Pub Date : 2007-07-02DOI: 10.23919/ECC.2007.7068969
J. Chiasson, M. Bodson
Indirect field oriented control of an induction machine requires knowledge of the rotor time constant to estimate the rotor flux linkages. Here an online method is presented for estimating the rotor time constant and the stator resistance both of which vary during operation of the machine due to ohmic heating. With the additional assumption of collecting the data while the motor runs at constant speed under load, two different methods are presented to estimate the rotor time constant and stator resistance. The first method formulates the problem using a nonlinear least-squares criterion and shows that the parameters that minimize the least-squares error can be found in a finite number of steps. The resulting algorithm requires significantly less computation than a previously reported algorithm by the authors developed under varying-speed conditions. The second method shows the parameters can also be found using a linear least-squares criterion provided that the rotor flux magnitude is varied with the speed held constant.
{"title":"Estimation of the rotor time constant of an induction machine at constant speed","authors":"J. Chiasson, M. Bodson","doi":"10.23919/ECC.2007.7068969","DOIUrl":"https://doi.org/10.23919/ECC.2007.7068969","url":null,"abstract":"Indirect field oriented control of an induction machine requires knowledge of the rotor time constant to estimate the rotor flux linkages. Here an online method is presented for estimating the rotor time constant and the stator resistance both of which vary during operation of the machine due to ohmic heating. With the additional assumption of collecting the data while the motor runs at constant speed under load, two different methods are presented to estimate the rotor time constant and stator resistance. The first method formulates the problem using a nonlinear least-squares criterion and shows that the parameters that minimize the least-squares error can be found in a finite number of steps. The resulting algorithm requires significantly less computation than a previously reported algorithm by the authors developed under varying-speed conditions. The second method shows the parameters can also be found using a linear least-squares criterion provided that the rotor flux magnitude is varied with the speed held constant.","PeriodicalId":407048,"journal":{"name":"2007 European Control Conference (ECC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116933734","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 : 2007-07-02DOI: 10.23919/ECC.2007.7068587
Sergio Saludes, M. J. Fuente
This paper describes a nonlinear Model Predictive Controller (MPC) which uses Support Vector Machines (SVM) as a model of the plant. Both batch and on-line trained SVM are presented. The fault tolerance capabilities of this kind of nonlinear MPC are discussed. A strategy for achieving fault tolerance in this nonlinear MPC based on on-line trained SVM is presented. Results obtained under simulation in a continuous stirred tank reactor are discussed.
{"title":"Fault tolerant SVM based nonlinear Model Predictive Control","authors":"Sergio Saludes, M. J. Fuente","doi":"10.23919/ECC.2007.7068587","DOIUrl":"https://doi.org/10.23919/ECC.2007.7068587","url":null,"abstract":"This paper describes a nonlinear Model Predictive Controller (MPC) which uses Support Vector Machines (SVM) as a model of the plant. Both batch and on-line trained SVM are presented. The fault tolerance capabilities of this kind of nonlinear MPC are discussed. A strategy for achieving fault tolerance in this nonlinear MPC based on on-line trained SVM is presented. Results obtained under simulation in a continuous stirred tank reactor are discussed.","PeriodicalId":407048,"journal":{"name":"2007 European Control Conference (ECC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116977323","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 : 2007-07-02DOI: 10.23919/ECC.2007.7068959
Martin Pomar, G. Gutiérrez, C. de Prada Moraga, J. E. Normey Rico
Frequently, the design of many processes or systems is performed without taking into account its dynamical behaviour. This leads to lost of performance when the process has to operate dynamically, as the design may impose constraints in the physically reachable dynamic. The joint design of equipment and its control system is an integration technique that incorporates control requirements in the process design stage. This paper illustrates the technique with a buck boost converter. The design has been performed including its control system and it is not only optimum from the point of view of the cost of its components, but it is also a system that complies with certain previously fixed dynamic characteristics.
{"title":"Integrated design and control applied to a buck boost converter","authors":"Martin Pomar, G. Gutiérrez, C. de Prada Moraga, J. E. Normey Rico","doi":"10.23919/ECC.2007.7068959","DOIUrl":"https://doi.org/10.23919/ECC.2007.7068959","url":null,"abstract":"Frequently, the design of many processes or systems is performed without taking into account its dynamical behaviour. This leads to lost of performance when the process has to operate dynamically, as the design may impose constraints in the physically reachable dynamic. The joint design of equipment and its control system is an integration technique that incorporates control requirements in the process design stage. This paper illustrates the technique with a buck boost converter. The design has been performed including its control system and it is not only optimum from the point of view of the cost of its components, but it is also a system that complies with certain previously fixed dynamic characteristics.","PeriodicalId":407048,"journal":{"name":"2007 European Control Conference (ECC)","volume":"74 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120925215","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 : 2007-07-02DOI: 10.23919/ECC.2007.7068384
N. Karcanias, S. Fatouros
The Greatest Common Divisor (GCD) of many polynomials is central to linear systems problems and its computation is a nongeneric problem. Defining the notion of “approximate” GCD, measuring and computing the strength of the approximation and determining the “best approximation” are challenging problems. This paper uses the Sylvester Resultant representation of the GCD of many polynomials, and the corresponding factorisation of generalised resultants. We define the notion of “approximate GCD” and then indicate how to compute the “optimal approximate GCD” of a given order, or degree and the corresponding order of the approximation. This optimisation problem is defined as a distance problem in a projective space and it is shown to have an analytic solution.
