This book merges two major areas of control: the design of control systems and adaptive control. Original contributions are made in the polynomial approach to stochastic optimal control and the resulting control laws are then manipulated into a form suitable for application in the self-tuning control framework. A major contribution is the derivation of both scalar and multivariable optimal controllers for the rejection of measurable disturbances using feedforward. A powerful feature of the book is the presentation of a case-study in which the LQG self-tuner was tested on the pressure control loop of a power station. The broad coverage of the book should appeal not only to research workers, teachers and students of control engineering, but also to practicing industrial control engineers.
to stochastic optimal control.- Stochastic tracking with measurable disturbance feedforward.- to self-tuning control.- Optimal self-tuning algorithm.- A power systems application.- Conclusions.
Inhaltsverzeichnis
to stochastic optimal control.- Stochastic tracking with measurable disturbance feedforward.- to self-tuning control.- Optimal self-tuning algorithm.- A power systems application.- Conclusions.
Klappentext
This book merges two major areas of control: the design of control systems and adaptive control. Original contributions are made in the polynomial approach to stochastic optimal control and the resulting control laws are then manipulated into a form suitable for application in the self-tuning control framework. A major contribution is the derivation of both scalar and multivariable optimal controllers for the rejection of measurable disturbances using feedforward. A powerful feature of the book is the presentation of a case-study in which the LQG self-tuner was tested on the pressure control loop of a power station. The broad coverage of the book should appeal not only to research workers, teachers and students of control engineering, but also to practicing industrial control engineers.
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