Pub Date : 2022-04-14DOI: 10.3389/fcteg.2022.861055
Tahmoores Farjam, Themistoklis Charalambous
In this paper, we consider the problem of power scheduling of a sensor that transmits over a (possibly) unknown Gilbert-Elliott (GE) channel for remote state estimation. The sensor supports two power modes, namely low power, and high power. The scheduling policy determines when to use low power or high power for data transmission over a fading channel with temporal correlation while satisfying the energy constraints. Although error-free acknowledgement/negative-acknowledgement (ACK/NACK) signals are provided by the remote estimator, they only provide meaningful information about the underlying channel state when low power is utilized. This leads to a partially observable Markov decision process (POMDP) problem and we derive conditions that preserve the optimality of a stationary schedule derived for its fully observable counterpart. However, implementing this schedule requires knowledge of the parameters of the GE model which are not available in practice. To address this, we adopt a Bayesian framework to learn these parameters online and propose an algorithm that is shown to satisfy the energy constraint while achieving near-optimal performance via simulation.
{"title":"Power Allocation for Remote Estimation Over Known and Unknown Gilbert-Elliott Channels","authors":"Tahmoores Farjam, Themistoklis Charalambous","doi":"10.3389/fcteg.2022.861055","DOIUrl":"https://doi.org/10.3389/fcteg.2022.861055","url":null,"abstract":"In this paper, we consider the problem of power scheduling of a sensor that transmits over a (possibly) unknown Gilbert-Elliott (GE) channel for remote state estimation. The sensor supports two power modes, namely low power, and high power. The scheduling policy determines when to use low power or high power for data transmission over a fading channel with temporal correlation while satisfying the energy constraints. Although error-free acknowledgement/negative-acknowledgement (ACK/NACK) signals are provided by the remote estimator, they only provide meaningful information about the underlying channel state when low power is utilized. This leads to a partially observable Markov decision process (POMDP) problem and we derive conditions that preserve the optimality of a stationary schedule derived for its fully observable counterpart. However, implementing this schedule requires knowledge of the parameters of the GE model which are not available in practice. To address this, we adopt a Bayesian framework to learn these parameters online and propose an algorithm that is shown to satisfy the energy constraint while achieving near-optimal performance via simulation.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42903757","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 : 2022-04-08DOI: 10.3389/fcteg.2021.778118
Tousif Rahman, R. Shafik, Ole-Christoffer Granmo, A. Yakovlev
Increased reliance on electronic health records and plethora of new sensor technologies has enabled the use of machine learning (ML) in medical diagnosis. This has opened up promising opportunities for faster and automated decision making, particularly in early and repetitive diagnostic routines. Nevertheless, there are also increased possibilities of data aberrance arising from environmentally induced noise. It is vital to create ML models that are resilient in the presence of data noise to minimize erroneous classifications that could be crucial. This study uses a recently proposed ML algorithm called the Tsetlin machine (TM) to study the robustness against noise-injected medical data. We test two different feature extraction methods, in conjunction with the TM, to explore how feature engineering can mitigate the impact of noise corruption. Our results show the TM is capable of effective classification even with a signal-to-noise ratio (SNR) of −15dB as its training parameters remain resilient to noise injection. We show that high testing data sensitivity can still be possible at very low SNRs through a balance of feature distribution–based discretization and a rule mining algorithm used as a noise filtering encoding method. Through this method we show how a smaller number of core features can be extracted from a noisy problem space resulting in reduced ML model complexity and memory footprint—in some cases up to 6x fewer training parameters while retaining equal or better performance. In addition, we investigate the cost of noise resilience in terms of energy when compared with recently proposed binarized neural networks.
