Fast Model Predictive Control of Input-Affine Systems: Application to the Hindmarsh-Rose Neuron Model

作     者：Alexandra Grancharova Junhong Xie Sorin Olaru 

作者机构：Department of Industrial Automation University of Chemical Technology and Metallurgy Bul. Kl. Ohridski 8 Sofia 1756 Bulgaria Laboratory of Signals and Systems CentraleSupélec-CNRS-Université Paris-Sud Université Paris-Saclay Gif-sur-Yvette Cedex France 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2024年第58卷第28期

页      面：354-359页

主　　题：Predictive control Nonlinear systems Constraints Convex programming Computational complexity 

摘      要：The paper presents a low complexity nonlinear MPC design for the class of constrained input-affine systems. Essentially, it builds on the idea of adding a contractive constraint in the NMPC problem formulation, which would ensure the closed-loop system stability when using a small prediction horizon. In particular, the one-step ahead NMPC problem with contractive constraint is considered and an approach to obtain an efficient online solution of the associated convex quadratically constrained quadratic programming problem is developed. The proposed technique is shown to be effective for embedded convex NMPC of input-affine systems, since it will reduce the computational complexity of the online NMPC and simplify the software and hardware implementation. The methodological developments are illustrated with simulations on the Hindmarsh-Rose neuron model.