Backup Plan Constrained Model Predictive Control

TL;DR

This paper introduces backup plan safety in control systems, ensuring alternative mission completion if the primary mission fails, using a multi-horizon, multi-objective model predictive control approach with simulation validation.

Contribution

It formulates a novel feasibility maximization problem incorporating backup plans and develops a multi-horizon, multi-objective MPPI control algorithm for nonlinear systems.

Findings

Demonstrates backup plan safety concept through simulations.

Shows effectiveness of the 3M algorithm in nonlinear control tasks.

Achieves computational efficiency via parallel processing.

Abstract

This article proposes a new safety concept: backup plan safety. The backup plan safety is defined as the ability to complete one of the alternative missions in the case of primary mission abortion. To incorporate this new safety concept in control problems, we formulate a feasibility maximization problem that adopts additional (virtual) input horizons toward the alternative missions on top of the input horizon toward the primary mission. Cost functions for the primary and alternative missions construct multiple objectives, and multi-horizon inputs evaluate them. To address the feasibility maximization problem, we develop a multi-horizon multi-objective model predictive path integral control (3M) algorithm. Model predictive path integral control (MPPI) is a sampling-based scheme that can help the proposed algorithm deal with nonlinear dynamic systems and achieve computational efficiency by parallel computation. Simulations of the aerial vehicle and ground vehicle control problems demonstrate the new concept of backup plan safety and the performance of the proposed algorithm.