{"title":"Resilient Biomedical Systems Design Under Noise Using Logic-Based Machine Learning","authors":"Tousif Rahman, R. Shafik, Ole-Christoffer Granmo, A. Yakovlev","doi":"10.3389/fcteg.2021.778118","DOIUrl":"https://doi.org/10.3389/fcteg.2021.778118","url":null,"abstract":"Increased reliance on electronic health records and plethora of new sensor technologies has enabled the use of machine learning (ML) in medical diagnosis. This has opened up promising opportunities for faster and automated decision making, particularly in early and repetitive diagnostic routines. Nevertheless, there are also increased possibilities of data aberrance arising from environmentally induced noise. It is vital to create ML models that are resilient in the presence of data noise to minimize erroneous classifications that could be crucial. This study uses a recently proposed ML algorithm called the Tsetlin machine (TM) to study the robustness against noise-injected medical data. We test two different feature extraction methods, in conjunction with the TM, to explore how feature engineering can mitigate the impact of noise corruption. Our results show the TM is capable of effective classification even with a signal-to-noise ratio (SNR) of −15dB as its training parameters remain resilient to noise injection. We show that high testing data sensitivity can still be possible at very low SNRs through a balance of feature distribution–based discretization and a rule mining algorithm used as a noise filtering encoding method. Through this method we show how a smaller number of core features can be extracted from a noisy problem space resulting in reduced ML model complexity and memory footprint—in some cases up to 6x fewer training parameters while retaining equal or better performance. In addition, we investigate the cost of noise resilience in terms of energy when compared with recently proposed binarized neural networks.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49127297","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 : 2022-03-25DOI: 10.3389/fcteg.2022.785795
A. Rauh, Simon Rohou, L. Jaulin
For linear time-invariant dynamic systems with exactly known coefficients of their system matrices for which measurements with bounded errors are available at discrete time instants, an optimal polygonal state estimation scheme was recently published. This scheme allows for tightly enclosing all possible state trajectories in presence of uncertain, but bounded, system inputs which may be varying arbitrarily within in their bounds. Moreover, this approach is also capable of accounting for uncertainty related to the measurement time instants. However, the drawback of this polygonal technique is its rapidly increasing complexity for larger system dimensions. For that reason, the polygonal state enclosures are replaced by a computationally less expensive, but nearly optimal, ellipsoidal enclosure technique in this paper. Numerical simulations for representative benchmark examples focusing both on applications with precisely known and uncertain parameters conclude this contribution.
{"title":"An Ellipsoidal Predictor–Corrector State Estimation Scheme for Linear Continuous-Time Systems With Bounded Parameters and Bounded Measurement Errors","authors":"A. Rauh, Simon Rohou, L. Jaulin","doi":"10.3389/fcteg.2022.785795","DOIUrl":"https://doi.org/10.3389/fcteg.2022.785795","url":null,"abstract":"For linear time-invariant dynamic systems with exactly known coefficients of their system matrices for which measurements with bounded errors are available at discrete time instants, an optimal polygonal state estimation scheme was recently published. This scheme allows for tightly enclosing all possible state trajectories in presence of uncertain, but bounded, system inputs which may be varying arbitrarily within in their bounds. Moreover, this approach is also capable of accounting for uncertainty related to the measurement time instants. However, the drawback of this polygonal technique is its rapidly increasing complexity for larger system dimensions. For that reason, the polygonal state enclosures are replaced by a computationally less expensive, but nearly optimal, ellipsoidal enclosure technique in this paper. Numerical simulations for representative benchmark examples focusing both on applications with precisely known and uncertain parameters conclude this contribution.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41344245","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 : 2022-03-18DOI: 10.3389/fcteg.2022.806558
A. Rojas, Hugo O. Garcés
In this work, we introduce signal-to-noise ratio (SNR) based fault detection and identification mechanisms for a networked control system feedback loop, where the network component is represented by an additive white noise (AWN) channel. The SNR approach is known to be a steady-state analysis and design tool, thus we first introduce a finite time approximation for the estimated AWN channel SNR. We then consider the case of a general linear time-invariant plant model with one unstable pole. The potential faults that we discuss here cover simultaneously the plant model gain and/or the unstable pole. The fault detection is performed relative to the estimated AWN channel SNR. The fault identification is performed using recursive least squares ideas and then further validated with the observed SNR value, when a fault has been previously detected. We show that the proposed SNR-based fault mechanism (fault detection plus fault identification) is capable of processing the proposed faults. We conclude discussing future research based on the contributions exposed in the present work.
