Extended state observer-based adaptive dynamic surface prescribed performance control for electro-hydraulic actuators...

Area of Science:

• Robotics
• Control Systems Engineering
• Hydraulic Systems

Background:

• Electro-Hydraulic Actuators (EHAs) face challenges with parametric uncertainties and disturbances, limiting tracking accuracy in existing control methods.
• Current robust adaptive control strategies often result in uniformly ultimately bounded tracking errors, which may not meet stringent performance requirements.

Purpose of the Study:

• To develop an advanced control scheme for EHAs that achieves asymptotic tracking performance and enhances anti-disturbance capabilities.
• To address limitations of existing methods by proposing a control strategy that guarantees precise tracking and stability.

Main Methods:

• An Extended-State-Observer (ESO) based asymptotic control scheme is proposed, integrating prescribed performance control with a backstepping framework.
• A novel ESO is designed for asymptotic estimation of mismatched disturbances and their derivatives without requiring prior bounds.
• Dynamic surface control with an adaptive adjustment mechanism is employed to prevent 'complexity explosion' and ensure closed-loop system stability.

Main Results:

• The proposed control strategy demonstrates the ability to achieve asymptotic tracking errors, surpassing the uniformly ultimately bounded errors of conventional methods.
• The novel ESO effectively estimates unknown disturbances and their derivatives, contributing to improved system robustness.
• Simulations and experimental validation confirm the efficacy and superior performance of the developed control scheme.

Conclusions:

• The ESO-based prescribed performance control strategy offers a significant advancement in robust adaptive control for EHAs.
• The method provides asymptotic tracking performance and robust anti-disturbance capabilities, validated through comprehensive testing.
• This approach enhances the reliability and precision of EHA systems in the presence of uncertainties and disturbances.