Finite-Time Projective Synchronization of Hyperjerk Systems Modeled With Fuzzy Recurrent Neural Networks
Baojie Zhang, Jun Wang, Yuming Feng, Zihui Zhang
Abstract
In this paper, we present a terminal slidingmode control method for the projective synchronization of unmodeled hyperjerk systems subject to parameter perturbation and external disturbances. We leverage fuzzy recurrent neural networks to identify unknown hyperjerk systems. We propose a control law for projective synchronization via the adaptive estimation of the unknown bounds of parameter perturbation and external disturbances. We theoretically prove that the proposed control law is able to achieve chattering-free projective synchronization in finite time. Finally, we elaborate on simulation results to demonstrate the efficacy of the methods.
Topics & Concepts
Leverage (statistics)Control theory (sociology)Synchronization (alternating current)Perturbation (astronomy)Artificial neural networkComputer scienceFuzzy logicMathematicsTopology (electrical circuits)Control (management)Artificial intelligencePhysicsQuantum mechanicsCombinatoricsNonlinear Dynamics and Pattern FormationChaos control and synchronizationNeural dynamics and brain function