Abstract
This study aims at establishing a robust intelligent control method with higher control performance and wider application region. In particular, this study deals with a nonlinear and failure-proof control problem. In this study, the dynamical energy principle is embedded into the integrator of Cubic Neural Network (CNN) that consists of multilevel parallel processing on different degrees of abstraction. The proposed CNN is applied to a control problem of a swung up and inverted pendulum. In order to confirm the effectiveness of the integrated CNN controllers, we carried out simulations and experiments with parameter variation and sensor failure. As a result of simulation and experiment, it was demonstrated that the integrated CNN controllers can stand up the pendulum taking into account the cart position limit at abnormal situations. Then, the robustness and the fault-tolerance of the proposed CNN controllers is confirmed by comparing sliding mode control techniques.
| Original language | English |
|---|---|
| Pages (from-to) | 1579-1586 |
| Number of pages | 8 |
| Journal | Nippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C |
| Volume | 69 |
| Issue number | 6 |
| Publication status | Published - 2003 Jun |
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Keywords
- Fault-Tolerance
- Intelligent Control
- Multi-Purpose Control
- Nonlinear Control
- Pendulum
- Robustness
ASJC Scopus subject areas
- Mechanical Engineering
Cite this
Robustness and fault-tolerance of cubic neural network intelligent control method (comparison with sliding mode control). / Takahashi, Masaki; Narukawa, Terumasa; Yoshida, Kazuo.
In: Nippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C, Vol. 69, No. 6, 06.2003, p. 1579-1586.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Robustness and fault-tolerance of cubic neural network intelligent control method (comparison with sliding mode control)
AU - Takahashi, Masaki
AU - Narukawa, Terumasa
AU - Yoshida, Kazuo
PY - 2003/6
Y1 - 2003/6
N2 - This study aims at establishing a robust intelligent control method with higher control performance and wider application region. In particular, this study deals with a nonlinear and failure-proof control problem. In this study, the dynamical energy principle is embedded into the integrator of Cubic Neural Network (CNN) that consists of multilevel parallel processing on different degrees of abstraction. The proposed CNN is applied to a control problem of a swung up and inverted pendulum. In order to confirm the effectiveness of the integrated CNN controllers, we carried out simulations and experiments with parameter variation and sensor failure. As a result of simulation and experiment, it was demonstrated that the integrated CNN controllers can stand up the pendulum taking into account the cart position limit at abnormal situations. Then, the robustness and the fault-tolerance of the proposed CNN controllers is confirmed by comparing sliding mode control techniques.
AB - This study aims at establishing a robust intelligent control method with higher control performance and wider application region. In particular, this study deals with a nonlinear and failure-proof control problem. In this study, the dynamical energy principle is embedded into the integrator of Cubic Neural Network (CNN) that consists of multilevel parallel processing on different degrees of abstraction. The proposed CNN is applied to a control problem of a swung up and inverted pendulum. In order to confirm the effectiveness of the integrated CNN controllers, we carried out simulations and experiments with parameter variation and sensor failure. As a result of simulation and experiment, it was demonstrated that the integrated CNN controllers can stand up the pendulum taking into account the cart position limit at abnormal situations. Then, the robustness and the fault-tolerance of the proposed CNN controllers is confirmed by comparing sliding mode control techniques.
KW - Fault-Tolerance
KW - Intelligent Control
KW - Multi-Purpose Control
KW - Nonlinear Control
KW - Pendulum
KW - Robustness
UR - http://www.scopus.com/inward/record.url?scp=0042932517&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0042932517&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0042932517
VL - 69
SP - 1579
EP - 1586
JO - Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C
JF - Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C
SN - 0387-5024
IS - 6
ER -