Extremum Seeking-based Iterative Learning Model Predictive Control (ESILC-MPC)
For control engineers, it offers a modular approach to handle parametric uncertainties in constrained MPC, but the contribution is incremental as it builds on existing methods.
The paper addresses tracking control for linear time-invariant systems with model uncertainties under constraints, proposing an MPC with ISS guarantee combined with extremum seeking to iteratively learn uncertainties. No concrete numerical results are provided.
In this paper, we study a tracking control problem for linear time-invariant systems, with model parametric uncertainties, under input and states constraints. We apply the idea of modular design introduced in Benosman et al. 2014, to solve this problem in the model predictive control (MPC) framework. We propose to design an MPC with input-to-state stability (ISS) guarantee, and complement it with an extremum seeking (ES) algorithm to iteratively learn the model uncertainties. The obtained MPC algorithms can be classified as iterative learning control (ILC)-MPC.