RMPC for constrained nonlinear systems based on interval arithmetic
LIU Gang
QIN Wei-wei
LIU Jie-yu
WANG Li-xin
Abstract：Based on the interval analysis, a robust nonlinear model predictive control (RNMPC) strategy with extended domain of attraction and reduced computation load is developed for a class of input-constrained and state-bounded uncertain nonlinear systems. Firstly, based on set theory, an effective and low conservatism algorithm for the robust one-step set of nonlinear systems is proposed with interval algorithm and interval extension of function in interval arithmetic. Secondly, an overlapped robust control-invariant set sequence around equilibrium points is calculated. And then, the robust multi-step sets of the robust control-invariant set sequence are calculated and used to design the robust nonlinear MPC based on one-step online optimization. By employing the one-step optimization, this algorithm extends the domain of attraction. Furthermore, the optimization function is simplex and the number of variable is reduced. Therefore, this algorithm reduces the on-line computation load of nonlinear optimization. Finally, numerical simulation results validate the proposed method.
Keywords：nonlinear model predictive controlrobust one step setinterval arithmeticrobust invariant set
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 735-740 )