The Experts below are selected from a list of 22014 Experts worldwide ranked by ideXlab platform
József Bokor - One of the best experts on this subject based on the ideXlab platform.
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System identification with generalized Orthonormal Basis functions: an application to flexible structures
Control Engineering Practice, 2003Co-Authors: Volkan Nalbantoglu, József Bokor, Gary J. Balas, Péter GáspárAbstract:Abstract This paper presents an application of a multi-input/multi-output identification technique based on system-generated Orthonormal Basis functions to a flexible structure. A priori information about the poles of the system, part of which corresponds to the natural frequencies of the structure, is used to generate the Orthonormal Basis functions. A multivariable model is identified for the experimental flexible structure by using these Orthonormal Basis functions. It is shown that including a priori knowledge of the system dynamics via the use of Orthonormal Basis functions into the identification process has the advantage of reducing the number of parameters to be estimated. The multivariable model is used to design an H∞ controller for the experimental structure to suppress vibrations. The controller is implemented on the structure and very good agreement is obtained between the simulations and the experimental results.
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minimal partial realization from generalized Orthonormal Basis function expansions
Automatica, 2002Co-Authors: T.j. De Hoog, Z Szabo, Peter S.c. Heuberger, József BokorAbstract:A solution is presented for the problem of realizing a discrete-time LTI state-space model of minimal McMillan degree such that its first N expansion coefficients in terms of generalized Orthonormal Basis match a given sequence. The Basis considered, also known as the Hambo Basis, can be viewed as a generalization of the more familiar Laguerre and two-parameter Kautz constructions, allowing general dynamic information to be incorporated in the Basis. For the solution of the problem use is made of the properties of the Hambo operator transform theory that underlies the Basis function expansion. As corollary results compact expressions are found by which the Hambo transform and its inverse can be computed efficiently. The resulting realization algorithms can be applied in an approximative sense, for instance, for computing a low-order model from a large Basis function expansion that is obtained in an identification experiment.
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A loop shaping design using weighted generalized Orthonormal Basis functions
2001 European Control Conference (ECC), 2001Co-Authors: Péter Gáspár, Z Szabo, József BokorAbstract:In the paper a robust control design method is proposed to obtain a compensator that realizes the required loop shape formed by nominal performance and robust stability specifications. First, a fictitious compensator is identified in such a way that the design loop transfer function tends toward the required loop transfer function. Since the compensator must be identified as accurately as possible in the frequency domains that are important in terms of the required loop shape, the identification is performed by a stable approximation using frequency-weighted generalized Orthonormal Basis functions (WGOBF). Second, a robust compensator is designed by using the H∞/μ method taking into consideration the approximation error of the designed loop transfer function. The steps of the control design are illustrated to demonstrate the method on a numerical example.
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l sup p norm convergence of rational Orthonormal Basis function expansions
Conference on Decision and Control, 1999Co-Authors: Z Szabo, József BokorAbstract:In this paper model sets for discrete-time LTI systems that are spanned by generalized Orthonormal Basis functions are investigated. It is established that the partial sums of Fourier series of generalized Orthonormal Basis expansions converge in all the spaces L/sup p/ and H/sup p/, 1
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Closed-loop identification using generalized Orthonormal Basis functions
Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), 1999Co-Authors: Péter Gáspár, Z Szabo, József BokorAbstract:This paper presents a closed-loop identification method based on the construction of generalized Orthonormal Basis functions (GOBF). It modifies the two-stage method, which applies the finite impulse response (FIR) model structure to one that uses GOBF functions. Identification of a FIR model has some important advantages, however it fails to be successful when the number of coefficients to be estimated becomes large. With appropriately chosen Basis functions where the Basis is generated by all-pass functions having poles close to the poles of the system, the convergence rate of the series expansion can be extremely fast. The paper gives the algorithm of the closed-loop identification, and it demonstrates the method in a numerical example.
Wagner C. Amaral - One of the best experts on this subject based on the ideXlab platform.
