In this paper, we present a novel solution for real-time, Non-Linear Model Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The proposed controller formulates the Optimal Control Problem (OCP) in terms of flat outputs over an adaptive lattice. In common approximated OCP solutions, the number of discretization points composing the lattice represents a critical upper bound for real-time applications. The proposed NMPC-based technique refines the initially uniform time horizon by adding time steps with a sampling criterion that aims to reduce the discretization error. This enables a higher accuracy in the initial part of the receding horizon, which is more relevant to NMPC, while keeping bounded the number of discretization points. By combining this feature with an efficient Least Square formulation, our solver is also extremely time-efficient, generating trajectories of multiple seconds within only a few milliseconds. The performance of the proposed approach has been validated in a high fidelity simulation environment, by using an UAV platform. We also released our implementation as open source C++ code.
2018, 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2018, Pages 74-80
Non-linear model predictive control with adaptive time-mesh refinement (04b Atto di convegno in volume)
Potena Ciro, DELLA CORTE Bartolomeo, Nardi Daniele, Grisetti Giorgio, Pretto Alberto
Gruppo di ricerca: Artificial Intelligence and Robotics