Interval type-2 fuzzy model predictive control of nonlinear networked control systems

Abstract

In this paper, the problem of fuzzy predictive control of nonlinear networked control systems subject to parameter un- certainties and defective communication links is studied. Stochas- tic variables with Bernoulli random binary distribution are used to represent the defective communication links with packets loss occurring intermittently between the controller and the physical plant. An interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy model is employed to describe the nonlinear plant subject to parameter un- certainties, which can be captured with the lower and upper mem- bership functions. The IT2 fuzzy model and IT2 fuzzy controller are not required to share the same lower and upper membership functions. In order to design the state-feedback fuzzy model pre- dictive controller, an optimization problem which minimizes the upper bound of a quadratic objective function subject to input constraints and packets dropout is formulated and solved at every sampling instant in the finite time horizon. By introducing some slack matrices, less conservative conditions are developed for sys- tem stability analysis. Two examples are given to demonstrate the effectiveness and merits of the proposed new design techniques.