arXiv subjects

Spencer Schutz

Publications and source records attributed to Spencer Schutz.

3 recordsLinked to original sources

arXiv2026

An Adaptive-Sampling Control Framework for Constrained Linear Systems with Robust Safety Guarantees

Adaptive-sampling control balances control performance with resource efficiency. However, existing methods either fail to guarantee robust constraint satisfaction during rate transitions or require computationally expensive online optimization. This paper proposes an adaptive-sampling control framework for linear systems subject to polytopic state and input constraints and bounded additive disturbances. Given a time-varying reference control update rate provided by a reasoner, our framework continuously calculates Model Predictive Control (MPC) update rates that ensure robust constraint satisfaction at all time steps. Offline, robust M-step hold control invariance is used to precompute invariant sets for a list of update rates and transition sets between them. Online, these sets are used in real time to guarantee recursive feasibility and finite-time transitions to the reference update rate. The utility of the architecture is demonstrated in a cruise control simulation.

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arXiv2026

Safe Adaptive-Sampling Control via Robust M-Step Hold Model Predictive Control

In adaptive-sampling control, the control frequency can be adjusted during task execution. Ensuring that these changes do not jeopardize the safety of the system being controlled requires attention. We introduce robust M-step hold model predictive control (MPC) to address this. Our formulation provides robust constraint satisfaction for an uncertain discrete-time system model with a fixed sampling time subject to an adaptable multi-step input hold (referred to as M-step hold). We show how to ensure recursive feasibility of the MPC utilizing M-step hold extensions of robust invariant sets, and demonstrate how to enable safe adaptive-sampling control via the online selection of M. We evaluate the utility of the robust M-step hold MPC formulation in a cruise control example.

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arXiv2025

On Sampling Time and Invariance

Invariant sets define regions of the state space where system constraints are always satisfied. The majority of numerical techniques for computing invariant sets have been developed for discrete-time systems with a fixed sampling time. Understanding how invariant sets change with sampling time is critical for designing adaptive-sampling control schemes that ensure constraint satisfaction. We introduce M-step hold control invariance, a generalization of traditional control invariance, and show its practical use to assess the link between control sampling frequency and constraint satisfaction. We robustify M-step hold control invariance against model mismatches and discretization errors, paving the way for adaptive-sampling control strategies.

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