An adaptive learning control for feedback linearizable systems

This paper addresses the problem of designing a state feedback control in order to track a given periodic output reference signal for single input single output feedback and linearizable systems with uncertain maximal relative degree matching unstructured uncertainties (no parameterization available). An adaptive learning control is designed which "learns" with arbitrary precision the unknown reference control input on the basis of its Fourier series expansion, while transient performances are guaranteed during the learning phase