Adaptive event-triggered efficient output feedback model predictive control for networked interval type-2 T-S fuzzy system with data loss

This article is concerned with the adaptive event-triggered (AET) efficient output feedback model predictive control (EOFMPC) for a set of nonlinear networked interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy control systems with data loss. For the reduction in data transmission burden and required control performance, the AET scheme and a novel dynamic triggering law are introduced jointly in this article. The efficient model predictive control approach as in Kouvaritakis et al. (IEEE Trans Automat Contr. 2000;45(8):1545-1549.), which transfers the main part of online optimization work to offline stage by incorporating an extra perturbation into the fixed feedback law, is extended to networked control systems to reduce their online computational burden. This article proposes the following: (1) an unified closed-loop model representation of data loss process and IT2 T-S fuzzy nonlinearities is established under the framework of EOFMPC, which explicitly considers the AET condition; (2) a new design method for the state observer gain is presented based on an ellipsoidal invariant set, so that a smaller bound of estimation error can be obtained through an extra optimization problem; (3) some optimization problems are formulated to design the proposed controllers, and the corresponding EOFMPC algorithm is summarized; (4) by means of the extra matrix partition technique, the online optimization problem subject to one ellipsoidal constraint is transformed into a novel convex form to solve the uncertainty of the estimation error. A simulation example and a few comparative simulation experiments illustrate the effectiveness and availability of the aforementioned approach.