An adaptive output feedback controller design procedure for uncertain nonlinear systems is developed which avoids the use of state estimation. To achieve this goal three separate problems are addressed independently: controller design, derivation of parameter update laws and approximate mapping of an unknown dynamic function from its input/output history. To handle the uncertainty, the controller, in the form of a dynamic compensator, is augmented by a single hidden layer (SHL) neural network that adjusts on-line for unknown nonlinearities. The parameter update laws for a SHL neural network are derived from stability analysis. Simulations illustrate the theoretical results.
ASJC Scopus subject areas
- Electrical and Electronic Engineering