The Experts below are selected from a list of 48498 Experts worldwide ranked by ideXlab platform
Petar V. Kokotović - One of the best experts on this subject based on the ideXlab platform.
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Multivariable adaptive control using high-frequency Gain Matrix factorization
IEEE Transactions on Automatic Control, 2004Co-Authors: A.k. Imai, R. R. Costa, Petar V. KokotovićAbstract:In this note, we extend the application of a less restrictive condition about the high-frequency Gain Matrix to design stable direct model reference adaptive control for a class of multivariable plants with relative degree greater than one. The new approach is based on a control parametrization derived from a factorization of the high-frequency Gain Matrix K/sub p/ in the form of a product of three matrices, one of them being diagonal. Three possible factorizations are presented. Only the signs of the diagonal factor or, equivalently, the signs of the leading principal minors of K/sub p/, are assumed known.
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Multivariable MRAC using high frequency Gain Matrix factorization
Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), 2001Co-Authors: A.k. Imai, R. R. Costa, Petar V. KokotovićAbstract:A MIMO (multiple-input, multiple-output) analog to the well-known Lyapunov-based SISO (single-input, single-output) design of MRAC (model-reference adaptive control) has been recently introduced by L. Hsu et al. (2001). The new design utilizes a control parametrization derived from a factorization of the high-frequency Gain Matrix K/sub p/=SDU, where S is symmetric positive-definite, D is diagonal and U is unity upper-triangular. Only the signs of the entries of D or, equivalently, the signs of the leading principal minors of K/sub p/, were assumed to be known. However, the result was restricted to plants with (vector) relative degree one. In this paper, we extend the MRAC for more general plants with relative degree greater than one. We present three possible factorizations of K/sub p/ and the resulting update laws.
Victor Sreeram - One of the best experts on this subject based on the ideXlab platform.
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a gradient flow approach to computing a non linear discrete time quadratic optimal feedback Gain Matrix
Optimal Control Applications & Methods, 1996Co-Authors: Michael Cantoni, K. L. Teo, W. Y. Yan, Victor SreeramAbstract:In this paper we propose an approach to solving infinite planning horizon quadratic optimal regulator problems with linear static state feedback for discrete time systems. The approach is based on solving a sequence of approximate problems constructed by combining a finite horizon problem with an infinite horizon linear problem. A gradient-flow algorithm is derived to solve the approximate problems. As part of this, a new algorithm is derived for computing the gradient of the cost functional, based on a system of difference equations to be solved completely forward in time. Two numerical examples are presented.
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A gradient flow approach to computing a nonlinear quadratic optimal feedback Gain Matrix for discrete time systems
Proceedings of 1994 33rd IEEE Conference on Decision and Control, 1994Co-Authors: M.w. Cantoni, Victor SreeramAbstract:Proposes an approach to solving infinite planning horizon quadratic optimal regulator problems with linear static state feedback, for discrete time systems. The approach is based on solving a sequence of approximate problems constructed by combining a finite horizon problem with an infinite horizon linear problem. A gradient flow based algorithm is derived to solve the approximate problems. As part of this, a new algorithm is derived for computing the gradient of the cost functional, based on a system of difference equations to be solved completely forward in time. A numerical example is presented.
Marc Bodson - One of the best experts on this subject based on the ideXlab platform.
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Multivariable model reference adaptive control without constraints on the high-frequency Gain Matrix
Automatica, 1995Co-Authors: Michel De Mathelin, Marc BodsonAbstract:Abstract A multivariable model reference adaptive control algorithm is presented for the case when the high-frequency Gain Matrix is unknown. Only an upper bound on the norm of the Matrix needs to be known a priori . A transformation of the parameters, with a sort of hysteresis, is used to guarantee that a controller Matrix, which is normally the inverse of the estimate of the high-frequency Gain Matrix, remains nonsingular. It is shown that all the signals in the adaptive system are bounded and that the tracking error and the regressor error converge to zero for all bounded reference inputs. Furthermore, exponential convergence is achieved when the regressor vector is persistently exciting.
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Multivariable model reference adaptive control without constraints on the high-frequency Gain Matrix
[1991] Proceedings of the 30th IEEE Conference on Decision and Control, 1991Co-Authors: Michel De Mathelin, Marc BodsonAbstract:Stability is improved for a multivariable model reference adaptive control algorithm when the high-frequency Gain Matrix is unknown. Only an upper bound on the norm of the Matrix is required. A transformation of the parameters, with a sort of 'hysteresis', is used to guarantee that a controller Matrix, which is nominally the inverse of the high-frequency Gain Matrix, remains nonsingular. It is shown that all the signals in the adaptive system are bounded and that the tracking error and the regressor error converge to zero for all bounded reference inputs. Furthermore, exponential convergence is achieved when the regressor vector is persistently exciting.
A.k. Imai - One of the best experts on this subject based on the ideXlab platform.
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Multivariable adaptive control using high-frequency Gain Matrix factorization
IEEE Transactions on Automatic Control, 2004Co-Authors: A.k. Imai, R. R. Costa, Petar V. KokotovićAbstract:In this note, we extend the application of a less restrictive condition about the high-frequency Gain Matrix to design stable direct model reference adaptive control for a class of multivariable plants with relative degree greater than one. The new approach is based on a control parametrization derived from a factorization of the high-frequency Gain Matrix K/sub p/ in the form of a product of three matrices, one of them being diagonal. Three possible factorizations are presented. Only the signs of the diagonal factor or, equivalently, the signs of the leading principal minors of K/sub p/, are assumed known.
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Multivariable MRAC using high frequency Gain Matrix factorization
Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), 2001Co-Authors: A.k. Imai, R. R. Costa, Petar V. KokotovićAbstract:A MIMO (multiple-input, multiple-output) analog to the well-known Lyapunov-based SISO (single-input, single-output) design of MRAC (model-reference adaptive control) has been recently introduced by L. Hsu et al. (2001). The new design utilizes a control parametrization derived from a factorization of the high-frequency Gain Matrix K/sub p/=SDU, where S is symmetric positive-definite, D is diagonal and U is unity upper-triangular. Only the signs of the entries of D or, equivalently, the signs of the leading principal minors of K/sub p/, were assumed to be known. However, the result was restricted to plants with (vector) relative degree one. In this paper, we extend the MRAC for more general plants with relative degree greater than one. We present three possible factorizations of K/sub p/ and the resulting update laws.
Hongye Su - One of the best experts on this subject based on the ideXlab platform.
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output feedback stabilization of nonlinear mimo systems having uncertain high frequency Gain Matrix
Systems & Control Letters, 2015Co-Authors: Lei Wang, Alberto Isidori, Hongye SuAbstract:Abstract The purpose of this paper is to provide a method for (semi-global) asymptotic stabilization of a nonlinear minimum-phase MIMO system, under a mild hypothesis of the so-called “high-frequency Gain” Matrix. This result is based on a non-trivial extension, to the MIMO setting, of the approach based on the use of extended observers. As a byproduct, a dynamic output feedback control is obtained, that asymptotically stabilizes the equilibrium of the closed-loop system, in spite of uncertainties in the high-frequency Gain Matrix.
