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 studies the control of a class of discrete event processes, i.e. processes that are discrete, asynchronous and possibly nondeter-ministic. The controlled process is described as the generator of a formal language, while the controller, or supervisor, is constructed from the grammar of a specified target language that incorporates the desired closed-loop system behavior. The existence problem for a supervisor is reduced to finding the largest controllable language contained in a given legal language. Two examples are provided.

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Supervisory control of a class of discrete event processes

Geared primarily to an audience consisting of mathematically advanced undergraduate or beginning graduate students, this text may additionally be used by engineering students interested in a rigorous, proof-oriented systems course that goes beyond the classical frequency-domain material and more applied courses. The minimal mathematical background required is a working knowledge of linear algebra and differential equations. The book covers what constitutes the common core of control theory and is unique in its emphasis on foundational aspects. While covering a wide range of topics written in a standard theorem/proof style, it also develops the necessary techniques from scratch. In this second edition, new chapters and sections have been added, dealing with time optimal control of linear systems, variational and numerical approaches to nonlinear control, nonlinear controllability via Lie-algebraic methods, and controllability of recurrent nets and of linear systems with bounded controls.

Mathematical Control Theory: Deterministic Finite Dimensional Systems

A long-run equilibrium theory of turnover is presented and is shown to explain the important regularities that have been observed by empirical investigators. A worker's productivity in a particular job is not known ex ante and becomes known more precisely as the worker's job tenure increases. Turnover is generated by the existence of a nondegenerate distribution of the worker's productivity across different. The nondegeneracy is caused by the assumed variation in the quality of the worker-employer match.

Job Matching and the Theory of Turnover

A discrete event system (DES) is a dynamic system that evolves in accordance with the abrupt occurrence, at possibly unknown irregular intervals, of physical events. Such systems arise in a variety of contexts ranging from computer operating systems to the control of complex multimode processes. A control theory for the logical aspects of such DESs is surveyed. The focus is on the qualitative aspects of control, but computation and the related issue of computational complexity are also considered. Automata and formal language models for DESs are surveyed. >

The control of discrete event systems

A flexible modular and hierarchical structure is presented to reduce computational effort in designing optimal nonblocking supervisors for discrete-event systems (DES). The structure organizes the system into modular subsystems that embody internal interacting dependencies. Verification and coordination among modular subsystems are achieved through their model abstractions. Sufficient conditions are presented to guarantee that these coordinators and modular supervisors result in optimal nonblocking control. A medium-sized example demonstrates the computational advantage of our approach.

Computationally efficient supervisor design: abstraction and modularity

Supervisory control of discrete-event systems: A brief history

Natural projections with the observer property have proved effective in reducing the computational complexity of nonblocking supervisory control design, and the state sizes of the resulting controllers. In this paper we present an algorithm to verify this property, or if necessary to achieve it. A natural projection is a special type of general causal reporter map; for the latter an algorithm is already known for verification and modification. This algorithm could be used to verify the observer property of a natural projection, but if the natural projection is not an observer the algorithm is not applicable to modify it to an observer. Also, while a general reporter map always admits a unique smallest refinement with the observer property, a natural projection does not. Indeed there may exist several minimal extensions to the original observable event set of a natural projection. We show that the problem of finding a minimal extension is NP-hard, but propose a polynomial-time algorithm that always finds an acceptable extension. While not guaranteed to be minimal, it is in practice often reasonably small.

On the Computation of Natural Observers in Discrete-Event Systems

In this paper the Supervisory Control Problem (SCP) for discrete event systems (DES) is generalized to a class of probabilistic discrete event systems (PDES). Necessary and sufficient conditions for the existence of a solution to the probabilistic SCP for a class of nonterminating PDES are developed. Methods of representing probabilistic supervisors for PDES are described and the computation of supervisors is briefly discussed. Finally, we discuss how the results may be extended to terminating probabilistic languages. >

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Supervisory control of probabilistic discrete event systems

This paper presents an approach for functionally dealing with multiple tasks in the supervisory control of discrete-event systems (DES). The colored marking generator (CMG), a special type of Moore automaton, is introduced as a model that distinguishes classes of tasks in DES. The main results of supervisory control theory are extended to this model, allowing the synthesis of minimally restrictive supervisors, which respect the safety specifications and ensure coreachability of multiple control objectives. Reversibility is also investigated as an alternative way of ensuring liveness of multiple tasks. Two examples illustrate the convenience of this approach.

https://link.springer.com/content/pdf/10.1007%2Fs10626-005-4058-y.pdf

W. M. Wonham

Papers

Computationally efficient supervisor design: abstraction and modularity

Supervisory control of discrete-event systems: A brief history

On the Computation of Natural Observers in Discrete-Event Systems

Supervisory control of probabilistic discrete event systems

Multitasking Supervisory Control of Discrete-Event Systems