• 한국정밀공학회지
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• 제29권1호
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• pp.79-86
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• 2012

This paper proposes PID and RIC (Robust Internal-loop Compensator) based motion controller using dual learning algorithm for position control of linear synchronous motor respectively. Its gains are auto-tuned by using two learning algorithms, reinforcement learning and neural network. The feedback controller gains are tuned by reinforcement learning, and then the feedforward controller gains are tuned by neural network. Experiments prove the validity of dual learning algorithm. The RIC controller has better performance than does the PID-feedforward controller in reducing tracking error and disturbance rejection. Neural network shows its ability to decrease tracking error and to reject disturbance in the stop range of the target position and home.

• 한국정밀공학회지
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• 제28권12호
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• pp.1379-1387
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• 2011

A PID-feedforward controller and Robust Internal-loop Compensator (RIC) based on reinforcement learning using random variable sequences are provided to auto-tune parameters for each controller in the high-precision position control of PMLSM (Permanent Magnet Linear Synchronous Motor). Experiments prove the well-tuned controller could be reduced up to one-fifth level of tracking errors before learning by reinforcement learning. The RIC compared to the PID-feedforward controller showed approximately twice the performance in reducing tracking error and disturbance rejection.