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Fuzzy Decentralized Controller Design with Internet of Things for Urban Trains

Due to the dynamic structure and physical constraints that exist in controlling the urban train system, the physical parameters of the urban train model are constantly changing, and therefore a static controller cannot fully control all of the control objectives to be defined. In this
 paper, a new method of using a PID controller based on fuzzy control with considering the uncertainty of the weight of the dynamic model of the system is presented for managing the dynamical model of the urban train. The primary objective of this research work is to provide a structure that
 simultaneously, in addition to guaranteeing the stability of the Kharitonov polynomials, the dynamic model of the sample system also automatically adjusts and controls the proposed controller control parameters. In order to introduce model uncertainties into the controller design calculations,
 Kharitonov polynomials related to the open loop conversion function are first extracted. Then the feedback loop is constructed with a parallel PID controller, and by rewriting the closed-loop conversion function equations, we obtain the set of all closed-loop system transformation functions,
 in which the model's indices are also considered. Subsequently, by examining the stability ranges of all closed-loop functions, the values obtained for the proportional, integral, and derivative parameters of the PID controller are obtained for its robust performance. Finally, a fuzzy-based
 structure is proposed for intelligent operation and online adjustment of proposed PID controller coefficients. Further, this research work outlines twolevel Internet of Things (IoT) sensor network that compliments the proposed mathematical model by providing real-time model parameters using
 the sensory information.