Abstract
In this paper, the optimal adaptive leader-follower consensus of linear continuous time multi-agent systems is considered. The error dynamics of each player depends on its neighbors’ information. Detailed analysis of online optimal leader-follower consensus under known and unknown dynamics is presented. The introduced reinforcement learning-based algorithms learn online the approximate solution to algebraic Riccati equations. An optimal adaptive control technique is employed to iteratively solve the algebraic Riccati equation based on the online measured error state and input information for each agent without requiring the priori knowledge of the system matrices. The decoupling of the multi-agent system global error dynamics facilitates the employment of policy iteration and optimal adaptive control techniques to solve the leaderfollower consensus problem under known and unknown dynamics. Simulation results verify the effectiveness of the proposed methods.
Highlights
In recent decades multi-agent systems (MASs) are applied as new methods for solving problems which cannot be solved by a single agent
Reinforcement learning (RL) is a class of methods, which provides online solution for optimal control problems by means of a reinforcement scalar signal measured from the environment, which indicates the level of control performance
This paper presents an online optimal adaptive algorithm for continuous time leader-follower consensus of MASs under known and unknown dynamics
Summary
In recent decades multi-agent systems (MASs) are applied as new methods for solving problems which cannot be solved by a single agent. RL is a class of methods, which provides online solution for optimal control problems by means of a reinforcement scalar signal measured from the environment, which indicates the level of control performance This is because a number of RL algorithms [22,23,24] do not require knowledge or identification/learning of the system dynamics, and RL is strongly connected with direct and indirect optimal adaptive control methods. This paper presents an online optimal adaptive algorithm for continuous time leader-follower consensus of MASs under known and unknown dynamics. We implement the decoupling of multi-agent global error dynamics which facilitates the employment of policy iteration and optimal adaptive control techniques to solve the leader-follower consensus problem under known and unknown dynamics. The introduced method employs PI technique to iteratively solve the ARE of each agent using the online information of error state and input without requiring a primary knowledge of system matrices.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
