Random Multiaccess Protocol‐Based Model Predictive Control for Nonlinear Systems in Takagi‐Sugeno's Form

This paper addresses the problem of fuzzy model predictive control (FMPC) for a class of nonlinear systems represented by Takagi‐Sugeno (T‐S) fuzzy models, subject to both hard constraints on control inputs and states, as well as communication network limitations. To mitigate data collisions and improve the reliability of data transmission, a random multiaccess protocol (RMP) is integrated into the FMPC framework, resulting in the proposed RMP‐based FMPC approach. Specifically, a unified representation is developed to model the interplay between T‐S fuzzy nonlinearities and the RMP for nonlinear systems with hard constraints. Utilizing a scheduling signal‐dependent fuzzy quadratic function approach, a “min‐max” problem is formulated by resorting to the objective function over the infinite horizon. The primary objective is to design a series of controllers using the FMPC approach, ensuring that the T‐S fuzzy system remains mean‐square stable under the prescribed constraints. By incorporating both the nonlinearities of the T‐S fuzzy system and the dynamics of the RMP, sufficient stability conditions are derived via an auxiliary online optimization problem. Finally, the efficacy and applicability of the proposed approach are validated through two simulation examples, which demonstrate the robustness and effectiveness of the RMP‐based FMPC strategy in handling both the network constraints and the system's nonlinear characteristics.