Magnetic Disk Learning Control for Transient-Free Head Positioning
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Solution Overview
Problem
Conventional learning control methods in digital control devices experience a transient response and accuracy deterioration when learning control is terminated, leading to a decrease in the operation result state of the control target relative to the target state.
Innovation Solution
The learning control device includes a feedback control unit and a learning control unit that extends the evaluation section length for tracking errors beyond the output section length, using an FIR filter to update learning control inputs, thereby mitigating transient responses and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the learning control input is updated using conventional learning control methods, then the tracking error is suppressed and the accuracy of the operation result state is improved, but a transient response occurs when learning control is terminated and the accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by extending the evaluation section to include post-learning control period tracking errors. This allows the learning control input to be updated in advance to account for transient responses that will occur when learning control is terminated. By evaluating tracking errors beyond the conventional output section boundary, the system proactively compensates for future accuracy deterioration before it happens.
2Productivity
If the learning control input is updated based on tracking errors within the output section only, then the control performance is improved during learning, but the transient response at termination cannot be mitigated
Solution Approach 1:
The patent applies dimensionality change by extending the evaluation section in the time dimension beyond the conventional output section boundary. Instead of only evaluating tracking errors during the output section, the system evaluates errors in the extended evaluation section that includes the post-learning control period. This temporal dimension extension allows the system to capture and compensate for transient responses without adding complex spatial or structural elements.
3Reliability
If the evaluation section length is extended beyond the output section length, then transient responses are suppressed and accuracy is maintained, but the computational load and data processing requirements increase
Solution Approach 1:
The patent applies continuity of useful action by making the evaluation section continuously overlap with and extend beyond the output section. This continuous evaluation approach ensures that tracking errors are monitored without interruption from the start of learning control through the post-learning control period. The continuous evaluation enables smooth updating of the learning control input, preventing gaps that would allow transient responses to develop unchecked while maintaining efficient processing through uninterrupted data flow.
Data Source
AI summary
A learning-control device includes a feedback-control unit 30 and a learning-control unit 40. The feedback-control unit 30 outputs, based on an input signal according to a tracking error between an operation-result-state of a control target operating according to an input-control-signal based on a feedback-signal and a target-state, the feedback-signal causing the operation-result-state of the control target 34 to track the target-state. The learning-control unit 40 outputs to the feedback-path F, through which the input signal according to the tracking error is input to the feedback-control unit 30, the learning-control input updated according to the tracking error causing the tracking error to approach zero asymptotically. The evaluation section length of an evaluation section by the learning-control unit 40 for the tracking error is longer than the output section length of the output section in which the learning-control unit 40 outputs the learning-control inputs to the feedback-path F.


