Adaptive Control For Linearly And Nonlinearly Parameterized Dynamic Uncertainties In Bilateral Teleoperation Systems
Abstract—Existing work concerning adaptive control of uncertain teleoperation systems can only deal with linearly parameterized (LP) dynamic uncertainties. Typical teleoperation system dynamics, however, posses terms with nonlinearly parameterized (NLP) structures. Stribeck friction is an example of NLP terms in robot dynamics. If not compensated for in the control scheme, uncertainties in the NLP dynamic terms may lead to significant tracking errors. In this paper, for a bilateral teleoperation system, adaptive controllers are designed for the master and slave robots with both LP and NLP dynamic uncertainties. Next, these controllers are incorporated into the 4-channel bilateral teleoperation framework. Then, transparency of the overall teleoperation is studied via a Lyapunov function analysis. A simulation study demonstrates the effectiveness of the proposed adaptive scheme.