Finite　control　set　model　predictive　control　(FCS-MPC)　has　recently　been　applied　to　solve　the　real-time　measurement　problems　of　DC-Microgrid　(DC　MG).　The　details　of　DC　MG　stochastic　modeling　have　not　been　presented　through　the　discretization　of　DC/DC　converters　that　approximate　the　DC　MG　stochastic　model.　Additionally,　a　detailed　description　of　the　simulations　and　real-time　verifications　is　not　included.　In　this　paper,　we　propose　an　improved　FCS-MPC　that　provides　details　stochastic　model　of　DC　MG　via　validations.　The　proposed　approach　utilizes　the　linear　Kalman　filtering　algorithm　(LKFA)　via　the　DC　MG　stochastic　average　dynamic　equation　model.　The　model　has　been　approximated　using　LKFA　and　the　DC　MG　stochastic　model.　The　stochastic　FCS-MPC　handles　the　real-time　information　required　to　achieve　the　proposed　approach.　A　highly　effective　algorithm　is　presented　that　allows　a　sample　time　to　be　executed　for　the　operation　of　the　DC　MGs　in　the　second　prediction　model.　A　DC　grid　estimated　voltage　reference　has　been　developed　using　LKFA,　state　feedback　regulation,　and　integral　action.　By　using　the　reference,　DC　grid　voltage　estimation　and　currents　are　regulated　via　a　weighted　expected　cost　function,　and　provide　equal　power　sharing　capability.　Improved　FCS-MPC　effectiveness　was　evaluated　through　Simulink　simulation　and　real-time　validation.