Sequence-based Anytime Control

TL;DR

This paper introduces two related anytime control algorithms for nonlinear systems that adapt to time-varying processing resources, improving stability and performance through tentative control sequences.

Contribution

The paper proposes novel anytime control algorithms that handle stochastic and Markovian processor availability, enhancing control robustness under variable computational resources.

Findings

Algorithms improve control stability with variable processing resources

Numerical simulations show significant performance gains

Stability analysis covers stochastic and Markov models

Abstract

We present two related anytime algorithms for control of nonlinear systems when the processing resources available are time-varying. The basic idea is to calculate tentative control input sequences for as many time steps into the future as allowed by the available processing resources at every time step. This serves to compensate for the time steps when the processor is not available to perform any control calculations. Using a stochastic Lyapunov function based approach, we analyze the stability of the resulting closed loop system for the cases when the processor availability can be modeled as an independent and identically distributed sequence and via an underlying Markov chain. Numerical simulations indicate that the increase in performance due to the proposed algorithms can be significant.