Improved model-free adaptive predictive control method for direct data-driven control of a wastewater treatment process with high performance. (February 2022) - Zhang, Shuai; Zhou, Ping; Xie, Yongfang; Chai, Tianyou
Abstract: In this paper, an improved model-free adaptive predictive control (IMFAPC) method with controller parameters sensitivity discrimination and optimization is proposed, to solve the problem that using conventional methods is difficult to effectively control the nitrate concentration and dissolved oxygen concentration of wastewater treatment process (WWTP). First, aiming at the nonstationary dynamic characteristics of WWTP and the limitation of control accuracy of conventional MFAPC method in the nonlinear dynamic system control, a novel improved MFAPC method is proposed considering the effect of the WWTP output tracking error variation on the control of nitrate concentration and dissolved oxygen concentration. Meanwhile, in order to obtain the optimum control performance, the sensitivity discrimination and optimization for the multi-parameter set of the proposed IMFAPC method are performed by combining the Monte Carlo experiment based multi-parameter sensitivity analysis and the hybrid intelligent optimization. Then, the stability of the proposed IMFAPC scheme is analyzed and proved by the BIBO method. Finally, various data experiments are carried out to verify the proposed method. The experimental results demonstrate that the proposed method can control the nitrate nitrogen and dissolved oxygen concentration rapidly and accurately under different operating conditions. Highlights: An improved model free adaptive predictive control method for WWTP is proposed. System tracking error variation is adopted to enhance the controller performance. Optimal controller parameter values are achieved by the MPSA and PSO-GA method. BIBO stability of the proposed IMFAPC scheme is proved. Data experiments verify the superiority of the proposed method.