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takagi sugeno fuzzy models in the framework of Orthonormal Basis functions
IEEE Transactions on Systems Man and Cybernetics, 2013Co-Authors: Jeremias Barbosa Machado, Ricardo J.g.b. Campello, Wagner C. AmaralAbstract:An approach to obtain Takagi-Sugeno (TS) fuzzy models of nonlinear dynamic systems using the framework of Orthonormal Basis functions (OBFs) is presented in this paper. This approach is based on an architecture in which local linear models with ladder-structured generalized OBFs (GOBFs) constitute the fuzzy rule consequents and the outputs of the corresponding GOBF filters are input variables for the rule antecedents. The resulting GOBF-TS model is characterized by having only real-valued parameters that do not depend on any user specification about particular types of functions to be used in the Orthonormal Basis. The fuzzy rules of the model are initially obtained by means of a well-known technique based on fuzzy clustering and least squares. Those rules are then simplified, and the model parameters (GOBF poles, GOBF expansion coefficients, and fuzzy membership functions) are subsequently adjusted by using a nonlinear optimization algorithm. The exact gradients of an error functional with respect to the parameters to be optimized are computed analytically. Those gradients provide exact search directions for the optimization process, which relies solely on input-output data measured from the system to be modeled. An example is presented to illustrate the performance of this approach in the modeling of a complex nonlinear dynamic system.
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Takagi–Sugeno Fuzzy Models in the Framework of Orthonormal Basis Functions
IEEE Transactions on Systems Man and Cybernetics, 2012Co-Authors: Jeremias Barbosa Machado, Ricardo J.g.b. Campello, Wagner C. AmaralAbstract:An approach to obtain Takagi-Sugeno (TS) fuzzy models of nonlinear dynamic systems using the framework of Orthonormal Basis functions (OBFs) is presented in this paper. This approach is based on an architecture in which local linear models with ladder-structured generalized OBFs (GOBFs) constitute the fuzzy rule consequents and the outputs of the corresponding GOBF filters are input variables for the rule antecedents. The resulting GOBF-TS model is characterized by having only real-valued parameters that do not depend on any user specification about particular types of functions to be used in the Orthonormal Basis. The fuzzy rules of the model are initially obtained by means of a well-known technique based on fuzzy clustering and least squares. Those rules are then simplified, and the model parameters (GOBF poles, GOBF expansion coefficients, and fuzzy membership functions) are subsequently adjusted by using a nonlinear optimization algorithm. The exact gradients of an error functional with respect to the parameters to be optimized are computed analytically. Those gradients provide exact search directions for the optimization process, which relies solely on input-output data measured from the system to be modeled. An example is presented to illustrate the performance of this approach in the modeling of a complex nonlinear dynamic system.
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Control of a bioprocess using Orthonormal Basis function fuzzy models
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004Co-Authors: Ricardo J.g.b. Campello, Luiz A.c. Meleiro, Wagner C. AmaralAbstract:Fuzzy models within the framework of Orthonormal Basis functions (OBF fuzzy models) were introduced in previous works and have shown to be a very promising approach to the areas of non-linear system identification and control since they exhibit several advantages over those dynamic model architectures usually adopted in the literature. In the present paper these models are reviewed and used as a Basis for a predictive control scheme which is applied to the control of a process for ethyl alcohol (ethanol) production.
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FUZZ-IEEE - Control of a bioprocess using Orthonormal Basis function fuzzy models
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004Co-Authors: Ricardo J.g.b. Campello, Luiz Augusto Da Cruz Meleiro, Wagner C. AmaralAbstract:Fuzzy models within the framework of Orthonormal Basis functions (OBF fuzzy models) were introduced in previous works and have shown to be a very promising approach to the areas of non-linear system identification and control since they exhibit several advantages over those dynamic model architectures usually adopted in the literature. In the present paper these models are reviewed and used as a Basis for a predictive control scheme which is applied to the control of a process for ethyl alcohol (ethanol) production.
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Fuzzy models within Orthonormal Basis function framework
FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315), 1999Co-Authors: G.h.c. Oliveira, Ricardo J.g.b. Campello, Wagner C. AmaralAbstract:Presents a framework for fuzzy modeling of dynamic systems using Orthonormal Basis functions in the representation of the model input signals. The main objective of using Orthonormal bases is to overcome the task of estimating the order and time delay of the process. The result is a nonlinear moving average fuzzy model which, consequently, has no feedback of prediction errors. Although any technique of fuzzy modeling can be used in the proposed framework, a relational approach is considered. The performance of fuzzy models with Orthonormal Basis functions is illustrated by examples and the results are compared with those provided by conventional fuzzy models and Volterra models.
