Neural-Adaptive Control of Waste-to-Energy Steam Generators

Steam generators offer a challenging control problem, exhibiting significant nonlinearities in both state and output equations. Here we consider a boiler heated by waste-gas incineration, modeled as a standard utility boiler using one known and one unknown (waste) fuel input. A nonlinear adaptive control design accounts for uncertainty in the plant parameters, and an adaptive neural network estimates the effect of the waste input. The simulation results show that the proposed method can obtain accurate performance and stability, in a situation where PI control performs poorly.