A Cascade Active Disturbance Rejection Control Method Based on SPSA Online Optimization for SCR Denitration System

Due to the characteristics of large inertia, significant lag, and model uncertainty in the Selective Catalytic Reduction (SCR) denitrification system, traditional control schemes encounter challenges in achieving precise control. To tackle this issue, this paper proposes an IMC cascade self-adaptive disturbance rejection controller based on SPSA online parameter optimization and applies it to the SCR denitrification control system. This approach effectively addresses the challenges of precise setpoint tracking, disturbance suppression due to model variations, and difficulties in controlling significant lag. The introduction of the SPSA algorithm allows the controller to update optimal parameters in real-time as the operating conditions change over time. Experimental results from setpoint tracking and disturbance rejection demonstrate that the proposed approach outperforms traditional PID control and linear self-adaptive disturbance rejection control.