Feedforward Control

Feedforward control aims to proactively guide a system towards a desired state by predicting and compensating for disturbances or inherent system dynamics, rather than solely reacting to errors. Current research emphasizes data-driven approaches, employing neural networks (including autoencoders and recurrent networks), Koopman operators, and physics-guided neural networks to learn complex system models for improved feedforward control design. This is particularly relevant in robotics, manufacturing, and building automation, where it enables more precise and efficient control of complex systems with reduced reliance on computationally expensive or inaccurate first-principles models. The resulting advancements improve performance, robustness, and energy efficiency across diverse applications.