Neuro-adaptive sliding mode control for underground coal gasification energy conversion process

Abstract

ABSTRACT Due to the non-availability of model parameters, the model-base control of a nonlinear and infinite-dimensional underground coal gasification (UCG) process is a challenging task. In this paper, a robust neuro-adaptive sliding mode control (NASMC) is designed for the UCG process to maintain a desired heating value level. The unknown model parameters used in NASMC are estimated using the feed-forward neural network. Moreover, the controller also requires time derivatives of some model parameters, which are estimated by uniform robust exact differentiator. As the relative degree of the output with respect to the input is zero, therefore, to apply NASMC, the relative degree is increased to one. This approach maintains the desired heating value and provides insensitivity to input disturbance and model uncertainties. A comparison is also made between NASMC and an already designed conventional SMC. The simulation results show that NASMC exhibits better performance as compared to the conservative SMC design.