrict ourselves to systems with a single switch switching signal, i.e. the system switches from one mode to another mode at a certain switching time. We provide two necessary and sufficient conditions for the observability characterization. The first condition is applied for arbitrary switching time and the second one is for switching times that are far enough from the initial time and the final time of observation. These two conditions explicitly contain the switching time variable that indicates that in general, the observability is dependent on the switching time. However, under some sufficient conditions we provide, the observability will not depend on the switching time anymore. Furthermore, the observability of systems with two-dimensional states is independent of the switching time. In addition, from the example we discussed, an observable switched system can be built from two unobservable modes and different mode sequences may produce different observability property; in particular, swapping the mode sequence may destroy observability.
{"title":"Observability of Singular Linear Switched Systems in Discrete Time: Single Switch Case","authors":"Sutrisno, Stephan Trenn","doi":"10.23919/ecc54610.2021.9654844","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9654844","url":null,"abstract":"In this paper, we investigate the observability of singular linear switched systems in discrete time. As a preliminary study, we restrict ourselves to systems with a single switch switching signal, i.e. the system switches from one mode to another mode at a certain switching time. We provide two necessary and sufficient conditions for the observability characterization. The first condition is applied for arbitrary switching time and the second one is for switching times that are far enough from the initial time and the final time of observation. These two conditions explicitly contain the switching time variable that indicates that in general, the observability is dependent on the switching time. However, under some sufficient conditions we provide, the observability will not depend on the switching time anymore. Furthermore, the observability of systems with two-dimensional states is independent of the switching time. In addition, from the example we discussed, an observable switched system can be built from two unobservable modes and different mode sequences may produce different observability property; in particular, swapping the mode sequence may destroy observability.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129985912","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 : 2021-06-29DOI: 10.23919/ecc54610.2021.9655177
Vittorio Casagrande, I. Prodan, S. Spurgeon, F. Boem
In this work we present a novel distributed MPC method for microgrid energy management based on distributed optimization. In order to cope with uncertainty in prices and renewable energy production, we adopt a robust min-max approach that optimizes at each time step the worst case scenario of the objective function. Combining the advantages of MPC and distributed optimization, the resulting algorithm is suitable for the control of large-scale microgrids in which renewable energy resources are employed. Moreover, since it is based on novel distributed optimization algorithms, the method allows the future power profiles to be computed for each microgrid component without sharing this information with the others. Simulation results for a DC microgrid system model show the effectiveness of the proposed method. The algorithm is tested in two different scenarios: in presence of uncertainties and considering perfect knowledge of the future price and power profiles.
{"title":"A robust MPC method for microgrid energy management based on distributed optimization","authors":"Vittorio Casagrande, I. Prodan, S. Spurgeon, F. Boem","doi":"10.23919/ecc54610.2021.9655177","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9655177","url":null,"abstract":"In this work we present a novel distributed MPC method for microgrid energy management based on distributed optimization. In order to cope with uncertainty in prices and renewable energy production, we adopt a robust min-max approach that optimizes at each time step the worst case scenario of the objective function. Combining the advantages of MPC and distributed optimization, the resulting algorithm is suitable for the control of large-scale microgrids in which renewable energy resources are employed. Moreover, since it is based on novel distributed optimization algorithms, the method allows the future power profiles to be computed for each microgrid component without sharing this information with the others. Simulation results for a DC microgrid system model show the effectiveness of the proposed method. The algorithm is tested in two different scenarios: in presence of uncertainties and considering perfect knowledge of the future price and power profiles.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130196596","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 : 2021-06-29DOI: 10.23919/ecc54610.2021.9655199
Domenico Natella, F. Vasca
The state of health of a battery characterizes its performance in terms of loss of capacity compared to the beginning of its life. This paper proposes a reinforcement learning algorithm for identifying the capacity of lithium-ion batteries. The training phase of the algorithm is based on data derived from constant current and constant voltage charging operations. The technique exploits a state observer based on a dynamic model of the battery and on the capacity estimation obtained with the reinforcement learning technique. The reward is defined as the error between the estimated and measured battery voltage. The effectiveness of the proposed solution is validated by considering different C-rates battery charging.
