Cooperative control strategies for autonomous agents using nonlinear model predictive control

Abstract

newline Autonomous aerial vehicles (AAVs) are extensively used in civilian and military applications like aerial surveying, search and rescue, transportation, border patrolling, etc. In most applications, achieving the objectives using a single AAV is difficult. Hence, multiple cooperative AAVs are used to accomplish the mission quickly and efficiently. However, achieving cooperation is challenging in real-world scenar- ios due to the uncertainties in obtaining other vehicle states (position, velocity, etc.) and measurement information. The constraints, such as limited sensor range and availability, noises, and environmental disturbances, must be handled properly to obtain an efficient system. In this thesis, we provide solutions for a three-agent and four-agent pursuit-evasion problem, path planning under localization constraints, and tracking ground vehicles for cinematography purposes. The optimal control commands for the coop- erative agents in each of these problems are found using the nonlinear model predictive control (NMPC) framework. We analyze the theoretical properties of the proposed solutions and show the performance through numerical simulations. Brief explanations of the proposed solutions are given in the following paragraphs. Chapter 2 presents a cooperative target defense guidance strategy using a nonlinear model predictive control (NMPC) framework for a target-attacker-defender (TAD) problem. The TAD problem 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 ex