© 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.This paper introduces a new hybrid adaptive model predictive control approach to control of uncertain dynamical systems where the matched uncertainty can be linearly parameterized by known basis functions. Introduced control framework respects the actuator position limit and actuator rate limit. Initially, an integration method in adaptive control is employed to identify the matched uncertainty in conjunction with Pseudo-Control Hedging (PCH). Thus, adaptation to matched uncertainty is not inhibited by the actuator saturation. Once the parameter convergence is achieved, control algorithm switches to online-learned model based Model Predictive Controller (MPC). Overall, the asymptotic stability of the system signals are ensured, and the improvements are illustrated on a simulation of wing-rock dynamics.