Adaptive Modular Geometric Control of Robotic Manipulators

TL;DR

This paper introduces an adaptive modular geometric control framework for robotic manipulators that improves tracking accuracy and robustness to uncertainties by decomposing dynamics into modules and incorporating geometric adaptation laws.

Contribution

It presents a novel modular control approach with adaptive laws that ensure physically consistent parameter estimates and stability, outperforming existing methods.

Findings

Reduces RMS position error by at least 12.2% compared to state-of-the-art controllers.

Demonstrates strong compensation for parametric uncertainties.

Ensures exponential stability in the nominal case.

Abstract

This paper proposes an adaptive modular geometric control framework for robotic manipulators. The proposed methodology decomposes the overall manipulator dynamics into individual modules, enabling the design of local geometric control laws at the module level. To address parametric uncertainties, geometric adaptation law is incorporated into the control structure, requiring only a single adaptation gain for the entire system while ensuring physically consistent and drift-free parameter estimates. Exponential stability of the proposed controller is established in the nominal case. Numerical simulations on a complex redundant robotic manipulator are conducted to evaluate the proposed approach against existing modular and geometric control methods. The results show that the proposed method reduces the RMS position error by at least 12.2% compared with state-of-the-art controllers under almost the same control effort. In addition, the adaptive extension demonstrates strong capability in compensating for parametric uncertainties and preserving high tracking performance.