Neural network based approach for tuning of SNS feedback and feedforward controllers.

Conference · Tue Jan 01 04:00:00 EST 2002

OSTI ID:974704

Kwon, S I ^[1]; Prokop, M S ^[2]; Regan, A H ^[3]

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1. Sung-Il
2. Mark S.
3. Amy H.

The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.

• Research Organization: Los Alamos National Laboratory
• Sponsoring Organization: DOE
• OSTI ID: 974704
• Report Number(s): LA-UR-02-5133
• Country of Publication: United States
• Language: English

Related Subjects

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES
43 PARTICLE ACCELERATORS
APPROXIMATIONS
AUTOMATION
CONTROL SYSTEMS
FEEDBACK
LEARNING
LINEAR ACCELERATORS
MINIMIZATION
NEURAL NETWORKS
PERFORMANCE
RF SYSTEMS
TRAINING
TUNING