Ebook: Functional Adaptive Control: An Intelligent Systems Approach

The field of intelligent control has recently emerged as a response to the challenge of controlling highly complex and uncertain nonlinear systems. It attempts to endow the controller with the key properties of adaptation, learn­ ing and autonomy. The field is still immature and there exists a wide scope for the development of new methods that enhance the key properties of in­ telligent systems and improve the performance in the face of increasingly complex or uncertain conditions. The work reported in this book represents a step in this direction. A num­ ber of original neural network-based adaptive control designs are introduced for dealing with plants characterized by unknown functions, nonlinearity, multimodal behaviour, randomness and disturbances. The proposed schemes achieve high levels of performance by enhancing the controller's capability for adaptation, stabilization, management of uncertainty, and learning. Both deterministic and stochastic plants are considered. In the deterministic case, implementation, stability and convergence is­ sues are addressed from the perspective of Lyapunov theory. When compared with other schemes, the methods presented lead to more efficient use of com­ putational storage and improved adaptation for continuous-time systems, and more global stability results with less prior knowledge in discrete-time sys­ tems.

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This book presents a wide range of techniques that lead to novel strategies for effecting intelligent control of complex systems that are typically characterised by uncertainty, nonlinear dynamics, component failure, unpredictable disturbances, multi-modality and high dimensional spaces. The underlying design philosophy is based on effecting closed-loop control in the presence of plant or environmental uncertainty and complexity by utilizing various types of neural network architectures, ranging from multilayer perceptron to radical basis function and modular network models. The uncertainty and complexity are typified by unknown nonlinear functionals, and temporal or spatial multi-modality. Deterministic and stochastic conditions, as well as continuous and discrete time dynamics are taken into consideration. The presented designs are firmly rooted in the techniques of adaptive control, reconfigurable control, multiple model control, stochastic adaptive control, lyapunov stability theory and neural networks. The techniques are shown to enhance the performance of the control system in the presence of the higher levels of complexity and uncertainty associated with modern plants, which demand superior intelligence and autonomy from the controller. The presented designs are supported both by theory and by numerous results from simulation experiments. The book also includes extensive reviews on general aspects concerning the fields of intelligent, nonlinear and stochastic control.

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This book presents a wide range of techniques that lead to novel strategies for effecting intelligent control of complex systems that are typically characterised by uncertainty, nonlinear dynamics, component failure, unpredictable disturbances, multi-modality and high dimensional spaces. The underlying design philosophy is based on effecting closed-loop control in the presence of plant or environmental uncertainty and complexity by utilizing various types of neural network architectures, ranging from multilayer perceptron to radical basis function and modular network models. The uncertainty and complexity are typified by unknown nonlinear functionals, and temporal or spatial multi-modality. Deterministic and stochastic conditions, as well as continuous and discrete time dynamics are taken into consideration. The presented designs are firmly rooted in the techniques of adaptive control, reconfigurable control, multiple model control, stochastic adaptive control, lyapunov stability theory and neural networks. The techniques are shown to enhance the performance of the control system in the presence of the higher levels of complexity and uncertainty associated with modern plants, which demand superior intelligence and autonomy from the controller. The presented designs are supported both by theory and by numerous results from simulation experiments. The book also includes extensive reviews on general aspects concerning the fields of intelligent, nonlinear and stochastic control.
Content:
Front Matter....Pages i-xxi
Front Matter....Pages 1-1
Introduction....Pages 3-19
Front Matter....Pages 21-21
Adaptive Control of Nonlinear Systems....Pages 23-46
Dynamic Structure Networks for Stable Adaptive Control....Pages 47-78
Composite Adaptive Control of Continuous-Time Systems....Pages 79-100
Functional Adaptive Control of Discrete-Time Systems....Pages 101-127
Front Matter....Pages 129-129
Stochastic Control....Pages 131-145
Dual Adaptive Control of Nonlinear Systems....Pages 147-164
Multiple Model Approaches....Pages 165-185
Multiple Model Dual Adaptive Control of Jump Nonlinear Systems....Pages 187-212
Multiple Model Dual Adaptive Control of Spatial Multimodal Systems....Pages 213-241
Front Matter....Pages 243-243
Conclusions....Pages 245-249
Back Matter....Pages 251-266

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This book presents a wide range of techniques that lead to novel strategies for effecting intelligent control of complex systems that are typically characterised by uncertainty, nonlinear dynamics, component failure, unpredictable disturbances, multi-modality and high dimensional spaces. The underlying design philosophy is based on effecting closed-loop control in the presence of plant or environmental uncertainty and complexity by utilizing various types of neural network architectures, ranging from multilayer perceptron to radical basis function and modular network models. The uncertainty and complexity are typified by unknown nonlinear functionals, and temporal or spatial multi-modality. Deterministic and stochastic conditions, as well as continuous and discrete time dynamics are taken into consideration. The presented designs are firmly rooted in the techniques of adaptive control, reconfigurable control, multiple model control, stochastic adaptive control, lyapunov stability theory and neural networks. The techniques are shown to enhance the performance of the control system in the presence of the higher levels of complexity and uncertainty associated with modern plants, which demand superior intelligence and autonomy from the controller. The presented designs are supported both by theory and by numerous results from simulation experiments. The book also includes extensive reviews on general aspects concerning the fields of intelligent, nonlinear and stochastic control.
Content:
Front Matter....Pages i-xxi
Front Matter....Pages 1-1
Introduction....Pages 3-19
Front Matter....Pages 21-21
Adaptive Control of Nonlinear Systems....Pages 23-46
Dynamic Structure Networks for Stable Adaptive Control....Pages 47-78
Composite Adaptive Control of Continuous-Time Systems....Pages 79-100
Functional Adaptive Control of Discrete-Time Systems....Pages 101-127
Front Matter....Pages 129-129
Stochastic Control....Pages 131-145
Dual Adaptive Control of Nonlinear Systems....Pages 147-164
Multiple Model Approaches....Pages 165-185
Multiple Model Dual Adaptive Control of Jump Nonlinear Systems....Pages 187-212
Multiple Model Dual Adaptive Control of Spatial Multimodal Systems....Pages 213-241
Front Matter....Pages 243-243
Conclusions....Pages 245-249
Back Matter....Pages 251-266
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