{"title":"Approximate Greatest Common Divisors of polynomials and the optimal solution","authors":"N. Karcanias, S. Fatouros","doi":"10.23919/ECC.2007.7068384","DOIUrl":"https://doi.org/10.23919/ECC.2007.7068384","url":null,"abstract":"The Greatest Common Divisor (GCD) of many polynomials is central to linear systems problems and its computation is a nongeneric problem. Defining the notion of “approximate” GCD, measuring and computing the strength of the approximation and determining the “best approximation” are challenging problems. This paper uses the Sylvester Resultant representation of the GCD of many polynomials, and the corresponding factorisation of generalised resultants. We define the notion of “approximate GCD” and then indicate how to compute the “optimal approximate GCD” of a given order, or degree and the corresponding order of the approximation. This optimisation problem is defined as a distance problem in a projective space and it is shown to have an analytic solution.","PeriodicalId":407048,"journal":{"name":"2007 European Control Conference (ECC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127085806","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 : 2007-07-02DOI: 10.23919/ECC.2007.7069035
S. Fekri
We propose a novel architecture, RMMAC, which represents a new variant in the class of multiple model adaptive control architectures. The general philosophy of the RMMAC was described in refs. [3] and [4], while extensive simulation evaluations were shown in [3-11]. The RMMAC methodology integrates robust controller synthesis, using the mixed-μ synthesis method, with dynamic hypothesis-testing concepts using explicit robust-performance requirements for the adaptive design.
{"title":"The robust multiple-model adaptive control (RMMAC) method","authors":"S. Fekri","doi":"10.23919/ECC.2007.7069035","DOIUrl":"https://doi.org/10.23919/ECC.2007.7069035","url":null,"abstract":"We propose a novel architecture, RMMAC, which represents a new variant in the class of multiple model adaptive control architectures. The general philosophy of the RMMAC was described in refs. [3] and [4], while extensive simulation evaluations were shown in [3-11]. The RMMAC methodology integrates robust controller synthesis, using the mixed-μ synthesis method, with dynamic hypothesis-testing concepts using explicit robust-performance requirements for the adaptive design.","PeriodicalId":407048,"journal":{"name":"2007 European Control Conference (ECC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127089239","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 : 2007-07-02DOI: 10.23919/ECC.2007.7068243
Jung-Yang Chen, Chuei-Tin Chang
A SDG-based reasoning procedure is presented in this paper to qualitatively predict the evolution process of one or more fault propagating in a multi-loop process system. A set of IF-THEN rules are then constructed accordingly for diagnosis purpose. The resulting fuzzy inference system can be used to identify not only the locations of fault origins but also their magnitude levels. Numerical simulation studies have been carried out to verify the feasibility of the proposed approach.
{"title":"Development of fault diagnosis methods based on qualitative predictions of symptom evolution behaviors","authors":"Jung-Yang Chen, Chuei-Tin Chang","doi":"10.23919/ECC.2007.7068243","DOIUrl":"https://doi.org/10.23919/ECC.2007.7068243","url":null,"abstract":"A SDG-based reasoning procedure is presented in this paper to qualitatively predict the evolution process of one or more fault propagating in a multi-loop process system. A set of IF-THEN rules are then constructed accordingly for diagnosis purpose. The resulting fuzzy inference system can be used to identify not only the locations of fault origins but also their magnitude levels. Numerical simulation studies have been carried out to verify the feasibility of the proposed approach.","PeriodicalId":407048,"journal":{"name":"2007 European Control Conference (ECC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126071888","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 : 2007-07-02DOI: 10.23919/ECC.2007.7068875
F. Hutu, S. Cauet, P. Coirault
In this paper, time delays effects on network synchronization are tackled in the case of a network of a diffusely coupled identical systems. It is assumed that the parameters of the networks are in a polytope. Using Lyapunov methods and LMI (Linear matrix inequality) framework coupled to dissipativity theory, a new condition is given to assure global exponential synchronization. The approach is successfully applied to network of oscillators used in smart antenna application.
{"title":"Passive synchronization of network of oscillators with delays: Application to antenna arrays","authors":"F. Hutu, S. Cauet, P. Coirault","doi":"10.23919/ECC.2007.7068875","DOIUrl":"https://doi.org/10.23919/ECC.2007.7068875","url":null,"abstract":"In this paper, time delays effects on network synchronization are tackled in the case of a network of a diffusely coupled identical systems. It is assumed that the parameters of the networks are in a polytope. Using Lyapunov methods and LMI (Linear matrix inequality) framework coupled to dissipativity theory, a new condition is given to assure global exponential synchronization. The approach is successfully applied to network of oscillators used in smart antenna application.","PeriodicalId":407048,"journal":{"name":"2007 European Control Conference (ECC)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123273230","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 : 2007-07-02DOI: 10.23919/ECC.2007.7069029
S. Ge, B. Ren
Recently, adaptive neural control has been attracting an increasing attention for nonlinear unknown dynamic systems [1][2]. This paper is dedicated to the discussions on a few techniques in the design of adaptive neural network control for non-affine systems which are known to be difficult to control. The techniques include implicit function theorem based neural control for classes of the non-affine systems in Brunovsky form, implicit function theorem with backstepping design for classes of the non-affine systems in pure-feedback form, and pseudo inverse control. This paper is aimed to provide an overview of the state of art of stable control design for non-affine systems using neural network parametrization, and to list the advantages and disadvantages of neural network control.
