Parallel reduced-order controllers for stochastic linear singularly perturbed discrete systems

Abstract

An approach to the decomposition and approximation of linear quadratic Gaussian control problems for singularly perturbed discrete systems at steady state is presented. The global Kalman filter is decomposed into separate reduced-order local filters through the use of a decoupling transformation. A near-optimal control law is derived by approximating coefficients of the optimal control law. The proposed method allows parallel processing of information and reduces offline and online computational requirements. A real-world example demonstrates the efficiency of the proposed method. >