Adaptive-Robust Control of Euler-Lagrange Systems With Linearly Parametrizable Uncertainty Bound

This brief proposes a new adaptive-robust control (ARC) architecture for a class of uncertain Euler- Lagrange (EL) systems where the upper bound of the uncertainty satisfies linear in the parameters structure. Conventional ARC strategies either require structural knowledge of the system or presume that the overall uncertainties or its time derivative are norm bounded by a constant. Due to the unmodeled dynamics and modeling imperfection, true structural knowledge of the system is not always available. Furthermore, for the class of systems under consideration, prior assumption, regarding the uncertainties (or its time derivative) being upper bounded by a constant, puts a restriction on the states beforehand. Conventional ARC laws invite overestimation-underestimation problem of switching gain. Toward this front, adaptive switching-gain-based robust control (ASRC) is proposed, which alleviates the overestimation-underestimation problem of switching gain. Moreover, ASRC avoids any presumption of constant upper bound on the overall uncertainties and can negotiate uncertainties regardless of being linear or nonlinear in parameters. Experimental results of ASRC using a wheeled mobile robot note improved control performance in comparison with the adaptive sliding mode control.