Z Szabo - One of the best experts on this subject based on the ideXlab platform.
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minimal partial realization from generalized Orthonormal Basis function expansions
Automatica, 2002Co-Authors: T.j. De Hoog, Z Szabo, Peter S.c. Heuberger, József BokorAbstract:A solution is presented for the problem of realizing a discrete-time LTI state-space model of minimal McMillan degree such that its first N expansion coefficients in terms of generalized Orthonormal Basis match a given sequence. The Basis considered, also known as the Hambo Basis, can be viewed as a generalization of the more familiar Laguerre and two-parameter Kautz constructions, allowing general dynamic information to be incorporated in the Basis. For the solution of the problem use is made of the properties of the Hambo operator transform theory that underlies the Basis function expansion. As corollary results compact expressions are found by which the Hambo transform and its inverse can be computed efficiently. The resulting realization algorithms can be applied in an approximative sense, for instance, for computing a low-order model from a large Basis function expansion that is obtained in an identification experiment.
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A loop shaping design using weighted generalized Orthonormal Basis functions
2001 European Control Conference (ECC), 2001Co-Authors: Péter Gáspár, Z Szabo, József BokorAbstract:In the paper a robust control design method is proposed to obtain a compensator that realizes the required loop shape formed by nominal performance and robust stability specifications. First, a fictitious compensator is identified in such a way that the design loop transfer function tends toward the required loop transfer function. Since the compensator must be identified as accurately as possible in the frequency domains that are important in terms of the required loop shape, the identification is performed by a stable approximation using frequency-weighted generalized Orthonormal Basis functions (WGOBF). Second, a robust compensator is designed by using the H∞/μ method taking into consideration the approximation error of the designed loop transfer function. The steps of the control design are illustrated to demonstrate the method on a numerical example.
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l sup p norm convergence of rational Orthonormal Basis function expansions
Conference on Decision and Control, 1999Co-Authors: Z Szabo, József BokorAbstract:In this paper model sets for discrete-time LTI systems that are spanned by generalized Orthonormal Basis functions are investigated. It is established that the partial sums of Fourier series of generalized Orthonormal Basis expansions converge in all the spaces L/sup p/ and H/sup p/, 1
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Closed-loop identification using generalized Orthonormal Basis functions
Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), 1999Co-Authors: Péter Gáspár, Z Szabo, József BokorAbstract:This paper presents a closed-loop identification method based on the construction of generalized Orthonormal Basis functions (GOBF). It modifies the two-stage method, which applies the finite impulse response (FIR) model structure to one that uses GOBF functions. Identification of a FIR model has some important advantages, however it fails to be successful when the number of coefficients to be estimated becomes large. With appropriately chosen Basis functions where the Basis is generated by all-pass functions having poles close to the poles of the system, the convergence rate of the series expansion can be extremely fast. The paper gives the algorithm of the closed-loop identification, and it demonstrates the method in a numerical example.
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Realization algorithms for expansions in generalized Orthonormal Basis functions
IFAC Proceedings Volumes, 1999Co-Authors: Peter S.c. Heuberger, Z Szabo, T.j. De Hoog, József BokorAbstract:Abstract In this paper a realization theory and associated algorithms are presented for the construction of minimal realizations on the Basis of a sequence of expansion coefficients in a generalized Orthonormal Basis. Both the exact and the partial realization problem are addressed and solved, leading to extended versions of the classical Ho-Kalman algorithm.In the construction of the realization algorithms, fruitful use is made of a system analysis in the transform domain, being induced by the choice of Basis functions. The resulting algorithms can also be applied in approximate realization and in system approximation.
Peter S.c. Heuberger - One of the best experts on this subject based on the ideXlab platform.
