Dynamic learning control design for interval type‐2 fuzzy singularly perturbed systems: A component‐based event‐triggering protocol

• Published in: International journal of robust and nonlinear control Vol. 32; no. 5; pp. 2518 - 2535
• Main Authors: Yang, Yekai, Niu, Yugang, Reza Karimi, Hamid
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 25.03.2022
• Subjects: Algorithms; component‐based dynamic event‐triggering protocol; Controllers; Design optimization; Domains; dynamic learning PSO algorithm; Fuzzy control; Fuzzy systems; interval type‐2 (IT2) fuzzy system; iterative optimization; Machine learning; singularly perturbed systems (SPSs); Sliding mode control
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.5661

In this work, the sliding mode control (SMC) problem is addressed for the discrete‐time interval type‐2 fuzzy singularly perturbed systems. A component‐based dynamic event‐triggering scheme is first proposed to determine the transmission of each measurement component according to the prespecified triggering condition, under which each sensor node will transmit independently its signal to the controller. Meanwhile, the SMC approach is used to design an effective interval‐type‐2 fuzzy controller by only utilizing the transmitted component signals, and the ε‐independent conditions are developed to attain the stability of the closed‐loop system and the reachability of the sliding domain. In addition, a framework of the optimization control design is established, where the learning‐based iterative optimization algorithm is proposed via reducing the convergence domain around the sliding surface. Finally, the proposed SMC scheme is verified via the simulation results.