Adaptive feedback-feedforward control scheme design based on neuro-fuzzy system

Abstract

Motivated by the designing idea of conventional feedforward control, an adaptive feedback-feedforward control scheme based on neuro-fuzzy system is proposed. Firstly, a class of SISO nonlinear processes is approximated by a composite model consisting of a linear ARX model and a FNN-based linearization error model (FNNM). The output of FNNM is taken as the measurable "disturbance". And then, a feedforward controller is employed, whose parameters are adjusted by using the input of the controlled process, the output of the error model, the output error between the linear ARX model and the system, and the gradient information of the composite model. Finally, both the proposed controller and the conventional PID are used in CSTR. Simulation results show that the proposed controller has better performance than the conventional PID.