Dynamic Output Feedback Predictive Control for Nonlinear Systems Represented by a Takagi-Sugeno Model
This paper addresses the output feedback predictive control for a Takagi-Sugeno (T-S) fuzzy system with bounded noise. The controller optimizes an infinite-horizon objective function respecting the input and state constraints. The control law is parameterized as a dynamic output feedback that is dependent on the membership functions, and the closed-loop stability is specified by the notion of quadratic boundedness. Online algorithms that guarantee the recursive feasibility of the convex optimization problem and the convergence of the augmented state to a neighborhood of the equilibrium point are proposed in this paper. A numerical example is given to illustrate the effectiveness of the proposed output feedback controllers.
dynamic output feedback linear matrix inequality (lmi) model predictive control (mpc) quadratic boundedness (qb) takagi-sugeno (t-s) fuzzy model