Overview Contents: Introduction to Adaptive Filters, Introduction to Stochastic Processes, Correlation Structure, FIR Wiener Filter, LMS Algorithm, Convergence Analysis, Sign LMS Algorithm, Block LMS Algorithm, Vector Space Treatment to Random Variables, Introduction to Linear Prediction, Lattice Filter and Recursions, Gradient Adaptive Lattice, RLS Lattice Recursions, Systolic Implementation, Singular Value Decomposition.
English
Last updated
Sun, 07-Aug-2022
A few of the well established methods of adaptive signal processing theory are modified and extended in order to address some of the basic issues of adaptive control.
An unknown plant will track an input command signal if the plant is preceded by a controller whose transfer function approximates the inverse of the plant transfer function. An adaptive inverse modeling process can be used to obtain a stable controller, whether the plant is minimum or non-minimum phase. A model-reference version of this idea allows system dynamics to closely approximate desired reference model dynamics. No direct feedback is involved. However the system output is monitored and utilized in order to adjust the parameters of the controller.
The proposed method performs very well in computer simulations of a wide range of stable plants, and it seems to be a promising alternative approach to the design of adaptive control systems.
Study any topic, anytime. explore thousands of courses for the lowest price ever!
