Dynamic intermittent Q‐learning–based model‐free suboptimal co‐design of ‐stabilization
Abstract
This paper proposes an intermittent model‐free learning algorithm for linear time‐invariant systems, where the control policy and transmission decisions are co‐designed simultaneously while also being subjected to worst‐case disturbances. The control policy is designed by introducing an internal dynamical system to further reduce the transmission rate and provide bandwidth flexibility in cyber‐physical systems. Moreover, a Q‐learning algorithm with two actors and a single critic structure is developed to learn the optimal parameters of a Q‐function. It is shown by using an impulsive system approach that the closed‐loop system has an asymptotically stable equilibrium and that no Zeno behavior occurs. Furthermore, a qualitative performance analysis of the model‐free dynamic intermittent framework is given and shows the degree of suboptimality concerning the optimal continuous updated controller. Finally, a numerical simulation of an unknown system is carried out to highlight the efficacy of the proposed framework.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 04, 2019
- Source ID
- 10.1002/rnc.4515
Entities
People
- Hamidreza Modares
- Henrique Ferraz
- Kyriakos G Vamvoudakis
- Yongliang Yang
Organizations
- China Postdoctoral Science Foundation
- Georgia Tech
- Michigan State University
- NATO
- National Natural Science Foundation of China
- National Science Foundation
- Office of Naval Research
- University of Science and Technology Beijing