Robust learning control for robotic manipulators with an extension to a class of non-linear systems

A robust learning control (RLC) scheme is developed for robotic manipulator s by a synthesis of learning control and robust control methods. The non-li near learning control strategy is applied directly to the structured system uncertainties that can be separated and expressed as products of unknown b ut repeatable (over iterations) state-independent time functions and known state-dependent functions. The non-linear uncertain terms in robotic dynami cs such as centrifugal, Coriolis and gravitational forces belong to this ca tegory. For unstructured uncertainties which may have non-repeatable factor s but are limited by a set of known bounding functions as the only a priori knowledge, e.g the frictions of a robotic manipulator, robust control stra tegies such as variable structure control strategy can be applied to ensure global asymptotic stability. By virtue of the learning and robust properti es, the new control system can easily fulfil control objectives that are di ? cult for either learning control or variable structure control alone to a chieve satisfactorily. The proposed RLC scheme is further shown to be appli cable to certain classes of non-linear uncertain systems which include robo tic dynamics as a subset. Various important properties concerning learning control, such as the need for a resetting condition and derivative signals, whether using iterative control mode or repetitive control mode, are also made clear in relation to different control objectives and plant dynamics.