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A hybrid model predictive control approach treats switching instants as explicit optimization variables instead of relying on a fixed time grid. Each step solves a single sparse quadratic program jointly optimizing inputs and impact timing. Optimality conditions are consistent with the hybrid minimum principle.
Why it matters
Accurate handling of switching times improves control of systems with event-driven dynamics.
Original headline
Switch-Time-Aware Hybrid Model Predictive Control Using the Hybrid Minimum Principle
arXiv:2610.29559v1 Announce Type: new Abstract: Model predictive control is a high-performance control method, but it incurs comparatively high online computational costs across many control tasks and is challenging to realize in low-level hardware settings.
arXiv:2610.29377v1 Announce Type: cross Abstract: Learning-based path planners based on diffusion and flow matching can generate diverse trajectories from demonstrations but typically lack mechanisms for constraint satisfaction and collision avoidance during deployment.
arXiv:2604.04455v3 Announce Type: replace Abstract: Nonlinear underactuated systems such as two-wheeled inverted pendulums (TWIPs) exhibit a limited region of attraction (RoA), which defines the set of initial conditions from which the closed-loop system converges to the equilibrium.
