In terms of structure, the complexity of the interconnections between neurons in the human brain is well established. A study of these interconnections shows that some are particularly similar to feedback loop control systems used in engineering to control certain production devices. These biological control systems are encountered between neurons belonging to the same macrostructure, or between different macrostructures of the human brain. They are involved in the control or regulation of several biological phenomena such as: hormonal secretions, temperature, body posture, coordination of movements, emotions, reasoning, decision-making, etc. The artificial intelligence research work presented in this paper concerns the mathematical modeling of these natural control systems with feedback loops. The objective is to contribute to the development of intelligent systems used to control the movement and behavior of robots, giving them the capacity and autonomy to react and adapt to changes in their environment, in a manner analogous to physiological homeostasis. After describing briefly the physiology and the structural organization of biological neurons, architecture and algorithm are designed to illustrate the behavior of some simple feedback control systems found in human brain. Then, simulations are carried out to validate the artificial model. The results show that the use of Artificial Intelligence (AI) improves the accuracy, efficiency and flexibility of feedback control systems in industrial process automation. In terms of perspectives, other models will be designed and combined with existing models, including this one, to produce reasoning and decision-making; which would allow robots to analyze situations and interact intelligently with their environment. This work can also serve as a reference for researchers and professionals interested in the development and applications of AI-inspired feedback control systems.