Ebook: Stochastic Optimal Control Theory with Application in Self-Tuning Control

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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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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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.
Content:
Front Matter....Pages -
Introduction to stochastic optimal control....Pages 1-14
Stochastic tracking with measurable disturbance feedforward....Pages 15-116
Introduction to self-tuning control....Pages 117-153
Optimal self-tuning algorithm....Pages 154-202
A power systems application....Pages 203-240
Conclusions....Pages 241-246
Back Matter....Pages -

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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.
Content:
Front Matter....Pages -
Introduction to stochastic optimal control....Pages 1-14
Stochastic tracking with measurable disturbance feedforward....Pages 15-116
Introduction to self-tuning control....Pages 117-153
Optimal self-tuning algorithm....Pages 154-202
A power systems application....Pages 203-240
Conclusions....Pages 241-246
Back Matter....Pages -
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