Optimal Control Input

Optimal control input research focuses on determining the best sequence of control actions to achieve desired system behavior, often balancing competing objectives like performance and safety. Current efforts concentrate on developing efficient algorithms, such as model predictive control (MPC), path integral methods, and reinforcement learning, often incorporating techniques like spline interpolation for smoother control and genetic algorithms or Stein Variational Gradient Descent for improved optimization. These advancements are crucial for improving the performance and safety of autonomous systems across diverse applications, from robotics and autonomous navigation to energy-efficient building control.