Dynamic event-triggered adaptive control for electro-hydraulic servomechanism.

Area of Science:

• Control Systems Engineering
• Robotics
• Artificial Intelligence

Background:

• Electro-hydraulic servomechanisms face challenges like limited communication, unmeasurable states, and model uncertainties.
• Existing control methods struggle to balance performance with communication efficiency.

Purpose of the Study:

• To develop an adaptive robust control algorithm for electro-hydraulic servomechanisms.
• To address restricted data communication, unmeasurable states, and modeling uncertainties.
• To improve control accuracy and reduce communication load.

Main Methods:

• A novel dynamic event-triggered adaptive robust control algorithm integrating a Pi-sigma fuzzy neural network (PSFNN) and a finite-time extended state observer (FTESO).
• The PSFNN-enhanced FTESO estimates unmeasurable states and uncertainties.
• A dynamic event-triggering mechanism is designed to minimize data transmission based on observed state deviations and virtual tracking errors.
• A finite-time backstepping control architecture is employed.

Main Results:

• The proposed algorithm effectively estimates unmeasurable states and modeling uncertainties.
• The dynamic event-triggering mechanism significantly reduces data transmission requirements.
• The adaptive robust control law ensures rapid convergence of position tracking errors.
• Simulations confirm superior performance compared to existing methods.

Conclusions:

• The developed control algorithm offers a robust and efficient solution for electro-hydraulic servomechanisms.
• It successfully mitigates challenges related to communication constraints and system uncertainties.
• The integration of FTESO and PSFNN provides accurate state estimation and adaptive control.