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Tracking Accuracy (tracking + accuracy)
Selected AbstractsTracking accuracy of a semi-Lagrangian method for advection,dispersion modelling in riversINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 1 2007S. Néelz Abstract There is an increasing need to improve the computational efficiency of river water quality models because: (1) Monte-Carlo-type multi-simulation methods, that return solutions with statistical distributions or confidence intervals, are becoming the norm, and (2) the systems modelled are increasingly large and complex. So far, most models are based on Eulerian numerical schemes for advection, but these do not meet the requirement of efficiency, being restricted to Courant numbers below unity. The alternative of using semi-Lagrangian methods, consisting of modelling advection by the method of characteristics, is free from any inherent Courant number restriction. However, it is subject to errors of tracking that result in potential phase errors in the solutions. The aim of this article is primarily to understand and estimate these tracking errors, assuming the use of a cell-based backward method of characteristics, and considering conditions that would prevail in practical applications in rivers. This is achieved separately for non-uniform flows and unsteady flows, either via theoretical considerations or using numerical experiments. The main conclusion is that, tracking errors are expected to be negligible in practical applications in both unsteady flows and non-uniform flows. Also, a very significant computational time saving compared to Eulerian schemes is achievable. Copyright © 2006 John Wiley & Sons, Ltd. [source] Observer-based adaptive robust control of a class of nonlinear systems with dynamic uncertainties,INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 4 2001Bin Yao Abstract In this paper, a discontinuous projection-based adaptive robust control (ARC) scheme is constructed for a class of nonlinear systems in an extended semi-strict feedback form by incorporating a nonlinear observer and a dynamic normalization signal. The form allows for parametric uncertainties, uncertain nonlinearities, and dynamic uncertainties. The unmeasured states associated with the dynamic uncertainties are assumed to enter the system equations in an affine fashion. A novel nonlinear observer is first constructed to estimate the unmeasured states for a less conservative design. Estimation errors of dynamic uncertainties, as well as other model uncertainties, are dealt with effectively via certain robust feedback control terms for a guaranteed robust performance. In contrast with existing conservative robust adaptive control schemes, the proposed ARC method makes full use of the available structural information on the unmeasured state dynamics and the prior knowledge on the bounds of parameter variations for high performance. The resulting ARC controller achieves a prescribed output tracking transient performance and final tracking accuracy in the sense that the upper bound on the absolute value of the output tracking error over entire time-history is given and related to certain controller design parameters in a known form. Furthermore, in the absence of uncertain nonlinearities, asymptotic output tracking is also achieved. Copyright © 2001 John Wiley & Sons, Ltd. [source] Fuzzy multipath filter with Kalman algorithm for tracking a low-altitude target,ASIAN JOURNAL OF CONTROL, Issue 3 2009Yee Ming Chen Abstract To process tracking low-altitude targets with multipath propagation is a difficult task. The difficulty is derived from a large number of uncertain knowledge (parameters) such as the radar waveform frequency, the specific signal processing, the surface reflectivity, the actual target elevation and range, etc. This paper offers a technique based on fuzzy logic to solve the uncertain measurement error by multipath propagation. Also, we apply quantitative analysis presenting the degree of multipath effect required to achieve the aforementioned goal. Meanwhile, tracking filter estimation is used to reduce the measurement error by the multipath propagation. After the simulation, the fuzzy-logic-based technique not only can simplify the process of the model building by the interacting multiple model but also can promote the tracking accuracy. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Multi-sensor track-to-track fusion via linear minimum variance sense estimatorsASIAN JOURNAL OF CONTROL, Issue 3 2008Li-Wei Fong Abstract An integrated approach that consists of sensor-based filtering algorithms, local processors, and a global processor is employed to describe the distributed fusion problem when several sensors execute surveillance over a certain area. For the sensor tracking systems, each filtering algorithm utilized in the reference Cartesian coordinate system is presented for target tracking, with the radar measuring range, bearing, and elevation angle in the spherical coordinate system (SCS). For the local processors, each track-to-track fusion algorithm is used to merge two tracks representing the same target. The number of 2-combinations of a set with N distinct sensors is considered for central track fusion. For the global processor, the data fusion algorithms, simplified maximum likelihood (SML) estimator and covariance matching method (CMM), based on linear minimum variance (LMV) estimation fusion theory, are developed for use in a centralized track-to-track fusion situation. The resulting global fusers can be implemented in a parallel structure to facilitate estimation fusion calculation. Simulation results show that the proposed SML estimator has a more robust capability of improving tracking accuracy than the CMM and the LMV estimators. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Neural Network Adaptive Robust Control Of Siso Nonlinear Systems In A Normal FormASIAN JOURNAL OF CONTROL, Issue 2 2001J.Q. Gong ABSTRACT In this paper, performance oriented control laws are synthesized for a class of single-input-single-output (SISO) n -th order nonlinear systems in a normal form by integrating the neural networks (NNs) techniques and the adaptive robust control (ARC) design philosophy. All unknown but repeat-able nonlinear functions in the system are approximated by the outputs of NNs to achieve a better model compensation for an improved performance. While all NN weights are tuned on-line, discontinuous projections with fictitious bounds are used in the tuning law to achieve a controlled learning. Robust control terms are then constructed to attenuate model uncertainties for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy. Furthermore, if the unknown nonlinear functions are in the functional ranges of the NNs and the ideal NN weights fall within the fictitious bounds, asymptotic output tracking is achieved to retain the perfect learning capability of NNs. The precision motion control of a linear motor drive system is used as a case study to illustrate the proposed NNARC strategy. [source] | ||||||||||