{"title":"Signal-to-Noise Ratio Based Fault Detection and Identification","authors":"A. Rojas, Hugo O. Garcés","doi":"10.3389/fcteg.2022.806558","DOIUrl":"https://doi.org/10.3389/fcteg.2022.806558","url":null,"abstract":"In this work, we introduce signal-to-noise ratio (SNR) based fault detection and identification mechanisms for a networked control system feedback loop, where the network component is represented by an additive white noise (AWN) channel. The SNR approach is known to be a steady-state analysis and design tool, thus we first introduce a finite time approximation for the estimated AWN channel SNR. We then consider the case of a general linear time-invariant plant model with one unstable pole. The potential faults that we discuss here cover simultaneously the plant model gain and/or the unstable pole. The fault detection is performed relative to the estimated AWN channel SNR. The fault identification is performed using recursive least squares ideas and then further validated with the observed SNR value, when a fault has been previously detected. We show that the proposed SNR-based fault mechanism (fault detection plus fault identification) is capable of processing the proposed faults. We conclude discussing future research based on the contributions exposed in the present work.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48705383","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 : 2022-03-15DOI: 10.3389/fcteg.2022.836720
John Ericksen, G. M. Fricke, S. Nowicki, T. Fischer, Julie Hayes, Karissa Rosenberger, Samantha R. Wolf, R. Fierro, M. Moses
We present methods for autonomous collaborative surveying of volcanic CO2 emissions using aerial robots. CO2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO2 collection flights in harsh environments. We implement two survey algorithms on teams of Dragonfly robots and demonstrate that they effectively map gas emissions and locate the highest gas concentrations. Our experiments culminate in a successful field test of collaborative rasterization and gradient descent algorithms in a challenging real-world environment at the edge of the Valles Caldera supervolcano. Both algorithms treat multiple flocking UAVs as a distributed flexible instrument. Simultaneous sensing in multiple UAVs gives scientists greater confidence in estimates of gas concentrations and the locations of sources of those emissions. These methods are also applicable to a range of other airborne concentration mapping tasks, such as pipeline leak detection and contaminant localization.
{"title":"Aerial Survey Robotics in Extreme Environments: Mapping Volcanic CO2 Emissions With Flocking UAVs","authors":"John Ericksen, G. M. Fricke, S. Nowicki, T. Fischer, Julie Hayes, Karissa Rosenberger, Samantha R. Wolf, R. Fierro, M. Moses","doi":"10.3389/fcteg.2022.836720","DOIUrl":"https://doi.org/10.3389/fcteg.2022.836720","url":null,"abstract":"We present methods for autonomous collaborative surveying of volcanic CO2 emissions using aerial robots. CO2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO2 collection flights in harsh environments. We implement two survey algorithms on teams of Dragonfly robots and demonstrate that they effectively map gas emissions and locate the highest gas concentrations. Our experiments culminate in a successful field test of collaborative rasterization and gradient descent algorithms in a challenging real-world environment at the edge of the Valles Caldera supervolcano. Both algorithms treat multiple flocking UAVs as a distributed flexible instrument. Simultaneous sensing in multiple UAVs gives scientists greater confidence in estimates of gas concentrations and the locations of sources of those emissions. These methods are also applicable to a range of other airborne concentration mapping tasks, such as pipeline leak detection and contaminant localization.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44274833","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 : 2022-03-10DOI: 10.3389/fcteg.2022.833146
J. Ramos-Teodoro, F. Rodríguez, M. Berenguel
Energy efficiency is a topic with many publications related to resource exploitation at a local scale; via well-performed energy management, substantial environmental and economic benefits can be achieved. In this article, the models used to forecast the photovoltaic power yield in two distinct facilities are described. These facilities are part of the same production plant, which makes use of different heterogeneous resources (carbon dioxide, water, thermal energy, and electricity) and has already been analyzed in a problem that consists in finding the set of variables that minimize the operation cost. In order to predict the power production for both photovoltaic fields, the expressions for radiation on sloped surfaces and the equivalent circuit for solar cells are employed, and the inverters and wire-transmission loss effects are considered. Furthermore, their integration within a general-purpose modeling framework for energy hubs is demonstrated. The comparison between validation results and production real data shows that despite the slight overestimation of the energy yield, the models are suitable to forecast the production of both facilities with a suitable accuracy, as the R 2 coefficients of both facilities lie between 0.95 and 0.96.