{"title":"Neural network control for non-affine nonlinear systems","authors":"S. Ge, B. Ren","doi":"10.23919/ECC.2007.7069029","DOIUrl":"https://doi.org/10.23919/ECC.2007.7069029","url":null,"abstract":"Recently, adaptive neural control has been attracting an increasing attention for nonlinear unknown dynamic systems [1][2]. This paper is dedicated to the discussions on a few techniques in the design of adaptive neural network control for non-affine systems which are known to be difficult to control. The techniques include implicit function theorem based neural control for classes of the non-affine systems in Brunovsky form, implicit function theorem with backstepping design for classes of the non-affine systems in pure-feedback form, and pseudo inverse control. This paper is aimed to provide an overview of the state of art of stable control design for non-affine systems using neural network parametrization, and to list the advantages and disadvantages of neural network control.","PeriodicalId":407048,"journal":{"name":"2007 European Control Conference (ECC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123291194","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 : 2007-07-02DOI: 10.23919/ECC.2007.7068649
A. Rahideh, A. Safavi, M. Shaheed
This paper presents a neural network (NN) based nonlinear dynamic modelling approach for a Twin Rotor MIMO System (TRMS), in terms of its 2 degree of freedom (DOF) dynamics. The TRMS is a highly nonlinear system with significant cross-coupling between its horizontal and vertical axes. It is perceived as an aerodynamic test rig representing the control challenges of modern air vehicles. Accurate dynamic modelling is a prerequisite to address such challenges satisfactorily. A feedforward neural network has been trained using resilient propagation (RPROP) learning algorithm. The trained NN based model has been tested with a set of data that are different from those used for training purpose. For more validation the power spectral density (PSD) of the model is compared with that of the real TRMS and also the correlation validations of the test results are presented in order to show the effectiveness of the proposed model. The results show that the developed model can adequately represent the highly nonlinear features of the system.
{"title":"NN-based modelling of a 2DOF TRMS using RPROP learning algorithm","authors":"A. Rahideh, A. Safavi, M. Shaheed","doi":"10.23919/ECC.2007.7068649","DOIUrl":"https://doi.org/10.23919/ECC.2007.7068649","url":null,"abstract":"This paper presents a neural network (NN) based nonlinear dynamic modelling approach for a Twin Rotor MIMO System (TRMS), in terms of its 2 degree of freedom (DOF) dynamics. The TRMS is a highly nonlinear system with significant cross-coupling between its horizontal and vertical axes. It is perceived as an aerodynamic test rig representing the control challenges of modern air vehicles. Accurate dynamic modelling is a prerequisite to address such challenges satisfactorily. A feedforward neural network has been trained using resilient propagation (RPROP) learning algorithm. The trained NN based model has been tested with a set of data that are different from those used for training purpose. For more validation the power spectral density (PSD) of the model is compared with that of the real TRMS and also the correlation validations of the test results are presented in order to show the effectiveness of the proposed model. The results show that the developed model can adequately represent the highly nonlinear features of the system.","PeriodicalId":407048,"journal":{"name":"2007 European Control Conference (ECC)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123423147","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 : 2007-07-02DOI: 10.23919/ECC.2007.7068989
J. M. Igreja, J. M. Lemos, R. N. Silva
Adaptive nonlinear model based predictive control of distributed plants involving transport phenomena, described by hyperbolic partial differential equations are considered. The method proposed relies on a control Lyapunov function derived from Sontag's formula and in a stable observer and tackles directly the infinite dimension class of systems without finite dimension approximations. The control of plug flow nonlinear systems (a tubular heat exchanger, a distributed collector solar filed and a tubular reactor) are presented as examples to illustrate the method.
{"title":"Adaptive control of hyperbolic systems: A CLF approach","authors":"J. M. Igreja, J. M. Lemos, R. N. Silva","doi":"10.23919/ECC.2007.7068989","DOIUrl":"https://doi.org/10.23919/ECC.2007.7068989","url":null,"abstract":"Adaptive nonlinear model based predictive control of distributed plants involving transport phenomena, described by hyperbolic partial differential equations are considered. The method proposed relies on a control Lyapunov function derived from Sontag's formula and in a stable observer and tackles directly the infinite dimension class of systems without finite dimension approximations. The control of plug flow nonlinear systems (a tubular heat exchanger, a distributed collector solar filed and a tubular reactor) are presented as examples to illustrate the method.","PeriodicalId":407048,"journal":{"name":"2007 European Control Conference (ECC)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123533307","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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