Editorial: Special issue on model predictive control under disturbances and uncertainties: Safety, stability, and learning

Model Predictive Control (MPC) has established itself as one of the most powerful advanced control strategies, combining conceptual simplicity with the ability to handle constraints and optimize performance. However, the presence of disturbances and uncertainties in real-world systems imposes significant challenges, necessitating robust, adaptive, and computationally efficient solutions. This Special Issue of the International Journal of Robust and Nonlinear Control is dedicated to advancing the state-of-the-art in MPC under disturbances and uncertainties, with a focus on safety, stability, and the integration of learning techniques. We are proud to present a collection of 19 high-quality papers that address these challenges and push the boundaries of MPC research.

The Call for Papers for this Special Issue invited contributions on novel theoretical developments, innovative design and analysis tools, and practical applications of MPC in the presence of disturbances and uncertainties. The response from the research community was overwhelming, and after a rigorous peer review process, 19 papers were selected for publication. These contributions reflect the latest advancements in disturbance and uncertainty modeling, robust and adaptive MPC, learning-based approaches, and applications across diverse domains such as robotics, automotive systems, energy systems, and aerospace.

The accepted papers cover a wide range of topics, organized around the following key themes: [...]