This　paper　presents　an　application　of　learning　automaton　(LA)　for　nonlinear　system　control.　The　proposed　control　strategy　utilizes　a　learning　automaton　in　which　the　reinforcement　scheme　is　based　on　the　Pursuit　Algorithm　interacting　with　a　nonstationary　environment.　Modulated　by　an　adaptive　mechanism,　the　LA　selects,　at　each　control　period,　a　local　optimal　action,　which　serves　as　input　to　the　controlled　system.　During　the　control　procedure,　the　system　output　value　takes　into　account　the　changes　occurring　inside　the　system　and　provides　reward/penalty　responses　to　the　learning　automaton.　©　2000　Elsevier　Science　Ltd.　All　rights　reserved.