Multiple Model Control for Teleoperation in Unknown Environments

This paper proposes a new adaptive control scheme for bilateral teleoperation in unknown environments. Traditional fixed-gain teleoperation methods often sacrifice performance in order to remain stable in the presence of large variations in the environment dynamics. In contrast, the proposed approach adjusts itself to the changes in the environment to maintain its stability without compromising performance. It is assumed that the dynamics of the environment are governed by a model from a finite set of environment models at any given time with Markov chain switching between these models. The first-order generalized pseudo-Bayesian (GPB1) multi-model estimation technique is used to identify the effective model at each time step given the sensory observations. The control action is a weighted sum of mode-based control laws that are designed for each mode of operation. Numerical and experimental studies demonstrate the effectiveness of the proposed method for teleoperation in free motion and in contact with rigid environments.