John B. Moore

Bio: John B. Moore is an academic researcher from Australian National University. The author has contributed to research in topics: Adaptive control & Linear-quadratic-Gaussian control. The author has an hindex of 50, co-authored 352 publications receiving 18573 citations. Previous affiliations of John B. Moore include Akita University & University of Hong Kong.

Applications of the Multivariable Popov Criterion

Abstract: Two classes of systems are considered for the application of the multivariable Popov criterion The first is obtained from a linear, finite-dimensional system with a state feedback law derived from a quadratic loss function minimization problem It is shown that a non-critical part of the system is the set of transducers producing the inputs to the system, in the sense that stability is retained even when the transducers are far from ideal The second class of systems is derived from linear, finite-dimensional systems which are stable It is shown that it is always it is possible to tolerate in general a small amount of non-linearity at virtually any point in the system without impairment of stability

Central tendency minimum variance adaptive control

Abstract: The authors propose designing indirect adaptive controllers using measures of central tendency of the a posteriori probability function of the control for parameters. That is, given a knowledge of plant uncertainty at each time instant from estimation algorithms, and a controller design rule, controller parameters are sought which maximize the likelihood of achieving the control objectives. The particular case of adaptive minimum variance control is studied in detail. >

Indefinite stochastic linear quadratic control and generalized differential Riccati equation

Abstract: We consider a stochastic linear-quadratic (LQ) problem with possible indefinite cost weighting matrices for the state and the control. An outstanding open problem is to identify an appropriate Riccati-type equation whose solvability is equivalent to the solvability of this possibly indefinite LQ problem. We introduce a new type of differential Riccati equation, called the generalized (differential) Riccati equation, which in turn provides a complete solution to the indefinite LQ problem. Moreover, all the optimal feedback/open-loop controls can be identified via the solution to this Riccati equation.

Dither signals, on-line spectral factorization and adaptive prewhitening in adaptive control

Abstract: Existing "globally" convergent adaptive schemes can fail to converge in stochastic environments if a certain strict passivity condition related to the noise colour is not satisfied. This paper presents an algorithm which applies white noise "dither" signals to guarantee global convergence when the passivity condition fails. A more sophisiticated derivative scheme exploits an on-line spectral factorization technique to recover the asymptotic optimality lost by the noise introduction. In a third scheme, building on the previous two schemes, convergence rates are enhanced and asymptotic efficiency is achieved employing adaptive prewhitening filters. When applied to adaptive control of unknown linear stochastic plants with unknown deterministic signals or disturbances, such as piecewise constant or sinusoidal ones, the simplest algorithm of the paper suffices and appears to be the first that is guaranteed to be globally convergent. The algorithm employing adaptive pre-whitening filters appears to be the first globally convergent adaptive control algorithm that exploits adaptive prewhitening.

In colored noise

Abstract: This paper presents a variation on a known extended least squares algorithm of the "output error" or "parallel model" type. Under reasonable conditions, the algorithms achieve global convergence of the one-step-ahead prediction error to the additive independent (possible colored) measurement noise. The convergence of the algorithms proposed is not critically sensitive to the color in the noise as are related extended least squares schemes which require a simultaneous noise model identification, nor is the convergence critically sensitive to the input signals as are realizations of the method of instrumental variables. The algorithms are also simpler to implement than for the competing schemes. In the paper, there is also studied an add on scheme which consists of additional processing of the prediction errors to achieve simultaneous noise model identification, and improved prediction. Such a scheme is attractive from the computational cost point of view. Global convergence results are developed for the algorithms based on martingale convergence theorems as in earlier theories for extended least squares schemes. The key contribution of the paper as far as the theory is concerned is to show how to cope with the colored noise in the martingale framework.

Linear Matrix Inequalities in System and Control Theory

Abstract: Preface 1. Introduction Overview A Brief History of LMIs in Control Theory Notes on the Style of the Book Origin of the Book 2. Some Standard Problems Involving LMIs. Linear Matrix Inequalities Some Standard Problems Ellipsoid Algorithm Interior-Point Methods Strict and Nonstrict LMIs Miscellaneous Results on Matrix Inequalities Some LMI Problems with Analytic Solutions 3. Some Matrix Problems. Minimizing Condition Number by Scaling Minimizing Condition Number of a Positive-Definite Matrix Minimizing Norm by Scaling Rescaling a Matrix Positive-Definite Matrix Completion Problems Quadratic Approximation of a Polytopic Norm Ellipsoidal Approximation 4. Linear Differential Inclusions. Differential Inclusions Some Specific LDIs Nonlinear System Analysis via LDIs 5. Analysis of LDIs: State Properties. Quadratic Stability Invariant Ellipsoids 6. Analysis of LDIs: Input/Output Properties. Input-to-State Properties State-to-Output Properties Input-to-Output Properties 7. State-Feedback Synthesis for LDIs. Static State-Feedback Controllers State Properties Input-to-State Properties State-to-Output Properties Input-to-Output Properties Observer-Based Controllers for Nonlinear Systems 8. Lure and Multiplier Methods. Analysis of Lure Systems Integral Quadratic Constraints Multipliers for Systems with Unknown Parameters 9. Systems with Multiplicative Noise. Analysis of Systems with Multiplicative Noise State-Feedback Synthesis 10. Miscellaneous Problems. Optimization over an Affine Family of Linear Systems Analysis of Systems with LTI Perturbations Positive Orthant Stabilizability Linear Systems with Delays Interpolation Problems The Inverse Problem of Optimal Control System Realization Problems Multi-Criterion LQG Nonconvex Multi-Criterion Quadratic Problems Notation List of Acronyms Bibliography Index.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Factor graphs and the sum-product algorithm

Abstract: Algorithms that must deal with complicated global functions of many variables often exploit the manner in which the given functions factor as a product of "local" functions, each of which depends on a subset of the variables. Such a factorization can be visualized with a bipartite graph that we call a factor graph, In this tutorial paper, we present a generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph. Following a single, simple computational rule, the sum-product algorithm computes-either exactly or approximately-various marginal functions derived from the global function. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sum-product algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's (1988) belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform (FFT) algorithms.

Sequential Monte Carlo methods in practice

Abstract: Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.

Planning Algorithms: Introductory Material

Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.