Adaptive neural control of the dissolved oxygen concentration in WWTPs based on disturbance observer

Dissolved oxygen (DO) concentration is a key variable in the activated sludge wastewater treatment processes. To solve the DO concentration control problem, a neural adaptive control design technique using a disturbance observer is developed. In the controller design, radial basis function (RBF) neural networks (NNs) are used to approximate the uncertain dynamics of the wastewater treatment process. First, the unknown external disturbance and the NN approximation error are combined into a compounded disturbance, and estimated by a nonlinear disturbance observer. Then, rigorously proved by Lyapunov method, the adaptive NN control based on the disturbance observer can guarantee semiglobal uniform boundedness of the closed-loop system signals and the disturbance estimate error. Finally, simulation studies are performed to demonstrate the effectiveness of the proposed controller. Comparing with the existing controllers, it is shown that satisfactory performances can be achieved using the proposed adaptive control technique.