{"title":"Integration of Photovoltaic Generation Within a Modeling Framework for Energy Hubs","authors":"J. Ramos-Teodoro, F. Rodríguez, M. Berenguel","doi":"10.3389/fcteg.2022.833146","DOIUrl":"https://doi.org/10.3389/fcteg.2022.833146","url":null,"abstract":"Energy efficiency is a topic with many publications related to resource exploitation at a local scale; via well-performed energy management, substantial environmental and economic benefits can be achieved. In this article, the models used to forecast the photovoltaic power yield in two distinct facilities are described. These facilities are part of the same production plant, which makes use of different heterogeneous resources (carbon dioxide, water, thermal energy, and electricity) and has already been analyzed in a problem that consists in finding the set of variables that minimize the operation cost. In order to predict the power production for both photovoltaic fields, the expressions for radiation on sloped surfaces and the equivalent circuit for solar cells are employed, and the inverters and wire-transmission loss effects are considered. Furthermore, their integration within a general-purpose modeling framework for energy hubs is demonstrated. The comparison between validation results and production real data shows that despite the slight overestimation of the energy yield, the models are suitable to forecast the production of both facilities with a suitable accuracy, as the R 2 coefficients of both facilities lie between 0.95 and 0.96.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44674970","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 : 2022-03-04DOI: 10.3389/fcteg.2021.786188
Fabrice Le Bars, Robin Sanchez , Luc Jaulin , Simon Rohou , Andreas Rauh
Interval analysis is a numerical tool classically used for solving nonlinear equations in a guaranteed way. It has been shown that it can be used to build reliable nonlinear state estimators for dynamical systems. Numerous simulations inspired from real-life applications have shown the applicability of the approach. This paper proposes to implement an interval-based INS (Inertial Navigation System) in an actual robot to estimate its orientation and position. It shows that some types of outliers can be naturally handled by the fusion algorithm, while the resulting controller can be both fast and reliable. Experiments with an actual autonomous boat conclude this article.
{"title":"An Online Interval-Based Inertial Navigation System for Control Purposes of Autonomous Boats","authors":"Fabrice Le Bars, Robin Sanchez , Luc Jaulin , Simon Rohou , Andreas Rauh ","doi":"10.3389/fcteg.2021.786188","DOIUrl":"https://doi.org/10.3389/fcteg.2021.786188","url":null,"abstract":"Interval analysis is a numerical tool classically used for solving nonlinear equations in a guaranteed way. It has been shown that it can be used to build reliable nonlinear state estimators for dynamical systems. Numerous simulations inspired from real-life applications have shown the applicability of the approach. This paper proposes to implement an interval-based INS (Inertial Navigation System) in an actual robot to estimate its orientation and position. It shows that some types of outliers can be naturally handled by the fusion algorithm, while the resulting controller can be both fast and reliable. Experiments with an actual autonomous boat conclude this article.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45607790","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 : 2022-03-02DOI: 10.3389/fcteg.2022.785123
A. Rauh, E. Auer
In various research projects, it has been demonstrated that feedforward neural network models (possibly extended toward dynamic representations) are efficient means for identifying numerous dependencies of the electrochemical behavior of high-temperature fuel cells. These dependencies include external inputs such as gas mass flows, gas inlet temperatures, and the electric current as well as internal fuel cell states such as the temperature. Typically, the research on using neural networks in this context is focused only on point-valued training data. As a result, the neural network provides solely point-valued estimates for such quantities as the stack voltage and instantaneous fuel cell power. Although advantageous, for example, for robust control synthesis, quantifying the reliability of neural network models in terms of interval bounds for the network’s output has not yet received wide attention. In practice, however, such information is essential for optimizing the utilization of the supplied fuel. An additional goal is to make sure that the maximum power point is not exceeded since that would lead to accelerated stack degradation. To solve the data-driven modeling task with the focus on reliability assessment, a novel offline and online parameterization strategy for interval extensions of neural network models is presented in this paper. Its functionality is demonstrated using real-life measured data for a solid oxide fuel cell stack that is operated with temporally varying electric currents and fuel gas mass flows.
