4.6 Article

Neuroadaptive Optimal Fixed-Time Synchronization and Its Circuit Realization for Unidirectionally Coupled FO Self-Sustained Electromechanical Seismograph Systems

Journal

IEEE TRANSACTIONS ON CYBERNETICS
Volume 53, Issue 4, Pages 2454-2466

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3121069

Keywords

Synchronization; Backstepping; Oscillators; Nonlinear dynamical systems; Analog circuits; Capacitors; Detectors; Chaos synchronization; circuit implementation; interval type-2 fuzzy neural network (IT2FNN); optimal fixed-time control; unidirectionally coupled fractional-order (FO) self-sustained electromechanical seismograph systems

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This article investigates the neuroadaptive optimal fixed-time synchronization and its circuit realization along with dynamical analysis for unidirectionally coupled fractional-order self-sustained electromechanical seismograph systems. The proposed scheme, which integrates HTTD, IT2FNN, and PPF, is proven to be effective in the synchronization of the coupled system.
This article investigates the neuroadaptive optimal fixed-time synchronization and its circuit realization along with dynamical analysis for unidirectionally coupled fractional-order (FO) self-sustained electromechanical seismograph systems under subharmonic and superharmonic oscillations. The synchronization model of the coupled FO seismograph system is established based on drive and response seismic detectors. The dynamical analysis reveals this coupled system generating transient chaos and homoclinic/heteroclinic oscillations. The test results of the constructed equivalent analog circuit further testify its complex nonlinear dynamics. Then, a neuroadaptive optimal fixed-time synchronization controller integrated with the FO hyperbolic tangent tracking differentiator (HTTD), interval type-2 fuzzy neural network (IT2FNN) with transformation, and prescribed performance function (PPF) together with the constraint condition is developed in the backstepping recursive design. Furthermore, it is proved that all signals of this closed-loop system are bounded, and the tracking errors fall into a trap of the prescribed constraint along with the minimized cost function. Extensive studies confirm the effectiveness of the proposed scheme.

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