Hybrid Control

Hybrid control integrates the strengths of different control approaches, aiming to overcome limitations of individual methods and achieve superior performance in complex systems. Current research focuses on developing hybrid controllers using diverse techniques, including model predictive control combined with reinforcement learning, adaptive control mechanisms, and iterative learning algorithms, often incorporating sensor fusion and bio-inspired models. This field is significant for advancing robotics, autonomous systems, and human-machine interaction, enabling more robust, adaptable, and efficient control in challenging and unpredictable environments.