Design of Syncretic Fuzzy-Neural Control for WWTP

Owing to the possible existence of system failures and packet dropouts in the wastewater treatment process (WWTP), it is difficult to obtain sufficient data, which will result in data shortage. Therefore, it is a challenge to design an effective data-driven controller with the above data shortage issue for WWTP. To solve this problem, a syncretic fuzzy-neural controller (SFNC) was developed and analyzed in this article. First, the knowledge obtained from the operation conditions was made full use by a knowledge reconstruction mechanism to construct the initial condition of SFNC. Then, the proposed SFNC was able to obtain the accurate parameters and compact structure in the initialization phase. Second, a syncretic-form strategy (SFS) was designed to syncretize the knowledge from the fuzzy rules and the data from the operation process to optimize the structure of SFNC. Then, the adaptability of SFNC can be improved to achieve good control performance in the presence of insufficient data. Third, the stability of the developed SFNC was proved by using Lyapunov stability theorem. Then, the stability of SFNC was given to guarantee its successful application. Finally, the proposed controller was tested on the Benchmark Simulation Model No.1 to confirm its effectiveness. The results demonstrated that the proposed SFNC can achieve superior control performance than some other existing controllers.

I. Introduction

The Protection of water resources has always been an important task around the world [1], [2]. To protect and save the water resource, it is necessary to utilize wastewater treatment process (WWTP) to purify the wastewater produced by human activities [3]–[5]. However, due to the possible existence of system failures and packet dropouts, the phenomenon of data inadequate or data loss is frequently encountered in the operation process of WWTP [6]–[8]. Thus, it is difficult for data-driven controllers to obtain the reliable performance [9]–[11] in case of insufficient data.