Feasibility Analysis and Constraint Selection in Optimization-Based Controllers
This addresses a crucial challenge in control synthesis for autonomous systems, offering incremental improvements in computational efficiency.
The paper tackled the problem of assessing feasibility and selecting feasible constraints in optimization-based controllers for autonomous systems, providing a novel theoretical analysis with necessary and sufficient conditions for linear constraints and developing methods that achieve performance comparable to state-of-the-art with improved computational efficiency.
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.