There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

This paper will very briefly review the history of the relationship between modern optimal control and robust control. The latter is commonly viewed as having arisen in reaction to certain perceived inadequacies of the former. More recently, the distinction has effectively disappeared. Once-controversial notions of robust control have become thoroughly mainstream, and optimal control methods permeate robust control theory. This has been especially true in H-infinity theory, the primary focus of this paper.

http://www.gbv.de/dms/goettingen/18606599X.pdf

Robust and Optimal Control

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

Simple state-space formulas are derived for all controllers solving the following standard H/sub infinity / problem: For a given number gamma >0, find all controllers such that the H/sub infinity / norm of the closed-loop transfer function is (strictly) less than gamma . It is known that a controller exists if and only if the unique stabilizing solutions to two algebraic Riccati equations are positive definite and the spectral radius of their product is less than gamma /sup 2/. Under these conditions, a parameterization of all controllers solving the problem is given as a linear fractional transformation (LFT) on a contractive, stable, free parameter. The state dimension of the coefficient matrix for the LFT, constructed using the two Riccati solutions, equals that of the plant and has a separation structure reminiscent of classical LQG (i.e. H/sub 2/) theory. This paper is intended to be of tutorial value, so a standard H/sub 2/ solution is developed in parallel. >

State-space solutions to standard H/sub 2/ and H/sub infinity / control problems

An improved method of ultimate bound computation for linear switched systems with bounded disturbances

Measurement-based Feedback Control of Linear Quantum Stochastic Systems with Quadratic-Exponential Criteria

Model predictive control (MPC) refers to a class of computer control algorithms which is commonly used when tracking a reference trajectory is the primary goal. It minimizes the steady-state tracking error, increases the closed-loop bandwidth, and enables the controller to track a reference signal, the major requirements for nanopositioning applications. These capabilities of MPC controllers have motivated this research to improve the positioning performance of the piezoelectric tube scanner (PTS) for better imaging performance of an atomic force microscope (AFM). The multi-input multi-output (MIMO) form of the controller designed in this work compensates for the cross-coupling effect while the damping compensator augmented with the plant enhances its damping capability. To evaluate the performance improvement using the proposed control frame work, an experimental comparison with the existing AFM proportionalintegral (PI) controller is conducted which demonstrates that it enhances image quality for scanning speeds up to 125 Hz.

Nanopositioning performance of MIMO MPC

In this paper, we study dynamical quantum networks which evolve according to Schrodinger equations but subject to sequential local or global quantum measurements. A network of qubits forms a composite quantum system whose state undergoes unitary evolution in between periodic measurements, leading to hybrid quantum dynamics with random jumps at discrete time instances along a continuous orbit. The measurements either act on the entire network of qubits, or only a subset of qubits. First of all, we reveal that this type of hybrid quantum dynamics induces probabilistic Boolean recursions representing the measurement outcomes. With global measurements, it is shown that such resulting Boolean recursions define Markov chains whose state-transitions are fully determined by the network Hamiltonian and the measurement observables. Particularly, we establish an explicit and algebraic representation of the underlying recursive random mapping driving such induced Markov chains. Next, with local measurements, the resulting probabilistic Boolean dynamics is shown to be no longer Markovian. The state transition probability at any given time becomes dependent on the entire history of the sample path, for which we establish a recursive way of computing such non-Markovian probability transitions. Finally, we adopt the classical bilinear control model for the continuous Schrodinger evolution, and show how the measurements affect the controllability of the quantum networks.

Measurement-Induced Boolean Dynamics and Controllability for Quantum Networks

A new approach to solving a nonlinear robust H∞ control problem using a stable nonlinear output feedback controller is presented in this paper. The particular class of nonlinear uncertain systems being considered is characterized in terms of Integral Quadratic Constraints and Global Lipschitz Conditions describing the admissible uncertainty and nonlinearity, respectively. The nonlinear controller is then constructed by including a copy of the system nonlinearity in the structure of the linear controller. The aim is to enable the controller to exploit the nonlinearity of the system such that it will absolutely stabilize the closed loop nonlinear system and achieve a specified disturbance attenuation level. This method involves the stabilizing solutions of a pair of algebraic Riccati equations.

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Ian R. Petersen

Papers

An improved method of ultimate bound computation for linear switched systems with bounded disturbances

Measurement-based Feedback Control of Linear Quantum Stochastic Systems with Quadratic-Exponential Criteria

Nanopositioning performance of MIMO MPC

Measurement-Induced Boolean Dynamics and Controllability for Quantum Networks

Robust H ∞ stabilization of a nonlinear uncertain system via a stable nonlinear output feedback controller