Efficient Model-Predictive Control for Nonlinear Systems in Interval Type-2 T-S Fuzzy Form Under Round-Robin Protocol

Abstract

This article is concerned with the efficient model-predictive control (EMPC) problem for interval type-2 Takagi–Sugeno (IT2 T-S) fuzzy systems with hard constraints. By applying the round-robin (RR) protocol, all controller nodes are activated in a pregiven order such that the occurrence of network congestion or data collisions can be effectively reduced. The aim of the proposed problem is to design a fuzzy controller in the framework of EMPC so as to obtain a good balance among the computation burden, the initial feasible region, and the control performance. With respect to the RR protocol and IT2 T-S fuzzy nonlinearities, a unified representation is modeled for the underlying system, and then, an augmentation state comprising of the system state, the token-dependent perturbation, and the previous input under the RR protocol is put forward; correspondingly, objective functions are constructed for the controller design. Subsequently, by using the min–max strategy, some token-dependent optimizations are established to facilitate the formation of the EMPC algorithm, where the feedback gain is designed offline, and the perturbation is calculated by solving an online optimization dependent of the token. Moreover, with the help of the matrix partition technique, the feasibility of the proposed EMPC algorithm and the stability of the underlying IT2 T-S fuzzy system are rigidly guaranteed. Finally, two numerical examples are utilized to illustrate the validity of the proposed EMPC strategy.