In this paper, we present an off-line approach for robust constrained MPC synthesis that gives an explicit control law using Linear Matrix Inequalities (LMIs). This off-line approach can address a broad class of model uncertainty descriptions with guaranteed robust stability of the closed-loop system and substantial reduction of the on-line MPC computation.
In this paper, we develop a finite horizon model predictive control algorithm which is robust to model uncertainties. A moving average system matrix is constructed to capture model uncertainties and facilitate future output predictions. The paper is focused on step tracking contr
In this paper, a robust model predictive control (MPC) algorithm is addressed for nonlinear uncertain systems in presence of the control input constraint. For achieving this goal, firstly, the additive and polytopic uncertainties are formulated in the nonlinear uncertain systems.
and Applied Analysis 3 The input and output constraints are satisfied if there exists symmetric matrixX and Y such that [ X ∗ Y T Q ] ≥ 0 with X rr ≤ u 2 r,max, r = 1, 2, . . . , m,