Task-Independent Robotic Uncalibrated Hand-Eye Coordination Based on the Extended State Observer

Abstract

Abstract-This paper proposes a standard method to approach the uncalibrated robotic hand-eye coordination problem that is system configuration-and task-independent. The unknown hand-eye relationship is first modeled as the modeling errors of a dynamic system. An extended state observer is then implemented to estimate summation of the system's modeling error and the system's external disturbances. With the estimation results as the compensation, the system control is accomplished from a nonlinear combination of the system state errors. A universal framework of controller design is provided for decoupled and coupled hand-eye systems of different configurations to execute dynamic tracking task.

Observers and observability for uncertain nonlinear systems: A necessary and sufficient condition

“…We point out that many physical systems can be modeled by system . Examples can be found from robotic systems, DC‐DC converter, flight systems, among many others (see, eg, the work of Xue et al. For the control of system , there is a widely used method that performs two steps .…”

L2Observers for a Class of Nonlinear Systems with Unknown Inputs

“…One way of relaxing that assumption is by using an extended state observer, that is, by appending the exogenous input to the system state. Exogenous input estimation via an extended state observer has been successful in various practical systems, including robotic systems [20], electric drive systems [21], power electronics [22], and avionics [23]. These exogenous inputs could be completely unknown, or partially unknown.…”

Control of uncertain nonlinear systems based on observers and estimators

“…Thus, the uncertainties may then be compensated in real time. Up to now, the idea of ADRC technique has been applied in solving various kinds of engineering problems, e.g., motor control (Feng, Liu, & Huang, 2004;Li & Liu, 2009), flight control (Huang, Xu, Han, & Lam, 2001;Xia, Zhu, Fu, & Wang, 2011), robot control (Su, Ma, Qiu, & Xi, 2004;Talole, Kolhe, & Phadke, 2010), etc. Meanwhile, some progress has also been made in the theoretical analysis of the ADRC (Guo & Zhao, 2011;Xue & Huang, 2011a,b;Yang & Huang, 2009;Zheng, Gao, & Gao, 2007).…”