Model predictive control for nonlinear systems in Takagi-Sugeno's form under round-robin protocol

In this paper, we focus on the investigation of the fuzzy model predictive control (FMPC) problem for nonlinear systems characterized by Takagi-Sugeno (T-S) fuzzy form with hard constraints on the control input and the state. To reduce the data transmission burden and improve the network utilization, a so-called Round-Robin (RR) communication protocol is employed to orchestrate data transmission orders via the shared communication network, which is located from controller nodes to the actuator node. In light of the token-dependent quadratic function approach, a switching system is constructed based on the data transmission period regarding the adopted RR protocol. Then, with respect to system nonlinearities in the case of the T-S fuzzy form, a min-max problem is formulated by resorting to the objective function over the infinite horizon. Our purpose is to find a series of desired controllers in terms of FMPC approach such that the underlying T-S fuzzy system is asymptotically stable. With taking the T-S fuzzy nonlinearities and the RR protocol fully into consideration, sufficient conditions are provided through an online auxiliary optimization problem. Finally, two simulation examples are used to demonstrate the effectiveness and the validity of the proposed methods. (c) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.