Hybrid Multivariable Adaptive Model-Matching Controllers for Robotic Manipulators

This paper proposes a hybrid, multivariable, adaptive model-matching scheme for controlling robotic manipulators and demonstrates its advantages over similar discrete schemes. In the hybrid approach, the controller itself is a continuously adjustable one, but the adaptation mechanism uses discrete algorithms. Thus it offers the advantages of less parameterization and complete freedom in the choice of the sampling rate. Furthermore, it assures stable operation because the minimum phase property of the original continuous time plant is retained.