{"title":"Battery State of Health Estimation via Reinforcement Learning","authors":"Domenico Natella, F. Vasca","doi":"10.23919/ecc54610.2021.9655199","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9655199","url":null,"abstract":"The state of health of a battery characterizes its performance in terms of loss of capacity compared to the beginning of its life. This paper proposes a reinforcement learning algorithm for identifying the capacity of lithium-ion batteries. The training phase of the algorithm is based on data derived from constant current and constant voltage charging operations. The technique exploits a state observer based on a dynamic model of the battery and on the capacity estimation obtained with the reinforcement learning technique. The reward is defined as the error between the estimated and measured battery voltage. The effectiveness of the proposed solution is validated by considering different C-rates battery charging.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134145103","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 : 2021-06-29DOI: 10.23919/ecc54610.2021.9654941
P. Gupta, Vaibhav Srivastava
We consider a human agent servicing a queue of homogeneous tasks. The agent can service a task with normal or high fidelity level, where fidelity refers to the degree of exactness and precision while servicing the task. We assume the parameters of the human’s service time distribution depend on the selected fidelity level and her cognitive state and are assumed to be unknown a priori. These parameters are learned online through Bayesian parameter estimation. We formulate a robust adaptive semi-Markov decision process (SMDP) to solve our optimal fidelity selection problem and extend the results on convergence of robust-adaptive Markov decision processes (MDP) to robust-adaptive SMDPs.
{"title":"On Robust and Adaptive Fidelity Selection for Human-in-the-loop Queues","authors":"P. Gupta, Vaibhav Srivastava","doi":"10.23919/ecc54610.2021.9654941","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9654941","url":null,"abstract":"We consider a human agent servicing a queue of homogeneous tasks. The agent can service a task with normal or high fidelity level, where fidelity refers to the degree of exactness and precision while servicing the task. We assume the parameters of the human’s service time distribution depend on the selected fidelity level and her cognitive state and are assumed to be unknown a priori. These parameters are learned online through Bayesian parameter estimation. We formulate a robust adaptive semi-Markov decision process (SMDP) to solve our optimal fidelity selection problem and extend the results on convergence of robust-adaptive Markov decision processes (MDP) to robust-adaptive SMDPs.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131466683","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 : 2021-06-29DOI: 10.23919/ecc54610.2021.9654882
J. Grover, Changliu Liu, K. Sycara
We consider the problem of estimating bounds on parameters that represent tasks being performed by robots in a multirobot system. In our previous work, we derived necessary conditions based on persistency of excitation analysis for exact parameter inference to be possible. We concluded that depending on the robot’s task, the dynamics of individual robots may fail to satisfy these conditions, thereby preventing exact inference. As an extension to that work, this paper focuses on estimating bounds on task parameters when such conditions are not satisfied. Each robot in the team uses optimization-based controllers for mediating between task satisfaction and collision avoidance. We use KKT conditions of this control synthesis optimization and SVD of active collision avoidance constraints to derive explicit relations between Lagrange multipliers, robot dynamics and task parameters. Using these relations, we are able to derive bounds on the task parameters of each robot. Through numerical simulations we show how our proposed region based identification approach generates feasible regions for parameters when a conventional estimator such as a UKF fails. Additionally, empirical evidence shows that this approach generates contracting sets which converge to the true parameters much faster than the rate at which a UKF based estimate converges. Videos of these results are available at https://bit.ly/2JDMgeJ
{"title":"Feasible Region-Based System Identification Using Duality","authors":"J. Grover, Changliu Liu, K. Sycara","doi":"10.23919/ecc54610.2021.9654882","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9654882","url":null,"abstract":"We consider the problem of estimating bounds on parameters that represent tasks being performed by robots in a multirobot system. In our previous work, we derived necessary conditions based on persistency of excitation analysis for exact parameter inference to be possible. We concluded that depending on the robot’s task, the dynamics of individual robots may fail to satisfy these conditions, thereby preventing exact inference. As an extension to that work, this paper focuses on estimating bounds on task parameters when such conditions are not satisfied. Each robot in the team uses optimization-based controllers for mediating between task satisfaction and collision avoidance. We use KKT conditions of this control synthesis optimization and SVD of active collision avoidance constraints to derive explicit relations between Lagrange multipliers, robot dynamics and task parameters. Using these relations, we are able to derive bounds on the task parameters of each robot. Through numerical simulations we show how our proposed region based identification approach generates feasible regions for parameters when a conventional estimator such as a UKF fails. Additionally, empirical evidence shows that this approach generates contracting sets which converge to the true parameters much faster than the rate at which a UKF based estimate converges. Videos of these results are available at https://bit.ly/2JDMgeJ","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"277 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133034105","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 : 2021-06-29DOI: 10.23919/ecc54610.2021.9654855
S. Rajendran, S. Spurgeon, G. Tsampardoukas, R. Hampson
It is challenging to achieve high braking efficiency as well as high directional stability in emergency μ –split braking manoeuvres. A self-learning adaptive integrated control scheme is presented for an electric vehicle (EV) which has a novel brake-circuit configuration. A self-learning time varying super twisting sliding mode-based anti-lock braking system (ABS) controller is integrated with a simple PID-based steering controller, adaptive super twisting sliding mode-based yaw moment controller and a yaw moment allocation module via a two-tier two-layer hierarchical scheme. The ABS controller is designed based on a model which includes the actuator dynamics, and a fuzzy module is employed to vary the slope of the sliding surface to achieve high performance levels in μ –split operation. The scheme effectively executes differential braking to attain high braking performance with optimal steering effort and improved vehicle stability. Moreover, the scheme exhibits high robustness and adaptability to uncertainties and disturbances. The design has the added benefit that it is straightforward to implement in real-time. The performance of the proposed scheme is demonstrated using a 15th – order high fidelity vehicle model whose performance has been correlated with an experimental vehicle.
