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.

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Linear Matrix Inequalities in System and Control Theory

On optimal e ∞ to e ∞ filtering

Survey paper: Set invariance in control

This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.

Model Predictive Control

The problem of associating data with targets in a cluttered multi-target environment is discussed and applied to passive sonar tracking. The probabilistic data association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, a new theoretical result is presented: the joint probabilistic data association (JPDA) algorithm, in which joint posterior association probabilities are computed for multiple targets (or multiple discrete interfering sources) in Poisson clutter. The algorithm is applied to a passive sonar tracking problem with multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e., low) probabilities of detection at each sample time. A simulation result is presented for two heavily interfering targets illustrating the dramatic tracking improvements obtained by estimating the targets' states using joint association probabilities.

Sonar tracking of multiple targets using joint probabilistic data association

This paper is concerned with the problem of estimating the state of a linear dynamic system using noise-corrupted observations, when input disturbances and observation errors are unknown except for the fact that they belong to given bounded sets. The cases of both energy constraints and individual instantaneous constraints for the uncertain quantities are considereal. In the former case, the set of possible system states compatible with the observations received is shown to be an ellipsoid, and equations for its center and weighting matrix are given, while in the latter case, equations describing a bounding ellipsoid to the set of possible states are derived. All three problems of filtering, prediction, and smoothing are examined by relating them to standard tracking problems of optimal control theory. The resulting estimators are similar in structure and comparable in simplicity to the corresponding stochastic linear minimum-variance estimators, and it is shown that they provide distinct advantages over existing schemes for recursive estimation with a set-membership description of uncertainty.

Recursive state estimation for a set-membership description of uncertainty

On the minimax reachability of target sets and target tubes

In this tutorial paper the basic principles of least squares estimation are introduced and applied to the solution of some filtering, prediction, and smoothing problems involving stochastic linear dynamic systems. In particular, the paper includes derivations of the discrete-time and continuous-time Kalman filters and their prediction and smoothing counterparts, with remarks on the modifications that are necessary if the noise processes are colored and correlated. The examination of these state estimation problems is preceded by a derivation of both the unconstrained and the linear least squares estimator of one random vector in terms of another, and an examination of the properties of each, with particular attention to the case of jointly Gaussian vectors. The paper concludes with a discussion of the duality between least squares estimation problems and least squares optimal control problems.

A tutorial introduction to estimation and filtering

The problem of optimal feedback control of uncertain discrete-time dynamic systems is considered where the uncertain quantities do not have a stochastic description but instead are known to belong to given sets The problem is converted to a sequential minimax problem and dynamic programming is suggested as a general method for its solution The notion of a sufficiently informative function, which parallels the notion of a sufficient statistic of stochastic optimal control, is introduced, and conditions under which the optimal controller decomposes into an estimator and an actuator are identified A limited class of problems for which this decomposition simplifies the computation and implementation of the optimal controller is delineated

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Sufficiently informative functions and the minimax feedback control of uncertain dynamic systems

This paper is the first in a series of three, the remaining two of which will appear at a later date. Performance bounds are derived in this series of papers for causal state estimation and regulation problems employing mean-square criteria. The systems considered are partially observed finite-dimensional continuous-time stochastic processes driven by additive white Gaussian noise. The particular systems to which the bounds apply are those which, when modeled by Ito differential equations, contain drift (.dt) coefficients which are, to within a uniformly Lipschitz residual, jointly linear in the system state and externally applied control. The bounds derived in the complete series of papers include both upper and lower bounds for causal state estimation and for a quadratic regulator problem. The upper bounds pertain to the performance of some simple, almost linear designs reminiscent of the designs which are optimal in the linear case. The lower bounds pertain to the optimum performance attainable within a broad class of candidate designs. This paper treats the upper bound for estimation. The second paper presents the estimation lower bound. The estimation performance bounds are independent of the control law, giving a particularly simple form to the control performance bounds which are developed in the third, and final, paper. All the bounds converge with vanishing nonlinearity (vanishing Lipschitz constants) to the known optimum performance for the limiting linear system. Consequently, the bounds are asymptotically tight and the simple designs studied are asymptotically optimal with vanishing nonlinearity.

I. Rhodes

Papers

Recursive state estimation for a set-membership description of uncertainty

On the minimax reachability of target sets and target tubes

A tutorial introduction to estimation and filtering

Sufficiently informative functions and the minimax feedback control of uncertain dynamic systems

Cone-bounded nonlinearities and mean-square bounds--Estimation lower bound