Theory and Application on Adaptive-Robust Control of Euler-Lagrange Systems with Linearly Parametrizable Uncertainty Bound
For control engineers dealing with uncertain Euler-Lagrange systems, this work offers a practical solution to relax restrictive assumptions on uncertainty bounds, though it is an incremental improvement over existing adaptive-robust control methods.
This paper proposes an Adaptive Switching-gain based Robust Control (ASRC) for Euler-Lagrange systems with linearly parametrizable uncertainty bounds, eliminating the need for constant upper bounds on uncertainties and avoiding overestimation-underestimation of switching gain. Experiments on a wheeled mobile robot show improved control performance over adaptive sliding mode control.
This work 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 parameters (LIP) 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 unmodelled dynamics and modelling imperfection, true structural knowledge of the system is not always available. Further, 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 states beforehand. Conventional ARC laws invite overestimation-underestimation problem of switching gain. Towards 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 notes improved control performance in comparison to adaptive sliding mode control.