Malaysia holds a variety of energy resources; oil, natural gas, coal and renewable energies such as biomass, solar and hydro. In spite of these abundant resources, the nation still reliant on fossil fuel for industrial and transportation sector even though fossil fuel is depleting worldwide. In this regard, renewable energy resources are becoming attractive for sustainable energy development in Malaysia. Bioethanol is one of a potential biofuel with a high octane number and has replaced lead as an octane enhancer in petrol. In bioethanol fermentation, the process possesses complex dynamics caused by the microorganisms involved in the process as well as the variation in the biomass composition with the operating conditions. In this work, a nonlinear model known as the Nonlinear Autoregressive with Exogenous input model was developed and embedded in the Model Predictive Control strategy to control the fermentation process. Then, the performance of the Nonlinear Autoregressive with Exogenous input Model Predictive Control was evaluated and compared with linear Model Predictive Control and Proportional Integral Derivative controller for set point tracking and disturbance rejections. The robustness tests have also been carried out using the linear and nonlinear Model Predictive Controls proposed. In the robustness test, the nonlinear Model Predictive Controller successfully settled back to the original product concentration and fermenter temperature set points with settling time three hours. Meanwhile the linear Model Predictive Controller only settled at 95.8% of the product concentration set point and 97.1% of the fermenter temperature set point with settling time more than five hours. From the results, Nonlinear Autoregressive with Exogenous input Model Predictive Control has shown better performance and more robust as compared to linear Model Predictive Control and Proportional Integral Derivative controller. (C) 2016 Elsevier Ltd. All rights reserved.