Control Period Adaptation for Resource-Constrained MPC Applications

The integration of control applications into cloud and edge expands the capabilities of modern control systems, but also introduces variability in shared resource availability and competition with other applications, posing new challenges for control design. This paper presents a multi-mode Model Predictive Control (MPC) framework tailored for resource-aware systems. By treating the controller period as a scalable parameter, our approach dynamically adjusts control accuracy and computational complexity in response to changing resource and state-space conditions. Unlike existing event- and self-triggered strategies, our multi-mode design allows users to actively manage trade-offs between computational load and control quality. We provide feasibility and stability guarantees for the proposed control framework and demonstrate its effectiveness in a simulated cart-pole system, showcasing significant improvements in computational resource efficiency without compromising control performance.