On the Design of Dynamical Neural Network Controller with Its Applications
碩士 === 國立交通大學 === 電機與控制工程系所 === 97 === Faults due to the aging of a controller for a control system are very common; once they happen, the controller is quite difficult to be repaired for some reasons. To solve this problem, in this thesis, we discuss the feasibility of replacing the existing contro...
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| Format: | Others |
| Language: | en_US |
| Published: |
2008
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| Online Access: | http://ndltd.ncl.edu.tw/handle/03062540062230459135 |
| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系所 === 97 === Faults due to the aging of a controller for a control system are very common; once they happen, the controller is quite difficult to be repaired for some reasons. To solve this problem, in this thesis, we discuss the feasibility of replacing the existing controller with a dynamical neural network (DNN) controller. A Hopfield neural network (HNN) controller is used as the DNN controller. The weightings of the HNN are first trained off line by the steepest descent algorithm to make the output of the HNN can mimic the existing controller. After the training is completed, the HNN is applied to the control system as a real-time controller. An inverted pendulum system (IPS) and a ball and beam system (BABS) are used to examine the effectiveness of the proposed HNN controller. The simulation results show that even with the initial condition different from that in the training data, the proposed HNN controller can mimic the existing controller and achieve favorable performance.
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