Neural Network Control Of Nonlinear Discrete-time Systems (automation And Control Engineering)
by Jagannathan Sarangapani /
2006 / English / PDF
11.7 MB Download
Intelligent systems are a hallmark of modern feedback control
systems. But as these systems mature, we have come to expect higher
levels of performance in speed and accuracy in the face of severe
nonlinearities, disturbances, unforeseen dynamics, and unstructured
uncertainties. Artificial neural networks offer a combination of
adaptability, parallel processing, and learning capabilities that
outperform other intelligent control methods in more complex
systems.
Intelligent systems are a hallmark of modern feedback control
systems. But as these systems mature, we have come to expect higher
levels of performance in speed and accuracy in the face of severe
nonlinearities, disturbances, unforeseen dynamics, and unstructured
uncertainties. Artificial neural networks offer a combination of
adaptability, parallel processing, and learning capabilities that
outperform other intelligent control methods in more complex
systems.
Borrowing from Biology
Borrowing from Biology
Examining neurocontroller design in discrete-time for the first
time, Neural Network Control of Nonlinear Discrete-Time Systems
presents powerful modern control techniques based on the
parallelism and adaptive capabilities of biological nervous
systems. At every step, the author derives rigorous stability
proofs and presents simulation examples to demonstrate the
concepts.
Examining neurocontroller design in discrete-time for the first
time, Neural Network Control of Nonlinear Discrete-Time Systems
presents powerful modern control techniques based on the
parallelism and adaptive capabilities of biological nervous
systems. At every step, the author derives rigorous stability
proofs and presents simulation examples to demonstrate the
concepts.
Progressive Development
Progressive Development
After an introduction to neural networks, dynamical systems,
control of nonlinear systems, and feedback linearization, the book
builds systematically from actuator nonlinearities and strict
feedback in nonlinear systems to nonstrict feedback, system
identification, model reference adaptive control, and novel optimal
control using the Hamilton-Jacobi-Bellman formulation. The author
concludes by developing a framework for implementing intelligent
control in actual industrial systems using embedded hardware.
After an introduction to neural networks, dynamical systems,
control of nonlinear systems, and feedback linearization, the book
builds systematically from actuator nonlinearities and strict
feedback in nonlinear systems to nonstrict feedback, system
identification, model reference adaptive control, and novel optimal
control using the Hamilton-Jacobi-Bellman formulation. The author
concludes by developing a framework for implementing intelligent
control in actual industrial systems using embedded hardware.
Neural Network Control of Nonlinear Discrete-Time Systems fosters
an understanding of neural network controllers and explains how to
build them using detailed derivations, stability analysis, and
computer simulations.
Neural Network Control of Nonlinear Discrete-Time Systems fosters
an understanding of neural network controllers and explains how to
build them using detailed derivations, stability analysis, and
computer simulations.