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State Estimator (state + estimator)
Selected AbstractsGuaranteed recursive non-linear state bounding using interval analysisINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2002Michel Kieffer Abstract The problem considered here is state estimation in the presence of unknown but bounded state perturbations and measurement noise. In this context, most available results are for linear models, and the purpose of the present paper is to deal with the non-linear case. Based on interval analysis and the notion of set inversion, a new state estimator is presented, which evaluates a set estimate guaranteed to contain all values of the state that are consistent with the available observations, given the perturbation and noise bounds and a set containing the initial value of the state. To the best of our knowledge, it is the first estimator for which this claim can be made. The precision of the set estimate can be improved, at the cost of more computation. Theoretical properties of the estimator are studied, and computer implementation receives special attention. A simple illustrative example is treated. Copyright © 2002 John Wiley & Sons, Ltd. [source] Sliding,window neural state estimation in a power plant heater lineINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 8 2001A. Alessandri Abstract The state estimation problem for a section of a real power plant is addressed by means of a recently proposed sliding-window neural state estimator. The complexity and the nonlinearity of the considered application prevent us from successfully using standard techniques as Kalman filtering. The statistics of the distribution of the initial state and of noises are assumed to be unknown and the estimator is designed by minimizing a given generalized least-squares cost function. The following approximations are enforced: (i) the state estimator is a finite-memory one, (ii) the estimation functions are given fixed structures in which a certain number of parameters have to be optimized (multilayer feedforward neural networks are chosen from among various possible nonlinear approximators), (iii) the algorithms for optimizing the parameters (i.e., the network weights) rely on a stochastic approximation. Extensive simulation results on a complex model of a part of a real power plant are reported to compare the behaviour of the proposed estimator with the extended Kalman filter. Copyright © 2001 John Wiley & Sons, Ltd. [source] Tracking of multiple target types with a single neural extended Kalman filterINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 5 2010Kathleen A. Kramer The neural extended Kalman filter is an adaptive state estimation routine that can be used in target-tracking systems to aid in the tracking through maneuvers without prior knowledge of the targets' dynamics. Within the neural extended Kalman filter, a neural network is trained using a Kalman filter training paradigm that is driven by the same residual as the state estimator. The difference between the a priori model used in the prediction steps of the estimator and the actual target dynamics is approximated. An important benefit of the technique is its versatility because little if any a priori knowledge of the target dynamics is needed. This allows the technique to be used in a generic tracking system that will encounter various classes of targets. In this paper, the neural extended Kalman filter is applied simultaneously to three separate classes of targets, each with different maneuver capabilities. The results show that the approach is well suited for use within a tracking system with multiple possible or unknown target characteristics. © 2010 Wiley Periodicals, Inc. [source] Robust Kalman filtering for uncertain discrete-time linear systemsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 13 2003Germain Garcia Abstract This paper presents a steady-state robust state estimator for a class of uncertain discrete-time linear systems with norm-bounded uncertainty. It is shown that if the system satisfies some particular structural conditions and if the uncertainty has a specific structure, the gain of the robust estimator (which assures a guaranteed cost) can be calculated using a formula only involving the original system matrices. Among the conditions the system has to satisfy, the strongest one relies on a minimum phase argument. It is also shown that under the assumptions considered, the robust estimator is in fact the Kalman filter for the nominal system. Copyright © 2003 John Wiley & Sons, Ltd. [source] Achieving state estimation equivalence for misassigned disturbances in offset-free model predictive controlAICHE JOURNAL, Issue 2 2009Murali R. Rajamani Abstract Integrated white noise disturbance models are included in advanced control strategies, such as Model Predictive Control, to remove offset when there are unmodeled disturbances or plant/model mismatch. These integrating disturbances are usually modeled to enter either through the plant inputs or the plant outputs or partially through both. There is currently a lack of consensus in the literature on the best choice for the structure of this disturbance model to obtain good feedback control. We show that the choice of the disturbance model does not affect the closed- loop performance if appropriate covariances are used in specifying the state estimator. We also present a data based autocovariance technique to estimate the appropriate covariances regardless of the plant's true unknown disturbance source. The covariances estimated using the autocovariance technique and the resulting estimator gain are shown to compensate for an incorrect choice of the source of the disturbance in the disturbance model. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Online estimation and control of polymer quality in a copolymerization reactorAICHE JOURNAL, Issue 5 2002Myung-June Park The validity of an online state estimator for a semi-batch MMA/MA solution copolymerization reactor was established using online densitometer and viscometer. Using the conventional extended Kalman filter (EKF) as the state estimator, the experiment was conducted under both isothermal and nonisothermal conditions for application to the control of copolymer properties. Further analysis was made by using ofline measurement data for the mol fraction of MMA in the remaining monomers and the solid content. The EKF was found to provide a good estimate for the state of the copolymerization system. A model predictive controller was designed and implemented to obtain copolymers with uniform copolymer composition and the desired weight average molecular weight by adopting the feed flow rate of MMA and the reaction temperature as control inputs. The controller was proven effective with a satisfactory performance for the control of polymer properties in the semi-batch copolymerization