Intelligent excitation for adaptive control with unknown parameters in reference input

Model reference adaptive control problem is considered for a class of reference inputs dependent upon the unknown parameters of the system. Due to the uncertainty in the reference input, the tracking objective cannot be achieved without parameter convergence. The common approach of injecting persistent excitation (PE) in the reference input leads to tracking of the excited reference input as opposed to the true one. A new technique, named intelligent excitation, is presented for introducing an excitation signal in the reference input and regulating its amplitude, dependent upon the convergence of the output tracking and parameter errors. Intelligent excitation ensures parameter convergence, similar to conventional PE; it vanishes as the errors converge to zero and reinitiates with every change in the unknown parameters. As a result, the regulated output tracks the desired reference input and not the excited one.