Direct adaptive neural network control for wastewater treatment process

In this paper, a direct adaptive neural network control (DANNC) method is developed to deal with the multi-variable (dissolved oxygen concentration and nitrate concentration) tracking control problem in wastewater treatment processes (WWTPs), which avoids the perplex issue of establishing the plant model of WWTP and has the excellent adaptive ability. The DANNC system is composed of neural controller and compensation controller. The neural controller is employed to approximate an ideal control law, and the compensation controller is designed to offset the network approximation error. The controller parameters' adaptive laws are deduced by the Lyapunov theorem. Simulation results, based on the international benchmark simulation model No.1 (BSM1), show that the control accuracy and dynamic performance of the DANNC method are improved nicely.

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