In　this　paper,　the　fuzzy　model　predictive　control　for　nonlinear　system　represented　by　a　discrete-time　Takagi-Sugeno　model　with　norm-bounded　disturbance　is　studied.　An　output　feedback　algorithm　is　proposed　by　parameterizing　the　infinite　horizon　control　moves　and　estimated　states　into　one　free　control　move,　one　free　estimated　state　followed　by　a　dynamic　output　feedback　law.　Since　the　introduced　free　control　move　and　free　estimated　state　are　decision　variables,　which　bring　more　degrees　of　freedom　for　the　optimization,　larger　feasibility　region　and　better　control　performance　can　be　achieved.　By　properly　designing　the　constraints　in　optimization　problem,　the　recursive　feasibility　is　guaranteed,　and　the　convergence　of　the　closed-loop　system　to　the　neighborhood　of　the　equilibrium　is　guaranteed.　A　numerical　example　is　given　to　illustrate　the　effectiveness　of　the　proposed　algorithm.　IEEE