A nonlinear control scheme for imprecisely known processes using the sliding mode and neural fuzzy techniques

This article considers the regulation control of nonlinear chemical processes whose dynamics are imprecisely known. A nonlinear control scheme that incorporates a sliding mode controller (SMC) and a neural fuzzy strategy is proposed to deal with this kind of processes. The sliding mode controller designed on the base of a previously known process model is implemented to keep system’s trajectory around the desired manifold. For extra and/or unknown dynamics that cannot be captured before the SMC design stage, an intelligent scheme of utilizing a neural fuzzy strategy is then used to provide an adaptive ability to accommodate the perturbation, which therefore is able to force the system output back to and maintain in the desired set point. The effectiveness and applicability of the proposed scheme are demonstrated through the control of a continuous stirred tank reactor with existing simultaneously the unmodeled side reactions, measuring error, and extra matched and unmatched disturbances. The potential use of a sliding observer along with the proposed scheme is also investigated in the work. Extensive simulation results reveal that the incorporation of the model-based SMC and the intelligent neural fuzzy technique appears to be an effective and promising approach to the nonlinear control of chemical processes whose dynamics are imprecisely known.