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Input Matrices (input + matrix)

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Engineering50%

Selected Abstracts

A novel dual-mode predictive control strategy for constrained Wiener systems

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 9 2010
Hai-Tao Zhang
Abstract In process industry, there exist many Wiener systems with input magnitude constraints for which, however, most of the existing control algorithms cannot guarantee to have sufficiently large regions of asymptotic stability. In this paper, the subspace method is applied to separate the nonlinear and linear blocks in a constrained multi-input/multi-output (MIMO) Wiener system and a novel dual-mode nonlinear model predictive control algorithm is developed to maximize the region of the asymptotic stability. Simulation results are presented to demonstrate the virtues of this new control algorithm. The limitation is the requirement that the state and input matrices of the Wiener system's linear block should be accurately identified. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Robust sliding mode design for uncertain stochastic systems based on H, control method

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 2 2010
Yugang Niu
Abstract In this paper, the design problem of sliding mode control (SMC) is addressed for uncertain stochastic systems modeled by Itô differential equations. There exist the parameter uncertainties in both the state and input matrices, as well as the unmatched external disturbance. The key feature of this work is the integration of SMC method with H, technique such that the robust stochastic stability with a prescribed disturbance attenuation level , can be achieved. A sufficient condition for the existence of the desired sliding mode controller is obtained via linear matrix inequalities. The reachability of the specified sliding surface is proven. Finally, a numerical simulation example is presented to illustrate the proposed method. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Robust decentralized H, control for interconnected descriptor systems with norm-bounded uncertainties,,

ASIAN JOURNAL OF CONTROL, Issue 1 2009
Ning Chen
Abstract This paper considers a robust decentralized H, control problem for interconnected descriptor systems. The uncertainties are assumed to be time-invariant, norm-bounded, and existing in both the system and control input matrices. Our interest is focused on dynamic output feedback. A sufficient condition for an uncertain interconnected descriptor system to be robustly stabilizable H, control with a specified disturbance attenuation level is derived in terms of a nonlinear matrix inequality (NMI). A two-stage homotopy method is employed to solve the NMI iteratively. First, a decentralized controller for the nominal descriptor system is computed by imposing block-diagonal constraints on the coefficient matrices of the controller gradually. Then, the decentralized controller is gradually modified from the nominal descriptor system (without uncertainties) to the original system with uncertainties. On each stage, groups of variables are fixed alternately at the iterations to reduce the NMI to linear matrix inequalities (LMIs). An example is given to show the usefulness of this method. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

Reduced modeling and state observation of an activated sludge process

BIOTECHNOLOGY PROGRESS, Issue 3 2009
Isabelle Queinnec
Abstract This article first proposes a reduction strategy of the activated sludge process model with alternated aeration. Initiated with the standard activated sludge model (ASM1), the reduction is based on some biochemical considerations followed by linear approximations of nonlinear terms. Two submodels are then obtained, one for the aerobic phase and one for the anoxic phase, using four state variables related to the organic substrate concentration, the ammonium and nitrate-nitrite nitrogen, and the oxygen concentration. Then, a two-step robust estimation strategy is used to estimate both the unmeasured state variables and the unknown inflow ammonium nitrogen concentration. Parameter uncertainty is considered in the dynamics and input matrices of the system. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009 [source]

Partial pole assignment for the quadratic pencil by output feedback control with feedback designs

NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, Issue 10 2005
Wen-Wei Lin
Abstract In this paper we study the partial pole assignment problem for the quadratic pencil by output feedback control where the output matrix is also a designing parameter. In addition, the input matrix is set to be the transpose of the output matrix. Under certain assumption, we give a solution to this partial pole assignment problem in which the unwanted eigenvalues are moved to desired values and all other eigenpairs remain unchanged. Copyright © 2005 John Wiley & Sons, Ltd. [source]

A Lanczos-type algorithm for the QR factorization of regular Cauchy matrices

NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, Issue 4 2002
Dario Fasino
Abstract We present a fast algorithm for computing the QR factorization of Cauchy matrices with real nodes. The algorithm works for almost any input matrix, does not require squaring the matrix, and fully exploits the displacement structure of Cauchy matrices. We prove that, if the determinant of a certain semiseparable matrix is non-zero, a three term recurrence relation among the rows or columns of the factors exists. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Robust hyperplane synthesis for sliding mode control systems via sensitivity minimization

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 3 2002
Hei Ka Tam
Abstract A robust hyperplane computation scheme for sliding mode control systems is proposed in this paper. A novel sensitivity index for the sliding eigenvalues with respect to perturbations in the system matrix, the input matrix and the hyperplane matrix is derived in the first instance. The robust hyperplane design problem is then formulated as an optimization task in which the proposed sensitivity index is accordingly minimized. Gradient information of the objective function is established which permits optimization to be proceeded effectively. A numerical example with statistical testing is employed to illustrate the design technique. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Neural Network Prediction of Biomass Digestibility Based on Structural Features

BIOTECHNOLOGY PROGRESS, Issue 2 2008
Jonathan P. O'Dwyer
Plots of biomass digestibility are linear with the natural logarithm of enzyme loading; the slope and intercept characterize biomass reactivity. The feed-forward back-propagation neural networks were performed to predict biomass digestibility by simulating the 1-, 6-, and 72-h slopes and intercepts of glucan, xylan, and total sugar hydrolyses of 147 poplar wood model samples with a variety of lignin contents, acetyl contents, and crystallinity indices. Regression analysis of the neural network models indicates that they performed satisfactorily. Increasing the dimensionality of the neural network input matrix allowed investigation of the influence glucan and xylan enzymatic hydrolyses have on each other. Glucan hydrolysis affected the last stage of xylan digestion, and xylan hydrolysis had no influence on glucan digestibility. This study has demonstrated that neural networks have good potential for predicting biomass digestibility over a wide range of enzyme loadings, thus providing the potential to design cost-effective pretreatment and saccharification processes. [source]