To a large extent, our lives on this earth depend on systems that operate auto matically. Manysuchsystems can be found in nature and others are man made. These systems can be biological, electrical, mechanical, chemical, or ecological, to namejust a few categories. Our human body is full ofsystems whose conti nued automatic operation is vital for our existence. On a daily basis we come in contact with man made systems whose automatic operation ensures increa sed productivity, promotes economic development and improves the quality of life. A primary component that is responsible for the automatic operation of a system is a device or mechanism called the controller. In man made systems one must first design and then implement such a controller either as a piece of hardware or as software code in a computer. The safe and efficient automatic operation of such systems is testimony to the success of control theorists and practitioners over the years. This book presents new methods {or controller design. The process ofdeveloping a controller or control strategy can be dramatically improved if one can generate an appropriate dynamic model for the system under consideration. Robust control design deals with the question of how to develop such controllers for system models with uncertainty. In many cases dynamic models can be expressed in terms oflinear, time invariant differential equations or transfer functions.
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To a large extent, our lives on this earth depend on systems that operate auto matically. Manysuchsystems can be found in nature and others are man made. These systems can be biological, electrical, mechanical, chemical, or ecological, to namejust a few categories. Our human body is full ofsystems whose conti nued automatic operation is vital for our existence. On a daily basis we come in contact with man made systems whose automatic operation ensures increa sed productivity, promotes economic development and improves the quality of life. A primary component that is responsible for the automatic operation of a system is a device or mechanism called the controller. In man made systems one must first design and then implement such a controller either as a piece of hardware or as software code in a computer. The safe and efficient automatic operation of such systems is testimony to the success of control theorists and practitioners over the years. This book presents new methods {or controller design. The process ofdeveloping a controller or control strategy can be dramatically improved if one can generate an appropriate dynamic model for the system under consideration. Robust control design deals with the question of how to develop such controllers for system models with uncertainty. In many cases dynamic models can be expressed in terms oflinear, time invariant differential equations or transfer functions.
Our heavy dependence on systems that are automatically controlled is undeniable. Many such systems can be found in nature; others are man-made. A primary component of such systems is a device or mechanism called the controller. In man-made systems, one must first design and then implement such a controller, either as a piece of hardware or as software code in a computer. The process of developing a controller can be dramatically improved if one can generate an appropriate dynamic model of the system under consideration. Robust Control Design: A Polynomial Approach explains how to develop such controllers for system models with uncertainty. In many cases, dynamic models can be expressed in terms of linear, time-invariant equations, or transfer functions. The book presents powerful, new methods for the robust design of system controllers, especially for systems with parameter uncertainty.
The book's approach is based on the Nyquist Theorem, exploiting the properties of polynomials and polynomial value sets. The most promising techniques are those derived from the Finite Nyquist Theorem (FNT) and its corollary, The Finite Inclusions Theorem (FIT). These results reduce the problem of synthesizing a robust controller to an iterative process in which each iteration solves a finite number of linear inequalities in the controller parameters. This formulation facilitates the development of methods for robust D-stabilization and robust multiobjective performance synthesis and can lead to a less conservative design. Furthermore, it provides new insight into the robust control synthesis problem and allows greater flexibility in the choice of controller order. Computationally efficient robust control algorithms can be developed using these results and then implemented in software, thus automating the design process. The focus is on single-input-single-output systems, but the final chapter lays the foundation of robust multivariable design.
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