reactor. [source] A state-dependent Riccati equation-based estimator approach for HIV feedback controlOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 2 2006H. T. Banks Abstract We consider optimal dynamic multidrug therapies for human immunodeficiency virus (HIV) type 1 infection. In this context, we describe an optimal tracking problem attempting to drive the states of the system to a stationary state in which the viral load is low and the immune response is strong. We consider optimal feedback control with full-state as well as with partial-state measurements. In the case of partial-state measurement, a state estimator is constructed based on viral load and T-cell count measurements. We demonstrate by numerical simulations that by anticipation of and response to the disease progression, the dynamic multidrug strategy reduces the viral load, increases the CD4+ T-cell count and improves the immune response. Copyright © 2006 John Wiley & Sons, Ltd. [source] Robust face tracking control of a mobile robot using self-tuning Kalman filter and echo state network,ASIAN JOURNAL OF CONTROL, Issue 4 2010Chi-Yi Tsai Abstract This paper presents a novel design of face tracking algorithm and visual state estimation for a mobile robot face tracking interaction control system. The advantage of this design is that it can track a user's face under several external uncertainties and estimate the system state without the knowledge about target's 3D motion-model information. This feature is helpful for the development of a real-time visual tracking control system. In order to overcome the change in skin color due to light variation, a real-time face tracking algorithm is proposed based on an adaptive skin color search method. Moreover, in order to increase the robustness against colored observation noise, a new visual state estimator is designed by combining a Kalman filter with an echo state network-based self-tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several experiments on a mobile robot validate the proposed control system. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Combining state estimator and disturbance observer in discrete-time sliding mode controller design,ASIAN JOURNAL OF CONTROL, Issue 5 2008Jeang-Lin Chang Abstract In response to a multiple input/multiple output discrete-time linear system with mismatched disturbances, an algorithm capable of performing estimated system states and unknown disturbances is proposed first, and then followed with the design of the controller. Attributed to the fact that both system states and disturbances can be estimated simultaneously with our proposed method, the estimation error is constrained at less than O(T) as the disturbance between the two sampling points is insignificant. In addition, the estimated system states and disturbances are then to be used in the controller when implementing our algorithm in a non-minimum phase system (with respect to the relation between the output and the disturbance). The tracking error is constrained in a small bounded region and the system stability is guaranteed. Finally, a numerical example is presented to demonstrate the applicability of the proposed control scheme. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Output Feedback Sliding Mode Controller Design Via H, THEORYASIAN JOURNAL OF CONTROL, Issue 1 2003Jeang-Lin Chang ABSTRACT For a linear system with mismatched disturbances, a sliding mode controller using only output feedback is developed in this paper. Through application of the H, control theory, the designed switching surface can achieve robust stabilization and guarantee a level of disturbance rejection during sliding mode. Although the system exhibits disturbances, a state estimator is used which, using only measured outputs, can asymptotically estimate the system states. The control law is designed with respect to the estimated signals. Finally, a numerical example is presented to demonstrate the proposed control scheme. [source] State estimation of a solid-state polymerization reactor for PET based on improved SR-UKFASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 2 2010Ji Liu Abstract A state estimator for the continuous solid-state polymerization (SSP) reactor of polyethylene terephthalate (PET) is designed in this study. Because of its invalidity in the application to some of the practical examples such as SSP processes, the square-root unscented Kalman filter (SR-UKF) algorithm is improved for the state estimation of arbitrary nonlinear systems with linear measurements. Discussions are given on how to avoid the filter invalidation and accumulating additional error. Orthogonal collocation method has been used to spatially discretize the reactor model described by nonlinear partial differential equations. The reactant concentrations on chosen collocation points are reconstructed from the outlet measurements corrupted with a large noise. Furthermore, the error performance of the developed ISR-UKF is investigated under the influence of various initial parameters, inaccurate measurement noise parameters and model mismatch. Simulation results show that this technique can produce fast convergence and good approximations for the state estimation of SSP reactor. Copyright © 2009 Curtin University of Technology and John Wiley & Sons, Ltd. [source] State estimation for time-delay systems with probabilistic sensor gain reductionsASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 6 2008Xiao He Abstract This paper presents a new state estimation problem for a class of time-delay systems with probabilistic sensor gain faults. The sensor gain reductions are described by a stochastic variable that obeys the uniform distribution in a known interval [,, ,], which is a natural reflection of the probabilistic performance deterioration of sensors when gain reduction faults occur. Attention is focused on the design of a state estimator such that for all possible sensor faults and all external disturbances, the filtering error dynamic is asymptotically mean-square stable as well as fulfils a prescribed disturbance attenuation level. The existence of desired filters is proved to depend on the feasibility of a certain linear matrix inequality (LMI), and a numerical example is given to illustrate the effectiveness of the proposed design approach. Copyright © 2008 Curtin University of Technology and John Wiley & Sons, Ltd. [source] Reduced-order state estimation for linear time-varying systems,ASIAN JOURNAL OF CONTROL, Issue 6 2009In Sung Kim Abstract We consider reduced-order and subspace state estimators for linear discrete-time systems with possibly time-varying dynamics. The reduced-order and subspace estimators are obtained using a finite-horizon minimization approach, and thus do not require the solution of algebraic Lyapunov or Riccati equations. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] | |||||||||||||