The　complex　dynamics　and　strong　nonlinearity　characteristics　will　bring　obstacle　for　the　accurate　and　stable　tracking　control　performance　of　unknown　nonlinear　systems.　To　solve　this　problem,　a　predictor-based　self-organizing　control　(PSOC)　method　is　designed　to　improve　the　control　performance　in　this　article.　The　merits　of　PSOC　are　three　aspects.　First,　a　self-organizing　fuzzy　neural　network　(SOFNN)　is　designed　to　adaptively　approximate　the　strong　nonlinearity　of　the　system.　Then,　the　self-organizing　strategy,　based　on　rule　similarity　and　validity,　is　studied　to　adjust　the　number　of　fuzzy　rules　in　SOFNN　to　improve　the　approximate　accuracy.　Second,　a　dynamic　surface　control　scheme　is　designed　to　describe　the　system　dynamics.　Then,　the　dynamic　surface　control　law　is　obtained　to　achieve　the　stable　control.　Third,　a　state　predictor　is　developed　to　predict　the　state　variable　accurately.　Then,　the　prediction　error　is　utilized　to　design　the　adaptive　law　of　PSOC　to　reduce　the　variation　range　of　tracking　control　errors　and　further　improve　the　control　accuracy.　Finally,　the　numerical　simulations　and　a　detailed　comparison　study　are　given　to　evaluate　the　efficiency　of　the　proposed　PSOC　method.