Data driven MRAC with parameter convergence

The convergence of parameters in model reference adaptive control (MRAC) requires that a restrictive persistence of excitation (PE) condition be satisfied. A recent data driven approach, concurrent learning, uses past input-output data in conjunction with standard adaptive laws to ensure parameter convergence without needing the PE condition. However, the concurrent learning method assumes the knowledge of the state derivative, which is a limitation. This paper combines a state derivative estimator with concurrent learning to guarantee parameter convergence, thus eliminating the need for both the PE condition and the knowledge of the state derivative. Simulation results are presented to demonstrate the effectiveness of the proposed control method.