Iterative learning control methods are represented as powerful tools to control dynamics nowadays. Our new controller based on particular case of iterative learning control is radically different from the presented conventional method, which attempts to stabilize a class of nonlinear systems by satisfying the conditions of Lyapunov Stability Theorem. Since our algorithm is model based, its robu...

This paper presents a novel iterative learning sliding mode controller (ILSMC) that can be applied to the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) subject model uncertainties and external disturbances. Here, proposed ILSMC is integrated in outer loop controlled system. The control development, conducted discrete-time domain, does not require priori information disturbanc...

This letter presents a data-based control approach to achieve high-performance trajectory tracking with Unmanned Aerial Vehicles (UAVs). We revisit an existing Iterative Learning Control (ILC) algorithm based on the notion that performance of system executes same task multiple times can be improved by learning from previous executions. While we will specifically refer multirotor platforms for e...

In this paper, we propose an integrated policy learning framework that fuses iterative learning control (ILC) and reinforcement learning. Integration is accomplished at the exploration level of the reinforcement learning algorithm. The proposed algorithm combines fast convergence properties of iterative learning control and robustness of reinforcement learning. This way, the advantages of both ...