Journal article
Adaptive Control of Uncertain Nonlinear Systems via Event-Triggered Communication and NN Learning
IEEE Transactions on Cybernetics, Vol.53(4), pp.2391-2401
2023
Abstract
This article concentrates on adaptive tracking control of strict-feedback uncertain nonlinear systems with an event-based learning scheme. A novel neural network (NN) learning law is proposed to design the adaptive control scheme. The NN weights information driven by the prediction-error-based control process is intermittently transmitted in the event-triggered context to the NN learning law mainly for signal tracking. The online stored sampled data of NN driven by the tracking error are utilized in the event context to update the learning law. With the adaptive control and NN learning law updated via the event-triggered communication, the improvements of NN learning capability, tracking performance, and system computing resource saving are guaranteed. In addition, it is proved that the minimum time interval for triggering errors of the two types of events is bounded and the Zeno behavior is strictly excluded. Finally, simulation results illustrate the effectiveness and good performance of the proposed control method.
Details
- Title
- Adaptive Control of Uncertain Nonlinear Systems via Event-Triggered Communication and NN Learning
- Authors/Creators
- X. Liu (Author/Creator) - Northwestern Polytechnical UniversityB. Xu (Author/Creator) - Northwestern Polytechnical UniversityY. Cheng (Author/Creator) - Northwestern Polytechnical UniversityH. Wang (Author/Creator) - Murdoch UniversityW. Chen (Author/Creator) - Xidian University
- Publication Details
- IEEE Transactions on Cybernetics, Vol.53(4), pp.2391-2401
- Publisher
- IEEE
- Identifiers
- 991005546346107891
- Copyright
- © 2022 IEEE.
- Murdoch Affiliation
- School of Engineering and Energy; Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
139 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
- 4.29 Automation & Control Systems
- 4.29.104 Adaptive Control
- Web Of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence
- Computer Science, Cybernetics
- ESI research areas
- Computer Science