In light of the complex and highly uncertain nature of dynamical systems
requiring controls, it is not surprising that reliable system models for many
high performance engineering and life science applications are unavailable. In
the face of such high levels of system uncertainty, robust controllers may
unnecessarily sacrifice system performance whereas adaptive controllers are
clearly appropriate since they can tolerate far greater system uncertainty
levels to improve system performance. In this dissertation, we develop a
Lyapunov-based direct adaptive and neural adaptive control framework that
addresses parametric uncertainty, unstructured uncertainty, disturbance
rejection, amplitude and rate saturation constraints, and digital
implementation issues. Specifically, we consider the following research topics:
direct adaptive control for nonlinear uncertain systems with exogenous
disturbances; robust adaptive control for nonlinear uncertain systems; adaptive
control for nonlinear uncertain systems with actuator amplitude and rate
saturation constraints; adaptive reduced-order dynamic compensation for
nonlinear uncertain systems; direct adaptive control for nonlinear matrix
second-order dynamical systems with state-dependent uncertainty; adaptive
control for nonnegative and compartmental dynamical systems with applications
to general anesthesia; direct adaptive control of nonnegative and compartmental
dynamical systems with time delay; adaptive control for nonlinear nonnegative
and compartmental dynamical systems with applications to clinical pharmacology;
neural network adaptive control for nonlinear nonnegative dynamical systems;
passivity-based neural network adaptive output feedback control for nonlinear
nonnegative dynamical systems; neural network adaptive dynamic output feedback
control for nonlinear nonnegative systems using tapped delay memory units;
Lyapunov-based adaptive control framework for discrete-time nonlinear systems
with exogenous disturbances; direct discrete-time adaptive control with
guaranteed parameter error convergence; and hybrid adaptive control for
nonlinear uncertain impulsive dynamical systems.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/5292 |
| Date | 26 November 2003 |
| Creators | Hayakawa, Tomohisa |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 7139317 bytes, application/pdf |
Page generated in 0.0026 seconds