http://nova.newcastle.edu.au/vital/access/services/Feed ${session.getAttribute("locale")} 5 http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:8678 Multilevel converters have emerged as a promising alternative to traditional two level converters, especially flying capacitor converters because of the fact that this topology requires only a main DC-link voltage and presents a easy way to increase the output voltage levels by increasing the number of cells. Unfortunately, a balancing of capacitor voltage is required. Recently, predictive control algorithms have been presented in order to control not only the output current but also to achieve good performance in the balancing of the capacitor voltages. For this purpose, it is necessary to know the state of these voltages generally taking a measurement of them, therefore the number of sensors required will be increased regarding the output voltage levels desired. This paper presents an estimation of the capacitor voltages using a discrete Kalman filter. This algorithm is employed to determinate correctly the system state and thus provides this information to the predictive controller in order to determinate the best switching combination to be applied in the next sample period. 2013-03-24T05:12:16.347Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11679 In this paper we study statistical properties of the error covariance matrix of a Kalman filter, when it is subject to random measurement losses. We introduce a sequence of tighter upper bounds for the asymptotic expected error covariance (EEC). This sequence starts with a given upper bound in the literature and converges to the actual asymptotic EEC. Although we have not yet shown the monotonic convergence of this whole sequence, monotonic convergent subsequences are identified. The feature of these subsequences is that a tighter upper bound is guaranteed if more computation is allowed. An iterative algorithm is provided for computing each of these upper bounds. A byproduct of this paper is a more compact proof for a known necessary condition on the measurement arrival probability for the asymptotic EEC to be finite. A similar analysis leads to a necessary condition on the measurement arrival probability for the error covariance to have a finite asymptotic variance. 2012-11-13T05:24:07.965Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11750 This paper presents a method for the estimation of thrust model parameters of uninhabited airborne systems using specific flight tests. Particular tests are proposed to simplify the estimation. The proposed estimation method is based on three steps. The first step uses a regression model in which the thrust is assumed constant. This allows us to obtain biased initial estimates of the aerodynamic coeficients of the surge model. In the second step, a robust nonlinear state estimator is implemented using the initial parameter estimates, and the model is augmented by considering the thrust as random walk. In the third step, the estimate of the thrust obtained by the observer is used to fit a polynomial model in terms of the propeller advanced ratio. We consider a numerical example based on Monte-Carlo simulations to quantify the sampling properties of the proposed estimator given realistic flight conditions. 2012-10-16T05:26:12.494Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:10815 This paper studies system identification of ARMA models whose outputs are subject to finite-level quantization and random packet dropouts. A simple adaptive quantizer and the corresponding recursive identification algorithm are proposed and shown to be optimal in the sense of asymptotically achieving the minimum mean square estimation error. The joint effects of finite-level quantization and random packet dropouts on identification accuracy are exactly quantified. The theoretic results are verified by simulations. 2012-05-18T03:21:38.985Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:7656 In this article, we have described the main components of a ship motion-control system and two particular motion-control problems that require wave filtering, namely, dynamic positioning and heading autopilot. Then, we discussed the models commonly used for vessel response and showed how these models are used for Kalman filter design. We also briefly discussed parameter and noise covariance estimation, which are used for filter tuning. To illustrate the performance, a case study based on numerical simulations for a ship autopilot was considered. The material discussed in this article conforms to modern commercially available ship motion-control systems. Most of the vessels operating in the offshore industry worldwide use Kalman filters for velocity estimation and wave filtering. Thus, the article provides an up-to-date tutorial and overview of Kalman-filter-based wave filtering. 2011-05-02T23:40:06.790Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:773 We study the problem of finite-horizon Kalman filtering for systems involving a norm-bounded uncertain block. A new technique is presented for robust Kalman filter design. This technique involves using multiple scaling parameters that ran be optimized by solving a semidefinite program. The use of optimized scaling parameters leads to an improved design. A recursive design method that can be applied to real-time applications is also proposed 2010-04-27T06:34:12.835Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:5113 This paper is concerned with the problem of robust filtering for uncertain linear discrete-time descriptor systems. The matrices of the system state-space model are uncertain, belonging to a given polytope. A linear matrix inequality based method is proposed for designing a linear stationary filter that guarantees the asymptotic stability of the estimation error and gives an optimized upper bound on the asymptotic error variance, irrespective of the parameter uncertainty. The proposed robust filter design is based on a parameter-dependent Lyapunov function, which is shown to outperform parameter-independent ones. 2010-04-27T04:42:03.426Z ]]>