SCR CONTROL SYSTEM DESIGN BASED ON ON-LINE RADIAL BASIS FUNCTION AND BACKPROPAGATION NEURAL NETWORKS

摘要

Because of its high NO_x reduction efficiency, selective catalyst reduction (SCR) has become an indispensable part of diesel vehicle aftertreatment. This paper presents a control strategy for SCR systems that is based on an on-line radial basis function neural network (RBFNN) and an on-line backpropagation neural network (BPNN). In this control structure, the radial basis function neural network is employed as an estimator to provide Jacobian information for the controller; and the backpropagation neural network is utilized as a controller, which dictates the appropriate urea-solution to be injected into the SCR system. This design is tested by simulations based in Gamma Technologies software (GT-ISE) as well as MATLAB Simulink. The results show that the RBF-BPNN control technique achieves a 1 - 5 % higher NO_x reduction efficiency than a PID controller.