IJCAS

This paper proposes a direct model reference adaptive control method for linear systems with unknown parameters in the presence of input constraints. First, we used the well-known linear quadratic regulator (LQR) technique to develop a modified reference model, which is the optimal model under input constraints. Second, a model reference adaptive controller, which tracked the mod-ified reference model instead of the reference model, was designed to compensate for parametric un-certainties. Using Lyapunov stability theory, we proved that the modified reference model tracking er-ror converges to zero. Simulation results demonstrate the effectiveness of the proposed controller.

Keywords: Adaptive control, input constraints, linear-quadratic regulator, optimal control.