Abstract
This paper is inspired by the automation of cleaning tasks required inside the endogenous environment. The intention of this paper, is to develop a robust adaptive strategy for force-position control, using robotic manipulators.With this objective, the operational/task space is decoupled into two sub-spaces, and the impedance model for the manipulator is designed using the standard second-order filters. The impedance filter generates the reference commands for the inner loop, which assures bounded position and force tracking. A delay estimation based adaptive sliding mode strategy is proposed for carrying out the tracking objective, and its convergence is proved using the Lyapunov-Razumikhin theorem. The controller uses past data to estimate the uncertainties in the error dynamics and exploits the sliding mode strategy to provide robustness in the closed-loop. This technique circumvents the under/overestimation issues, and linear/nonlinear parametrization requirements in conventional adaptive schemes. Multiple numerical simulations and experiments are performed, and the results point to the validity of the proposed control law in real-world settings.