• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.561-566
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• 2002

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.146-151
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• 2001

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.10a
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• pp.207-212
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• 2003

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2004.10a
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• pp.181-186
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• 2004

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• Journal of IKEEE
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• v.2 no.2 s.3
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• pp.255-262
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• 1998

The neural network can roughly be classified as the specialized control, indirect control and general schemes. Neural network is adopted for MPPT of solar array. And back propagation algorithm also is used to train neural network controller. We investigate the possibilities of $P_{max}$ control using the neural networks, and then we also examine about operating the solar cell at an optimal voltage comprise of temperature compensated voltage with boost converter. Proposed boost converter of MPPT system is studied by simulation and is implemented by using a microprocessor(80c196kc) which controls duty ratio of the boost converter.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.241-246
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• 2004

This paper proposed trajectory tracking control of mobile robot. Trajectory tracking control scheme are real coding genetic-algorithm and back-propergation algorithm. Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studios have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using Real coding Genetic Algorithm(RCGA) and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verify numerical simulations and the results show better performance than constant gain controller.