Pub Date : 2018-06-01DOI: 10.23919/ECC.2018.8550130
D. Gromov, F. Castaños, Alexander L. Fradkov
A novel formulation for the description of implicit port-Hamiltonian control systems is proposed and its potential use for the design of the control laws stabilizing a given submanifold described as a zero level set of an admissible energy function is shown. Using the developed formulation, a number of results on the stabilization of port-Hamiltonian systems are presented. The obtained results are formulated in a way that allows for direct application.
{"title":"PROJECTED DYNAMICS OF CONSTRAINED HAMILTONIAN SYSTEMS*","authors":"D. Gromov, F. Castaños, Alexander L. Fradkov","doi":"10.23919/ECC.2018.8550130","DOIUrl":"https://doi.org/10.23919/ECC.2018.8550130","url":null,"abstract":"A novel formulation for the description of implicit port-Hamiltonian control systems is proposed and its potential use for the design of the control laws stabilizing a given submanifold described as a zero level set of an admissible energy function is shown. Using the developed formulation, a number of results on the stabilization of port-Hamiltonian systems are presented. The obtained results are formulated in a way that allows for direct application.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"423 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115929886","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 : 2018-06-01DOI: 10.23919/ECC.2018.8550620
A. Matveev, K. Ovchinnikov
Multiple fully actuated mobile robots travel in a plane with upper-limited speeds and are driven by accelerations limited in magnitude. The robots should arrive at a pre-specified distance from an unpredictable speedy target, then maintain this distance and achieve an even self-distribution over the respective moving circle, along with a given angular velocity of rotation about the target. The robots do not communicate with anybody and are anonymous to one another; pre-assignment of different roles to various robots is impossible. Every robot measures only the relative position of the target and companion robots (within a finite range of “visibility” in the latter case) and has access to the angular velocity of its own pure rotation; access to its own linear velocity may also be needed in some cases. Necessary conditions for the solvability of the mission are disclosed and a distributed control strategy is proposed. Its global convergence and collision avoidance property are rigor- ously proved under slight and partly unavoidable enhancement of the just mentioned necessary conditions. The performance of the control law is illustrated by computer simulations.
{"title":"Distributed Communication-Free Control of Multiple Robots for Circumnavigation of a Speedy Unpredictably Maneuvering Target","authors":"A. Matveev, K. Ovchinnikov","doi":"10.23919/ECC.2018.8550620","DOIUrl":"https://doi.org/10.23919/ECC.2018.8550620","url":null,"abstract":"Multiple fully actuated mobile robots travel in a plane with upper-limited speeds and are driven by accelerations limited in magnitude. The robots should arrive at a pre-specified distance from an unpredictable speedy target, then maintain this distance and achieve an even self-distribution over the respective moving circle, along with a given angular velocity of rotation about the target. The robots do not communicate with anybody and are anonymous to one another; pre-assignment of different roles to various robots is impossible. Every robot measures only the relative position of the target and companion robots (within a finite range of “visibility” in the latter case) and has access to the angular velocity of its own pure rotation; access to its own linear velocity may also be needed in some cases. Necessary conditions for the solvability of the mission are disclosed and a distributed control strategy is proposed. Its global convergence and collision avoidance property are rigor- ously proved under slight and partly unavoidable enhancement of the just mentioned necessary conditions. The performance of the control law is illustrated by computer simulations.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134072071","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 : 2018-06-01DOI: 10.23919/ECC.2018.8550139
M. Khlebnikov
A stabilization problem for discrete-time bilinear controlsystemsisconsidered.Usingthelinearmatrixinequality technique and the concept of quadratic Lyapunov functions, an approach is proposed to the construction of the so-called stabilizability ellipsoid such that the trajectories of the closedloop system starting from any point inside this ellipsoid asymptotically tend to the origin. The approach allows for an efficient construction of nonconvex approximations to stabilizability domains of bilinear control systems. The obtained results can be extended to various robust statements of the problem, to bilinear systems with many dimensional control, and to bilinear control systems subjected to exogenous disturbances.
