Abstract: In this article, we proposed a fuzzy controller for nonlinear two-time-scale systems with uncertain system parameters. The designed controller is implemented using integral control and event ...
Abstract: This article investigates adaptive dynamic programming (ADP)-based optimal control issue of nonlinear stochastic systems with asymmetric input constraints. The solution starts with ...
Modern power systems face growing stability challenges due to rising network complexity and dynamic operating conditions. Traditional control mechanisms often struggle to effectively mitigate ...
Abstract: This article addresses the periodic event-triggered adaptive output feedback control problem for networked system with unknown nonlinear dynamics. Based on the output-dependent periodic ...
Abstract: Contribution: This research designs a student evaluation framework integrating the fuzzy-logic system that assesses the student’s performances in the soft boundary system for outcome-based ...
Abstract: In nonlinear systems, monitoring control behavior, fault occurrence, and latency factor continue to be major obstacles. Traditional control models frequently handle edge–case situations ...
Abstract: It is difficult to ensure tracking performance even for general nonlinear systems under the condition that only intermittent output signals are used. This article studies the problem of ...
Abstract: This paper introduces a fault-tolerant fuzzy adaptive controller for a specific class of strict-feedback fractional-order nonlinear systems. Systems are susceptible to actuator and sensor ...
Abstract: This article studies the stability and stabilization of delayed Takagi–Sugeno fuzzy systems (TSFSs). First, by introducing the membership functions into the free matrices, a ...
Abstract: This paper proposes a method to synthesize a decentralized polynomial observer for the large-scale nonlinear system. The polynomial Takagi-Sugeno (T-S) fuzzy framework is employed to model ...
Abstract: The parameter adaptation enhanced differential evolution (DE) algorithm has demonstrated promising performance for noiseless optimization. However, its efficiency degrades when confronted ...