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Unknown Inputs (unknown + input)

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Engineering100%

Terms modified by Unknown Inputs

  • unknown input observer
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    Selected Abstracts

    A subspace algorithm for simultaneous identification and input reconstruction

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 12 2009
    Harish J. Palanthandalam-Madapusi
    Abstract This paper considers the concept of input and state observability, that is, conditions under which both the unknown input and initial state of a known model can be determined from output measurements. We provide necessary and sufficient conditions for input and state observability in discrete-time systems. Next, we develop a subspace identification algorithm that identifies the state-space matrices and reconstructs the unknown input using output measurements and known inputs. Finally, we present several illustrative examples, including a nonlinear system in which the unknown input is due to the endogenous nonlinearity. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    On sliding mode observers for systems with unknown inputs

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 8-9 2007
    T. Floquet
    Abstract This paper considers the problem of designing an observer for a linear system subject to unknown inputs. This problem has been extensively studied in the literature with respect to both linear and nonlinear (sliding mode) observers. Necessary and sufficient conditions to enable a linear unknown input observer to be designed have been established for many years. One way to express these conditions is that the transfer function matrix between the unknown input and the measured output must be minimum phase and relative degree one. Identical conditions must be met in order to design a ,classical' sliding mode observer for the same problem. This paper shows how the relative degree condition can be weakened if a classical sliding mode observer is combined with sliding mode exact differentiators to essentially generate additional independent output signals from the available measurements. A practical example dedicated to actuator fault detection and identification of a winding machine demonstrates the efficacy of the approach. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Simultaneous input and parameter estimation with input observers and set-membership parameter bounding: theory and an automotive application

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2006
    I. Kolmanovsky
    Abstract The paper addresses an on-line, simultaneous input and parameter estimation problem for a first-order system affected by measurement noise. This problem is motivated by practical applications in the area of engine control. Our approach combines an input observer for the unknown input with a set-membership algorithm to estimate the parameter. The set-membership algorithm takes advantage of a priori available information such as (i) known bounds on the unknown input, measurement noise and time rate of change of the unknown input; (ii) the form of the input observer in which the unknown parameter affects only the observer output; and (iii) the input observer error bounds for the case when the parameter is known exactly. The asymptotic properties of the algorithm as the observer gain increases are delineated. It is shown that for accurate estimation the unknown input needs to approach the known bounds a sufficient number of times (these time instants need not be known). Powertrain control applications are discussed and a simulation example based on application to engine control is reported. A generalization of the basic ideas to higher order systems is also elaborated. Copyright © 2006 John Wiley & Sons, Ltd. [source]

    On sliding mode observers for systems with unknown inputs

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 8-9 2007
    T. Floquet
    Abstract This paper considers the problem of designing an observer for a linear system subject to unknown inputs. This problem has been extensively studied in the literature with respect to both linear and nonlinear (sliding mode) observers. Necessary and sufficient conditions to enable a linear unknown input observer to be designed have been established for many years. One way to express these conditions is that the transfer function matrix between the unknown input and the measured output must be minimum phase and relative degree one. Identical conditions must be met in order to design a ,classical' sliding mode observer for the same problem. This paper shows how the relative degree condition can be weakened if a classical sliding mode observer is combined with sliding mode exact differentiators to essentially generate additional independent output signals from the available measurements. A practical example dedicated to actuator fault detection and identification of a winding machine demonstrates the efficacy of the approach. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Fuzzy model-based fault detection for Markov jump systems

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 11 2009
    Shuping He
    Abstract The robust fault detection filter (RFDF) design problems are studied for nonlinear stochastic time-delay Markov jump systems. By means of the Takagi,Sugeno fuzzy models, the fuzzy RFDF system and the dynamics of filtering error generator are constructed. Moreover, taking into account the sensitivity to faults while guaranteeing robustness against unknown inputs, the H, filtering scheme is proposed to minimize the influences of the unknown inputs and another new performance index is introduced to enhance the sensitivity to faults. A sufficient condition is first established on the stochastic stability using stochastic Lyapunov,Krasovskii function. Then in terms of linear matrix inequalities techniques, the sufficient conditions on the existence of fuzzy RFDF are presented and proved. Finally, the design problem is formulated as a two-objective optimization algorithm. Simulation results illustrate that the proposed RFDF can detect the faults shortly after the occurrences. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Higher-order sliding-mode observer for state estimation and input reconstruction in nonlinear systems

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 4-5 2008
    Leonid Fridman
    Abstract In this paper, a higher-order sliding-mode observer is proposed to estimate exactly the observable states and asymptotically the unobservable ones in multi-input,multi-output nonlinear systems with unknown inputs and stable internal dynamics. In addition the unknown inputs can be identified asymptotically. Numerical examples illustrate the efficacy of the proposed observer. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    New results in robust actuator fault reconstruction for linear uncertain systems using sliding mode observers

