Nonlinear Control for Systems Containing Input Uncertainty Via a Lyapunov-Based Approach

Controllers are often designed based on the assumption that a control actuation can be directly applied to the system. This assumption may not be valid, however, for systems containing parametric input uncertainty or unmodeled actuator dynamics.In this dissertation, a tracking control methodology is proposed for aircaft and aerospace systems for which the corresponding dynamic models contain uncertainty in the control actuation. The dissertation will focus on five problems of interest: (1) adaptive CMG-actuated satellite attitude control in the presence of inertia uncertainty and uncertain CMG gimbal friction; (2) adaptive neural network (NN)-based satellite attitude control for CMG-actuated small-sats in the presence of uncertain satellite inertia, nonlinear disturbance torques, uncertain CMG gimbal friction, and nonlinear electromechanical CMG actuator disturbances; (3) dynamic inversion (DI) control for aircraft systems containing parametric input uncertainty and additive, nonlinearly parameterizable (non-LP) disturbances; (4) adaptive dynamic inversion (ADI) control for aircraft systems as described in (3); and (5) adaptive output feedback control for aircraft systems as described in (3) and (4)