{"title":"Quadratic Stabilization of Discrete-Time Bilinear Control Systems","authors":"M. Khlebnikov","doi":"10.23919/ECC.2018.8550139","DOIUrl":"https://doi.org/10.23919/ECC.2018.8550139","url":null,"abstract":"A stabilization problem for discrete-time bilinear controlsystemsisconsidered.Usingthelinearmatrixinequality technique and the concept of quadratic Lyapunov functions, an approach is proposed to the construction of the so-called stabilizability ellipsoid such that the trajectories of the closedloop system starting from any point inside this ellipsoid asymptotically tend to the origin. The approach allows for an efficient construction of nonconvex approximations to stabilizability domains of bilinear control systems. The obtained results can be extended to various robust statements of the problem, to bilinear systems with many dimensional control, and to bilinear control systems subjected to exogenous disturbances.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131474261","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 : 2018-06-01DOI: 10.23919/ECC.2018.8550302
Gian Antonio Susto, Marco Maggipinto, G. Zannon, Fabio Altinier, E. Pesavento, A. Beghi
In laundry treatment appliances, the weight of the laundry loaded by the user inside the drum dramatically affects the operating behavior. Therefore, it is important to obtain a good estimate of the said quantity in order to correctly configure the machine before the washing/drying starts. In Vertical Axis Washing Machines the laundry weight is computed by exploiting the quantity of water absorbed by the clothes. However, such approach does not grant accurate results because the water absorption depends on the clothes fabric. For this reason, we propose a Soft Sensing approach for weight estimation that exploits the information obtained from physical sensors available on board without added costs. Data-driven Soft Sensors are developed, where, using Machine Learning techniques, a statistical model of the phenomenon of interest is created from a set of sample data.
{"title":"Machine Learning-based Laundry Weight Estimation for Vertical Axis Washing Machines","authors":"Gian Antonio Susto, Marco Maggipinto, G. Zannon, Fabio Altinier, E. Pesavento, A. Beghi","doi":"10.23919/ECC.2018.8550302","DOIUrl":"https://doi.org/10.23919/ECC.2018.8550302","url":null,"abstract":"In laundry treatment appliances, the weight of the laundry loaded by the user inside the drum dramatically affects the operating behavior. Therefore, it is important to obtain a good estimate of the said quantity in order to correctly configure the machine before the washing/drying starts. In Vertical Axis Washing Machines the laundry weight is computed by exploiting the quantity of water absorbed by the clothes. However, such approach does not grant accurate results because the water absorption depends on the clothes fabric. For this reason, we propose a Soft Sensing approach for weight estimation that exploits the information obtained from physical sensors available on board without added costs. Data-driven Soft Sensors are developed, where, using Machine Learning techniques, a statistical model of the phenomenon of interest is created from a set of sample data.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129074867","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 : 2018-06-01DOI: 10.23919/ECC.2018.8550149
Michelle S. Chong, Maria Sandsten, A. Rantzer
An estimation algorithm for the Wigner distribution (time-frequency representation) of the unmeasured states of a linear time-invariant system is presented. Given that the inputs and outputs are measured, the algorithm involves designing a Luenberger-like observer for each frequency of interest. Under noise-free conditions, we show that the es- timates converge to the true Wigner distribution under a detectability assumption on the time-frequency representation. The estimation algorithm provides estimates which converge to a neighbourhood of the true Wigner distribution where its norm is dependent on the norm of the measurement noise. We also illustrate the efficacy of the estimation algorithm on an academic example and a model of neuron populations.
{"title":"Estimating the Wigner distribution of linear time-invariant dynamical systems","authors":"Michelle S. Chong, Maria Sandsten, A. Rantzer","doi":"10.23919/ECC.2018.8550149","DOIUrl":"https://doi.org/10.23919/ECC.2018.8550149","url":null,"abstract":"An estimation algorithm for the Wigner distribution (time-frequency representation) of the unmeasured states of a linear time-invariant system is presented. Given that the inputs and outputs are measured, the algorithm involves designing a Luenberger-like observer for each frequency of interest. Under noise-free conditions, we show that the es- timates converge to the true Wigner distribution under a detectability assumption on the time-frequency representation. The estimation algorithm provides estimates which converge to a neighbourhood of the true Wigner distribution where its norm is dependent on the norm of the measurement noise. We also illustrate the efficacy of the estimation algorithm on an academic example and a model of neuron populations.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134183863","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 : 2018-06-01DOI: 10.23919/ECC.2018.8550125
Jun Yang, Cunjia Liu, Zongyu Zuo, Wen‐Hua Chen
In this paper, we present a simple optimal path following algorithm for a generic small fixed-wing unmanned aerial vehicle by virtue of a predictive control approach. Different from most of exiting path following algorithms, the proposed algorithm is designed in an optimal manner where the control action is generated based on a well-defined cost function. In addition, the presented approach is designed without resorting to any complex geometric coordinate transformation. Thereby the resultant optimal control law is straightforward for practical implementation. The effectiveness of the present method is validated by three cases of simulation studies.