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 14 2007
    Kok Yew Ng
    Abstract This paper presents a robust actuator fault reconstruction scheme for linear uncertain systems using sliding mode observers. In existing work, fault reconstruction via sliding mode observers is limited to either linear certain systems subject to unknown inputs, relative degree one systems or a specific class of relative degree two systems. This paper presents a new method that is applicable to a wider class of systems with relative degree higher than one, and can also be used for systems with more unknown inputs than outputs. The method uses two sliding mode observers in cascade. Signals from the first observer are processed and used to drive the second observer. Overall, this results in actuator fault reconstruction being feasible for a wider class of systems than using existing methods. A simulation example verifies the claims made in this paper. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Inverse filtering and deconvolution

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 2 2001
    Ali Saberi
    Abstract This paper studies the so-called inverse filtering and deconvolution problem from different angles. To start with, both exact and almost deconvolution problems are formulated, and the necessary and sufficient conditions for their solvability are investigated. Exact and almost deconvolution problems seek filters that can estimate the unknown inputs of the given plant or system either exactly or almostly whatever may be the unintended or disturbance inputs such as measurement noise, external disturbances, and model uncertainties that act on the system. As such they require strong solvability conditions. To alleviate this, several optimal and suboptimal deconvolution problems are formulated and studied. These problems seek filters that can estimate the unknown inputs of the given system either exactly, almostly or optimally in the absence of unintended (disturbance) inputs, and on the other hand, in the presence of unintended (disturbance) inputs, they seek that the influence of such disturbances on the estimation error be as small as possible in a certain norm (H2 or H,) sense. Both continuous- and discrete-time systems are considered. For discrete-time systems, the counter parts of all the above problems when an ,,-step delay in estimation is present are introduced and studied. Next, we focus on the exact and almost deconvolution but this time when the uncertainties in plant dynamics can be structurally modeled by a ,-block as a feedback element to the nominally known plant dynamics. This is done either in the presence or absence of external disturbances. Copyright © 2001 John Wiley & Sons, Ltd. [source]

    On the optimality of two-stage Kalman filtering for systems with unknown inputs,

    ASIAN JOURNAL OF CONTROL, Issue 4 2010
    Chien-Shu Hsieh
    Abstract This paper is concerned with the optimal solution of two-stage Kalman filtering for linear discrete-time stochastic time-varying systems with unknown inputs affecting both the system state and the outputs. By means of a newly-presented modified unbiased minimum-variance filter (MUMVF), which appears to be the optimal solution to the addressed problem, the optimality of two-stage Kalman filtering for systems with unknown inputs is defined and explored. Two extended versions of the previously proposed robust two-stage Kalman filter (RTSKF), augmented-unknown-input RTSKF (ARTSKF) and decoupled-unknown-input RTSKF (DRTSKF), are presented to solve the general unknown input filtering problem. It is shown that under less restricted conditions, the proposed ARTSKF and DRTSKF are equivalent to the corresponding MUMVFs. An example is given to illustrate the usefulness of the proposed results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

    An LMI approach to design observer for unknown inputs Takagi-Sugeno fuzzy models

    ASIAN JOURNAL OF CONTROL, Issue 4 2010
    M. Chadli
    Abstract This paper considers the design of an observer for a Takagi-Sugeno (T-S) fuzzy model subject to unknown inputs affecting states and outputs of the system simultaneously. Uncertainties affecting state matrices are also considered. Based on the Lyapunov method, sufficient conditions in Linear Matrix Inequalities (LMI) terms are proposed to design the given unknown input T-S observer. In order to improve the performances of the proposed T-S observer, the pole placement in an LMI region is also considered. An numerical example is given to illustrate the validity of the derived results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

    AN ITERATIVE LMI APPROACH TO RFDF FOR LINEAR SYSTEM WITH TIME-VARYING DELAYS

    ASIAN JOURNAL OF CONTROL, Issue 1 2006
    Maiying Zhong
    ABSTRACT This paper deals with robust fault detection filter (RFDF) problem for a class of linear uncertain systems with time-varying delays and model uncertainties. The RFDF design problem is formulated as an optimization problem by using L2 -induced norm to represent the robustness of residual to unknown inputs and modelling errors, and the sensitivity to faults. A sufficient condition to the solvability of formulated problem is established in terms of certain matrix inequalities, which can be solved with the aid of an iterative linear matrix inequality (ILMI) algorithm. Finally, a numerical example is given to illustrate the effectiveness of the proposed method. [source]

    FAULT DETECTION, ISOLATION AND RECONSTRUCTION FOR DESCRIPTOR SYSTEMS

    ASIAN JOURNAL OF CONTROL, Issue 4 2005
    Tae-Kyeong Yeu
    ABSTRACT In this paper, we consider fault detection, isolation and reconstruction problem for descriptor systems with actuator faults and sensor faults, respectively. When actuator faults exist in the system, the fault detection and isolation (FDI) problem is solved through an unknown input observer regarding remaining faults excluded a specified fault as unknown inputs. Whereas, in existing sensor faults, the fault detection is only achieved by the unknown input observer and residual signals. Since the derivative signal of sensor fault is generated in the error dynamics between the actual system and the derived observer. The main objective of this work attempts the reconstruction of the faults. The reconstruction can be achieved by sliding mode observer including feedforward injection map and compensation signal. Finally, the isolation problem of sensor faults is solved by reconstructing all of the faults. [source]