  A Learning Model Predictive Controller (LMPC) for linear system in presented.
The proposed controller is an extension of the LMPC [1] and it aims to decrease
the computational burden. The control scheme is reference-free and is able to
improve its performance by learning from previous iterations. A convex safe set
and a terminal cost function are used in order to guarantee recursive
feasibility and non-increasing performance at each iteration. The paper
presents the control design approach, and shows how to recursively construct
the convex terminal set and the terminal cost from state and input trajectories
of previous iterations. Simulation results show the effectiveness of the
proposed control logic.
