Efficient Nonlinear Model Predictive Control via quasi-LPV representation

Nonlinear Model Predictive Control often suffers from excessive computational complexity, which becomes critical when fast plants are to be controlled. This papers presents an approach to NMPC that exploits the quasi-LPV framework. For quasi-LPV systems, the scheduling variables are determined by the state variables and/or inputs. By calculating an estimate of the state variables during prediction, the prediction model can be adapted to the estimated state evolution in each step. Stability of the proposed algorithm is enforced by the offline solution of an optimization problem with Linear Matrix Inequality (LMI) constraints. Furthermore, an iterative approach is presented with which the NMPC optimization problem can be handled by solving a series of Quadratic Programs (QP) or Second Order Cone Programs (SOCP) in each time step, which leads to computational efficiency. The algorithm is tested in simulation to highlight convergence of the prediction and stability of the closed-loop under contraints.