A passivity-based control strategy for nonlinear bilateral teleoperation employing estimated external forces

Abstract

Teleoperation systems have been presented to handle objects in environments in which the presence of operators are impracticable, unsafe or less effective. In this paper, a passive control strategy employing the new outputs to state synchronization of the master and slave robots is developed to attain position coordination during contact tasks. The proposed control scheme includes position signals plus force signals. However, force measurement in such applications is a major limitation. Therefore, a modified force estimation algorithm is proposed to predict external forces applied on the master and slave robots. The closed-loop bilateral teleoperation system is investigated by employing Lyapunov stability criteria. Finally, the experimental results demonstrate the efficiency of the proposed control scheme. It is observed that, in the presence of the estimated external forces in the control scheme, the slave robot follows the master position in both free and contact motion, and force reflecting occurs properly as well. Moreover, it is verified that the external forces are estimated appropriately through the proposed force estimation algorithm.

Position synchronization for an uncertain teleoperation system with time delays using L1 theory

“…Z e : y z (t) = D z ()y s (t); (7) where D z () = k z () b z () denote the vector of the sti ness and damping coe cient of the environment. Upon substituting the output Eq.…”

“…Adaptive control methods have also been widely used in teleoperation systems [5]. They have been applied both in the classical form, such as model-reference adaptive control [6], and in more modern forms in combination with other control approaches, such as passivity-based [7] and model predictive [8] control.…”

L1 impedance control for bilateral teleoperation containing model uncertainty

“…Position control, on one hand, basically aims at synchronizing the position of master and slave robots. Sliding-mode control (Ji et al, 2020), adaptive control (Chan et al, 2014), passivity-based control (Namnabat et al, 2020), predictive control (Nikpour et al, 2021; Yazdankhoo et al, 2019), robust H ∞ control (Gormus et al, 2022), Lyapunov–Krasovskii-based control (Bavili et al, 2021), and also relatively more modern methods such as neural networks (Kebria et al, 2020) and fuzzy logic (Li et al, 2013) are among the most well-known approaches to controlling the position of the slave side.…”