A Fast NMPC Approach based on Bounded-Variable Nonlinear Least Squares. Issue 20 (2018)

• Record Type: Journal Article
• Authors: Saraf, Nilay; Zanon, Mario; Bemporad, Alberto
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 20(2018)
• Page Start: 337
• Page End: 342
• Publication Date: 2018
• Subjects: Nonlinear model predictive control -- Bounded-variable least squares -- Gauss-Newton method; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.11.056
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper, we present an approach for real-time nonlinear model predictive control (NMPC) of constrained multivariable dynamical systems described by nonlinear difference equations. The NMPC problem is formulated by means of a quadratic penalty function as an always feasible, sparse nonlinear least-squares problem subject to box constraints on the decision variables. This formulation is exploited by the proposed fast solution algorithm, which is based on the Gauss-Newton method and bounded-variable least squares (BVLS). Linear time-invariant and linear time-varying model predictive control based on BVLS are special cases of the proposed NMPC framework. The proposed approach and its benefits are demonstrated through a typical numerical example in simulation.