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Asymptotically optimal Orthonormal Basis functions for LPV system identification
Automatica, 2009Co-Authors: Roland Toth, Peter S.c. HeubergerAbstract:A global model structure is developed for parametrization and identification of a general class of Linear Parameter-Varying (LPV) systems. By using a fixed Orthonormal Basis function (OBF) structure, a linearly parametrized model structure follows for which the coefficients are dependent on a scheduling signal. An optimal set of OBFs for this model structure is selected on the Basis of local linear dynamic properties of the LPV system (system poles) that occur for different constant scheduling signals. The selected OBF set guarantees in an asymptotic sense the least worst-case modeling error for any local model of the LPV system. Through the fusion of the Kolmogorov n-width theory and Fuzzy c-Means clustering, an approach is developed to solve the OBF-selection problem for discrete-time LPV systems, based on the clustering of observed sample system poles.
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minimal partial realization from generalized Orthonormal Basis function expansions
Automatica, 2002Co-Authors: T.j. De Hoog, Z Szabo, Peter S.c. Heuberger, József BokorAbstract:A solution is presented for the problem of realizing a discrete-time LTI state-space model of minimal McMillan degree such that its first N expansion coefficients in terms of generalized Orthonormal Basis match a given sequence. The Basis considered, also known as the Hambo Basis, can be viewed as a generalization of the more familiar Laguerre and two-parameter Kautz constructions, allowing general dynamic information to be incorporated in the Basis. For the solution of the problem use is made of the properties of the Hambo operator transform theory that underlies the Basis function expansion. As corollary results compact expressions are found by which the Hambo transform and its inverse can be computed efficiently. The resulting realization algorithms can be applied in an approximative sense, for instance, for computing a low-order model from a large Basis function expansion that is obtained in an identification experiment.
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A frequency-domain iterative identification algorithm using general Orthonormal Basis functions
Automatica, 2001Co-Authors: Hüseyin Akçay, Peter S.c. HeubergerAbstract:In this paper, a two-step frequency-domain identification algorithm is proposed, in which the identified model is parameterized in terms of general Orthonormal Basis functions. Furthermore an update scheme is presented, where the poles of the Basis functions are adjusted iteratively and the convergence properties of this scheme with respect to unknown-but-bounded noise are studied. The algorithm is applied to experimental frequency response data obtained from a compact disk player.
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Realization algorithms for expansions in generalized Orthonormal Basis functions
IFAC Proceedings Volumes, 1999Co-Authors: Peter S.c. Heuberger, Z Szabo, T.j. De Hoog, József BokorAbstract:Abstract In this paper a realization theory and associated algorithms are presented for the construction of minimal realizations on the Basis of a sequence of expansion coefficients in a generalized Orthonormal Basis. Both the exact and the partial realization problem are addressed and solved, leading to extended versions of the classical Ho-Kalman algorithm.In the construction of the realization algorithms, fruitful use is made of a system analysis in the transform domain, being induced by the choice of Basis functions. The resulting algorithms can also be applied in approximate realization and in system approximation.
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Identification of a fluidized catalytic cracking unit: an Orthonormal Basis function approach
Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207), 1998Co-Authors: E.t. Van Donkelaar, Peter S.c. HeubergerAbstract:Multivariable system identification of a model IV fluidized catalytic cracking unit is performed using a linear time invariant model parametrization based on Orthonormal Basis functions. This model structure is a linear regression structure which results in a simple convex optimization problem for least squares prediction error identification. Unknown initial conditions are estimated simultaneously with the system dynamics to account for the slow drift of the measured output from the given initial condition to a stationary working point. The model accuracy for low frequencies is improved by a steady-state constraint on the estimated model and incorporation of prior knowledge of the large time constants in the model structure. The model accuracy is furthermore improved by an iteration over identification of a high order model and model reduction. First a high order model is estimated using an Orthonormal Basis. This model is reduced and used to generate a new Orthonormal Basis which is used in the following iteration step for high order estimation. With the approach followed accurate models over a large frequency range are estimated with only a limited amount of data.
Ricardo J.g.b. Campello - One of the best experts on this subject based on the ideXlab platform.
