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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Ito, Yuji Toyota Cent Res & Dev Labs Inc Nagakute Aichi 4801192 Japan

ISBN: (纸本)9781713872344

This study presents a set-based method to estimate unknown parameters of continuous-time structured nonlinear systems under uncertainties. sets containing the parameters are found by using data sets and structures of the systems. Such estimation is challenging because the uncertainties are caused by disturbances, data noises, and partially unknown dynamics. In addition, the estimation for continuous-time nonlinear systems faces nonlinear problems that are difficult to address. To overcome these issues, the proposed set-based method solves linear programming (LP) problems iteratively instead of the nonlinear problems. The inequalities in the LP are derived such that they are consistent with the true parameters, using the boundedness of the uncertainties. A theoretical analysis guarantees that solving the iterative LP gives sets containing the true parameters.

关键词： Continuous time system estimation Grey box modeling Linear programming Nonlinear system identification parameter set estimation Uncertain systems

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Iteratively-Linearized set-Based parameter estimation for Uncertain Nonlinear Systems

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IFAC-PapersOnLine 2023年第2期56卷 5861-5868页

作者： Yuji Ito Toyota Central R&D Labs. Inc. Nagakute 480-1192 Japan

关键词： Continuous time system estimation Grey box modeling Linear programming Nonlinear system identification parameter set estimation Uncertain systems

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Robust adaptive model predictive control: Performance and parameter estimation

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2021年第18期31卷 8703-8724页

作者： Lu, Xiaonan Cannon, Mark Koksal-Rivet, Denis Univ Oxford Dept Engn Sci Oxford England Univ Chicago Dept Math Chicago IL 60637 USA

For systems with uncertain linear models, bounded additive disturbances and state and control constraints, a robust model predictive control (MPC) algorithm incorporating online model adaptation is proposed. sets of model parameters are identified online and employed in a robust tube MPC strategy with a nominal cost. The algorithm is shown to be recursively feasible and input-to-state stable. Computational tractability is ensured by using polytopic sets of fixed complexity to bound parameter sets and predicted states. Convex conditions for persistence of excitation are derived and are related to probabilistic rates of convergence and asymptotic bounds on parameter set estimates. We discuss how to balance conflicting requirements on control signals for achieving good tracking performance and parameter set estimate accuracy. Conditions for convergence of the estimated parameter set are discussed for the case of fixed complexity parameter set estimates, inexact disturbance bounds, and noisy measurements.

关键词： adaptive control input-to-state stability parameter set estimation receding horizon control uncertain linear systems

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A sequence-dependent coarse-grain model of B-DNA with explicit description of bases and phosphate groups parametrised from large scale Molecular Dynamics simulations

A sequence-dependent coarse-grain model of B-DNA with explic...

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作者： Alessandro Samuele PATELLI ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

学位级别：博士

We introduce a sequence–dependent coarse–grain model of double–stranded DNA with an explicit description of both the bases and the phosphate groups as interacting rigid–bodies. The model parameters are trained on extensive, state–of–the–art large scale molecular dynamics (MD) simulations. The model paradigm relies on three main approximations: 1) nucleic acid bases and phosphate groups are rigid, 2) in- teractions are nearest–neighbour and can be modelled with a quadratic energy, 3) model parameters have dimer sequence dependence. For an arbitrary sequence, the model predicts a sequence–dependent Gaussian equilibrium probability distribution. The parameter set comprises dimer–based elements, which are used to reconstruct mean configurations, called ground–states, which can have strong non–local sequence dependence, and precision matrices, or stiffness matrices, for any sequence of any length. This prediction step is sufficiently efficient that it is straightforward to con- struct probability density functions for millions of fragments each of length a few hundred base–pairs. The estimation of a parameter set consists in minimising the sum of Kullback–Leibler divergences between Gaussians predicted by the model and analogous Gaussians estimated directly from MD simulations of a training library of sequences. The training library comprises a short list of short palindromic DNA sequences. We designed the palindromic library using an ad hoc algorithm to include multiple instances of all independent tetramer sub–sequences. We exploit palindromic symmetry properties to study the convergence of the statistics extracted from MD simulations of palindromes and to define palindromically symmetrised estimators of first and second centred moments. The computation of the parameter set is delicate and needs the use of sophisticated numerics. We present an efficient and reliable procedure for estimating a complete parameter set which involves a generalisation of the classic Fisher

关键词： aaa coarse–grain DNA phosphate group palindromic sequence molecular dynamics simulation Kullback–Leibler divergence Fisher information parameter set estimation sugar puckering BI–BII conformations groove width

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Multiple weighted prediction models for video coding with brightness variations

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IET IMAGE PROCESSING 2012年第4期6卷 434-443页

作者： Tsang, S. -H. Chan, Y. -L. Siu, W. -C. Hong Kong Polytech Univ Dept Elect & Informat Engn Ctr Signal Proc Kowloon Hong Kong Peoples R China

Weighted prediction (WP) is a tool introduced in H. 264 to improve motion-compensation performance for video sequences with brightness variations. Various WP models to estimate the parameter set have been discussed in the literature. However, no single WP model works well for all types of brightness variations. A single reference frame multiple WP models (SRefMWP) scheme is proposed to facilitate the use of multiple WP models in a single reference frame. The proposed scheme makes a new arrangement of the multiple frame buffers in multiple reference frame motion estimation. It enables different macroblocks in the same frame to use different WP models even when they are predicted from the same reference frame. Furthermore, a new re-ordering mechanism for SRefMWP is also proposed to guarantee that the list of the reference picture is in the best order for further decreasing the bit rate. To reduce the implementation cost, a reduction in the memory requirement is achieved via look-up tables (LUTs). Experimental results show that SRefMWP can achieve significant coding gain in scenes with different types of brightness variations. Furthermore, SRefMWP with LUTs can reduce the memory requirement by about 80% while keeping the same coding efficiency as SRefMWP.

关键词： motion compensation H.264 multiple weighted prediction models video sequences table lookup brightness variations Image and video coding Data handling techniques parameter set estimation single reference frame motion estimation look-up tables video coding image sequences LUT macroblocks multiple frame buffers memory requirement reduction reference picture Video signal processing single reference frame multiple WP models re-ordering mechanism motion-compensation performance SRefMWP scheme multiple reference frame motion estimation

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