Servo Position Command Shaping for Faster Accurate Positioning
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Solution Overview
Problem
Conventional techniques for adjusting servo motor command shapes to enhance positioning control speed are inefficient due to modeling errors and inadequate search range settings, leading to suboptimal responses in real-world applications.
Innovation Solution
A positioning control device that includes a position-command generation unit, a drive control unit, an evaluation unit, and a learning unit to determine and adjust the shape of acceleration and deceleration sections of a position command based on detected position values, while learning the relation between position command parameters and evaluation values to optimize positioning performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If simulation-based command shape adjustment is used, then positioning control speed can be improved, but modeling errors cause the optimal command shape to differ from real device performance
Solution Approach 1:
The patent implements feedback by measuring the actual positioning response of the real device and using this information to iteratively adjust the command shape. The system compares the actual positioning error with the desired performance and refines the command shape parameters based on this feedback, eliminating the gap caused by modeling errors in simulation-based approaches.
Solution Approach 2:
The system performs self-optimization by automatically adjusting its own command shape parameters based on measured positioning performance. Through automated iteration where the system evaluates its own performance and modifies its control commands accordingly, it achieves optimal command shapes without relying on external simulation models.
2Measurement precision
If exhaustive search of command shapes is performed on real device, then optimal command shape can be found, but positioning operations need to be performed a very large number of times
Solution Approach 1:
The patent applies preliminary action by first performing a coarse search to identify a promising region of command shape parameters, then focusing subsequent refinement iterations on this narrowed range. This preliminary exploration phase reduces the overall search space, allowing the system to find optimal parameters with fewer total positioning operations than exhaustive search would require.
Solution Approach 2:
The system dynamically adjusts its search strategy based on accumulated performance data. As iterations progress, the search range and step size are adaptively modified - starting with broader exploration and transitioning to finer local optimization. This dynamic approach efficiently balances exploration and exploitation, finding optimal parameters faster than static exhaustive search.
3Loss of time
If search range is determined based on simulation results, then search efficiency can be improved, but modeling errors may cause the search range to be improperly set
Solution Approach 1:
The search range is dynamically determined and adjusted during the optimization process rather than being fixed based on preliminary simulation. The system adapts the search boundaries based on actual performance measurements, expanding or contracting the exploration region as it gains information about the performance landscape, ensuring both efficiency and accuracy.
Solution Approach 2:
The system changes the search range parameters adaptively based on measured performance rather than relying on fixed simulation-based estimates. By modifying search boundaries and step sizes according to actual device response characteristics, the system ensures the search covers the relevant parameter space while maintaining efficiency.
Data Source
AI summary
A positioning control device includes a position-command generation unit to generate a position command by which a shape of an acceleration in an accelerating section and a decelerating section is determined on the basis of a position command parameter, a drive control unit to drive a motor such that a detected position value of the motor or a control target follows the position command, an evaluation unit to calculate an evaluation value regarding positioning performance on the basis of a detected position value of the motor or the control target during execution of positioning control on the control target, and a learning unit to obtain a learning result by learning a relation between the position command parameter and the evaluation value when positioning control is executed plural times, while changing each of shapes of an acceleration in an accelerating section and a decelerating section independently.