Motion Control of an Inverted Pendulum Using Neural Networks based on optimal Tracking Control
This paper deals with motion control of an inverted pendulum using neural networks (NN)based on optimal tracking control. First, we examined the control performance in the nonlinear region, applying the optimal tracking control theory to the inverted pendulum. Second, the control system in which the...
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| Veröffentlicht in: | TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C 1993/09/25, Vol.59(565), pp.2694-2700 |
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| container_issue | 565 |
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| container_title | TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C |
| container_volume | 59 |
| creator | Nonami, Kenzo Uchida, Hiroaki Miyakawa, Shohei |
| description | This paper deals with motion control of an inverted pendulum using neural networks (NN)based on optimal tracking control. First, we examined the control performance in the nonlinear region, applying the optimal tracking control theory to the inverted pendulum. Second, the control system in which the optical tracking control system was replaced with two NNs and a feedback (FB)was designed. In the nonlinear region, the NN control performance was compared with optimal tracking control from the points of view of simulation and experiment. As a result, it was shown that NN control is valid for nonlinear problems in the case where the pendulum angle is large and the motor is saturated. |
| doi_str_mv | 10.1299/kikaic.59.2694 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 0387-5024 |
| ispartof | Transactions of the Japan Society of Mechanical Engineers Series C, 1993/09/25, Vol.59(565), pp.2694-2700 |
| issn | 0387-5024 1884-8354 |
| language | eng ; jpn |
| recordid | cdi_crossref_primary_10_1299_kikaic_59_2694 |
| source | J-STAGE Free; EZB-FREE-00999 freely available EZB journals |
| subjects | 0ptimal Tracking Control Experiment Feedback Control Feedforward Control Inverted Pendulum Neural Networks Learning Control |
| title | Motion Control of an Inverted Pendulum Using Neural Networks based on optimal Tracking Control |
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