Output Feedback Bilateral Teleoperation with Force Estimation in the Presence of Time Delays

Abstract

This thesis presents a novel bilateral teleoperation algorithm for n degree of freedom nonlinear manipulators connected through time delays. Teleoperation has many practical uses, as there are many benefits that come from being able to operate machines from a distance. For instance, the ability to send a remote controlled robotic vehicle into a hazardous environment can be a great asset in many industrial applications. As well, the field of remote medicine can benefit from these technologies. A highly skilled surgeon could perform surgery on a patient who is located in another city, or even country. Earth to space operations and deep sea exploration are other areas where teleoperation is quite useful.

Central to the approach presented in this work is the use of second order sliding mode unknown input observers for estimating the external forces acting on the manipulators. The use of these observers removes the need for both velocity and force sensors, leading to a lower cost hardware setup that provides all of the advantages of a position-force teleoperation algorithm. Stability results for this new algorithm are presented for several cases. Stability of each of the master and slave sides of the teleoperation system is demonstrated, showing that the master and slave are both stabilized by their respective controllers when the unknown input observers are used for state and force estimation. Additionally, closed loop stability results for the teleoperation system connected to a variety of slave side environments are presented. Delay-independent stability results for a linear spring-damper environment as well as a general finite-gain stable nonlinear environment are given. Delay-dependent stability results for the case where the slave environment is a liner spring-damper and the delays are commensurate are also presented. As well, stability results for the closed loop under the assumption that the human operator is modeled as a finite-gain stable nonlinear environment are given. Following the theoretical presentation, numerical simulations illustrating the algorithm are presented, and experimental results verifying the practical application of the approach are given.