Robust Model Free Adaptive Predictive Control for Wastewater Treatment Process With Packet Dropouts

Area of Science:

• Environmental Engineering
• Control Systems Engineering
• Process Automation

Background:

• Wastewater treatment processes (WWTP) are susceptible to performance degradation due to external disturbances and data packet dropouts.
• Existing control strategies often struggle to maintain optimal performance under such challenging conditions.

Purpose of the Study:

• To propose a robust model-free adaptive predictive control (RMFAPC) strategy with a packet dropout compensation mechanism (PDCM) for enhanced WWTP performance.
• To ensure robust control and stability in WWTP despite external disturbances and communication uncertainties.

Main Methods:

• Dynamic linearization approach (DLA) using perturbed process data to approximate system dynamics.
• Predictive control strategy combined with an extended state observer (ESO) for disturbance attenuation.
• Packet dropout compensation mechanism (PDCM) specifically designed to handle data loss.

Main Results:

• RMFAPC significantly reduced integrated absolute error (IAE) by 0.0223 and 0.1976 in simulation scenarios.
• Demonstrated superior robustness against disturbances compared to the standard model-free adaptive predictive control (MFAPC).
• Ablation experiments confirmed the effectiveness of the PDCM in mitigating issues caused by packet dropouts.

Conclusions:

• The proposed RMFAPC strategy offers a robust and effective solution for controlling WWTP under disturbances and packet dropouts.
• The integration of DLA, ESO, and PDCM ensures reliable and stable process operation.
• Simulation results validate the significant performance improvements and robustness of RMFAPC.