{"title":"Self-learning Adaptive Integrated Control of an Electric Vehicle in Emergency Braking","authors":"S. Rajendran, S. Spurgeon, G. Tsampardoukas, R. Hampson","doi":"10.23919/ecc54610.2021.9654855","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9654855","url":null,"abstract":"It is challenging to achieve high braking efficiency as well as high directional stability in emergency μ –split braking manoeuvres. A self-learning adaptive integrated control scheme is presented for an electric vehicle (EV) which has a novel brake-circuit configuration. A self-learning time varying super twisting sliding mode-based anti-lock braking system (ABS) controller is integrated with a simple PID-based steering controller, adaptive super twisting sliding mode-based yaw moment controller and a yaw moment allocation module via a two-tier two-layer hierarchical scheme. The ABS controller is designed based on a model which includes the actuator dynamics, and a fuzzy module is employed to vary the slope of the sliding surface to achieve high performance levels in μ –split operation. The scheme effectively executes differential braking to attain high braking performance with optimal steering effort and improved vehicle stability. Moreover, the scheme exhibits high robustness and adaptability to uncertainties and disturbances. The design has the added benefit that it is straightforward to implement in real-time. The performance of the proposed scheme is demonstrated using a 15th – order high fidelity vehicle model whose performance has been correlated with an experimental vehicle.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133582501","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 : 2021-06-29DOI: 10.23919/ECC54610.2021.9655083
Mohammed Al-Kharaz, B. Ananou, M. Ouladsine, Michel Combal, J. Pinaton
Process diagnostic and monitoring during production is a fundamental task of the control and alarm system. However, many defected products are still related to various issues of health states of production equipment. Therefore, quality inspection is a crucial step during the manufacturing process, ensuring that a product’s quality is maintained or improved with a reduced or total absence of errors. The final product quality determines whether or not a product unit satisfies its intended use. In this paper, we propose a final quality inspection framework based on alarm events data. In this framework, we first transform the textual alarm data into numeric using binary scoring. Then, we reduce the dimension of the obtained numeric matrix using an appropriate alarms grouping method. After that, we apply the reduced data to learn a classifier and to make a decision. Finally, we compare several machine learning algorithms’ performance in the prediction of scrap-per-lot, namely Decision Tree, Logistic Regression, K-nearest neighbors, Linear Support Vector Machine, and Multi-Layer Perceptron. The results show a satisfactory performance of the compared models that we effectively prove on a dataset collected over the whole semiconductor fabrication facility.
{"title":"From Alarm System Events Towards Quality Inspection of The Final Product: Application to a Semiconductor Industry","authors":"Mohammed Al-Kharaz, B. Ananou, M. Ouladsine, Michel Combal, J. Pinaton","doi":"10.23919/ECC54610.2021.9655083","DOIUrl":"https://doi.org/10.23919/ECC54610.2021.9655083","url":null,"abstract":"Process diagnostic and monitoring during production is a fundamental task of the control and alarm system. However, many defected products are still related to various issues of health states of production equipment. Therefore, quality inspection is a crucial step during the manufacturing process, ensuring that a product’s quality is maintained or improved with a reduced or total absence of errors. The final product quality determines whether or not a product unit satisfies its intended use. In this paper, we propose a final quality inspection framework based on alarm events data. In this framework, we first transform the textual alarm data into numeric using binary scoring. Then, we reduce the dimension of the obtained numeric matrix using an appropriate alarms grouping method. After that, we apply the reduced data to learn a classifier and to make a decision. Finally, we compare several machine learning algorithms’ performance in the prediction of scrap-per-lot, namely Decision Tree, Logistic Regression, K-nearest neighbors, Linear Support Vector Machine, and Multi-Layer Perceptron. The results show a satisfactory performance of the compared models that we effectively prove on a dataset collected over the whole semiconductor fabrication facility.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133647705","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 : 2021-06-29DOI: 10.23919/ecc54610.2021.9655036
L. Bascetta, G. P. Incremona
Unmanned Aerial Vehicles (UAVs) have to operate in complex environments, characterized by disturbances of different nature that affect the system performance. Moreover, system dynamics can be altered by unavoidable modeling uncertainties, that can further decrease the control performance. This motivates the introduction of robust control strategies and, among them, Sliding Mode Control (SMC) represents a viable solution, provided that the UAV model is led back to a normal form, suitable for control design purposes. This paper investigates two flatness-based linearization approaches, a feedback and a feedforward one, that transform the nonlinear and coupled quadrotor model into a canonical form eligible to design a trajectory tracking controller based on a battery of Higher-Order Sliding Mode (HOSM) regulators. Simulation results, based on a realistic model of a quadrotor, are presented to assess the performance of the proposed control system.
