Online sequential extreme learning machine based adaptive control for wastewater treatment plant - Cao, Weiwei; Yang, Qinmin (Neurocomputing (Amsterdam), 2020)
Wastewater Treatment Plant (WWTP) is challenging to regulate for its complex chemical and biological characteristics, and its precise mathematical model is usually not accessible due to the limitation of available measurement. Traditional methods highly rely on human intervention and cannot adapt to the varying environment. Meanwhile, adaptive neural network based control strategies are encountered with dilemmas of local minima, slow convergence, huge time consumption, and low efficiency. To overcome such challenges, in this paper, a novel Online Sequential Extreme Learning Machine (OS-ELM) based adaptive control is discussed. In contrast, no a prior human experience or off-line training phase is required. It also has fast training speed thanks to randomly generated parameters of the hidden layer and Moore–Penrose pseudo-inverse. Moreover, the performance of the system is guaranteed even in the presence of time-varying dynamics and uncertainties through a learning mechanism in an online manner. The stability of the closed-loop system is shown strictly and extensive comparison case studies substantiate the feasibility of the scheme.