A hybrid adaptive control approach for robust tracking of robotic manipulators: theory and experiment

In this work, a novel hybrid control strategy is proposed for robust trajectory tracking control of robotic systems. The main interest of using hybrid design is to reduce the controller gains so as to reduce control efforts from the single model certainty equivalence principle- based adaptive controllers. For this purpose, we allow the parameter estimate of conventional adaptive control design to be switched into a model that best approximates the plant among a finite set of models. First, we uniformly divide the compact set of unknown parameters into a finite number of smaller compact subsets. Then we construct a finite set of candidate controller for each of these smaller compact subsets. The derivative of the Lyapunov function candidate is employed to identify a controller that closely approximates the plant at each instant of time. The idea of introducing hybrid approach in adaptive control framework is to achieve good transient tracking performance with smaller values of controller gains in the presence of large-scale parametric uncertainties. The proposed method is implemented and evaluated on two 3 degree-of-freedom Phantom premimum (TM) 1.5 telerobotic systems to demonstrate the effectiveness of the theoretical development.