Predictive Control for Networked Interval Type-2 T–S Fuzzy System via an Event-Triggered Dynamic Output Feedback Scheme

In this paper, the problem of event-triggered dynamic output feedback model predictive control (OFMPC) for nonlinear networked control systems (NCSs) with packet loss and bounded disturbance is studied. Interval type-2 (IT2) Takagi–Sugeno fuzzy model is exploited to represent the nonlinear plant with parameter uncertainties, which can be captured by the lower and upper membership functions. Whether or not the measured output should be released into unreliable network links is determined by the error between the current measured output and the latest event-triggered output. The Bernoulli random binary distribution is used to describe the process of packet loss in NCSs. This paper proposes the following: 1) the synthesis approach, including the design of the parameter-dependent dynamic output feedback controller by solving an online MPC optimization problem, which minimizes the upper bound of an infinite time horizon quadratic objective function respecting input and state constraints; 2) the guarantee of recursive feasibility and quadratical stability of the closed-loop system by applying the quadratic boundedness technique. Moreover, an algorithm of tightening the ellipsoidal bounds of state error is added to improve the control performance. The simulation and comparison studies are performed to demonstrate the usefulness and availability of the presented new techniques.