Sequencing batch reactors (SBR) are widely used in wastewater treatment due to flexibility in operation and low investment costs. However, the main drawback of this technology is the large energy requirements for its operation. In addition, SBR performance is mostly determined by the quality of treated water, which is affected by model uncertainty. Efficient control systems that can meet the operational goals for this process under uncertainty are therefore desired. In this work, we propose an efficient control approach for composition control of organic matter and nitrogen in SBR systems in the presence of the model uncertainty. A local sensitivity analysis was performed to identify the variables that have a major impact on SBR behavior. Robust and stochastic model predictive controllers were designed to effectively control the SBR process under uncertainty. Our results indicate that water quality requirements can be achieved even in the presence of uncertainty without sacrificing SBR performance.