Stochastic Control of Two-Level Nonlinear Large-Scale Systems; Part I-Interaction Prediction Principle

In this paper a new two-level computational algorithm is proposed for stochastic control of nonlinear large-scale systems. This two-level algorithm is composed of a two-level controller that uses Interaction Prediction Principle (Model coordination) to solve the optimization problems and a two-level computational form of extended Kalman filter for the estimation problem. The two-level optimizer uses a new coordination strategy, which is based on the gradient of interaction errors. Based on this idea, the new method can be applicable to a general optimization problem whilst the classical model coordination approach works only in special cases. In addition, the proposed approach improves the convergence rate of the solution and produces savings in computational time of the algorithm. The significance and applicability of the theoretical developments of this paper are also shown by a numerical example.