Dynamic Analysis and Neural-Adaptive Prescribed-Time Control of the FO Memristive Magnetic-Field Electromechanical...

Area of Science:

• Electromechanical Systems
• Nonlinear Dynamics
• Control Theory

Background:

• Memristors offer unique properties for advanced electronic circuits.
• Fractional-order dynamics are crucial for modeling complex dielectric materials.
• Controlling chaotic systems in electromechanical transducers is challenging.

Purpose of the Study:

• To develop a fractional-order model for memristor-based magnetic-field electromechanical transducers.
• To design a neural-adaptive prescribed-time control scheme for chaotic dynamics.
• To address actuator faults and system uncertainties while ensuring precise tracking.

Main Methods:

• Fractional-order modeling of dielectric properties and magnetic flux-electric charge relationship.
• Dynamical analysis using Bifurcation diagrams and sample entropy.
• Neural-adaptive control incorporating a deferred constraint function, type-2 fuzzy wavelet neural network (FWNN), and fractional-order tracking differentiator (TD).

Main Results:

• The fractional-order model accurately characterizes system dynamics.
• The proposed control scheme effectively transforms chaotic oscillations into orderly motions.
• The controller ensures boundedness of all closed-loop system signals and handles faults/uncertainties.

Conclusions:

• The developed fractional-order model and neural-adaptive control scheme are effective for memristor-based electromechanical transducers.
• The approach provides robust and precise tracking control under various conditions.
• Simulations confirm the scheme's effectiveness and robustness.