Model Predictive Control with Foreseeing Horizon Designed to Mitigate Extreme Events in Chaotic Dynamical Systems

Abstract. Practical applications of weather control are being explored under Japan's Moonshot Research and Development Program to mitigate extreme weather events such as heavy rainfall. One of the most significant challenges in this endeavor is identifying effective and efficient control inputs to mitigate extreme weather events within limited energy and computational time. To address this difficulty, the development of mathematical weather control approaches is being promoted. However, further improvements to the conventional approaches are required for the practical applications of weather control. In this study, we propose a novel framework called model predictive control with foreseeing horizon (MPCF), designed to mitigate extreme events in chaotic dynamical systems. The MPCF aims to improve control effectiveness by leveraging the sensitivity to initial conditions of chaotic dynamical systems. We evaluated the MPCF through control simulation experiments using the Lorenz 96 model. Our results demonstrated that introducing the foreseeing horizon improved the success rate without substantially increasing the computational cost of optimization, particularly when the control horizon was short. Furthermore, a comparison with the conventional method showed that the MPCF achieved success rates comparable to the conventional method with lower computational costs. This study showed that the MPCF is a promising control framework for mitigating extreme events in chaotic dynamical systems.