Abstract: A novel internal model control strategy is presented based on the improved extreme learning machine. Specifically, the improved extreme learning machine is used to estimate the internal model, and internal model is expanded with first-order term by Taylor series to calculate the controller for the control system indirectly, which avoids calculating the inverse for internal model directly. Moreover, the system stability and error are also analyzed for this control system with model errors and disturbance. The proposed control strategy is simulated to control in the continuous stirred tank reactor. The results indicate that the proposed control strategy has an excellent system performance to control the consistence with a strong anti-noise performance. Moreover, the system based on the improved extreme learning machine has a better performance than that based on extreme learning machine.