Energy Shaping

Energy shaping is a control technique aiming to modify a system's inherent energy landscape to achieve desired behavior, often by influencing its dynamics and stability. Current research focuses on applying this approach across diverse domains, utilizing machine learning algorithms like reinforcement learning (particularly PPO and A2C), neural networks (including Hamiltonian and convolutional networks), and optimization methods to design controllers that achieve specific energy profiles. This methodology shows promise in improving efficiency and autonomy in applications ranging from robotics and nuclear reactor control to industrial process optimization and exoskeleton design, offering a powerful tool for achieving precise and robust control in complex systems.