{"title":"A Simple Optimal Planer Path Following Algorithm for Unmanned Aerial Vehicles∗","authors":"Jun Yang, Cunjia Liu, Zongyu Zuo, Wen‐Hua Chen","doi":"10.23919/ECC.2018.8550125","DOIUrl":"https://doi.org/10.23919/ECC.2018.8550125","url":null,"abstract":"In this paper, we present a simple optimal path following algorithm for a generic small fixed-wing unmanned aerial vehicle by virtue of a predictive control approach. Different from most of exiting path following algorithms, the proposed algorithm is designed in an optimal manner where the control action is generated based on a well-defined cost function. In addition, the presented approach is designed without resorting to any complex geometric coordinate transformation. Thereby the resultant optimal control law is straightforward for practical implementation. The effectiveness of the present method is validated by three cases of simulation studies.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134375432","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 : 2018-06-01DOI: 10.23919/ECC.2018.8550408
Milan Papez
Jump Markov nonlinear models (JMNMs) characterize a dynamical system by a finite number of presumably nonlinear and possibly non-Gaussian state-space configurations that switch according to a discrete-valued hidden Markov process. In this context, the smoothing problem– the task of estimating fixed points or sequences of hidden variables given all available data-is of key relevance to many objectives of statistical inference, including the estimation of static parameters. The present paper proposes a particle Gibbs with ancestor sampling (PGAS)-based smoother for JMNMs. The design methodology relies on integrating out the discrete process in order to increase the efficiency through Rao-Blackwellization. The experimental evaluation illustrates that the proposed method achieves higher estimation accuracy in less computational time compared to the original PGAS procedure.
{"title":"Rao-Blackwellized Particle Gibbs Kernels for Smoothing in Jump Markov Nonlinear Models","authors":"Milan Papez","doi":"10.23919/ECC.2018.8550408","DOIUrl":"https://doi.org/10.23919/ECC.2018.8550408","url":null,"abstract":"Jump Markov nonlinear models (JMNMs) characterize a dynamical system by a finite number of presumably nonlinear and possibly non-Gaussian state-space configurations that switch according to a discrete-valued hidden Markov process. In this context, the smoothing problem– the task of estimating fixed points or sequences of hidden variables given all available data-is of key relevance to many objectives of statistical inference, including the estimation of static parameters. The present paper proposes a particle Gibbs with ancestor sampling (PGAS)-based smoother for JMNMs. The design methodology relies on integrating out the discrete process in order to increase the efficiency through Rao-Blackwellization. The experimental evaluation illustrates that the proposed method achieves higher estimation accuracy in less computational time compared to the original PGAS procedure.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132229258","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 : 2018-06-01DOI: 10.23919/ECC.2018.8550314
M. Seslija, R. Sepulchre
This paper addresses the issue of modeling meanfield behavior in heterogeneous populations of linear timeinvariant SISO systems. Our analysis is conducted in the frequency domain, where the heterogeneity of input-output mappings (transfer functions) is modeled as a complex-valued Gaussian process. The mean-field model of diffusively coupled agents is obtained as a Gaussian approximation of averaged input-output behavior. It is shown that the strong coupling and the large number of agents reduce the population variance.
