Journal article
Fault-tolerant tracking control based on reinforcement learning with application to a steer-by-wire system
Journal of the Franklin Institute, Vol.359(3), pp.1152-1171
2022
Abstract
In this paper, a novel complete model-free integral reinforcement learning (CMFIRL) algorithm based fault tolerant control scheme is proposed to solve the tracking problem of steer-by-wire (SBW) system. We begin with the recognition that the reference errors can eventually converge to zero based on the command generator model. Then an augmented tracking system is constructed with a corresponding performance index which is considered as a type of actuator failure. By using the reinforcement learning (RL) technique, three novel online update strategies are respectively developed to cope with the following three cases, i.e., model-based, partially model-free, and completely model-free. Especially, the RL algorithm for the complete model-free case eliminates the constraints of requiring the known system dynamics in fault-tolerant tracking controlling. The system stability and the convergence of the CMFIRL iteration algorithm are also rigorously proved. Finally, a simulation example is given to illustrate the effectiveness of the proposed approach.
Details
- Title
- Fault-tolerant tracking control based on reinforcement learning with application to a steer-by-wire system
- Authors/Creators
- H. Chen (Author/Creator) - Anhui UniversityY. Tu (Author/Creator) - Anhui UniversityH. Wang (Author/Creator) - Murdoch UniversityK. Shi (Author/Creator) - Chengdu UniversityS. He (Author/Creator) - Anhui University
- Publication Details
- Journal of the Franklin Institute, Vol.359(3), pp.1152-1171
- Publisher
- Elsevier Ltd
- Identifiers
- 991005544973607891
- Copyright
- © 2021 The Franklin Institute.
- Murdoch Affiliation
- School of Engineering and Energy; Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
Metrics
25 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.116 Robotics
- 4.116.862 Reinforcement Learning
- Web Of Science research areas
- Automation & Control Systems
- Engineering, Electrical & Electronic
- Engineering, Multidisciplinary
- Mathematics, Interdisciplinary Applications
- ESI research areas
- Engineering