5 research outputs found
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach
Multi-Agent Zero-Sum Differential Graphical Games for Disturbance Rejection in Distributed Control
This paper addresses distributed optimal tracking control of multi-agent linear systems subject to external disturbances. The concept of differential game theory is utilized to formulate this distributed control problem into a multi-player zero-sum differential graphical game, which provides a new perspective on distributed tracking of multiple agents influenced by disturbances. In the presented differential graphical game, the dynamics and performance indices for each node depend on local neighbor information and disturbances. It is shown that the solution to the multi-agent differential graphical games in the presence of disturbances requires the solution to coupled Hamilton-Jacobi-Isaacs (HJI) equations. Multi-agent learning policy iteration (PI) algorithm is provided to find the solution to these coupled HJI equations and its convergence is proven. It is also shown that L2-bounded synchronization errors can be guaranteed using this technique. An online PI algorithm is given to solve the zero-sum game in real time. A simulation example is provided to show the effectiveness of the online approach