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