Optimal control and decision making using machine learning
Keywords:
Machine learning, Optimal control, Reinforcement learning, Neural networks, Decision-making systems, Industrial automationAbstract
Optimal control plays a central role in modern industrial, energy, transportation, and economic systems, where accurate decision-making and stability are essential. Traditional control approaches rely on analytical mathematical models; however, many real-world systems are highly nonlinear, time-varying, and influenced by uncertainties, making classical modeling insufficient. Recent advances in machine learning (ML) and artificial intelligence (AI) have enabled data-driven modeling, real-time forecasting, and adaptive decision-making. This paper presents an in-depth analysis of machine-learning-based optimal control methods, including neural network modeling, reinforcement learning (Q-learning, deep RL), and hybrid MPC-ML approaches. Practical applications in energy load control, industrial automation, robotics, transport optimization, and economic decision-making are discussed. A laboratory experiment on nonlinear system stabilization demonstrates that ML-based control achieves high prediction accuracy (97.8%) and effective system regulation. The results show that ML significantly enhances control efficiency in complex and uncertain environments. Future technological systems are expected to increasingly rely on intelligent, self-learning control strategies.
References
1. Sutton R., Barto A. Reinforcement Learning: An Introduction, MIT Press, 2020.
2. Goodfellow I., Bengio Y., Courville A. Deep Learning, MIT Press, 2016.
3. Camacho E., Alba C. Model Predictive Control, Springer, 2019.
4. Kiumarsi B., et al. “Optimal control via reinforcement learning,” IEEE Transactions on Automatic Control, 2022.
5. Wang M., Li Z. “Machine-learning-based control systems,” Journal of Systems Engineering, 2023.
6. Silver D., et al. “Deterministic Policy Gradient Algorithms,” ICML, 2014.
7. Li X., Chen Y. “Neural network modeling of nonlinear systems,” Control Engineering Practice, 2021.
8. Zhang L., Wu Y. “Deep RL for industrial process control,” IEEE Access, 2019.
9. Huang P. “Predictive control using learned models,” Automation Science and Engineering, 2022.
