A stable adaptive implementation of the internal model principle

The authors provide a novel adaptive implementation of the internal model principle for linear time-invariant systems, which allows for rejection of unknown deterministically modeled disturbances. The minimal representation of the system model is used for parameter estimation, and the global convergence and stability of the algorithm are investigated with unmodeled dynamics. With proper modification of parameter estimation algorithm, the authors present global convergence and stability for the algorithm without the requirement of persistent excitation condition. Some simulation results are given to support the analysis