Feasibility Analysis and Constraint Selection in Optimization-Based Controllers

TL;DR

This paper introduces a new theoretical framework for assessing and selecting feasible constraints in optimization-based control of autonomous systems, improving computational efficiency and providing insights into constraint infeasibility.

Contribution

It offers necessary and sufficient conditions for feasibility of linear constraints and develops novel methods for constraint selection in control systems.

Findings

Algorithms achieve performance comparable to state-of-the-art methods.

Methods offer improved computational efficiency.

Provides a new theoretical framework for handling constraint infeasibility.

Abstract

Control synthesis under constraints is at the forefront of research on autonomous systems, in part due to its broad application from low-level control to high-level planning, where computing control inputs is typically cast as a constrained optimization problem. Assessing feasibility of the constraints and selecting among subsets of feasible constraints is a challenging yet crucial problem. In this work, we provide a novel theoretical analysis that yields necessary and sufficient conditions for feasibility assessment of linear constraints and based on this analysis, we develop novel methods for feasible constraint selection in the context of control of autonomous systems. Through a series of simulations, we demonstrate that our algorithms achieve performance comparable to state-of-the-art methods while offering improved computational efficiency. Importantly, our analysis provides a novel theoretical framework for assessing, analyzing and handling constraint infeasibility.