Optimal　control　is　developed　to　guarantee　nonlinear　systems　run　in　an　optimum　operating　state.　However,　since　the　operation　demands　of　systems　are　dynamically　changeable,　it　is　difficult　for　optimal　control　to　obtain　reliable　optimal　solutions　to　achieve　satisfying　operation　performance.　To　overcome　this　problem,　a　knowledge-data　driven　optimal　control　(KDDOC)　for　nonlinear　systems　is　designed　in　this　article.　First,　an　adaptive　initialization　strategy,　using　the　knowledge　from　historical　operation　information　of　nonlinear　systems,　is　employed　to　dynamically　preset　parameters　of　KDDOC.　Then,　the　initial　performance　of　KDDOC　can　be　enhanced　for　nonlinear　systems.　Second,　a　knowledge　guide-based　global　best　selection　mechanism　is　used　to　assist　KDDOC　in　searching　for　the　optimal　solutions　under　different　operation　demands.　Then,　dynamic　optimal　solutions　of　KDDOC　can　be　obtained　to　adapt　to　flexible　changes　in　nonlinear　systems.　Third,　a　knowledge　direct-based　exploitation　mechanism　is　presented　to　accelerate　the　solving　process　of　KDDOC.　Then,　the　demand　response　speed　of　KDDOC　can　be　improved　to　ensure　nonlinear　systems　with　optimal　operation　performance　in　different　states.　Finally,　the　performance　of　KDDOC　is　validated　on　a　simulation　and　a　practical　process.　Several　experimental　results　illustrate　the　effectiveness　of　the　proposed　optimal　control　for　nonlinear　systems.