{"title":"Gaussian mean-field models of linear systems","authors":"M. Seslija, R. Sepulchre","doi":"10.23919/ECC.2018.8550314","DOIUrl":"https://doi.org/10.23919/ECC.2018.8550314","url":null,"abstract":"This paper addresses the issue of modeling meanfield behavior in heterogeneous populations of linear timeinvariant SISO systems. Our analysis is conducted in the frequency domain, where the heterogeneity of input-output mappings (transfer functions) is modeled as a complex-valued Gaussian process. The mean-field model of diffusively coupled agents is obtained as a Gaussian approximation of averaged input-output behavior. It is shown that the strong coupling and the large number of agents reduce the population variance.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132802562","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 : 2018-06-01DOI: 10.23919/ECC.2018.8550436
N. A. Nguyen, Sorin Olaru
This paper presents the construction of a convex piecewise affine control Lyapunov function for constrained linear discrete-time systems, affected by bounded additive disturbances. Exploiting the properties of this control Lyapunov function, the closed-loop dynamics are shown to converge to a given full-dimensional robust positively invariant set. Moreover, the proposed method leads to a simple robust control algorithm which only requires solving a linear programming problem at each sampling instant. Finally, the controller design is illustrated via a numerical example.
{"title":"A piecewise affine control Lyapunov function for robust control","authors":"N. A. Nguyen, Sorin Olaru","doi":"10.23919/ECC.2018.8550436","DOIUrl":"https://doi.org/10.23919/ECC.2018.8550436","url":null,"abstract":"This paper presents the construction of a convex piecewise affine control Lyapunov function for constrained linear discrete-time systems, affected by bounded additive disturbances. Exploiting the properties of this control Lyapunov function, the closed-loop dynamics are shown to converge to a given full-dimensional robust positively invariant set. Moreover, the proposed method leads to a simple robust control algorithm which only requires solving a linear programming problem at each sampling instant. Finally, the controller design is illustrated via a numerical example.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117139641","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 : 2018-06-01DOI: 10.23919/ECC.2018.8550495
R. Baklouti, M. Mansouri, A. Hamida, H. Nounou, M. Nounou
This paper deals with fault detection (FD) of chemical processes. Our previous study [1] has proved the effectiveness of multiscale principal component analysis (MSPCA)-based Moving Window (MW)-Generalized Likelihood Ratio Test (GLRT) to detect faults by maximizing the detection probability for a particular false alarm rate with different values of windows. However, the conventional PCA method is not suitable in nonlinear processes. In fact, this lack affects the monitoring system. To address this problem, we propose, first, to use multistage kernel PCA (MSKPCA) technique to extract the deterministic features and compute the principal components (PCs) in the original space. Second, integrate exponentially weighted moving average (EWMA), that has shown better abilities to reduce the false alarm rates and enhance the (FD) performances. Therefore, this work focuses on extending MSKPCA, and developing a MSKPCA-based EWMA-GLRT technique in order to improve the (FD) performance. The performances of the MSKPCA -based EWMA- GLRT are illustrated using Tennessee Eastman benchmark process.
{"title":"Improved Statistical Method Based Exponentially Weighted GLRT Chart and Its Application to Fault Detection*","authors":"R. Baklouti, M. Mansouri, A. Hamida, H. Nounou, M. Nounou","doi":"10.23919/ECC.2018.8550495","DOIUrl":"https://doi.org/10.23919/ECC.2018.8550495","url":null,"abstract":"This paper deals with fault detection (FD) of chemical processes. Our previous study [1] has proved the effectiveness of multiscale principal component analysis (MSPCA)-based Moving Window (MW)-Generalized Likelihood Ratio Test (GLRT) to detect faults by maximizing the detection probability for a particular false alarm rate with different values of windows. However, the conventional PCA method is not suitable in nonlinear processes. In fact, this lack affects the monitoring system. To address this problem, we propose, first, to use multistage kernel PCA (MSKPCA) technique to extract the deterministic features and compute the principal components (PCs) in the original space. Second, integrate exponentially weighted moving average (EWMA), that has shown better abilities to reduce the false alarm rates and enhance the (FD) performances. Therefore, this work focuses on extending MSKPCA, and developing a MSKPCA-based EWMA-GLRT technique in order to improve the (FD) performance. The performances of the MSKPCA -based EWMA- GLRT are illustrated using Tennessee Eastman benchmark process.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"17 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121018316","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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