A fast dissipative robust nonlinear model predictive control procedure via quasi?linear parameter varying embedding and parameter extrapolation

In this article, a robust model predictive control (MPC) procedure for quasi-linear parameter varying (qLPV) systems is proposed. The novelty resides in considering recursive extrapolation algorithm to estimate the values of scheduling parameters along prediction horizon , which fastens sluggish performances achieved with qLPV MPCs from literature. bounds on estimation errors through are taken into account by MPC, solves an online min-max problem: first, constrained convex program resolved order determine worst-case bound cost function and, subsequently, second quadratic solved minimize respect sequence vector. Since error usually much smaller than actual parameter, conservativeness solution quite reduced. Recursive feasibility and stability proposed demonstrated dissipativity arguments given form linear matrix inequality remedy, determines zone attraction input-to-state ensured. nonlinear temperature regulation problem flat solar collector considered as case study. Using realistic simulation benchmark, technique compared other LPV MPC algorithms literature, proving itself efficient while achieving good performances.