Aiming at the characteristics of large inertia, large hysteresis and unstable parameters of the air and flue gas system of coalfired generator sets, a sliding mode active disturbance rejection control strategy based on generator sets is proposed. The fuzzy radial basis function (RBF) algorithm is selected to identify the model, the gradient descent method is used to coarse-tune the neural network weights, and the genetic algorithm is used to fine-tune the neural network weights. The internal and external disturbances of the system are estimated by the extended state observer, the nonlinear state error feedback rate is designed and sliding mode control strategies are designed to overcome the inertia, hysteresis and disturbances of the system, and Lyapunov functions are designed to evaluate the stability of the control system. The simulation results show that the designed control strategy reaches the set value in 38 s with no overshoot compared with the cascaded proportion integration differentiation (PID) control, sliding mode control and self-rejecting control in the case of model mismatch. When a 20% backward step disturbance is applied to the system, the system regulation time is 39. 5 s with 3. 4% overshoot. The regulation time in the case of model mismatch is 43. 2 s with no overshoot. When the system applies 20% reverse step disturbance, the system regulation time is 46. 4 s with 3. 87% overshoot. The engineering application results show that the primary air volume control deviation is within ±10 000 m 3 / h, which is 21% lower than the fluctuation range of the cascaded PID control, and the anti-disturbance capability and robustness of the system are improved.