Adaptive output feedback control for robot manipulators using lyapunov-based switching

In the face of large scale parametric uncertainties, the single model (SM)-based classical adaptive control approach demands high observer, controller and adaptation gains in order to achieve good tracking performance. The well known problem of having high-gain based design is that it amplifies the input and output disturbance as well as excites hidden unmodeled dynamics causing poor tracking performance. In this paper, a multi-model based adaptive design is proposed to reduce the level of parametric uncertainty in order to reduce the observer-controller gains. The key idea of this approach is to allow the parameter estimate of the SM-based classical adaptive control design to be reset into a model that best approximates the plant among a finite set of candidate models. For this purpose, we uniformly distribute the compact set of unknown parameters into a finite number of smaller compact subsets. Then we design a family of candidate controllers for each of these smaller compact subsets. The derivative of the Lyapunov function candidate is used as a resetting criterion to identify a candidate model that closely approximates the plant at each instant of time. The proposed method is evaluated on a 2-DOF robot manipulator to demonstrate the effectiveness of the theoretical development.