Proceedings of SPIE - the International Society for Optical Engineering
Using an Artificial Neural Network (ANN) trained with the Least Mean Square (LMS) algorithm we have designed a robust linear quadratic regulator for a range of plant uncertainty. Since there is a trade-off between performance and robustness in the conventional design techniques, we propose a design technique to provide the best mix of robustness and performance. Our approach is to provide different control strategies for different levels of uncertainty. We describe how to measure these uncertainties. We will compare our multiple strategies results with those of conventional techniques e.g. H∞ control theory. A Lyapunov equation is used to define stability in all cases.
Yoo, K., & Thursby, M. H. (1993). Robust linear quadratic regulation using neural network. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 1919 157-161.