Nonlinear Model Predictive Control Framework For Cooperative Three-Agent Target Defense Game

TL;DR

This paper develops a nonlinear model predictive control framework for a cooperative target defense game involving a target, attacker, and defender, demonstrating improved robustness and performance through simulations.

Contribution

It introduces a novel NMPC-based guidance strategy for cooperative defense in TAD games with state estimation and capture analysis.

Findings

NMPC strategy outperforms CLOS and A-CLOS methods

The approach is robust to various attacker models

Numerical simulations validate effectiveness

Abstract

This paper presents cooperative target defense guidance strategies using nonlinear model predictive control (NMPC) framework for a target-attacker-defender (TAD) game. The TAD game consists of an attacker and a cooperative target-defender pair. The attacker's objective is to capture the target, whereas the target-defender team acts together such that the defender can intercept the attacker and ensure target survival. We assume that the cooperative target-defender pair do not have perfect knowledge of the attacker states, and hence the states are estimated using an Extended Kalman Filter (EKF). The capture analysis based on the Apollonius circles is performed to identify the target survival regions. The efficacy of the NMPC-based solution is evaluated through extensive numerical simulations. The results show that the NMPC-based solution offers robustness to the different unknown attacker models and has better performance than CLOS and A-CLOS based strategies.