{"title":"Interval Extension of Neural Network Models for the Electrochemical Behavior of High-Temperature Fuel Cells","authors":"A. Rauh, E. Auer","doi":"10.3389/fcteg.2022.785123","DOIUrl":"https://doi.org/10.3389/fcteg.2022.785123","url":null,"abstract":"In various research projects, it has been demonstrated that feedforward neural network models (possibly extended toward dynamic representations) are efficient means for identifying numerous dependencies of the electrochemical behavior of high-temperature fuel cells. These dependencies include external inputs such as gas mass flows, gas inlet temperatures, and the electric current as well as internal fuel cell states such as the temperature. Typically, the research on using neural networks in this context is focused only on point-valued training data. As a result, the neural network provides solely point-valued estimates for such quantities as the stack voltage and instantaneous fuel cell power. Although advantageous, for example, for robust control synthesis, quantifying the reliability of neural network models in terms of interval bounds for the network’s output has not yet received wide attention. In practice, however, such information is essential for optimizing the utilization of the supplied fuel. An additional goal is to make sure that the maximum power point is not exceeded since that would lead to accelerated stack degradation. To solve the data-driven modeling task with the focus on reliability assessment, a novel offline and online parameterization strategy for interval extensions of neural network models is presented in this paper. Its functionality is demonstrated using real-life measured data for a solid oxide fuel cell stack that is operated with temporally varying electric currents and fuel gas mass flows.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42650117","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 : 2022-02-25DOI: 10.3389/fcteg.2022.806543
Patrick Flüs, O. Stursberg
This paper introduces a method to control a class of jump Markov linear systems with uncertain initialization of the continuous state and affected by disturbances. Both types of uncertainties are modeled as stochastic processes with arbitrarily chosen probability distributions, for which however, the expected values and (co-)variances are known. The paper elaborates on the control task of steering the uncertain system into a target set by use of continuous controls, while chance constraints have to be satisfied for all possible state sequences of the Markov chain. The proposed approach uses a stochastic model predictive control approach on moving finite-time horizons with tailored constraints to achieve the control goal with prescribed confidence. Key steps of the procedure are (i) to over-approximate probabilistic reachable sets by use of the Chebyshev inequality, and (ii) to embed a tightened version of the original constraints into the optimization problem, in order to obtain a control strategy satisfying the specifications. Convergence of the probabilistic reachable sets is attained by suitable bounding of the state covariance matrices for arbitrary Markov chain sequences. The paper presents the main steps of the solution approach, discusses its properties, and illustrates the principle for a numeric example.
{"title":"Control of Jump Markov Uncertain Linear Systems With General Probability Distributions","authors":"Patrick Flüs, O. Stursberg","doi":"10.3389/fcteg.2022.806543","DOIUrl":"https://doi.org/10.3389/fcteg.2022.806543","url":null,"abstract":"This paper introduces a method to control a class of jump Markov linear systems with uncertain initialization of the continuous state and affected by disturbances. Both types of uncertainties are modeled as stochastic processes with arbitrarily chosen probability distributions, for which however, the expected values and (co-)variances are known. The paper elaborates on the control task of steering the uncertain system into a target set by use of continuous controls, while chance constraints have to be satisfied for all possible state sequences of the Markov chain. The proposed approach uses a stochastic model predictive control approach on moving finite-time horizons with tailored constraints to achieve the control goal with prescribed confidence. Key steps of the procedure are (i) to over-approximate probabilistic reachable sets by use of the Chebyshev inequality, and (ii) to embed a tightened version of the original constraints into the optimization problem, in order to obtain a control strategy satisfying the specifications. Convergence of the probabilistic reachable sets is attained by suitable bounding of the state covariance matrices for arbitrary Markov chain sequences. The paper presents the main steps of the solution approach, discusses its properties, and illustrates the principle for a numeric example.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41630408","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 : 2022-02-15DOI: 10.3389/fcteg.2022.835052
Xiang Li, Jing Zhu
This paper investigates the consensus of multi-agent systems (MASs) by virtue of event-triggered mechanism. Considering the existence of external disturbances, we use a disturbance observer to estimate the disturbance signals and eliminate the corresponding effects by using estimators to compensate the input control terms. The self-triggered condition is designed and proved that there is no Zeno behavior. We show that the proposed disturbance observer can estimate the external disturbance signals well under the self-triggered condition. Finally, simulation examples are presented to verify the theoretical results.
{"title":"Self-Triggered Control of Multi-Agent Systems With External Disturbances","authors":"Xiang Li, Jing Zhu","doi":"10.3389/fcteg.2022.835052","DOIUrl":"https://doi.org/10.3389/fcteg.2022.835052","url":null,"abstract":"This paper investigates the consensus of multi-agent systems (MASs) by virtue of event-triggered mechanism. Considering the existence of external disturbances, we use a disturbance observer to estimate the disturbance signals and eliminate the corresponding effects by using estimators to compensate the input control terms. The self-triggered condition is designed and proved that there is no Zeno behavior. We show that the proposed disturbance observer can estimate the external disturbance signals well under the self-triggered condition. Finally, simulation examples are presented to verify the theoretical results.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45580921","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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