Learning via Algorithm

One of the most exciting advancements in controls engineering over the last 2 years has been the development of new control algorithms that make use of machine learning techniques. These algorithms are designed to improve the accuracy and efficiency of control systems by allowing them to adapt to changing conditions and learn from data in real-time.

One example of this type of algorithm is the deep reinforcement learning (DRL) algorithm, which has been used to optimize control systems in a variety of applications, from robotics to energy management. DRL algorithms are able to learn from experience by training on data from simulations or real-world systems, and can quickly adapt to changing conditions or unexpected events.

Another important development in controls engineering is the increased use of model-based control techniques, which use mathematical models to predict the behavior of a system and optimize control signals in real-time. Model-based control has been used in a variety of applications, including automotive control, aerospace control, and industrial process control.

Overall, these advances in controls engineering are helping to improve the performance and efficiency of control systems across a wide range of industries and are paving the way for new applications and innovations in the field.