An efficient inexact NMPC scheme with stability and feasibility guarantees


In this paper, an inexact nonlinear model predictive control scheme with reduced computational complexity is proposed. The presented approach exploits fixed sensitivity information precomputed offline at a reference value. This allows one to avoid the online computational effort resulting from the propagation of sensitivities and possibly the corresponding condensing routine when solving the optimal control problem with a sequential quadratic programming method. By performing a numerical simulation of the nonlinear dynamics online, feasibility of the closed-loop trajectories can be preserved in contrast to linear model predictive control schemes. Nominal stability guarantees of the approach are derived and the effectiveness of the scheme is demonstrated on a non-trivial example.

Andrea Zanelli
PhD Researcher

Interested in nonlinear optimization, model predictive control and embedded systems.