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Combines nonlinear optimization, mathematical control theory, and numerical solution of ordinary differential/differential-algebraic equations to solve optimal control problems.
The authors provide a comprehensive treatment of stochastic systems from the foundations of probability to stochastic optimal control.
The authors present a unified computational methodology for the analysis and synthesis of piecewise affine controllers, taking an approach that is capable of handling sliding modes, sampled-data, and networked systems.
Discusses sliding mode control of uncertain nonlinear systems, with a particular emphasis on advanced and optimization based algorithms. This is the first book to systematize the theory of optimization based higher order sliding mode control and illustrate advanced algorithms and their applications to real problems.
Outlines the main contributions to the field of passive network synthesis and presents new research into the enumerative approach and the classification of networks of restricted complexity. This book serves as both an ideal introduction to the topic and a definitive treatment of the Ladenheim catalogue.
Presents new computational tools for the H8 control of distributed parameter systems in which transfer functions are considered as input-output descriptions for the plants to be controlled. The emphasis is on the computation of the controller parameters and reliable implementation.
For over a quarter of a century, high-gain observers have been used extensively in the design of output feedback control of nonlinear systems. This book presents a clear, unified treatment of the theory of high-gain observers and their use in feedback control. Also provided is a discussion of the separation principle for nonlinear systems.
Analyses Lagrange multiplier theory and demonstrates its impact on the development of numerical algorithms for variational problems in function spaces.
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