LEARNING CONTROL ALGORITHM FOR NONLINEAR MAPS

A feedback optimal control algorithm is developed for N-dimensional ma ps, which uses learning-based feedback optimal control techniques. The algorithm has two steps: (1) Learn the control of a reference map con taining a stochastic term. (2) Apply the learned control to the labora tory system employing real time feedback. The stochastic component of the learning step is important to provide a close knit family of contr ols to handle laboratory uncertainty and noise. As an example, the for malism is applied to simulated two- and three-dimensional nonlinear la boratory maps in the presence of noise.