{"title":"Design of Sliding Mode Controllers for Quadrotor Vehicles via Flatness-based Feedback and Feedforward Linearization Strategies","authors":"L. Bascetta, G. P. Incremona","doi":"10.23919/ecc54610.2021.9655036","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9655036","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) have to operate in complex environments, characterized by disturbances of different nature that affect the system performance. Moreover, system dynamics can be altered by unavoidable modeling uncertainties, that can further decrease the control performance. This motivates the introduction of robust control strategies and, among them, Sliding Mode Control (SMC) represents a viable solution, provided that the UAV model is led back to a normal form, suitable for control design purposes. This paper investigates two flatness-based linearization approaches, a feedback and a feedforward one, that transform the nonlinear and coupled quadrotor model into a canonical form eligible to design a trajectory tracking controller based on a battery of Higher-Order Sliding Mode (HOSM) regulators. Simulation results, based on a realistic model of a quadrotor, are presented to assess the performance of the proposed control system.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"63 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134608814","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 : 2021-06-29DOI: 10.23919/ecc54610.2021.9654887
R. Mourouvin, J. Dai, S. Boersma, S. Bacha, D. Georges, A. Benchaib
In this paper, an optimization problem is formulated and a complete control solution is proposed that uses the Model Predictive Control (MPC) for AC/DC energy management of a Modular Multilevel Converter (MMC) controlled in grid-forming mode. For the implementation of the MPC, a relevant model of the grid is found by system identification. The identified model is then validated before its usage in the MPC. The simulation results show a better response to disturbances from AC grid and DC grid, as well as a better coordination in the management of the MMC internal energy and DC grid voltage compared to the commonly used dual PI structure.
{"title":"Model Predictive Control for AC/DC Energy Management of a Modular Multilevel Converter Operated in Grid-Forming Mode","authors":"R. Mourouvin, J. Dai, S. Boersma, S. Bacha, D. Georges, A. Benchaib","doi":"10.23919/ecc54610.2021.9654887","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9654887","url":null,"abstract":"In this paper, an optimization problem is formulated and a complete control solution is proposed that uses the Model Predictive Control (MPC) for AC/DC energy management of a Modular Multilevel Converter (MMC) controlled in grid-forming mode. For the implementation of the MPC, a relevant model of the grid is found by system identification. The identified model is then validated before its usage in the MPC. The simulation results show a better response to disturbances from AC grid and DC grid, as well as a better coordination in the management of the MMC internal energy and DC grid voltage compared to the commonly used dual PI structure.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131660062","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 : 2021-06-29DOI: 10.23919/ecc54610.2021.9654908
J. Giovagnola, D. Rigamonti, M. Corno, Weidong Chen, S. Savaresi
In this paper, we propose a tuning method for Adaptive Monte Carlo Localization (AMCL). The proposed method tunes the most important AMCL parameters without the need of a continuous ground truth by optimizing the estimated path smoothness and using the passage through a finite number of gateways as constraints. The optimization algorithm exploits Bayesian Optimization in order to limit the number of tuning runs.Data collected with an instrumented robot on a public road validate the approach. The proposed tuning yields a robust localization with minimal manual intervention in the tuning.
{"title":"Data-Driven, Ground Truth-Free Tuning of an Adaptive Monte Carlo Localization Method for Urban Scenarios","authors":"J. Giovagnola, D. Rigamonti, M. Corno, Weidong Chen, S. Savaresi","doi":"10.23919/ecc54610.2021.9654908","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9654908","url":null,"abstract":"In this paper, we propose a tuning method for Adaptive Monte Carlo Localization (AMCL). The proposed method tunes the most important AMCL parameters without the need of a continuous ground truth by optimizing the estimated path smoothness and using the passage through a finite number of gateways as constraints. The optimization algorithm exploits Bayesian Optimization in order to limit the number of tuning runs.Data collected with an instrumented robot on a public road validate the approach. The proposed tuning yields a robust localization with minimal manual intervention in the tuning.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115661862","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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