DeepAI AI Chat
Log In Sign Up

Observer-based Adaptive Optimal Output Containment Control problem of Linear Heterogeneous Multi-agent Systems with Relative Output Measurements

by   Majid Mazouchi, et al.

This paper develops an optimal relative output-feedback based solution to the containment control problem of linear heterogeneous multi-agent systems. A distributed optimal control protocol is presented for the followers to not only assure that their outputs fall into the convex hull of the leaders' output (i.e., the desired or safe region), but also optimizes their transient performance. The proposed optimal control solution is composed of a feedback part, depending of the followers' state, and a feed-forward part, depending on the convex hull of the leaders' state. To comply with most real-world applications, the feedback and feed-forward states are assumed to be unavailable and are estimated using two distributed observers. That is, since the followers cannot directly sense their absolute states, a distributed observer is designed that uses only relative output measurements with respect to their neighbors (measured for example by using range sensors in robotic) and the information which is broadcasted by their neighbors to estimate their states. Moreover, another adaptive distributed observer is designed that uses exchange of information between followers over a communication network to estimate the convex hull of the leaders' state. The proposed observer relaxes the restrictive requirement of knowing the complete knowledge of the leaders' dynamics by all followers. An off-policy reinforcement learning algorithm on an actor-critic structure is next developed to solve the optimal containment control problem online, using relative output measurements and without requirement of knowing the leaders' dynamics by all followers. Finally, the theoretical results are verified by numerical simulations.


Fully-HeterogeneousContainment Control of a Network of Leader-Follower Systems

This paper develops a distributed solution to the fully-heterogeneous co...

Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control

The distributed Volt/Var control (VVC) methods have been widely studied ...

Optimal Distributed Control of Multi-agent Systems in Contested Environments via Reinforcement Learning

This paper presents a model-free reinforcement learning (RL) based distr...

Distributed optimal steady-state regulation for high-order multi-agent systems with external disturbances

In this paper, a distributed optimal steady-state regulation problem is ...

Distributed Global Output-Feedback Control for a Class of Euler-Lagrange Systems

This published paper investigates the distributed tracking control probl...

Distributed estimation from relative measurements of heterogeneous and uncertain quality

This paper studies the problem of estimation from relative measurements ...

Distributed optimization for a class of high-order nonlinear multi-agent systems with unknown dynamics

In this paper, we study a distributed optimization problem for a class o...