Learning Controller for Servo Positioning Accuracy
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Solution Overview
Problem
Existing servo control systems for machine tools lack an efficient method for automatically adjusting learning controller characteristics based on actual measurement, leading to suboptimal performance and stability in repetitive operations.
Innovation Solution
A servo control system that includes a position command generator, position detector, band limiting filter, dynamic characteristic compensation element, sine wave sweep input unit, frequency characteristic calculator, and learning control characteristic evaluation function calculator, which together allow for the automatic adjustment of the learning controller's configuration to optimize servo characteristics through actual measurement and offline calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a learning controller is used to optimize feedforward signals for repetitive operations, then manufacturing precision is improved, but device complexity increases due to the need for additional control components and adjustment mechanisms
Solution Approach 1:
The learning controller automatically adjusts its own parameters by evaluating frequency characteristics and convergence behavior of the control system. The system performs self-diagnosis and self-optimization through automated evaluation function calculation, eliminating the need for manual tuning and reducing operational complexity despite the added control components
Solution Approach 2:
The system dynamically adjusts control parameters including band limiting filter characteristics and dynamic characteristic compensation element settings based on measured frequency responses. By automatically modifying these parameters according to actual system behavior, the learning controller achieves high positioning accuracy while adapting to varying operational conditions
2Adaptability or versatility
If manual adjustment of learning controller parameters is performed, then adaptability is improved, but loss of time increases due to trial and error adjustment processes
Solution Approach 1:
The system measures the actual frequency characteristics of the control system and uses this feedback to automatically evaluate learning controller performance. The evaluation function calculator continuously monitors convergence behavior and adjusts parameters based on measured data, enabling rapid adaptation without manual trial and error
Solution Approach 2:
The system performs preliminary measurement of frequency characteristics and evaluation function calculation before final parameter optimization. By pre-assessing system behavior through sine wave sweep measurements and offline calculations, the learning controller can be quickly configured for specific applications without extensive on-site adjustment
3Measurement precision
If frequency characteristics are measured through actual measurement, then measurement precision is improved, but device complexity increases due to additional measurement and calculation components
Solution Approach 1:
The frequency characteristic measurement system uses the existing position control loop and sine wave sweep input to obtain transfer function data. By utilizing already-present system components for dual purposes (control and measurement), the system achieves high measurement precision without requiring entirely separate measurement equipment
Solution Approach 2:
The evaluation function calculator serves as an intermediary that processes measured frequency characteristics and translates them into optimized controller parameters. This intermediary component bridges the gap between raw measurement data and actionable control settings, automating the complex calculation process while maintaining measurement accuracy
Data Source
AI summary
A servo control system includes a position command generator, a position detector for a feed axis, a positional deviation obtainer for calculating a positional deviation, a position control loop, a band limiting filter for attenuating a high frequency component of the positional deviation, a dynamic characteristic compensation element for advancing a phase, a learning controller including the band limiting filter and the dynamic characteristic compensation element, a sine wave sweep input unit for applying a sine wave sweep to the position control loop, a frequency characteristic calculator for estimating the gain and phase of position control loop input and output signals, and a learning control characteristic evaluation function calculator for calculating an evaluation function, which indicates a position control characteristic with the learning controller based on a frequency characteristic by actual measurement and a frequency characteristic of the learning controller.