Journal article
Position control of spherical inverted pendulum via improved discrete-time neural network approach
Nonlinear Dynamics, Vol.99, pp.2867-2875
2020
Abstract
Much recently, a discrete-time neural network (NN) approach from the output regulation theory was adopted to solve the position tracking problem of the spherical inverted pendulum (SIP) system. The key of this approach is to find the approximate solution of the corresponding discrete regulator equations (DREs) of the SIP system, which are composed of 10 nonlinear algebraic functional equations. However, the procedure for calculating the approximate solution of the DREs is quite tedious and is dependent on the system parameters. In this paper, an improved discrete-time NN control algorithm is proposed, which relies on the NN approximation of the feedforward function. Since the feedforward function is two-dimensional, the improved NN approach is much simpler compared with the existing NN approach. Moreover, a distinct advantage of our approach is that it allows certain robustness to the system parameters when every state is available. Simulation results demonstrate that our approach leads to much smaller tracking errors than the existing NN approach.
Details
- Title
- Position control of spherical inverted pendulum via improved discrete-time neural network approach
- Authors/Creators
- C. Liu (Author/Creator) - Hefei University of TechnologyZ. Ping (Author/Creator) - Hefei University of TechnologyY. Huang (Author/Creator) - Hefei University of TechnologyJ-G Lu (Author/Creator) - Shanghai Jiao Tong UniversityH. Wang (Author/Creator) - Murdoch University
- Publication Details
- Nonlinear Dynamics, Vol.99, pp.2867-2875
- Publisher
- Springer
- Identifiers
- 991005543387607891
- Copyright
- © 2020 Springer Nature B.V.
- Murdoch Affiliation
- College of Science, Health, Engineering and Education
- 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
90 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
- Engineering, Mechanical
- Mechanics
- ESI research areas
- Engineering