Model-Free Event-Triggered Optimal Consensus Control of Multiple Euler-Lagrange Systems via Reinforcement LearningShow others and affiliations
2021 (English)In: IEEE Transactions on Network Science and Engineering, E-ISSN 2327-4697, Vol. 8, no 1, p. 246-258Article in journal (Refereed) Published
Abstract [en]
This paper develops a model-free approach to solve the event-triggered optimal consensus of multiple Euler-Lagrange systems (MELSs) via reinforcement learning (RL). Firstly, an augmented system is constructed by defining a pre-compensator to circumvent the dependence on system dynamics. Secondly, the Hamilton-Jacobi-Bellman (HJB) equations are applied to the deduction of the model-free event-triggered optimal controller. Thirdly, we present a policy iteration (PI) algorithm derived from reinforcement learning (RL), which converges to the optimal policy. Then, the value function of each agent is represented through a neural network to realize the PI algorithm. Moreover, the gradient descent method is used to update the neural network only at a series of discrete event-triggered instants. The specific form of the event-triggered condition is then proposed, and it is guaranteed that the closed-loop augmented system under the event-triggered mechanism is uniformly ultimately bounded (UUB). Meanwhile, the Zeno behavior is also eliminated. Finally, the validity of this approach is verified by a simulation example.
Place, publisher, year, edition, pages
IEEE, 2021. Vol. 8, no 1, p. 246-258
Keywords [en]
Reinforcement learning, event-triggered control, Euler-Lagrange system, augmented system
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-81463DOI: 10.1109/TNSE.2020.3036604ISI: 000631202700021Scopus ID: 2-s2.0-85096869102OAI: oai:DiVA.org:ltu-81463DiVA, id: diva2:1502244
Note
Validerad;2021;Nivå 2;2021-04-06 (alebob);
Finansiär: National Key Research and Development Program of China (2018YFC0809302); National Natural Science Foundation of China (61751305, 61673176); Program of Shanghai Academic Research Leader (20XD1401300); Programme of Introducing Talents of Discipline to Universities (B17017)
2020-11-192020-11-192024-01-05Bibliographically approved