Several difficulties are still encountered in the direct use of a nonlinear model in the area of model based control. A time series rule based model is employed in this work to perform nonlinear control in cases where linear approaches have failed. The rule based model is basically comprised of a set of rules which are related to the time series of input and output data. The proposed control approach filtered out the high-frequency disturbances using possibility theory. An on-line identification phase is required if persistent changes of some parameters frequently occur. The identification algorithm maximizes the membership of the disturbance parameter in the immediate past. The control objective minimizes the square errors of the output and set point in a time horizon projected into the immediate future. Physical examples are simulated to demonstrate the implementation of this approach.