An analytical adaptive single-neuron compensation control law for nonlinear process

Abstract

To circumvent the drawbacks in nonlinear controller designing of chemical processes, an analytical adaptive single-neuron compensation control scheme is proposed in this paper. A class of nonlinear processes with modest nonlinearities is approximated by a composite model consisting a linear ARX model and a fuzzy neural network-based linearization error model. Motivated by the conventional feedforward control design technique in process industries, the output of FNNM can be viewed as measurable disturbance and a compensator can be designed to eliminate the disturbance influence. Simulation results show that the adaptive single-neuron compensation control plays a major role in improving the control performance, and the proposed adaptive control possesses better performance. © 2008 Springer-Verlag Berlin Heidelberg.