Non-linear state dynamics: computational methods and manufacturing application

Stochastic optimal control problems are considered that are non-linear in t he state dynamics. but otherwise are an LQGP problem in the control, i.e. t he dynamics an linear in the control vector and the costs are quadratic in the control. In addition the system is randomly perturbed by both continuou s Gaussian (G) and discontinuous Poisson (P) noise. The approach to the sol ution is by way of computational stochastic dynamic programming using a new enhancement with a least squares equivalent LQGP problem in the state to a ccelerate the iterative convergence, without adding to the slate space comp utational complexity since the LQGP coefficient equations are: independent of the state. General Gauss statistics quadratures are developed to numeric ally handle Poisson jump integrals. The methods are illustrated for a multi stage manufacturing system (MMS) with sufficient realism in an uncertain en vironment, together with implementation procedures needed to modify the for mal general theory.