Flue Gas Oxygen Content Control Based on Interval Type-2 Fuzzy Broad Learning System and PID for MSWI Process

The flue gas oxygen content is critical for ensuring the efficient and stable operation of the municipal solid waste incineration (MSWI) process. This article proposes a novel control method based on an interval type-2 fuzzy broad learning system and proportional-integral-derivative (IT2FBLS-PID). First, a parameter self-learning complex controller based on IT2FBLS is designed, alongside a simple adaptive PID controller. To reduce computational complexity and resource consumption while maintaining control accuracy, a double event-triggering (DET) mechanism is introduced. Additionally, to enhance the controller’s adaptability and robustness, structural self-organization adjustments are made to the IT2FBLS. Finally, a stability analysis of the parameter learning and structural self-organization algorithms of the complex controller is conducted. The superiority of the proposed control method is demonstrated through actual data from a MSWI power plant in Beijing. Note to Practitioners—The MSWI process is characterized by strong nonlinearity and inherent uncertainty. In actual MSWI plants, operators typically rely on accumulated experience for manual control, which can be inconsistent and unstable. Traditional PID control and neural network control (NNC) methods often suffer from poor robustness, while fuzzy NNC struggles to maintain operational stability and converge quickly to the setpoint value during the initial control phase. In contrast, the method proposed leverages the wide applicability of PID and the fast response and uncertainty-handling capabilities of the IT2FBLS controller. It introduces a dual event-driven mechanism for both algorithms, offering a solution that practitioners can apply to similar complex industrial processes.