A Self-Learning Sliding Mode Controller for Biological Wastewater Treatment System

The control system, as an important part in biological wastewater treatment system (BWTS), is employed to meet the operational goals for reaching required effluent quality; however, the control performance will be degraded under drastic uncertainties and various conditions. In this paper, a self-learning sliding mode controller (SLSMC) is proposed for BWTS without the knowledge of uncertainties. First, a mathematical kernel function (MKF) is established to estimate the bounds of uncertainties, which is used to pursue the optimized control law of SLSMC. Second, a self-learning optimization algorithm is designed to modify the parameters of MKF, which ensure that there are no overestimation parameters of SLSMC. Third, a gain adaptation mechanism, based on MKF and conventional conditions of BWTS, is developed to suppress the chattering and maintain control accuracy simultaneously. Finally, to show the effectiveness of SLSMC, it is applied to BWTS under uncertainties and different conditions in comparison with other existing methods. The results demonstrate that SLSMC performs favorably in terms of both chattering reduction and control accuracy.