Neural net-based adaptive linear quadratic control
A new indirect adaptive control scheme based on recurrent neural networks is proposed. The certainty equivalence principle is used to combine an adaptive law with a control structure derived from the linear quadratic (LQ) control problem. The proposed approach includes two sets of neural networks each with two feedback connected layers to solve for two types of algebraic matrix Riccati equations. One is for the Kalman filter and the other one is for the LQ controller design. The gradient algorithm is used as an adaptive law for identifying plant parameters and is used as the update rule for neural networks.