A lot has been discussed about the shortage of petrochemical feedstocks in a long-term scenario, encouraging the development of the biobased industry to reduce fossil chemical dependence and greenhouse gas emissions. One key point of challenge for the implementation of the biotechnological unit composing this industry is its modeling and control, due to the microbial systems complexity and variations associated with the bioreactor operation. This work presents a critical review of the main control strategies and algorithms applied to bioprocesses. The classic PID feedback algorithm was found to be the main subject of study in the last decade. In the last five years, however, more studies were reported with advanced control strategies, such as nonlinear model predictive control and artificial neural networks, suggesting an increasing interest to use them to deal with non-linearities of biological systems.