Neuro-based optimal regulator for a class of system with uncertainties

This paper proposes a neuro-based optimal regulator (NBOR) for a class of system with uncertainties. In this paper, we show how the neural network output compensates the control input based on the Riccati equation and how the compensatory solution of the Riccati equation is estimated by the least-squares method. Then, the NBOR is applied to systems with uncertainties in order to illustrate its effectiveness and applicability.