A multilayer recurrent neural network is proposed for synthesizing linear-quadratic optimal control systems by means of solving algebraic matrix Riccati equations in real time. The proposed recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic control systems in real time. The operating characteristics of the recurrent neural network and closed-loop control systems are also demonstrated through two illustrative examples.
|Number of pages||6|
|Publication status||Published - 1994|
|Event||Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA|
Duration: 27 Jun 1994 → 29 Jun 1994
|Conference||Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)|
|City||Orlando, FL, USA|
|Period||27/06/94 → 29/06/94|