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takagi sugeno fuzzy models in the framework of Orthonormal Basis functions
IEEE Transactions on Systems Man and Cybernetics, 2013Co-Authors: Jeremias Barbosa Machado, Ricardo J.g.b. Campello, Wagner C. AmaralAbstract:An approach to obtain Takagi-Sugeno (TS) fuzzy models of nonlinear dynamic systems using the framework of Orthonormal Basis functions (OBFs) is presented in this paper. This approach is based on an architecture in which local linear models with ladder-structured generalized OBFs (GOBFs) constitute the fuzzy rule consequents and the outputs of the corresponding GOBF filters are input variables for the rule antecedents. The resulting GOBF-TS model is characterized by having only real-valued parameters that do not depend on any user specification about particular types of functions to be used in the Orthonormal Basis. The fuzzy rules of the model are initially obtained by means of a well-known technique based on fuzzy clustering and least squares. Those rules are then simplified, and the model parameters (GOBF poles, GOBF expansion coefficients, and fuzzy membership functions) are subsequently adjusted by using a nonlinear optimization algorithm. The exact gradients of an error functional with respect to the parameters to be optimized are computed analytically. Those gradients provide exact search directions for the optimization process, which relies solely on input-output data measured from the system to be modeled. An example is presented to illustrate the performance of this approach in the modeling of a complex nonlinear dynamic system.
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Takagi–Sugeno Fuzzy Models in the Framework of Orthonormal Basis Functions
IEEE Transactions on Systems Man and Cybernetics, 2012Co-Authors: Jeremias Barbosa Machado, Ricardo J.g.b. Campello, Wagner C. AmaralAbstract:An approach to obtain Takagi-Sugeno (TS) fuzzy models of nonlinear dynamic systems using the framework of Orthonormal Basis functions (OBFs) is presented in this paper. This approach is based on an architecture in which local linear models with ladder-structured generalized OBFs (GOBFs) constitute the fuzzy rule consequents and the outputs of the corresponding GOBF filters are input variables for the rule antecedents. The resulting GOBF-TS model is characterized by having only real-valued parameters that do not depend on any user specification about particular types of functions to be used in the Orthonormal Basis. The fuzzy rules of the model are initially obtained by means of a well-known technique based on fuzzy clustering and least squares. Those rules are then simplified, and the model parameters (GOBF poles, GOBF expansion coefficients, and fuzzy membership functions) are subsequently adjusted by using a nonlinear optimization algorithm. The exact gradients of an error functional with respect to the parameters to be optimized are computed analytically. Those gradients provide exact search directions for the optimization process, which relies solely on input-output data measured from the system to be modeled. An example is presented to illustrate the performance of this approach in the modeling of a complex nonlinear dynamic system.
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Control of a bioprocess using Orthonormal Basis function fuzzy models
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004Co-Authors: Ricardo J.g.b. Campello, Luiz A.c. Meleiro, Wagner C. AmaralAbstract:Fuzzy models within the framework of Orthonormal Basis functions (OBF fuzzy models) were introduced in previous works and have shown to be a very promising approach to the areas of non-linear system identification and control since they exhibit several advantages over those dynamic model architectures usually adopted in the literature. In the present paper these models are reviewed and used as a Basis for a predictive control scheme which is applied to the control of a process for ethyl alcohol (ethanol) production.
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FUZZ-IEEE - Control of a bioprocess using Orthonormal Basis function fuzzy models
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004Co-Authors: Ricardo J.g.b. Campello, Luiz Augusto Da Cruz Meleiro, Wagner C. AmaralAbstract:Fuzzy models within the framework of Orthonormal Basis functions (OBF fuzzy models) were introduced in previous works and have shown to be a very promising approach to the areas of non-linear system identification and control since they exhibit several advantages over those dynamic model architectures usually adopted in the literature. In the present paper these models are reviewed and used as a Basis for a predictive control scheme which is applied to the control of a process for ethyl alcohol (ethanol) production.
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Fuzzy models within Orthonormal Basis function framework
FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315), 1999Co-Authors: G.h.c. Oliveira, Ricardo J.g.b. Campello, Wagner C. AmaralAbstract:Presents a framework for fuzzy modeling of dynamic systems using Orthonormal Basis functions in the representation of the model input signals. The main objective of using Orthonormal bases is to overcome the task of estimating the order and time delay of the process. The result is a nonlinear moving average fuzzy model which, consequently, has no feedback of prediction errors. Although any technique of fuzzy modeling can be used in the proposed framework, a relational approach is considered. The performance of fuzzy models with Orthonormal Basis functions is illustrated by examples and the results are compared with those provided by conventional fuzzy models and Volterra models.
